To examine whether Tmprss6 overexpression can affect NB tumor growth, we subcutaneously implanted neuro-2a cells into nude mice. The tumors derived from cells overexpressing Tmprss6 grew at a significantly lower rate than those in the Vector group (Fig. ##FIG##7##8A##). We also measured the tumor growth, and the weights of the tumors. As shown in Fig. ##FIG##7##8B, C##, compared with the Vector group, all two of these values were significantly decreased in the Tmprss6 group, and the results of hematoxylin and eosin staining in tumor sections showed that the Vector group tumors are quite dense, while the tumor structure in the Tmprss6 group was looser and with lighter nuclear staining (Supplementary Fig. ##SUPPL##0##S3##). We confirmed the continuous overexpression of Tmprss6 and FLAG in the process of tumor growth by western blot analysis (Fig. ##FIG##7##8D, E##). We also used western blot analysis to examine the levels of ATF3, P-p38, p38, Bcl-2, Bax, and cleaved-caspase3 in tumor tissue (Fig. ##FIG##7##8F–L##). Consistent with our cell culture experimental results, ATF3, P-p38, Bax, and cleaved-caspase3 expression increased significantly, while Bcl-2 expression decreased significantly in the Tmprss6 group. TUNEL staining further confirmed that overexpression of Tmprss6 induced the production of apoptotic bodies in tumor tissues (Fig. ##FIG##7##8M##). These results suggest that overexpression of Tmprss6 inhibits tumor growth and initiates apoptosis through the ATF3/P38 signaling pathway.</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>Overexpression of Tmprss6 in neuro-2a cells</title>\",\n \"<p id=\\\"Par6\\\">To explore the relationship between Tmprss6 and NB, we manipulated the levels of Tmprss6 in the NB cell line, neuro-2a [##REF##35531888##25##]. We confirmed increases in Tmprss6 and FLAG expression in the cells by qRT-PCR and western blot analysis, respectively (Fig. ##FIG##0##1A–D##), and the distribution of the overexpressed protein on the neuro-2a cell membrane by immunofluorescence staining (Fig. ##FIG##0##1E##). These results indicate successful overexpression of Tmprss6 in neuro-2a cells.</p>\",\n \"<title>Tmprss6 overexpression inhibits the Bmp-Smad signaling pathway and regulates the expression of hepcidin by cleaved HJV</title>\",\n \"<p id=\\\"Par7\\\">To identify the role of Tmprss6 in neuro-2a cells, we evaluated the levels of HJV, a substrate of Tmprss6 [##REF##20200349##26##]. As expected, Tmprss6 overexpression (Tmprss6 group) decreased the levels of HJV via cleavage of the proteins compared with the Vector and WT groups (Fig. ##FIG##1##2A–C##). Since HJV is a co-receptor of Bmp [##REF##30213871##27##], a decrease in HJV levels inhibits cytosolic Smad1/5/8 phosphorylation (P-Smad1/5/8) and significantly reduces the levels of P-Smad1/5/8 in the nucleus (Fig. ##FIG##1##2D, E##). Nuclear translocation of P-Smad1/5/8 in the cytoplasm requires binding to Smad4. We found that, after Tmprss6 overexpression (Tmprss6 group), the expression of Smad4 in the cytoplasm was decreased, while the levels of the protein in the nucleus were increased compared with the Vector and WT groups (Fig. ##FIG##1##2D, F##). The decreased P-Smad1/5/8 levels in the nucleus inhibited pro-hepcidin expression (Fig. ##FIG##1##2G, H##). To investigate if the low levels of pro-hepcidin in neuro-2a cells regulated FPN1, possibly affecting intracellular iron content, we analyzed FPN1 levels by western blot analysis. As shown in Fig. ##FIG##1##2G, H##, FPN1 levels significantly increased in neuro-2a cells overexpressing Tmprss6 compared with the Vector and WT groups.</p>\",\n \"<title>Overexpression of Tmprss6 decreases intracellular iron content by increasing the expression of FPN1, thus inhibiting RNR activity and preventing progression to the cell cycle S phase</title>\",\n \"<p id=\\\"Par8\\\">FPN1, as the only known iron-exporter protein, plays an important role in the regulation of intracellular iron [##REF##29620054##28##]. As shown in Fig. ##FIG##1##2I, J##, compared to the control groups, the levels of FtH and FtL, the subunits of ferritin, a ubiquitous iron storage protein, were significantly decreased, while the levels of TfR1 protein, the gateway to cellular iron uptake, were significantly increased in neuro-2a cells with elevated Tmprss6. Consistent with these results, we found that the intracellular total iron content (Fig. ##FIG##1##2K##) and Fe<sup>2+</sup> content (Fig. ##FIG##1##2L, M##) decreased significantly in the Tmprss6 group. Given these indication that the cells were iron starved, we proceeded to evaluate RNR activity to see if the low levels of iron limited RNR function. The RNR activity was indeed significantly decreased in the Tmprss6 group (Fig. ##FIG##1##2N##). The decrease of RNR activity coincided with cell cycle arrest in the Tmprss6 group, where there was a significant decrease in cells in the S phase compared with the Vector and WT groups (Fig. ##FIG##1##2O, P##). These results suggest that Tmprss6 can slow cell proliferation by decreasing their on available for RNR activity.</p>\",\n \"<title>Overexpression of Tmprss6 induces apoptosis in neuro-2a cells</title>\",\n \"<p id=\\\"Par9\\\">In our cell culture experiments, we were surprised to find that Tmprss6 overexpression not only inhibited cell proliferation, but also stimulated cell death. Therefore, we proceeded to examine which forms of cell death, including apoptosis, necrosis, and ferroptosis, may have been occurring. Our results demonstrate that Tmprss6 overexpression was not associated with ferroptosis (Supplementary Fig. ##SUPPL##0##S2A, B##) or necrosis (supplementary Fig. ##SUPPL##0##S2C, D##), but was closely associated with apoptosis (Fig. ##FIG##2##3##). As shown in the annexin V assay results in Fig. ##FIG##2##3A, B##, the percentage of apoptotic cells was ~1% in the Vector and WT groups, while the Tmprss6 group was increased significantly, by about 12-fold. TUNEL assay also revealed that Tmprss6 overexpression caused a significant increase in apoptotic bodies compared to the Vector and WT groups (Fig. ##FIG##2##3C##). Finally, we evaluated the expression of Bcl-2, Bax, and cleaved-caspase3 to find that the ratio of Bcl-2/Bax ratio was significantly decreased (Fig. ##FIG##2##3D, E##), while cleaved-caspase3 levels (Fig. ##FIG##2##3D, F##) significantly increased in the Tmprss6 group, compared with the Vector and WT groups. Together these results indicate that the cell death caused by Tmprss6 overexpression was due to apoptosis.</p>\",\n \"<title>Tmprss6 overexpression-mediated apoptosis in neuro-2a cells is due to activation of the ATF3/P38 signaling pathway</title>\",\n \"<p id=\\\"Par10\\\">To explore how Tmprss6 overexpression induces apoptosis, we used RNA sequencing to screen for changes in apoptosis-stimulating gene expression. Compared with the Vector group, Tmprss6 overexpression significantly up-regulated the expression of ATF3 and Bnip3 in the volcano diagrams (Fig. ##FIG##3##4A##). The most closely associated with the changes in gene expression are Bmp signaling pathways in the GO analysis in Fig. ##FIG##3##4B##. We validated the changes in ATF3 and BCL2/ adenovirus E1B 19 kDa interacting 3 (Bnip3) gene expression by qRT-PCR (Fig. ##FIG##3##4C##). We also examined ATF3 protein levels and found them to be increased (Fig. ##FIG##3##4D, E##). To investigate whether overexpression of Tmprss6 causes nuclear translocation of ATF3, we performed western blot analysis on cytoplasmic and nuclear cell isolates. As shown in Fig. ##FIG##3##4F, G##, compared with the controls, there was a shift from the cytoplasm to the nucleus in the intracellular distribution of ATF3 after Tmprss6 overexpression. The increased nuclear translocation of ATF3 in the Tmprss6 group was also apparent in immunofluorescence experiments (Fig. ##FIG##3##4H##). We also found an increase in phosphorylated p38 (Fig. ##FIG##3##4I, J##)—the nuclear translocation of ATF3 is known to activate p38 mitogen-activated protein kinases, ultimately leading to apoptosis [##REF##33921748##29##].</p>\",\n \"<title>Overexpression of Tmprss6 decreases iron levels to inhibit RNR activity and mediate apoptosis in SH-SY5Y cells</title>\",\n \"<p id=\\\"Par11\\\">In addition, we also investigated the downstream effects of Tmprss6 activity in human neuroblastoma cell lines, SH-SY5Y. As shown in Fig. ##FIG##4##5A, B##, we overexpressed Tmprss6 (Tmprss6 group) in the SH-SY5Y cells. We then assessed the levels of HJV, P-Smad1/5/8, Smad4, ATF3, Pro-hepcidin, FPN1, TfR1, FtL, FtH, RNR activity, P-p38, Bcl-2, Bax and Cleaved-caspase3 (Fig. ##FIG##4##5C–R##) in the Tmprss6-overexpressing cells. Although the ratio of the changes was somewhat different, these results are consistent with our findings in neuro-2a cells overexpressing Tmprss6, demonstrating that the downstream effects of Tmprss6 activity in neuro-2a cells is also applicable to SH-SY5Y cells.</p>\",\n \"<title>Overexpression of Smad4 induces nuclear translocation of ATF3</title>\",\n \"<p id=\\\"Par12\\\">To explore the mechanism whereby Tmprss6 overexpression results in ATF3 nuclear translocation, we constructed a Smad4-FLAG overexpression plasmid to simulate the increased Smad4 expression in the nucleus by Tmprss6 overexpression (Tmprss6-FLAG is not expressed in the experiments in Fig. ##FIG##5##6##). As shown in Fig. ##FIG##5##6A, B##, compared with the controls, the increased expression of Smad4 was accompanied increased expression of ATF3. Overexpression of Smad4 also induced nuclear translocation of ATF3 compared to the Vector and WT groups (Fig. ##FIG##5##6C–E##). We confirmed this shift of ATF3 to the nucleus by immunofluorescence experiments (Fig. ##FIG##5##6F##). To explore the molecular mechanism of the ATF3 nuclear translocation caused by Smad4 overexpression, we performed immunoprecipitation experiments, the results of which suggest that an interaction between ATF3 and Smad4 occurs (Fig. ##FIG##5##6G##). Thus, ATF3 nuclear transposition may be the consequence of its binding to Smad4.</p>\",\n \"<title>Disruption of ATF3 expression alleviates apoptosis induced by overexpression of Tmprss6 in neuro-2a cells</title>\",\n \"<p id=\\\"Par13\\\">To further confirm the role of ATF3 in Tmprss6 overexpression-mediated apoptosis, we inhibited the expression of ATF3 via shRNA. As shown in Fig. ##FIG##6##7A–C##, the expression of ATF3 was significantly inhibited in the ATF3-targeting short hairpin RNA (shRNA) group compared with the Scrambled shRNA and WT groups. Inhibition of ATF3 expression by the shRNA in Tmprss6-overexpressing cells decreased the ratio of P-p38/p38 (Fig. ##FIG##6##7D, E##) and the levels of cleaved-caspase3 (Fig. ##FIG##6##7H, I##), while the ratio of Bcl-2/Bax was elevated (Fig. ##FIG##6##7F, G##). TUNEL assay also revealed that inhibition of ATF3 expression in Tmprss6-overexpressing cells significantly reduced the formation of apoptotic bodies (Fig. ##FIG##6##7J##). These results confirm that ATF3 plays a central role in Tmprss6 overexpression-mediated apoptosis.</p>\",\n \"<title>Overexpression of Tmprss6 inhibits tumor growth and initiates apoptosis</title>\",\n \"<p id=\\\"Par14\\\">To examine whether Tmprss6 overexpression can affect NB tumor growth, we subcutaneously implanted neuro-2a cells into nude mice. The tumors derived from cells overexpressing Tmprss6 grew at a significantly lower rate than those in the Vector group (Fig. ##FIG##7##8A##). We also measured the tumor growth, and the weights of the tumors. As shown in Fig. ##FIG##7##8B, C##, compared with the Vector group, all two of these values were significantly decreased in the Tmprss6 group, and the results of hematoxylin and eosin staining in tumor sections showed that the Vector group tumors are quite dense, while the tumor structure in the Tmprss6 group was looser and with lighter nuclear staining (Supplementary Fig. ##SUPPL##0##S3##). We confirmed the continuous overexpression of Tmprss6 and FLAG in the process of tumor growth by western blot analysis (Fig. ##FIG##7##8D, E##). We also used western blot analysis to examine the levels of ATF3, P-p38, p38, Bcl-2, Bax, and cleaved-caspase3 in tumor tissue (Fig. ##FIG##7##8F–L##). Consistent with our cell culture experimental results, ATF3, P-p38, Bax, and cleaved-caspase3 expression increased significantly, while Bcl-2 expression decreased significantly in the Tmprss6 group. TUNEL staining further confirmed that overexpression of Tmprss6 induced the production of apoptotic bodies in tumor tissues (Fig. ##FIG##7##8M##). These results suggest that overexpression of Tmprss6 inhibits tumor growth and initiates apoptosis through the ATF3/P38 signaling pathway.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par15\">Proteolytic enzymes have long been thought to be involved in carcinogenesis, since proteolytic enzymes can hydrolyze the extracellular matrix (ECM), allowing cancer cells to escape the basement membrane and invade surrounding tissues [##REF##27870503##22##]. Notably, cell surface proteases, such as Tmprss6, also activate a variety of growth factors and their associated receptors, which are essential for the activation of oncogenic signaling pathways [##REF##12784999##30##–##REF##32868877##32##]. So far, several members of the TTSP family have been linked to cancer progression [##REF##32868877##32##–##REF##17131055##34##].</p>","<p id=\"Par16\">Multiple clinical studies have shown that Tmprss6 levels are decreased in tumor progression, while low gene expression correlated with poor prognosis in triple-negative breast cancer [##REF##23649491##23##, ##REF##32385772##35##, ##REF##17575220##36##]. Consistent with the clinical observations, overexpression of Tmprss6 has been found to inhibit the invasion and growth of breast and prostate cancer cells in both in vivo and in vitro experiments [##REF##17575220##36##, ##REF##22858929##37##]. Webb et al. [##REF##22858929##37##] suggested that Tmprss6 may inhibit the development of prostate cancer cells by reducing the levels of β-catenin in the tumor cell membrane. Knockout of Tmprss6 at the cellular level resulted in increased levels of β-catenin, while overexpression of Tmprss6 had the opposite effect. Although Tmprss6 is well known for its association with some types of cancer, surprisingly little is known about the mechanisms by which it is involved in the development and growth of cancer, especially in the molecular control of the cell cycle and apoptotic processes in tumor tissues.</p>","<p id=\"Par17\">Here, we evaluated the role of Tmprss6 in a neuroblastoma cell line and its derived tumors. Since elevated Tmprss6 interfered with cell cycle progression and triggered apoptosis, we continued to test the overexpression model by investigating the mechanism of neuro-2a growth inhibition. Previous studies have shown that Tmprss6 cleaves HJV, a BMP co-receptor, on the surface of hepatocytes, modulating the BMP/SMAD signaling pathway that influences <italic>HAMP</italic> expression [##REF##20200349##26##]. Consistent with this, we observed that Tmprss6 overexpression in neuro-2a cells reduced the level of HJV by cleaved HJV (Fig. ##FIG##1##2A–C##), thereby inhibiting the Bmp-Smad signaling pathway (Fig. ##FIG##1##2D–F##), and reducing the expression of pro-hepcidin (Fig. ##FIG##1##2G, H##). Meanwhile, we also tested the effects of Tmprss6 knockdown in neuro-2a cells, which significantly activated the Bmp-Smad signaling pathway (Supplementary Fig. ##SUPPL##0##S4##). The decreased expression of pro-hepcidin decreased the total intracellular iron and Fe<sup>2+</sup> content by increasing the level of FPN1 (Fig. ##FIG##1##2G–M##). The decrease in Fe<sup>2+</sup> content inhibited the activity of RNR, thus arresting the cell cycle ahead of the S phase, ultimately leading to inhibited tumoral cell proliferation (Fig. ##FIG##1##2N–P##).</p>","<p id=\"Par18\">Apoptosis plays a key role in the pathogenesis of numerous diseases [##REF##21943236##38##]. In neurodegenerative diseases, pathogenesis entails an excess of apoptosis [##REF##24211851##39##], whereas in cancer, too little apoptosis can be the culprit, enabling the expansion of neoplastic cells [##REF##25936818##40##]. The mechanism of apoptosis is complex and involves several pathways. Importantly, apoptosis is an important target in the treatment of cancer [##REF##35725836##41##]. In our study, we found that Tmprss6 overexpression in neuro-2a cells leads to a decrease in proliferation by stimulating apoptosis (Fig. ##FIG##2##3##), without affecting ferroptosis or necrosis (supplementary Fig. ##SUPPL##0##S2##). RNA sequencing revealed that Tmprss6 overexpression led to increased levels of ATF3 and Bnip3, while GO analysis showed functional enrichment of Bmp pathways (Fig. ##FIG##3##4A–B##). Meanwhile, we confirmed there was increased expression of ATF3 and Bnip3 (Fig. ##FIG##3##4C–E##). Elevated ATF3 levels promote nuclear translocation and activate downstream signaling via phosphorylation of p38, which mediates apoptosis (Fig. ##FIG##3##4F–J##).</p>","<p id=\"Par19\">We also found that the expression and nuclear translocation of ATF3 was increased in neuro-2a cells when Smad4 was overexpressed (Fig. ##FIG##5##6A–F##), which is likely the result of an interaction between the two proteins, as we demonstrated by immunoprecipitation experiments (Fig. ##FIG##5##6G##). Thus, Smad4 not only assists in the nuclear translocation of P-Smad1/5/8, but also stimulates the nuclear translocation of ATF3. We conjecture that since the overexpression of Tmprss6 inhibits the phosphorylation of Smad1/5/8, the amount of Smad4 bound to P-Smad1/5/8 is accordingly decreased, freeing up Smad4 to promote an increased nuclear translocation of ATF3.</p>","<p id=\"Par20\">Figure ##FIG##8##9## presents a schematic representation of the possible mechanism of Tmprss6-mediated inhibition of tumor growth. We propose Tmprss6 as a new candidate target for inhibiting neuronal tumor cell proliferation and mediating apoptosis in cancer.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par15\\\">Proteolytic enzymes have long been thought to be involved in carcinogenesis, since proteolytic enzymes can hydrolyze the extracellular matrix (ECM), allowing cancer cells to escape the basement membrane and invade surrounding tissues [##REF##27870503##22##]. Notably, cell surface proteases, such as Tmprss6, also activate a variety of growth factors and their associated receptors, which are essential for the activation of oncogenic signaling pathways [##REF##12784999##30##–##REF##32868877##32##]. So far, several members of the TTSP family have been linked to cancer progression [##REF##32868877##32##–##REF##17131055##34##].</p>\",\n \"<p id=\\\"Par16\\\">Multiple clinical studies have shown that Tmprss6 levels are decreased in tumor progression, while low gene expression correlated with poor prognosis in triple-negative breast cancer [##REF##23649491##23##, ##REF##32385772##35##, ##REF##17575220##36##]. Consistent with the clinical observations, overexpression of Tmprss6 has been found to inhibit the invasion and growth of breast and prostate cancer cells in both in vivo and in vitro experiments [##REF##17575220##36##, ##REF##22858929##37##]. Webb et al. [##REF##22858929##37##] suggested that Tmprss6 may inhibit the development of prostate cancer cells by reducing the levels of β-catenin in the tumor cell membrane. Knockout of Tmprss6 at the cellular level resulted in increased levels of β-catenin, while overexpression of Tmprss6 had the opposite effect. Although Tmprss6 is well known for its association with some types of cancer, surprisingly little is known about the mechanisms by which it is involved in the development and growth of cancer, especially in the molecular control of the cell cycle and apoptotic processes in tumor tissues.</p>\",\n \"<p id=\\\"Par17\\\">Here, we evaluated the role of Tmprss6 in a neuroblastoma cell line and its derived tumors. Since elevated Tmprss6 interfered with cell cycle progression and triggered apoptosis, we continued to test the overexpression model by investigating the mechanism of neuro-2a growth inhibition. Previous studies have shown that Tmprss6 cleaves HJV, a BMP co-receptor, on the surface of hepatocytes, modulating the BMP/SMAD signaling pathway that influences <italic>HAMP</italic> expression [##REF##20200349##26##]. Consistent with this, we observed that Tmprss6 overexpression in neuro-2a cells reduced the level of HJV by cleaved HJV (Fig. ##FIG##1##2A–C##), thereby inhibiting the Bmp-Smad signaling pathway (Fig. ##FIG##1##2D–F##), and reducing the expression of pro-hepcidin (Fig. ##FIG##1##2G, H##). Meanwhile, we also tested the effects of Tmprss6 knockdown in neuro-2a cells, which significantly activated the Bmp-Smad signaling pathway (Supplementary Fig. ##SUPPL##0##S4##). The decreased expression of pro-hepcidin decreased the total intracellular iron and Fe<sup>2+</sup> content by increasing the level of FPN1 (Fig. ##FIG##1##2G–M##). The decrease in Fe<sup>2+</sup> content inhibited the activity of RNR, thus arresting the cell cycle ahead of the S phase, ultimately leading to inhibited tumoral cell proliferation (Fig. ##FIG##1##2N–P##).</p>\",\n \"<p id=\\\"Par18\\\">Apoptosis plays a key role in the pathogenesis of numerous diseases [##REF##21943236##38##]. In neurodegenerative diseases, pathogenesis entails an excess of apoptosis [##REF##24211851##39##], whereas in cancer, too little apoptosis can be the culprit, enabling the expansion of neoplastic cells [##REF##25936818##40##]. The mechanism of apoptosis is complex and involves several pathways. Importantly, apoptosis is an important target in the treatment of cancer [##REF##35725836##41##]. In our study, we found that Tmprss6 overexpression in neuro-2a cells leads to a decrease in proliferation by stimulating apoptosis (Fig. ##FIG##2##3##), without affecting ferroptosis or necrosis (supplementary Fig. ##SUPPL##0##S2##). RNA sequencing revealed that Tmprss6 overexpression led to increased levels of ATF3 and Bnip3, while GO analysis showed functional enrichment of Bmp pathways (Fig. ##FIG##3##4A–B##). Meanwhile, we confirmed there was increased expression of ATF3 and Bnip3 (Fig. ##FIG##3##4C–E##). Elevated ATF3 levels promote nuclear translocation and activate downstream signaling via phosphorylation of p38, which mediates apoptosis (Fig. ##FIG##3##4F–J##).</p>\",\n \"<p id=\\\"Par19\\\">We also found that the expression and nuclear translocation of ATF3 was increased in neuro-2a cells when Smad4 was overexpressed (Fig. ##FIG##5##6A–F##), which is likely the result of an interaction between the two proteins, as we demonstrated by immunoprecipitation experiments (Fig. ##FIG##5##6G##). Thus, Smad4 not only assists in the nuclear translocation of P-Smad1/5/8, but also stimulates the nuclear translocation of ATF3. We conjecture that since the overexpression of Tmprss6 inhibits the phosphorylation of Smad1/5/8, the amount of Smad4 bound to P-Smad1/5/8 is accordingly decreased, freeing up Smad4 to promote an increased nuclear translocation of ATF3.</p>\",\n \"<p id=\\\"Par20\\\">Figure ##FIG##8##9## presents a schematic representation of the possible mechanism of Tmprss6-mediated inhibition of tumor growth. We propose Tmprss6 as a new candidate target for inhibiting neuronal tumor cell proliferation and mediating apoptosis in cancer.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Transmembrane serine protease 6 (Tmprss6) has been correlated with the occurrence and progression of tumors, but any specific molecular mechanism linking the enzyme to oncogenesis has remained elusive thus far. In the present study, we found that Tmprss6 markedly inhibited mouse neuroblastoma N2a (neuro-2a) cell proliferation and tumor growth in nude mice. Tmprss6 inhibits Smad1/5/8 phosphorylation by cleaving the bone morphogenetic protein (BMP) co-receptor, hemojuvelin (HJV). Ordinarily, phosphorylated Smad1/5/8 binds to Smad4 for nuclear translocation, which stimulates the expression of hepcidin, ultimately decreasing the export of iron through ferroportin 1 (FPN1). The decrease in cellular iron levels in neuro-2a cells with elevated Tmprss6 expression limited the availability of the metal forribo nucleotide reductase activity, thereby arresting the cell cycle prior to S phase. Interestingly, Smad4 promoted nuclear translocation of activating transcription factor 3 (ATF3) to activate the p38 mitogen-activated protein kinases signaling pathway by binding to ATF3, inducing apoptosis of neuro-2a cells and inhibiting tumor growth. Disruption of ATF3 expression significantly decreased apoptosis in Tmprss6 overexpressed neuro-2a cells. Our study describes a mechanism whereby Tmprss6 regulates the cell cycle and apoptosis. Thus, we propose Tmprss6 as a candidate target for inhibiting neuronal tumor growth.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Transmembrane serine protease 6 (Tmprss6) has been correlated with the occurrence and progression of tumors, but any specific molecular mechanism linking the enzyme to oncogenesis has remained elusive thus far. In the present study, we found that Tmprss6 markedly inhibited mouse neuroblastoma N2a (neuro-2a) cell proliferation and tumor growth in nude mice. Tmprss6 inhibits Smad1/5/8 phosphorylation by cleaving the bone morphogenetic protein (BMP) co-receptor, hemojuvelin (HJV). Ordinarily, phosphorylated Smad1/5/8 binds to Smad4 for nuclear translocation, which stimulates the expression of hepcidin, ultimately decreasing the export of iron through ferroportin 1 (FPN1). The decrease in cellular iron levels in neuro-2a cells with elevated Tmprss6 expression limited the availability of the metal forribo nucleotide reductase activity, thereby arresting the cell cycle prior to S phase. Interestingly, Smad4 promoted nuclear translocation of activating transcription factor 3 (ATF3) to activate the p38 mitogen-activated protein kinases signaling pathway by binding to ATF3, inducing apoptosis of neuro-2a cells and inhibiting tumor growth. Disruption of ATF3 expression significantly decreased apoptosis in Tmprss6 overexpressed neuro-2a cells. Our study describes a mechanism whereby Tmprss6 regulates the cell cycle and apoptosis. Thus, we propose Tmprss6 as a candidate target for inhibiting neuronal tumor growth.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary information</title>","<p>\n\n\n\n</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>\\n\\n\\n\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary information</title>","<p>The online version contains supplementary material available at 10.1038/s41419-024-06442-x.</p>","<title>Acknowledgements</title>","<p>This study was funded by the National Natural Science Foundation of China (grant number 31471035), Foundations of the Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology of Hebei Province, Ministry of Education Key Laboratory of Molecular and Cellular Biology, Hebei Collaborative Innovation Center for Eco-Environment, China and Hebei Research Center of the Basic Discipline of Cell Biology (C2023205049).</p>","<title>Author contributions</title>","<p>Y-ZC and YZ conceived and designed the experiments. YZ, JB, HB, ST, and HS performed the experiments. YZ, ZS, PY, GG, and YL completed the statistical analysis of the data. Yong Zuo wrote the manuscript with help from Y-ZC.</p>","<title>Data availability</title>","<p>Additional data can be found in the Supplementary materials. The remaining datasets and material generated in the study are available from the corresponding authors upon reasonable request.</p>","<title>Competing interests</title>","<p id=\"Par45\">The authors declare no competing interest.</p>","<title>Ethical approval</title>","<p id=\"Par46\">All procedures were carried out in accordance with the Guide for the Care and Use of Laboratory Animals issued by the National Institutes of Health and were approved by the Animal Care and Use Committee of the Hebei Science and Technical Bureau in the PRC.</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41419-024-06442-x.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>This study was funded by the National Natural Science Foundation of China (grant number 31471035), Foundations of the Key Laboratory of Animal Physiology, Biochemistry and Molecular Biology of Hebei Province, Ministry of Education Key Laboratory of Molecular and Cellular Biology, Hebei Collaborative Innovation Center for Eco-Environment, China and Hebei Research Center of the Basic Discipline of Cell Biology (C2023205049).</p>\",\n \"<title>Author contributions</title>\",\n \"<p>Y-ZC and YZ conceived and designed the experiments. YZ, JB, HB, ST, and HS performed the experiments. YZ, ZS, PY, GG, and YL completed the statistical analysis of the data. Yong Zuo wrote the manuscript with help from Y-ZC.</p>\",\n \"<title>Data availability</title>\",\n \"<p>Additional data can be found in the Supplementary materials. The remaining datasets and material generated in the study are available from the corresponding authors upon reasonable request.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par45\\\">The authors declare no competing interest.</p>\",\n \"<title>Ethical approval</title>\",\n \"<p id=\\\"Par46\\\">All procedures were carried out in accordance with the Guide for the Care and Use of Laboratory Animals issued by the National Institutes of Health and were approved by the Animal Care and Use Committee of the Hebei Science and Technical Bureau in the PRC.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Overexpression of Tmprss6 in neuro-2a cells.</title><p><bold>A</bold> qRT-PCR detection of Tmprss6 mRNA expression in neuro-2a cells. Western blot detection of FLAG <bold>B</bold> and Tmprss6 <bold>C</bold> expression in neuro-2a cells. A representative blot image for each protein and the respective β-actin levels are shown. Quantification of Tmprss6 <bold>D</bold> expression in neuro-2a cells. Each sample’s relative expression level was calculated by normalizing its specific band to its corresponding β-actin or Histone3 band, and then comparing the mean ratio to that of the control group to get the relative fold change. <bold>E</bold> Immunofluorescence was used to detect the location of FLAG tags in neuro-2a cells (Scale bar = 20 μm). Data are expressed as the mean ± SD, <italic>n</italic> = 4. ∗∗∗<italic>p</italic> < 0.001.</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Effect of Tmprss6 overexpression on the cell cycle, iron content, and Bmp-Smad signaling pathway.</title><p><bold>A</bold> The interaction between FLAG (Tmprss6) and HJV was detected by immunoprecipitation. <bold>B</bold> Western blot analysis of HJV expression in neuro-2a cells (<italic>n</italic> = 4). <bold>C</bold> Quantification of HJV expression from the experiment shown in <bold>B</bold>. <bold>D</bold> The expression of P-Smad1/5/8, Smad1 and Smad4 proteins in the cytoplasm and nucleus were detected by western blot analysis (<italic>n</italic> = 4). Quantification of P-Smad1/5/8 / Smad1 <bold>E</bold> and Smad4 <bold>F</bold> expression from the experiment shown in <bold>D</bold>. <bold>G</bold> Western blot analysis of pro-hepcidin and FPN1 levels in neuro-2a cells (<italic>n</italic> = 4). <bold>H</bold> Quantification of pro-hepcidin and FPN1 expression from the experiment shown in <bold>G</bold>. <bold>I</bold> Western blot analysis of FtH, FtL, and TfR1 levels in neuro-2a cells (<italic>n</italic> = 4). <bold>J</bold> Quantification of FtH, FtL, and TfR1 levels from the experiment shown in <bold>I</bold>. Each sample’s relative expression level was calculated as described for Fig. ##FIG##0##1D##. <bold>K</bold> Total intracellular iron content was determined by ICP-MS (<italic>n</italic> = 4). <bold>L</bold> FerroOrange probe was used to detect intracellular Fe<sup>2+</sup> content (Scale bar = 100 μm). <bold>M</bold> Quantitative evaluation of Fe<sup>2+</sup> staining. <bold>N</bold> RNR activity was tested using a kit from Mlbio (<italic>n</italic> = 5). <bold>O</bold> The cell cycle was assessed by flow cytometry. <bold>P</bold> The percentage of cells in the G0/G1, S, and G2/M phases. Data are presented as the mean ± SD. ∗<italic>p</italic> < 0.05, ∗∗<italic>p</italic> < 0.01.</p></caption></fig>","<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Overexpression of Tmprss6 induced apoptosis in neuro-2a cells.</title><p><bold>A</bold> Apoptosis, as measured by flow cytometry. <bold>B</bold> Proportion of apoptotic cells in the indicated groups. <bold>C</bold> Apoptosis induced by Tmprss6 overexpression by the TUNEL method, as described in the Methods section (Scale bar = 100 μm). <bold>D</bold> Levels of Bcl-2, Bax, and cleaved-caspase3, as assessed by western blot analysis (<italic>n</italic> = 4). <bold>E</bold>, <bold>F</bold> Quantification of Bcl-2/Bax <bold>E</bold> and Cleaved-caspase3 <bold>F</bold> expression from the experiment shown in <bold>D</bold>. Each sample’s relative expression level was calculated as described in the legend for Fig. ##FIG##0##1D##. Data are expressed as the mean ± SD. ∗∗<italic>p</italic> < 0.01, ∗∗∗<italic>p</italic> < 0.001.</p></caption></fig>","<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Activation of the ATF3/P38 signaling pathway in Tmprss6-overexpressing neuro-2a cells.</title><p><bold>A</bold> Volcano plot showing the significantly changed mRNAs in Tmprss6-overexpressing cells. <bold>B</bold> Pathway analysis was performed using GO data bases to identify functional enrichment based on the differentially expressed mRNA. <bold>C</bold> The ATF3 and Bnip3 expression was detected by qRT-PCR. <bold>D</bold> Levels of total ATF3, as assessed by western blot analysis (<italic>n</italic> = 4). <bold>E</bold> Quantification of total ATF3 expression from the experiment shown in <bold>D</bold>. <bold>F</bold> The levels of ATF3 in the cytoplasm and nucleus were detected by western blot analysis. <bold>G</bold> Quantification of ATF3 levels in the cytoplasm and nucleus from the experiment shown in <bold>F</bold>. <bold>H</bold> The subcellular localization of ATF3 in neuro-2a cells was determined by immunofluorescence (Scale bar = 20 μm). <bold>I</bold> The levels of P-p38 and p38 protein were detected by western blot analysis (<italic>n</italic> = 4). <bold>J</bold> Quantification of P-p38/p38 from the experiment shown in <bold>I</bold>. Each sample’s relative expression level was calculated as described in the legend of Fig. ##FIG##0##1D##. Data are expressed as the mean ± SD. ∗<italic>p</italic> < 0.05. ∗∗<italic>p</italic> < 0.01.</p></caption></fig>","<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Overexpression of Tmprss6 decreases iron levels to inhibit RNR activity and mediate apoptosis in SH-SY5Y cells.</title><p><bold>A</bold> Western blot analysis of Tmprss6 expression in SH-SY5Y cells. <bold>B</bold> Quantification of Tmprss6 expression in SH-SY5Y cells. <bold>C</bold> Western blot analysis of HJV expression in SH-SY5Y cells. <bold>D</bold> Quantification of HJV expression in SH-SY5Y cells. <bold>E</bold> Expression of P-Smad1/5/8, Smad1, Smad4 and ATF3 proteins in the cytoplasm and nucleus, as detected by western blot analysis. Quantification of P-Smad1/5/8/Smad1 <bold>F</bold>, Smad4 <bold>G</bold>, and ATF3 <bold>H</bold> expression from the experiment shown in <bold>E</bold>. <bold>I</bold> Western blot analysis of pro-hepcidin and FPN1 levels in SH-SY5Y cells. <bold>J</bold> Quantification of pro-hepcidin and FPN1 expression from the experiment shown in I. <bold>K</bold> Western blot analysis of FtH, FtL, and TfR1 levels in SH-SY5Y cells. <bold>L</bold> Quantification of FtH, FtL and TfR1 levels from the experiment shown in <bold>K</bold>. <bold>M</bold> RNR activity, as tested using a kit from Mlbio. <bold>N</bold> Levels of P-p38 and p38 protein, as detected by western blot analysis. <bold>O</bold> Quantification of P-p38/p38 from the experiment shown in <bold>N</bold>. <bold>P</bold> Levels of Bcl-2, Bax and Cleaved-caspase3, as assessed by western blot analysis. <bold>Q</bold>, <bold>R</bold> Quantification of Bcl-2/Bax <bold>Q</bold> and Cleaved-caspase3 <bold>R</bold> expression from the experiment shown in <bold>P</bold>. The data are expressed as the mean ± SD, <italic>n</italic> = 3. ∗<italic>p</italic> < 0.05, ∗∗<italic>p</italic> < 0.01.</p></caption></fig>","<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Nuclear translocation of ATF3 in Smad4-overexpressing neuro-2a cells.</title><p>Neuro-2a cells were transfected with pcDNA3.1 (Vector group) or pcDNA3.1-Smad4-3Flag (Smad4 group) plasmids. <bold>A</bold> Levels of total FLAG, Smad4, and ATF3, as assessed by western blot analysis (<italic>n</italic> = 4). <bold>B</bold> Quantification of total Smad4 and ATF3 expression from the experiment shown in <bold>A</bold>. <bold>C</bold> The levels of Smad4 and ATF3 in the cytoplasm and nucleus were detected by western blot analysis (<italic>n</italic> = 4). <bold>D</bold>, <bold>E</bold> Quantification of Smad4 and ATF3 levels in the cytoplasm and nucleus in the experiment shown in E. Each sample’s relative expression level was calculated as described in the legend of Fig. ##FIG##0##1D##. <bold>F</bold> The subcellular localization of ATF3 in neuro-2a cells was examined by immunofluorescence (Scale bar = 20 μm). <bold>G</bold> The interaction between FLAG (Smad4) and ATF3 was examined by immunoprecipitation. Data are expressed as the mean ± SD. ∗<italic>p</italic> < 0.05.∗∗<italic>p</italic> < 0.01.***<italic>p</italic> < 0.001.</p></caption></fig>","<fig id=\"Fig7\"><label>Fig. 7</label><caption><title>Effects of ATF3 expression on Tmprss6-induced apoptosis in neuro-2a cells.</title><p><bold>A</bold> Live ZSgreen1 imaging was performed 72 h after transfection with Scrambled shRNA or ATF3 shRNA plasmids. <bold>B</bold> Levels of ATF3, as assessed by western blot analysis (<italic>n</italic> = 4). <bold>C</bold> Quantification of ATF3 expression from the experiment shown in <bold>B</bold>. <bold>D</bold>–<bold>I</bold> Levels of P-p38 <bold>D</bold>, p38 <bold>D</bold>, Bcl-2 <bold>F</bold>, Bax <bold>F</bold>, and cleaved-caspase3 <bold>H</bold>, as assessed by western blot analysis (<italic>n</italic> = 4), from the first to the seventh lane are the WT group, Vector pcDNA3.1-transfected cells (Vector group), Scrambled shRNA-transfected cells (Scrambled shRNA group), pcDNA3.1 and Scrambled shRNA co-transfection group, Tmprss6-transfected cells (Tmprss6 group), ATF3 shRNA-transfected cells (ATF3 shRNA group), Tmprss6 and ATF3 shRNA co-transfection group. Quantification of P-p38/p38 <bold>E</bold>, Bcl-2/Bax <bold>G</bold>, and Cleaved-caspase3 <bold>I</bold> expression from the experiments shown in <bold>D</bold>, <bold>F</bold>, and <bold>H</bold>, respectively. Each sample’s relative expression level was calculated as described in the legend of Fig. ##FIG##0##1D##. <bold>J</bold> TUNEL method showing the induction of apoptosis in the indicated groups (Scale bar = 100 μm). Data are expressed as the mean ± SD. ∗<italic>p</italic> < 0.05. ∗∗<italic>p</italic> < 0.01, ∗∗∗<italic>p</italic> < 0.001.</p></caption></fig>","<fig id=\"Fig8\"><label>Fig. 8</label><caption><title>Effect of Tmprss6 overexpression on tumor growth and tumor cell apoptosis.</title><p><bold>A</bold> Photo of ex vivo tumors at 5 weeks post cell implantation in nude mice. <bold>B</bold> Tumor growth curves of nude mice within 5 weeks after cell implantation. <bold>C</bold> Tumor weights of nude mice at 5 weeks post cell implantation. <bold>D</bold>–<bold>L</bold> Levels of FLAG <bold>D</bold>, Tmprss6 <bold>D</bold>, ATF3 <bold>F</bold>, P-p38 <bold>F</bold>, p38 <bold>F</bold>, Bcl-2 <bold>I</bold>, Bax <bold>I</bold>, and Cleaved-caspase3 <bold>K</bold>, as assessed by western blot analysis (<italic>n</italic> = 4). Quantification of Tmprss6 <bold>E</bold>, ATF3 <bold>G</bold>, P-p38/p38 <bold>H</bold>, Bcl-2/Bax <bold>J</bold>, and Cleaved-caspase3 <bold>L</bold> expression from the experiments shown in <bold>D</bold>, <bold>F</bold>, <bold>I</bold>, and <bold>K</bold>, respectively. Each sample’s relative expression level was calculated as described in the legend of Fig. ##FIG##0##1D##. <bold>M</bold> TUNEL method showing the induction of apoptosis in the tumors in the indicated groups (Scale bar = 100 μm). Data are expressed as the mean ± SD. ∗<italic>p</italic> < 0.05. ∗∗<italic>p</italic> < 0.01, ∗∗∗<italic>p</italic> < 0.001.</p></caption></fig>","<fig id=\"Fig9\"><label>Fig. 9</label><caption><title>Schematic diagram of a working model of the inhibition of tumor growth by Tmprss6 overexpression.</title><p>The overexpression of Tmprss6 reduces HJV levels by cleaving HJV, thereby inhibiting the phosphorylation of Smad1/5/8 and retention of the latter in the cytoplasm. Due to the decreased P-Smad1/5/8 levels, the transport of P-Smad1/5/8 to the nucleus by Smad4 is reduced. Decreased P-Smad1/5/8 in the nucleus inhibits pro-hepcidin expression. The decreased pro-hepcidin levels block the internalization and degradation of FPN1. The increased FPN1 level leads to the export of cellular Fe<sup>2+</sup>, limiting the activity of RNR, ultimately arresting the cell cycle before the S phase. Additionally, the decreased amount of P-Smad1/5/8 frees up Smad4 to bind to ATF3 and translocate it to the nucleus, thus activating p38 signaling and the expression of Bnip3, leading to apoptosis.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><title>Overexpression of Tmprss6 in neuro-2a cells.</title><p><bold>A</bold> qRT-PCR detection of Tmprss6 mRNA expression in neuro-2a cells. Western blot detection of FLAG <bold>B</bold> and Tmprss6 <bold>C</bold> expression in neuro-2a cells. A representative blot image for each protein and the respective β-actin levels are shown. Quantification of Tmprss6 <bold>D</bold> expression in neuro-2a cells. Each sample’s relative expression level was calculated by normalizing its specific band to its corresponding β-actin or Histone3 band, and then comparing the mean ratio to that of the control group to get the relative fold change. <bold>E</bold> Immunofluorescence was used to detect the location of FLAG tags in neuro-2a cells (Scale bar = 20 μm). Data are expressed as the mean ± SD, <italic>n</italic> = 4. ∗∗∗<italic>p</italic> < 0.001.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><title>Effect of Tmprss6 overexpression on the cell cycle, iron content, and Bmp-Smad signaling pathway.</title><p><bold>A</bold> The interaction between FLAG (Tmprss6) and HJV was detected by immunoprecipitation. <bold>B</bold> Western blot analysis of HJV expression in neuro-2a cells (<italic>n</italic> = 4). <bold>C</bold> Quantification of HJV expression from the experiment shown in <bold>B</bold>. <bold>D</bold> The expression of P-Smad1/5/8, Smad1 and Smad4 proteins in the cytoplasm and nucleus were detected by western blot analysis (<italic>n</italic> = 4). Quantification of P-Smad1/5/8 / Smad1 <bold>E</bold> and Smad4 <bold>F</bold> expression from the experiment shown in <bold>D</bold>. <bold>G</bold> Western blot analysis of pro-hepcidin and FPN1 levels in neuro-2a cells (<italic>n</italic> = 4). <bold>H</bold> Quantification of pro-hepcidin and FPN1 expression from the experiment shown in <bold>G</bold>. <bold>I</bold> Western blot analysis of FtH, FtL, and TfR1 levels in neuro-2a cells (<italic>n</italic> = 4). <bold>J</bold> Quantification of FtH, FtL, and TfR1 levels from the experiment shown in <bold>I</bold>. Each sample’s relative expression level was calculated as described for Fig. ##FIG##0##1D##. <bold>K</bold> Total intracellular iron content was determined by ICP-MS (<italic>n</italic> = 4). <bold>L</bold> FerroOrange probe was used to detect intracellular Fe<sup>2+</sup> content (Scale bar = 100 μm). <bold>M</bold> Quantitative evaluation of Fe<sup>2+</sup> staining. <bold>N</bold> RNR activity was tested using a kit from Mlbio (<italic>n</italic> = 5). <bold>O</bold> The cell cycle was assessed by flow cytometry. <bold>P</bold> The percentage of cells in the G0/G1, S, and G2/M phases. Data are presented as the mean ± SD. ∗<italic>p</italic> < 0.05, ∗∗<italic>p</italic> < 0.01.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Fig. 3</label><caption><title>Overexpression of Tmprss6 induced apoptosis in neuro-2a cells.</title><p><bold>A</bold> Apoptosis, as measured by flow cytometry. <bold>B</bold> Proportion of apoptotic cells in the indicated groups. <bold>C</bold> Apoptosis induced by Tmprss6 overexpression by the TUNEL method, as described in the Methods section (Scale bar = 100 μm). <bold>D</bold> Levels of Bcl-2, Bax, and cleaved-caspase3, as assessed by western blot analysis (<italic>n</italic> = 4). <bold>E</bold>, <bold>F</bold> Quantification of Bcl-2/Bax <bold>E</bold> and Cleaved-caspase3 <bold>F</bold> expression from the experiment shown in <bold>D</bold>. Each sample’s relative expression level was calculated as described in the legend for Fig. ##FIG##0##1D##. Data are expressed as the mean ± SD. ∗∗<italic>p</italic> < 0.01, ∗∗∗<italic>p</italic> < 0.001.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Fig. 4</label><caption><title>Activation of the ATF3/P38 signaling pathway in Tmprss6-overexpressing neuro-2a cells.</title><p><bold>A</bold> Volcano plot showing the significantly changed mRNAs in Tmprss6-overexpressing cells. <bold>B</bold> Pathway analysis was performed using GO data bases to identify functional enrichment based on the differentially expressed mRNA. <bold>C</bold> The ATF3 and Bnip3 expression was detected by qRT-PCR. <bold>D</bold> Levels of total ATF3, as assessed by western blot analysis (<italic>n</italic> = 4). <bold>E</bold> Quantification of total ATF3 expression from the experiment shown in <bold>D</bold>. <bold>F</bold> The levels of ATF3 in the cytoplasm and nucleus were detected by western blot analysis. <bold>G</bold> Quantification of ATF3 levels in the cytoplasm and nucleus from the experiment shown in <bold>F</bold>. <bold>H</bold> The subcellular localization of ATF3 in neuro-2a cells was determined by immunofluorescence (Scale bar = 20 μm). <bold>I</bold> The levels of P-p38 and p38 protein were detected by western blot analysis (<italic>n</italic> = 4). <bold>J</bold> Quantification of P-p38/p38 from the experiment shown in <bold>I</bold>. Each sample’s relative expression level was calculated as described in the legend of Fig. ##FIG##0##1D##. Data are expressed as the mean ± SD. ∗<italic>p</italic> < 0.05. ∗∗<italic>p</italic> < 0.01.</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Fig. 5</label><caption><title>Overexpression of Tmprss6 decreases iron levels to inhibit RNR activity and mediate apoptosis in SH-SY5Y cells.</title><p><bold>A</bold> Western blot analysis of Tmprss6 expression in SH-SY5Y cells. <bold>B</bold> Quantification of Tmprss6 expression in SH-SY5Y cells. <bold>C</bold> Western blot analysis of HJV expression in SH-SY5Y cells. <bold>D</bold> Quantification of HJV expression in SH-SY5Y cells. <bold>E</bold> Expression of P-Smad1/5/8, Smad1, Smad4 and ATF3 proteins in the cytoplasm and nucleus, as detected by western blot analysis. Quantification of P-Smad1/5/8/Smad1 <bold>F</bold>, Smad4 <bold>G</bold>, and ATF3 <bold>H</bold> expression from the experiment shown in <bold>E</bold>. <bold>I</bold> Western blot analysis of pro-hepcidin and FPN1 levels in SH-SY5Y cells. <bold>J</bold> Quantification of pro-hepcidin and FPN1 expression from the experiment shown in I. <bold>K</bold> Western blot analysis of FtH, FtL, and TfR1 levels in SH-SY5Y cells. <bold>L</bold> Quantification of FtH, FtL and TfR1 levels from the experiment shown in <bold>K</bold>. <bold>M</bold> RNR activity, as tested using a kit from Mlbio. <bold>N</bold> Levels of P-p38 and p38 protein, as detected by western blot analysis. <bold>O</bold> Quantification of P-p38/p38 from the experiment shown in <bold>N</bold>. <bold>P</bold> Levels of Bcl-2, Bax and Cleaved-caspase3, as assessed by western blot analysis. <bold>Q</bold>, <bold>R</bold> Quantification of Bcl-2/Bax <bold>Q</bold> and Cleaved-caspase3 <bold>R</bold> expression from the experiment shown in <bold>P</bold>. The data are expressed as the mean ± SD, <italic>n</italic> = 3. ∗<italic>p</italic> < 0.05, ∗∗<italic>p</italic> < 0.01.</p></caption></fig>\",\n \"<fig id=\\\"Fig6\\\"><label>Fig. 6</label><caption><title>Nuclear translocation of ATF3 in Smad4-overexpressing neuro-2a cells.</title><p>Neuro-2a cells were transfected with pcDNA3.1 (Vector group) or pcDNA3.1-Smad4-3Flag (Smad4 group) plasmids. <bold>A</bold> Levels of total FLAG, Smad4, and ATF3, as assessed by western blot analysis (<italic>n</italic> = 4). <bold>B</bold> Quantification of total Smad4 and ATF3 expression from the experiment shown in <bold>A</bold>. <bold>C</bold> The levels of Smad4 and ATF3 in the cytoplasm and nucleus were detected by western blot analysis (<italic>n</italic> = 4). <bold>D</bold>, <bold>E</bold> Quantification of Smad4 and ATF3 levels in the cytoplasm and nucleus in the experiment shown in E. Each sample’s relative expression level was calculated as described in the legend of Fig. ##FIG##0##1D##. <bold>F</bold> The subcellular localization of ATF3 in neuro-2a cells was examined by immunofluorescence (Scale bar = 20 μm). <bold>G</bold> The interaction between FLAG (Smad4) and ATF3 was examined by immunoprecipitation. Data are expressed as the mean ± SD. ∗<italic>p</italic> < 0.05.∗∗<italic>p</italic> < 0.01.***<italic>p</italic> < 0.001.</p></caption></fig>\",\n \"<fig id=\\\"Fig7\\\"><label>Fig. 7</label><caption><title>Effects of ATF3 expression on Tmprss6-induced apoptosis in neuro-2a cells.</title><p><bold>A</bold> Live ZSgreen1 imaging was performed 72 h after transfection with Scrambled shRNA or ATF3 shRNA plasmids. <bold>B</bold> Levels of ATF3, as assessed by western blot analysis (<italic>n</italic> = 4). <bold>C</bold> Quantification of ATF3 expression from the experiment shown in <bold>B</bold>. <bold>D</bold>–<bold>I</bold> Levels of P-p38 <bold>D</bold>, p38 <bold>D</bold>, Bcl-2 <bold>F</bold>, Bax <bold>F</bold>, and cleaved-caspase3 <bold>H</bold>, as assessed by western blot analysis (<italic>n</italic> = 4), from the first to the seventh lane are the WT group, Vector pcDNA3.1-transfected cells (Vector group), Scrambled shRNA-transfected cells (Scrambled shRNA group), pcDNA3.1 and Scrambled shRNA co-transfection group, Tmprss6-transfected cells (Tmprss6 group), ATF3 shRNA-transfected cells (ATF3 shRNA group), Tmprss6 and ATF3 shRNA co-transfection group. Quantification of P-p38/p38 <bold>E</bold>, Bcl-2/Bax <bold>G</bold>, and Cleaved-caspase3 <bold>I</bold> expression from the experiments shown in <bold>D</bold>, <bold>F</bold>, and <bold>H</bold>, respectively. Each sample’s relative expression level was calculated as described in the legend of Fig. ##FIG##0##1D##. <bold>J</bold> TUNEL method showing the induction of apoptosis in the indicated groups (Scale bar = 100 μm). Data are expressed as the mean ± SD. ∗<italic>p</italic> < 0.05. ∗∗<italic>p</italic> < 0.01, ∗∗∗<italic>p</italic> < 0.001.</p></caption></fig>\",\n \"<fig id=\\\"Fig8\\\"><label>Fig. 8</label><caption><title>Effect of Tmprss6 overexpression on tumor growth and tumor cell apoptosis.</title><p><bold>A</bold> Photo of ex vivo tumors at 5 weeks post cell implantation in nude mice. <bold>B</bold> Tumor growth curves of nude mice within 5 weeks after cell implantation. <bold>C</bold> Tumor weights of nude mice at 5 weeks post cell implantation. <bold>D</bold>–<bold>L</bold> Levels of FLAG <bold>D</bold>, Tmprss6 <bold>D</bold>, ATF3 <bold>F</bold>, P-p38 <bold>F</bold>, p38 <bold>F</bold>, Bcl-2 <bold>I</bold>, Bax <bold>I</bold>, and Cleaved-caspase3 <bold>K</bold>, as assessed by western blot analysis (<italic>n</italic> = 4). Quantification of Tmprss6 <bold>E</bold>, ATF3 <bold>G</bold>, P-p38/p38 <bold>H</bold>, Bcl-2/Bax <bold>J</bold>, and Cleaved-caspase3 <bold>L</bold> expression from the experiments shown in <bold>D</bold>, <bold>F</bold>, <bold>I</bold>, and <bold>K</bold>, respectively. Each sample’s relative expression level was calculated as described in the legend of Fig. ##FIG##0##1D##. <bold>M</bold> TUNEL method showing the induction of apoptosis in the tumors in the indicated groups (Scale bar = 100 μm). Data are expressed as the mean ± SD. ∗<italic>p</italic> < 0.05. ∗∗<italic>p</italic> < 0.01, ∗∗∗<italic>p</italic> < 0.001.</p></caption></fig>\",\n \"<fig id=\\\"Fig9\\\"><label>Fig. 9</label><caption><title>Schematic diagram of a working model of the inhibition of tumor growth by Tmprss6 overexpression.</title><p>The overexpression of Tmprss6 reduces HJV levels by cleaving HJV, thereby inhibiting the phosphorylation of Smad1/5/8 and retention of the latter in the cytoplasm. Due to the decreased P-Smad1/5/8 levels, the transport of P-Smad1/5/8 to the nucleus by Smad4 is reduced. Decreased P-Smad1/5/8 in the nucleus inhibits pro-hepcidin expression. The decreased pro-hepcidin levels block the internalization and degradation of FPN1. The increased FPN1 level leads to the export of cellular Fe<sup>2+</sup>, limiting the activity of RNR, ultimately arresting the cell cycle before the S phase. Additionally, the decreased amount of P-Smad1/5/8 frees up Smad4 to bind to ATF3 and translocate it to the nucleus, thus activating p38 signaling and the expression of Bnip3, leading to apoptosis.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":["<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>"],"string":"[\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM1\\\"></supplementary-material>\",\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM2\\\"></supplementary-material>\",\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM3\\\"></supplementary-material>\"\n]","isConversational":false},"footnote":{"kind":"list like","value":["<fn-group><fn><p>Edited by Professor Massimiliano Agostini</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"],"string":"[\n \"<fn-group><fn><p>Edited by Professor Massimiliano Agostini</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41419_2024_6442_Fig1_HTML\" id=\"d32e365\"/>","<graphic xlink:href=\"41419_2024_6442_Fig2_HTML\" id=\"d32e495\"/>","<graphic xlink:href=\"41419_2024_6442_Fig3_HTML\" id=\"d32e595\"/>","<graphic xlink:href=\"41419_2024_6442_Fig4_HTML\" id=\"d32e688\"/>","<graphic xlink:href=\"41419_2024_6442_Fig5_HTML\" id=\"d32e791\"/>","<graphic xlink:href=\"41419_2024_6442_Fig6_HTML\" id=\"d32e864\"/>","<graphic xlink:href=\"41419_2024_6442_Fig7_HTML\" id=\"d32e967\"/>","<graphic xlink:href=\"41419_2024_6442_Fig8_HTML\" id=\"d32e1086\"/>","<graphic xlink:href=\"41419_2024_6442_Fig9_HTML\" id=\"d32e1211\"/>"],"string":"[\n \"<graphic xlink:href=\\\"41419_2024_6442_Fig1_HTML\\\" id=\\\"d32e365\\\"/>\",\n \"<graphic xlink:href=\\\"41419_2024_6442_Fig2_HTML\\\" id=\\\"d32e495\\\"/>\",\n \"<graphic xlink:href=\\\"41419_2024_6442_Fig3_HTML\\\" id=\\\"d32e595\\\"/>\",\n \"<graphic xlink:href=\\\"41419_2024_6442_Fig4_HTML\\\" id=\\\"d32e688\\\"/>\",\n \"<graphic xlink:href=\\\"41419_2024_6442_Fig5_HTML\\\" id=\\\"d32e791\\\"/>\",\n \"<graphic xlink:href=\\\"41419_2024_6442_Fig6_HTML\\\" id=\\\"d32e864\\\"/>\",\n \"<graphic xlink:href=\\\"41419_2024_6442_Fig7_HTML\\\" id=\\\"d32e967\\\"/>\",\n \"<graphic xlink:href=\\\"41419_2024_6442_Fig8_HTML\\\" id=\\\"d32e1086\\\"/>\",\n \"<graphic xlink:href=\\\"41419_2024_6442_Fig9_HTML\\\" id=\\\"d32e1211\\\"/>\"\n]","isConversational":false},"media":{"kind":"list like","value":["<media xlink:href=\"41419_2024_6442_MOESM1_ESM.docx\"><caption><p>SUPPLEMENTAL MATERIAL</p></caption></media>","<media xlink:href=\"41419_2024_6442_MOESM2_ESM.pdf\"><caption><p>Original Data File</p></caption></media>","<media xlink:href=\"41419_2024_6442_MOESM3_ESM.pdf\"><caption><p>The reproducibility checklist</p></caption></media>"],"string":"[\n \"<media xlink:href=\\\"41419_2024_6442_MOESM1_ESM.docx\\\"><caption><p>SUPPLEMENTAL MATERIAL</p></caption></media>\",\n \"<media xlink:href=\\\"41419_2024_6442_MOESM2_ESM.pdf\\\"><caption><p>Original Data File</p></caption></media>\",\n \"<media xlink:href=\\\"41419_2024_6442_MOESM3_ESM.pdf\\\"><caption><p>The reproducibility checklist</p></caption></media>\"\n]","isConversational":false},"unknown_pub":{"kind":"string","value":"[{\"label\": [\"5.\"], \"surname\": [\"Ward\", \"DeSantis\", \"Robbins\", \"Kohler\", \"Jemal\"], \"given-names\": [\"E\", \"C\", \"A\", \"B\", \"A\"], \"article-title\": [\"Childhood and adolescent cancer statistics, 2014\"], \"source\": [\"Cancer J Clin\"], \"year\": [\"2014\"], \"volume\": [\"64\"], \"fpage\": [\"83\"], \"lpage\": [\"103\"], \"pub-id\": [\"10.3322/caac.21219\"]}, {\"label\": [\"17.\"], \"surname\": [\"Rohini\", \"Haritha Menon\", \"Selvamurugan\"], \"given-names\": [\"M\", \"A\", \"N\"], \"article-title\": [\"Role of activating transcription factor 3 and its interacting proteins under physiological and pathological conditions\"], \"source\": [\"Int J Biol macromolecules\"], \"year\": [\"2018\"], \"volume\": [\"120\"], \"fpage\": [\"310\"], \"lpage\": [\"7\"], \"pub-id\": [\"10.1016/j.ijbiomac.2018.08.107\"]}, {\"label\": [\"24.\"], \"surname\": [\"Gitlin-Domagalska\", \"Mangold\", \"Debowski\", \"Ptaszynska\", \"Legowska\", \"Gutschow\"], \"given-names\": [\"A\", \"M\", \"D\", \"N\", \"A\", \"M\"], \"article-title\": [\"Matriptase-2: monitoring and inhibiting its proteolytic activity\"], \"source\": [\"Future Med Chem\"], \"year\": [\"2018\"], \"volume\": [\"23\"], \"fpage\": [\"1328\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":42,"string":"42"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:55"},"citation":{"kind":"string","value":"Cell Death Dis. 2024 Jan 13; 15(1):49"},"package_file":{"kind":"string","value":"oa_package/96/d1/PMC10787746.tar.gz"}}},{"rowIdx":77,"cells":{"accession_id":{"kind":"string","value":"PMC10787747"},"pmid":{"kind":"string","value":"38218732"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par2\">Wafer bonding technology has evolved significantly, particularly in the field of electronic applications manufactured at the wafer level to achieve high throughput. This advancement is especially critical for the packaging and assembly of MEMS devices, optical systems, and CMOS applications. Among these, wafer bonding of Si and Si-based materials is widely adapted for various applications including the packaging of MEMS sensors<sup>##UREF##0##1##</sup>, optical system sealing<sup>##UREF##1##2##</sup>, microchannel devices<sup>##REF##34739717##3##</sup>, and 3D stacking of memory and logic devices<sup>##UREF##2##4##</sup>.</p>","<p id=\"Par3\">The formation of a layer at the bonding interface is the fundamental and highly effective approach for wafer bonding. One of the representing methods in this regard is hydrophilic bonding, where the Si surface containing physisorbed water and chemisorbed OH groups forms hydrogen and molecular bonds at the bonding interface<sup>##UREF##3##5##,##UREF##4##6##</sup>. In order to enhance the bonding quality and lower the process temperature, plasma treatments typically using or gas are widely employed to render Si bonding surfaces hydrophilic, facilitating water adsorption. Subsequent post-bonding annealing at 200-600 promotes Si oxidation at the bonding interface, thereby increasing the bond strength<sup>##UREF##5##7##,##UREF##6##8##</sup>.</p>","<p id=\"Par4\">In the field of MEMS packaging, glass frit bonding is also widely adapted through the formation of an interface using low-melting-point glass pastes<sup>##UREF##7##9##,##UREF##8##10##</sup>. Here, the bonding surfaces are coated with such glass pastes, and thermo compression bonding at 350-600 induces adhesion between the substrates through the reflowed glass layer. Hence, the formation of the layer at the bonding interface is pivotal for ensuring mechanical reliability.</p>","<p id=\"Par5\">However, the bonding process temperature is a crucial factor in industrial applications. Elevated temperatures can deteriorate device performance and the bonding interface’s reliability due to mismatches in the coefficient of thermal expansion between substrates. Consequently, low-temperature bonding, particularly at room temperature, is needed to accommodate next-generation electronics.</p>","<p id=\"Par6\">Regarding to the formation of , the conversion of polysilazane into is a distinctive characteristic, represented by perhydropolysilazane (PHPS) , which undergoes hydrolysis when it interacts with water. This reaction leads to the formation of , along with the release of byproducts such as and ()<sup>##UREF##9##11##,##UREF##10##12##</sup>.</p>","<p id=\"Par7\">PHPS can be converted into through various methods, including annealing within the temperature range of 200-1000<sup>##UREF##10##12##,##UREF##11##13##</sup>, infrared (IR) irradiation<sup>##UREF##12##14##–##UREF##14##16##</sup>, treatment with pH-controlled chemicals<sup>##UREF##15##17##</sup>, and exposure to moisture<sup>##UREF##16##18##,##UREF##17##19##</sup>. Since PHPS forms membrane by simple approaches compared to vacuum process such as chemical vapor deposition (CVD), PHPS is widely adapted to the coating of polymer films and metals<sup>##UREF##18##20##–##UREF##20##22##</sup> and interlayer dielectrics<sup>##UREF##21##23##,##UREF##22##24##</sup>.</p>","<p id=\"Par8\">It is worth noting that the PHPS conversion into proceeds even at room temperature in the presence of water<sup>##UREF##9##11##,##UREF##11##13##,##UREF##23##25##,##UREF##24##26##</sup>. Hence, the conversion of PHPS to holds the potential for creating a bonding interface consisting of at room temperature, by introducing at the PHPS interface, for instance, through the use of plasma hydrophilic treatment.</p>","<p id=\"Par9\">Based on this concept, this study proposes a novel approach of room temperature wafer bonding by forming bonding interface utilizing the PHPS conversion. The plasma hydrophilic treatment is applied to the Si wafer surface or the PHPS layer, thereby introducing adsorbed water to facilitate the PHPS conversion at the bonding interface. This is followed by wafer bonding via PHPS, leading to the formation of a bonding interface at room temperature.</p>","<p id=\"Par10\">To investigate the proposed wafer bonding process via PHPS, two approaches were explored. The first approach involved utilizing the PHPS layer as a source of water and oxygen for the conversion of the PHPS bonding interface. In this case, the PHPS layers on the bonding pair wafers were directly subjected to plasma hydrophilic treatment. Wafer bonding was then performed via both side PHPS layers on the both of bonding pair wafers. The effects of plasma treatment were examined by comparing conditions where both sides, one side, or none of the PHPS layers underwent plasma treatment.</p>","<p id=\"Par11\">In the second approach, plasma treatment was applied to the Si wafer surfaces. This scenario involved plasma hydrophilic treatment that facilitated water adsorption on the Si wafer surfaces, followed by the PHPS coating on the Si wafers. This implies that the PHPS receives water not from the ambient air but from the Si wafer surface. Subsequently, wafer bonding was performed via one side PHPS layer using the PHPS-coated wafer and the plasma-treated Si wafer. For comparison, wafer bonding was also performed via one side PHPS layer without plasma treatment to the wafer surface.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">Wafer bonding technology has evolved significantly, particularly in the field of electronic applications manufactured at the wafer level to achieve high throughput. This advancement is especially critical for the packaging and assembly of MEMS devices, optical systems, and CMOS applications. Among these, wafer bonding of Si and Si-based materials is widely adapted for various applications including the packaging of MEMS sensors<sup>##UREF##0##1##</sup>, optical system sealing<sup>##UREF##1##2##</sup>, microchannel devices<sup>##REF##34739717##3##</sup>, and 3D stacking of memory and logic devices<sup>##UREF##2##4##</sup>.</p>\",\n \"<p id=\\\"Par3\\\">The formation of a layer at the bonding interface is the fundamental and highly effective approach for wafer bonding. One of the representing methods in this regard is hydrophilic bonding, where the Si surface containing physisorbed water and chemisorbed OH groups forms hydrogen and molecular bonds at the bonding interface<sup>##UREF##3##5##,##UREF##4##6##</sup>. In order to enhance the bonding quality and lower the process temperature, plasma treatments typically using or gas are widely employed to render Si bonding surfaces hydrophilic, facilitating water adsorption. Subsequent post-bonding annealing at 200-600 promotes Si oxidation at the bonding interface, thereby increasing the bond strength<sup>##UREF##5##7##,##UREF##6##8##</sup>.</p>\",\n \"<p id=\\\"Par4\\\">In the field of MEMS packaging, glass frit bonding is also widely adapted through the formation of an interface using low-melting-point glass pastes<sup>##UREF##7##9##,##UREF##8##10##</sup>. Here, the bonding surfaces are coated with such glass pastes, and thermo compression bonding at 350-600 induces adhesion between the substrates through the reflowed glass layer. Hence, the formation of the layer at the bonding interface is pivotal for ensuring mechanical reliability.</p>\",\n \"<p id=\\\"Par5\\\">However, the bonding process temperature is a crucial factor in industrial applications. Elevated temperatures can deteriorate device performance and the bonding interface’s reliability due to mismatches in the coefficient of thermal expansion between substrates. Consequently, low-temperature bonding, particularly at room temperature, is needed to accommodate next-generation electronics.</p>\",\n \"<p id=\\\"Par6\\\">Regarding to the formation of , the conversion of polysilazane into is a distinctive characteristic, represented by perhydropolysilazane (PHPS) , which undergoes hydrolysis when it interacts with water. This reaction leads to the formation of , along with the release of byproducts such as and ()<sup>##UREF##9##11##,##UREF##10##12##</sup>.</p>\",\n \"<p id=\\\"Par7\\\">PHPS can be converted into through various methods, including annealing within the temperature range of 200-1000<sup>##UREF##10##12##,##UREF##11##13##</sup>, infrared (IR) irradiation<sup>##UREF##12##14##–##UREF##14##16##</sup>, treatment with pH-controlled chemicals<sup>##UREF##15##17##</sup>, and exposure to moisture<sup>##UREF##16##18##,##UREF##17##19##</sup>. Since PHPS forms membrane by simple approaches compared to vacuum process such as chemical vapor deposition (CVD), PHPS is widely adapted to the coating of polymer films and metals<sup>##UREF##18##20##–##UREF##20##22##</sup> and interlayer dielectrics<sup>##UREF##21##23##,##UREF##22##24##</sup>.</p>\",\n \"<p id=\\\"Par8\\\">It is worth noting that the PHPS conversion into proceeds even at room temperature in the presence of water<sup>##UREF##9##11##,##UREF##11##13##,##UREF##23##25##,##UREF##24##26##</sup>. Hence, the conversion of PHPS to holds the potential for creating a bonding interface consisting of at room temperature, by introducing at the PHPS interface, for instance, through the use of plasma hydrophilic treatment.</p>\",\n \"<p id=\\\"Par9\\\">Based on this concept, this study proposes a novel approach of room temperature wafer bonding by forming bonding interface utilizing the PHPS conversion. The plasma hydrophilic treatment is applied to the Si wafer surface or the PHPS layer, thereby introducing adsorbed water to facilitate the PHPS conversion at the bonding interface. This is followed by wafer bonding via PHPS, leading to the formation of a bonding interface at room temperature.</p>\",\n \"<p id=\\\"Par10\\\">To investigate the proposed wafer bonding process via PHPS, two approaches were explored. The first approach involved utilizing the PHPS layer as a source of water and oxygen for the conversion of the PHPS bonding interface. In this case, the PHPS layers on the bonding pair wafers were directly subjected to plasma hydrophilic treatment. Wafer bonding was then performed via both side PHPS layers on the both of bonding pair wafers. The effects of plasma treatment were examined by comparing conditions where both sides, one side, or none of the PHPS layers underwent plasma treatment.</p>\",\n \"<p id=\\\"Par11\\\">In the second approach, plasma treatment was applied to the Si wafer surfaces. This scenario involved plasma hydrophilic treatment that facilitated water adsorption on the Si wafer surfaces, followed by the PHPS coating on the Si wafers. This implies that the PHPS receives water not from the ambient air but from the Si wafer surface. Subsequently, wafer bonding was performed via one side PHPS layer using the PHPS-coated wafer and the plasma-treated Si wafer. For comparison, wafer bonding was also performed via one side PHPS layer without plasma treatment to the wafer surface.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Methods</title>","<p id=\"Par32\">4 inch Si wafers, with a thickness of 525 m, were utilized for the wafer bonding experiments. A 20 wt% solution of PHPS in dibutyl ether solvent was purchased.</p>","<p id=\"Par33\">In the case of the wafer bonding via both side PHPS layers, first, the wafers were coated with PHPS solution by spin coating at 2000 rpm for 20 s. The coated PHPS layers underwent a baking process on a hot plate at 100 for 5 min to volatilize the solvent. This baking step completely removes the solvent from the PHPS layer<sup>##UREF##28##32##,##UREF##39##43##</sup>, that is also supported by XPS and EDX analysis (data not shown). Sequentially, the PHPS layers were treated with plasma with 150 W power for 60 s to induce hydrophilicity on the PHPS layer’s surface. After the surface treatment, the PHPS-coated surfaces of the Si wafers were manually brought into contact under ambient air conditions at room temperature. For comparison, wafer bonding was also conducted with plasma treatment applied to both sides of the PHPS layers, one side of the PHPS layers, and without any plasma treatment.</p>","<p id=\"Par34\">In the case of the wafer bonding via one side PHPS layer, one wafer of a bonding pair was coated with PHPS. First, the bonding pair of Si wafers were treated with plasma followed by plasma to enhance water adsorption<sup>##UREF##40##44##,##UREF##41##45##</sup>, with both treatments conducted at a power of 150 W for 60 s each. After the plasma treatment, the wafers underwent a DI water rinse and were subsequently spin-dried. Following these surface preparations, the PHPS solution was spin-coated onto the one of the treated Si wafer pair, and solvent volatilization was conducted under the same conditions as described earlier. Finally, the Si wafer coated with PHPS and the uncoated wafer were bonded together in the ambient air at room temperature. The wafer bonding was also performed without the plasma hydrophilic treatment of Si wafers for comparison.</p>","<p id=\"Par35\">The PHPS layer underwent analysis using XPS to investigate surface compositions. The bonding quality was evaluated by IR imaging of the bonded wafers and bond strength measurements using the blade test<sup>##UREF##42##46##</sup>. The properties of the cross-sectional bonding interface were observed using SEM with EDX.</p>"],"string":"[\n \"<title>Methods</title>\",\n \"<p id=\\\"Par32\\\">4 inch Si wafers, with a thickness of 525 m, were utilized for the wafer bonding experiments. A 20 wt% solution of PHPS in dibutyl ether solvent was purchased.</p>\",\n \"<p id=\\\"Par33\\\">In the case of the wafer bonding via both side PHPS layers, first, the wafers were coated with PHPS solution by spin coating at 2000 rpm for 20 s. The coated PHPS layers underwent a baking process on a hot plate at 100 for 5 min to volatilize the solvent. This baking step completely removes the solvent from the PHPS layer<sup>##UREF##28##32##,##UREF##39##43##</sup>, that is also supported by XPS and EDX analysis (data not shown). Sequentially, the PHPS layers were treated with plasma with 150 W power for 60 s to induce hydrophilicity on the PHPS layer’s surface. After the surface treatment, the PHPS-coated surfaces of the Si wafers were manually brought into contact under ambient air conditions at room temperature. For comparison, wafer bonding was also conducted with plasma treatment applied to both sides of the PHPS layers, one side of the PHPS layers, and without any plasma treatment.</p>\",\n \"<p id=\\\"Par34\\\">In the case of the wafer bonding via one side PHPS layer, one wafer of a bonding pair was coated with PHPS. First, the bonding pair of Si wafers were treated with plasma followed by plasma to enhance water adsorption<sup>##UREF##40##44##,##UREF##41##45##</sup>, with both treatments conducted at a power of 150 W for 60 s each. After the plasma treatment, the wafers underwent a DI water rinse and were subsequently spin-dried. Following these surface preparations, the PHPS solution was spin-coated onto the one of the treated Si wafer pair, and solvent volatilization was conducted under the same conditions as described earlier. Finally, the Si wafer coated with PHPS and the uncoated wafer were bonded together in the ambient air at room temperature. The wafer bonding was also performed without the plasma hydrophilic treatment of Si wafers for comparison.</p>\",\n \"<p id=\\\"Par35\\\">The PHPS layer underwent analysis using XPS to investigate surface compositions. The bonding quality was evaluated by IR imaging of the bonded wafers and bond strength measurements using the blade test<sup>##UREF##42##46##</sup>. The properties of the cross-sectional bonding interface were observed using SEM with EDX.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<p id=\"Par12\">In order to investigate the PHPS conversion into at room temperature by plasma hydrophilic treatment, chemical components of the PHPS coating on wafers were analyzed by x-ray photoelectron spectroscopy (XPS). Figure ##FIG##0##1## presents the XPS core spectra of the PHPS surface before and after plasma treatment. When the PHPS layer is just coated, oxygen peak is not observed, accompanied by a significant peak of nitrogen. The silicon peak at around 101.3 eV indicates silicon nitride<sup>##UREF##25##27##,##REF##25585707##28##</sup>. This is attributed to the intrinsic composition of PHPS<sup>##UREF##14##16##,##UREF##26##29##,##REF##30836334##30##</sup>. However, when the PHPS layer is treated with plasma, an increase in the oxygen peak and a decrease in the nitrogen peak are significant. Moreover, the silicon peak shifts towards a higher binding energy around 103.5 eV, indicating the presence of silicon oxide<sup>##UREF##25##27##,##REF##25585707##28##</sup>. Since the plasma itself does not contain and , it is suggested that the water adsorbs on the PHPS surface after the plasma treatment. Subsequently, the PHPS layer consumes the adsorbed water present on its hydrophilic surface for the conversion reaction.</p>","<p id=\"Par13\">The stitched IR images of the typical bonded wafers with PHPS layers are depicted in Fig. ##FIG##1##2##. These images reveal the presence of interfacial voids, which appear as patterns with interference fringes. Basically the bonding interface shows a good adhesion without significant interfacial voids except the bonding via both side PHPS layer with both side plasma treatment (Fig. ##FIG##1##2## (<bold>a</bold>)). Due to the high viscosity of PHPS before its conversion to , good adhesion is achieved through the PHPS layers. Additionally, it is indicated that submicron-sized particles can become embedded within the soft PHPS layers<sup>##UREF##15##17##,##UREF##27##31##</sup>.</p>","<p id=\"Par14\">Conversely, bonding via both sides plasma-treated PHPS layers results in large interfacial voids, as shown in Fig. ##FIG##1##2## (<bold>a</bold>). As indicated by the XPS analysis, plasma treatment converts the PHPS surface to hard . However, since the spin-coated PHPS layer does not possess a smooth and flat surface, the converted surface does not fully compensate for surface asperities, leading to lower quality adhesion.</p>","<p id=\"Par15\">The results of the bond strength measurements obtained through blade insertion tests are presented in Fig. ##FIG##2##3##. For the bonding via both side PHPS layers, the bond strength is low when both PHPS layers are subjected to plasma treatment or when plasma treatment is not performed, yielding bond strengths of 0.64 J/m and 0.30 J/m, respectively. In the scenario where both sides of the PHPS layers undergo plasma treatment, both surfaces are converted into with surface asperities. Consequently, weak adhesion and a low bond strength result, which aligns with the results of the IR observations. In cases where plasma treatment is not performed, an insufficient supply of water at the bonding interface prevents the PHPS layers from conversion, resulting in a low bond strength.</p>","<p id=\"Par16\">Conversely, when only one side of the PHPS layers is treated with plasma, a significantly improved bond strength of 5.54 J/m is achieved. In this case, the bonding interface features the adhesive PHPS layer in contact with the hydrophilic PHPS layer. The hydrophilic layer with adsorbed water facilitates the conversion of the untreated PHPS layer into . As a result, both strong adhesion and high bond strength are attained.</p>","<p id=\"Par17\">In the cases of the bondings via the single side PHPS layer, when wafer bonding is conducted without the plasma hydrophilic treatment, the resulting bond strength measures 1.07 J/m. In contrast, bonding with plasma treatment exhibits a significantly improved bond strength of 6.02 J/m. Consistent with the bonding using both side PHPS layers, the plasma treatment introduces adsorbed water to the PHPS layer, thereby promoting its conversion to a mechanically stable interface at room temperature.</p>","<p id=\"Par18\">Figure ##FIG##3##4##(a) shows the cross-sectional scanning electron microscopic (SEM) images of the bonding interface for the Si wafers bonded using PHPS layers on both sides with one side subjected to plasma treatment. In the low magnification image, the bonding interface appears uniform and devoid of voids. At higher magnification, the bonding interface is seen to consist of PHPS layers, each with a thickness of 0.4 m, resulting in a total thickness of 0.8 m. For the bonding via single side PHPS layer with plasma treatment shown in Fig. ##FIG##3##4##(b), a uniform and void-free bonding interface is also clearly visible, with the thickness of the PHPS layer measuring 0.4 m, consistent with the thickness observed in the bonding using double side PHPS layers.</p>","<p id=\"Par19\">Energy dispersive x-ray spectroscopy (EDX) analysis was performed across the bonding interface, and the results are presented in Fig. ##FIG##4##5##. For the bonding via double side PHPS layers with one side plasma, the presence of O and N is detected at the interface of the PHPS layers, while the Si signal is relatively lower compared to the bulk Si area. This can be attributed to the lower density of Si atoms in the converted PHPS layers. Moreover, the O intensity appears to be uniformly distributed across the PHPS layers, while the N intensity is relatively weak on the left side of the PHPS layers but as strong as O on the right side.</p>","<p id=\"Par20\">This distribution of N intensity is attributed to the differing treatment of the two PHPS layers. Specifically, the left side PHPS layer in Fig. ##FIG##4##5##(a) is treated with plasma, while the right side is not. The dominant O signal on the left side is consistent with the PHPS conversion by plasma treatment, while the strong N signal on the right side suggests that the PHPS layer in this region is partially converted to .</p>","<p id=\"Par21\">The EDX line profile across the bonding interface of the single PHPS layer is illustrated in Fig. ##FIG##4##5##(b). At the PHPS layer area, the Si intensity is lower, while the presence of O and N is detected at the bonding interface. In comparison to the bonding utilizing both side PHPS layers as shown in Fig. ##FIG##4##5##(a), the composition of the PHPS layer appears to distribute more uniformly across the bonding interface, and the N intensity is closer to the background level. This suggests a more uniform conversion of the PHPS layer to than the bonding via both side PHPS layers. Given that the single PHPS layer at the bonding interface is situated between hydrophilic Si surfaces, the adsorbed water diffuses into the PHPS layer from both sides. As a result, the conversion of PHPS to proceeds more uniformly than the bonding via both sides PHPS layers.</p>","<p id=\"Par22\">Furthermore, the N signal in EDX can be partly attributed to the byproducts of resulting from the PHPS conversion. Given that N is generally spread throughout the PHPS layers, it suggests the diffusion of and byproducts into the PHPS layer. As the converted PHPS into has an amorphous structure, both and are expected to diffuse similarly to .</p>","<p id=\"Par23\">To investigate the PHPS conversion at the bonding interface, XPS analysis was performed on the debonded surface for both the bondings using both side PHPS layers and a single side PHPS layer, with and without plasma treatment. The atomic ratio of O and N was calculated from the O1s and N1s peaks for each condition, as illustrated in Fig. ##FIG##5##6##.</p>","<p id=\"Par24\">For bonding via both side PHPS layers without plasma treatment, the N ratio was 0.88, suggesting that the N in the PHPS molecules was slightly replaced by O from the natural adsorption of water on the Si wafers<sup>##UREF##28##32##</sup>. However, when one of the both side PHPS layers was treated with plasma, the N ratio decreased to 0.50, indicating that the adsorbed water from the plasma treatment replaced the N at the bonding interface.</p>","<p id=\"Par25\">On the other hand, the debonded surface with a single side PHPS layer also exhibited a high N ratio of 0.75 without plasma treatment. Similarly to bonding via both side PHPS layers, plasma treatment reduced the N ratio to 0.30 for bonding via a single side PHPS layer. As indicated by the cross-sectional EDX analysis, the single PHPS layer is sandwiched between the hydrophilic Si surfaces. The conversion of the single side PHPS layer proceeded further due to the relatively larger amount of water present at the bonding interface, as the amount of PHPS was half of that in the bonding via both side PHPS layers. Consequently, the replacement of N by O proceeded more largely compared to the bonding via double side PHPS layers.</p>"],"string":"[\n \"<title>Results</title>\",\n \"<p id=\\\"Par12\\\">In order to investigate the PHPS conversion into at room temperature by plasma hydrophilic treatment, chemical components of the PHPS coating on wafers were analyzed by x-ray photoelectron spectroscopy (XPS). Figure ##FIG##0##1## presents the XPS core spectra of the PHPS surface before and after plasma treatment. When the PHPS layer is just coated, oxygen peak is not observed, accompanied by a significant peak of nitrogen. The silicon peak at around 101.3 eV indicates silicon nitride<sup>##UREF##25##27##,##REF##25585707##28##</sup>. This is attributed to the intrinsic composition of PHPS<sup>##UREF##14##16##,##UREF##26##29##,##REF##30836334##30##</sup>. However, when the PHPS layer is treated with plasma, an increase in the oxygen peak and a decrease in the nitrogen peak are significant. Moreover, the silicon peak shifts towards a higher binding energy around 103.5 eV, indicating the presence of silicon oxide<sup>##UREF##25##27##,##REF##25585707##28##</sup>. Since the plasma itself does not contain and , it is suggested that the water adsorbs on the PHPS surface after the plasma treatment. Subsequently, the PHPS layer consumes the adsorbed water present on its hydrophilic surface for the conversion reaction.</p>\",\n \"<p id=\\\"Par13\\\">The stitched IR images of the typical bonded wafers with PHPS layers are depicted in Fig. ##FIG##1##2##. These images reveal the presence of interfacial voids, which appear as patterns with interference fringes. Basically the bonding interface shows a good adhesion without significant interfacial voids except the bonding via both side PHPS layer with both side plasma treatment (Fig. ##FIG##1##2## (<bold>a</bold>)). Due to the high viscosity of PHPS before its conversion to , good adhesion is achieved through the PHPS layers. Additionally, it is indicated that submicron-sized particles can become embedded within the soft PHPS layers<sup>##UREF##15##17##,##UREF##27##31##</sup>.</p>\",\n \"<p id=\\\"Par14\\\">Conversely, bonding via both sides plasma-treated PHPS layers results in large interfacial voids, as shown in Fig. ##FIG##1##2## (<bold>a</bold>). As indicated by the XPS analysis, plasma treatment converts the PHPS surface to hard . However, since the spin-coated PHPS layer does not possess a smooth and flat surface, the converted surface does not fully compensate for surface asperities, leading to lower quality adhesion.</p>\",\n \"<p id=\\\"Par15\\\">The results of the bond strength measurements obtained through blade insertion tests are presented in Fig. ##FIG##2##3##. For the bonding via both side PHPS layers, the bond strength is low when both PHPS layers are subjected to plasma treatment or when plasma treatment is not performed, yielding bond strengths of 0.64 J/m and 0.30 J/m, respectively. In the scenario where both sides of the PHPS layers undergo plasma treatment, both surfaces are converted into with surface asperities. Consequently, weak adhesion and a low bond strength result, which aligns with the results of the IR observations. In cases where plasma treatment is not performed, an insufficient supply of water at the bonding interface prevents the PHPS layers from conversion, resulting in a low bond strength.</p>\",\n \"<p id=\\\"Par16\\\">Conversely, when only one side of the PHPS layers is treated with plasma, a significantly improved bond strength of 5.54 J/m is achieved. In this case, the bonding interface features the adhesive PHPS layer in contact with the hydrophilic PHPS layer. The hydrophilic layer with adsorbed water facilitates the conversion of the untreated PHPS layer into . As a result, both strong adhesion and high bond strength are attained.</p>\",\n \"<p id=\\\"Par17\\\">In the cases of the bondings via the single side PHPS layer, when wafer bonding is conducted without the plasma hydrophilic treatment, the resulting bond strength measures 1.07 J/m. In contrast, bonding with plasma treatment exhibits a significantly improved bond strength of 6.02 J/m. Consistent with the bonding using both side PHPS layers, the plasma treatment introduces adsorbed water to the PHPS layer, thereby promoting its conversion to a mechanically stable interface at room temperature.</p>\",\n \"<p id=\\\"Par18\\\">Figure ##FIG##3##4##(a) shows the cross-sectional scanning electron microscopic (SEM) images of the bonding interface for the Si wafers bonded using PHPS layers on both sides with one side subjected to plasma treatment. In the low magnification image, the bonding interface appears uniform and devoid of voids. At higher magnification, the bonding interface is seen to consist of PHPS layers, each with a thickness of 0.4 m, resulting in a total thickness of 0.8 m. For the bonding via single side PHPS layer with plasma treatment shown in Fig. ##FIG##3##4##(b), a uniform and void-free bonding interface is also clearly visible, with the thickness of the PHPS layer measuring 0.4 m, consistent with the thickness observed in the bonding using double side PHPS layers.</p>\",\n \"<p id=\\\"Par19\\\">Energy dispersive x-ray spectroscopy (EDX) analysis was performed across the bonding interface, and the results are presented in Fig. ##FIG##4##5##. For the bonding via double side PHPS layers with one side plasma, the presence of O and N is detected at the interface of the PHPS layers, while the Si signal is relatively lower compared to the bulk Si area. This can be attributed to the lower density of Si atoms in the converted PHPS layers. Moreover, the O intensity appears to be uniformly distributed across the PHPS layers, while the N intensity is relatively weak on the left side of the PHPS layers but as strong as O on the right side.</p>\",\n \"<p id=\\\"Par20\\\">This distribution of N intensity is attributed to the differing treatment of the two PHPS layers. Specifically, the left side PHPS layer in Fig. ##FIG##4##5##(a) is treated with plasma, while the right side is not. The dominant O signal on the left side is consistent with the PHPS conversion by plasma treatment, while the strong N signal on the right side suggests that the PHPS layer in this region is partially converted to .</p>\",\n \"<p id=\\\"Par21\\\">The EDX line profile across the bonding interface of the single PHPS layer is illustrated in Fig. ##FIG##4##5##(b). At the PHPS layer area, the Si intensity is lower, while the presence of O and N is detected at the bonding interface. In comparison to the bonding utilizing both side PHPS layers as shown in Fig. ##FIG##4##5##(a), the composition of the PHPS layer appears to distribute more uniformly across the bonding interface, and the N intensity is closer to the background level. This suggests a more uniform conversion of the PHPS layer to than the bonding via both side PHPS layers. Given that the single PHPS layer at the bonding interface is situated between hydrophilic Si surfaces, the adsorbed water diffuses into the PHPS layer from both sides. As a result, the conversion of PHPS to proceeds more uniformly than the bonding via both sides PHPS layers.</p>\",\n \"<p id=\\\"Par22\\\">Furthermore, the N signal in EDX can be partly attributed to the byproducts of resulting from the PHPS conversion. Given that N is generally spread throughout the PHPS layers, it suggests the diffusion of and byproducts into the PHPS layer. As the converted PHPS into has an amorphous structure, both and are expected to diffuse similarly to .</p>\",\n \"<p id=\\\"Par23\\\">To investigate the PHPS conversion at the bonding interface, XPS analysis was performed on the debonded surface for both the bondings using both side PHPS layers and a single side PHPS layer, with and without plasma treatment. The atomic ratio of O and N was calculated from the O1s and N1s peaks for each condition, as illustrated in Fig. ##FIG##5##6##.</p>\",\n \"<p id=\\\"Par24\\\">For bonding via both side PHPS layers without plasma treatment, the N ratio was 0.88, suggesting that the N in the PHPS molecules was slightly replaced by O from the natural adsorption of water on the Si wafers<sup>##UREF##28##32##</sup>. However, when one of the both side PHPS layers was treated with plasma, the N ratio decreased to 0.50, indicating that the adsorbed water from the plasma treatment replaced the N at the bonding interface.</p>\",\n \"<p id=\\\"Par25\\\">On the other hand, the debonded surface with a single side PHPS layer also exhibited a high N ratio of 0.75 without plasma treatment. Similarly to bonding via both side PHPS layers, plasma treatment reduced the N ratio to 0.30 for bonding via a single side PHPS layer. As indicated by the cross-sectional EDX analysis, the single PHPS layer is sandwiched between the hydrophilic Si surfaces. The conversion of the single side PHPS layer proceeded further due to the relatively larger amount of water present at the bonding interface, as the amount of PHPS was half of that in the bonding via both side PHPS layers. Consequently, the replacement of N by O proceeded more largely compared to the bonding via double side PHPS layers.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par26\">Based on the experimental results, we propose a room temperature bonding mechanism as illustrated in Fig. ##FIG##6##7##. For bonding with both-side PHPS layers, the plasma treatment initiates the conversion of one PHPS layer to , facilitated by adsorbed water, as confirmed by the XPS analysis. Following the bonding of the plasma-treated PHPS layer with the untreated PHPS layer, the latter adheres to the substrates due to its ability to deform and compensate for surface asperities, as evidenced by IR imaging of the bonding interface. Subsequently, the water from the plasma-treated PHPS layer diffuses into the untreated PHPS layer, proceeding the hydrolysis of the untreated PHPS layer<sup>##UREF##26##29##</sup>. As the condensation polymerization of Si-OH groups proceeds and enhances the bond strength even at room temperature<sup>##UREF##4##6##,##UREF##29##33##,##UREF##30##34##</sup>, is formed at the bonding interface proceeds, resulting in a increased bond strength. The plasma-treated PHPS layer serves as a source of water and oxygen, supporting this conversion process, as indicated by cross-sectional EDX analysis.</p>","<p id=\"Par27\">In the case of bonding with a single PHPS layer, the PHPS layer is coated on the hydrophilic surface with adsorbed water, thus facilitating PHPS conversion including hydrolysis step starting from the rear side. It has also been reported that the adsorbed water on the substrates react with the PHPS layer coated on the substrate<sup>##UREF##28##32##</sup>. This conversion is driven by the diffusion of the adsorbed water originating from the Si surface into the PHPS layer. Concurrently, the PHPS layer serves as an adhesive during the bonding of the PHPS-coated wafer to the uncoated wafer. Given that the surface of the uncoated wafer is also hydrophilic due to adsorbed water, water similarly diffuses into the PHPS layer from the front side. Consequently, the PHPS layer, situated between two hydrophilic Si surfaces, undergoes more uniform conversion into compared to bonding via PHPS layers on both sides, as supported by the EDX analysis.</p>","<p id=\"Par28\">Compared to other wafer bonding techniques, the wafer bonding via PHPS is comparable to water glass bonding<sup>##UREF##31##35##–##UREF##33##37##</sup> and sol-gel bonding<sup>##UREF##34##38##–##UREF##36##40##</sup>. Both of these techniques involve bonding through a liquid precursor of , followed by an annealing step to create the bonding interface composed of . However, a fundamental distinction lies in the mechanism of transformation to . In sol-gel or water glass bonding, the conversion to occurs through condensation polymerization, necessitating annealing to eliminate from the bonding interface. In contrast, the proposed method utilizing a PHPS layer achieves the formation of the bonding interface through hydrolysis, allowing for bonding at room temperature without the need for annealing.</p>","<p id=\"Par29\">Regarding to the conventional direct bonding of Si or , wafer bonding via PHPS exhibits a comparable bonding quality. In conventional hydrophilic bonding of Si or with plasma hydrophilic treatment, the bond strength typically increases with post-bonding annealing. For Si/Si and / wafer bonding, the typical bond strength is less than 1 J/m without post-bonding annealing<sup>##UREF##4##6##,##UREF##37##41##</sup>. The bond strength increases to over 2 J/m after annealing at temperatures no lower than 300 . Despite the conventional hydrophilic wafer bonding requiring a heating step for a robust bonding interface, wafer bonding via PHPS achieves a high bond strength without annealing.</p>","<p id=\"Par30\">In addition, conventional hydrophilic wafer bonding is often associated with concerns about bubble formation at the bonding interface. Post-bonding annealing in hydrophilic bonding facilitates the condensation polymerization of Si-OH at the bonding interface. This process generates byproducts such as and residual , resulting in the formation of bubbles and voids at the bonding interface<sup>##UREF##37##41##,##UREF##38##42##</sup>. In contrast, wafer bonding via PHPS, as depicted in Fig. ##FIG##1##2##, does not exhibit the formation of bubbles, contributing to an improved bonding quality.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par26\\\">Based on the experimental results, we propose a room temperature bonding mechanism as illustrated in Fig. ##FIG##6##7##. For bonding with both-side PHPS layers, the plasma treatment initiates the conversion of one PHPS layer to , facilitated by adsorbed water, as confirmed by the XPS analysis. Following the bonding of the plasma-treated PHPS layer with the untreated PHPS layer, the latter adheres to the substrates due to its ability to deform and compensate for surface asperities, as evidenced by IR imaging of the bonding interface. Subsequently, the water from the plasma-treated PHPS layer diffuses into the untreated PHPS layer, proceeding the hydrolysis of the untreated PHPS layer<sup>##UREF##26##29##</sup>. As the condensation polymerization of Si-OH groups proceeds and enhances the bond strength even at room temperature<sup>##UREF##4##6##,##UREF##29##33##,##UREF##30##34##</sup>, is formed at the bonding interface proceeds, resulting in a increased bond strength. The plasma-treated PHPS layer serves as a source of water and oxygen, supporting this conversion process, as indicated by cross-sectional EDX analysis.</p>\",\n \"<p id=\\\"Par27\\\">In the case of bonding with a single PHPS layer, the PHPS layer is coated on the hydrophilic surface with adsorbed water, thus facilitating PHPS conversion including hydrolysis step starting from the rear side. It has also been reported that the adsorbed water on the substrates react with the PHPS layer coated on the substrate<sup>##UREF##28##32##</sup>. This conversion is driven by the diffusion of the adsorbed water originating from the Si surface into the PHPS layer. Concurrently, the PHPS layer serves as an adhesive during the bonding of the PHPS-coated wafer to the uncoated wafer. Given that the surface of the uncoated wafer is also hydrophilic due to adsorbed water, water similarly diffuses into the PHPS layer from the front side. Consequently, the PHPS layer, situated between two hydrophilic Si surfaces, undergoes more uniform conversion into compared to bonding via PHPS layers on both sides, as supported by the EDX analysis.</p>\",\n \"<p id=\\\"Par28\\\">Compared to other wafer bonding techniques, the wafer bonding via PHPS is comparable to water glass bonding<sup>##UREF##31##35##–##UREF##33##37##</sup> and sol-gel bonding<sup>##UREF##34##38##–##UREF##36##40##</sup>. Both of these techniques involve bonding through a liquid precursor of , followed by an annealing step to create the bonding interface composed of . However, a fundamental distinction lies in the mechanism of transformation to . In sol-gel or water glass bonding, the conversion to occurs through condensation polymerization, necessitating annealing to eliminate from the bonding interface. In contrast, the proposed method utilizing a PHPS layer achieves the formation of the bonding interface through hydrolysis, allowing for bonding at room temperature without the need for annealing.</p>\",\n \"<p id=\\\"Par29\\\">Regarding to the conventional direct bonding of Si or , wafer bonding via PHPS exhibits a comparable bonding quality. In conventional hydrophilic bonding of Si or with plasma hydrophilic treatment, the bond strength typically increases with post-bonding annealing. For Si/Si and / wafer bonding, the typical bond strength is less than 1 J/m without post-bonding annealing<sup>##UREF##4##6##,##UREF##37##41##</sup>. The bond strength increases to over 2 J/m after annealing at temperatures no lower than 300 . Despite the conventional hydrophilic wafer bonding requiring a heating step for a robust bonding interface, wafer bonding via PHPS achieves a high bond strength without annealing.</p>\",\n \"<p id=\\\"Par30\\\">In addition, conventional hydrophilic wafer bonding is often associated with concerns about bubble formation at the bonding interface. Post-bonding annealing in hydrophilic bonding facilitates the condensation polymerization of Si-OH at the bonding interface. This process generates byproducts such as and residual , resulting in the formation of bubbles and voids at the bonding interface<sup>##UREF##37##41##,##UREF##38##42##</sup>. In contrast, wafer bonding via PHPS, as depicted in Fig. ##FIG##1##2##, does not exhibit the formation of bubbles, contributing to an improved bonding quality.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":["<title>Conclusions</title>","<p id=\"Par31\">In this paper, we investigated and demonstrated wafer bonding via PHPS at room temperature. Through the strategic use of plasma hydrophilic treatment on either the Si surface or the PHPS layer, we have effectively facilitated the conversion of PHPS into a robust bonding interface composed of . This approach achieves the bond strength exceeding 5 J/m and the void-free bonding interfaces, which are attributed to the combined effects of PHPS adhesion and formation. The XPS and EDX analysis also suggest the bonding mechanism that the adsorbed water by hydrophilic plasma treatment diffuses into the PHPS layer which results in the partial conversion of PHPS into , contributing to the robust bonding interface. This technique will provide a new approach for the wafer bonding at room temperature for electronics packaging.</p>"],"string":"[\n \"<title>Conclusions</title>\",\n \"<p id=\\\"Par31\\\">In this paper, we investigated and demonstrated wafer bonding via PHPS at room temperature. Through the strategic use of plasma hydrophilic treatment on either the Si surface or the PHPS layer, we have effectively facilitated the conversion of PHPS into a robust bonding interface composed of . This approach achieves the bond strength exceeding 5 J/m and the void-free bonding interfaces, which are attributed to the combined effects of PHPS adhesion and formation. The XPS and EDX analysis also suggest the bonding mechanism that the adsorbed water by hydrophilic plasma treatment diffuses into the PHPS layer which results in the partial conversion of PHPS into , contributing to the robust bonding interface. This technique will provide a new approach for the wafer bonding at room temperature for electronics packaging.</p>\"\n]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Room temperature wafer bonding is a desirable approach for the packaging and assembly of diverse electronic devices. The formation of layer at the bonding interface is crucial for a reliable wafer bonding as represented by conventional bonding techniques such as hydrophilic bonding and glass frit bonding. This paper reports a novel concept of room temperature wafer bonding based on the conversion of polysilazane to at the bonding interface. As polysilazane is converted to by hydrolysis, in this work, adsorbed water is introduced to the bonding interface by plasma treatment, thereby facilitating the formation of at the wafer bonding interface. The experimental results indicate that the adsorbed water from the plasma treatment diffuses into the polysilazane layer and facilitates its hydrolysis and conversion. The proposed method demonstrates the successful wafer bonding at room temperature with high bond strength without interfacial voids. This technique will provide a new approach of bonding wafers at room temperature for electronics packaging.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Room temperature wafer bonding is a desirable approach for the packaging and assembly of diverse electronic devices. The formation of layer at the bonding interface is crucial for a reliable wafer bonding as represented by conventional bonding techniques such as hydrophilic bonding and glass frit bonding. This paper reports a novel concept of room temperature wafer bonding based on the conversion of polysilazane to at the bonding interface. As polysilazane is converted to by hydrolysis, in this work, adsorbed water is introduced to the bonding interface by plasma treatment, thereby facilitating the formation of at the wafer bonding interface. The experimental results indicate that the adsorbed water from the plasma treatment diffuses into the polysilazane layer and facilitates its hydrolysis and conversion. The proposed method demonstrates the successful wafer bonding at room temperature with high bond strength without interfacial voids. This technique will provide a new approach of bonding wafers at room temperature for electronics packaging.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":[],"string":"[]","isConversational":false},"back":{"kind":"list like","value":["<title>Acknowledgements</title>","<p>This work is supported by the Amada foundation, Tokyo Ohka Foundation for The Promotion of Science and Technology, and JSPS KAKENHI Grant Number JP23K13554.</p>","<title>Author contributions</title>","<p>K.T. designed and carried out the experiments, analyzed the experimental results, and wrote the manuscript. K.T., T.S., and E.H. discussed results, commented on, and edited the manuscript.</p>","<title>Data availability</title>","<p>The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.</p>","<title>Competing interests</title>","<p id=\"Par39\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Acknowledgements</title>\",\n \"<p>This work is supported by the Amada foundation, Tokyo Ohka Foundation for The Promotion of Science and Technology, and JSPS KAKENHI Grant Number JP23K13554.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>K.T. designed and carried out the experiments, analyzed the experimental results, and wrote the manuscript. K.T., T.S., and E.H. discussed results, commented on, and edited the manuscript.</p>\",\n \"<title>Data availability</title>\",\n \"<p>The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par39\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>XPS core spectra of the PHPS surfaces before and after the plasma hydrophilic treatment for (<bold>a</bold>) O1s, (<bold>b</bold>) N1s, and (<bold>c</bold>) Si2p peaks.</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Stitched IR images of the bonded wafers via PHPS. (<bold>a</bold>) Bonding via both side PHPS layers with plasma treatment for both PHPS layers, (<bold>b</bold>) bonding via both side PHPS layers with plasma treatment for one PHPS layer, (<bold>c</bold>) bonding via both side PHPS layers without plasma treatment, (<bold>d</bold>) bonding via one side PHPS layers with plasma treatment, and (<bold>e</bold>) bonding via one side PHPS layers without plasma treatment.</p></caption></fig>","<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Bond strength of the bonded wafers via PHPS layers at room temperature.</p></caption></fig>","<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Cross-sectional SEM observation of the bonding interface of (<bold>a</bold>) both side PHPS layers with one side plasma treatment and (<bold>b</bold>) one side PHPS layer with plasma treatment.</p></caption></fig>","<fig id=\"Fig5\"><label>Figure 5</label><caption><p>EDX line profile across the bonding interface via (<bold>a</bold>) both side PHPS layers with one side plasma treatment and (<bold>b</bold>) single side PHPS layer with plasma treatment.</p></caption></fig>","<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Atomic ratio of O and N on the debonded wafer surface of the bonding via both and one side PHPS layers with and without plasma treatment.</p></caption></fig>","<fig id=\"Fig7\"><label>Figure 7</label><caption><p>The room temperature wafer bonding mechanism via (<bold>a</bold>) both side PHPS layers with plasma treatment for one side PHPS layer and (<bold>b</bold>) one side PHPS layer with plasma treatment.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>XPS core spectra of the PHPS surfaces before and after the plasma hydrophilic treatment for (<bold>a</bold>) O1s, (<bold>b</bold>) N1s, and (<bold>c</bold>) Si2p peaks.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>Stitched IR images of the bonded wafers via PHPS. (<bold>a</bold>) Bonding via both side PHPS layers with plasma treatment for both PHPS layers, (<bold>b</bold>) bonding via both side PHPS layers with plasma treatment for one PHPS layer, (<bold>c</bold>) bonding via both side PHPS layers without plasma treatment, (<bold>d</bold>) bonding via one side PHPS layers with plasma treatment, and (<bold>e</bold>) bonding via one side PHPS layers without plasma treatment.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Figure 3</label><caption><p>Bond strength of the bonded wafers via PHPS layers at room temperature.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Figure 4</label><caption><p>Cross-sectional SEM observation of the bonding interface of (<bold>a</bold>) both side PHPS layers with one side plasma treatment and (<bold>b</bold>) one side PHPS layer with plasma treatment.</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Figure 5</label><caption><p>EDX line profile across the bonding interface via (<bold>a</bold>) both side PHPS layers with one side plasma treatment and (<bold>b</bold>) single side PHPS layer with plasma treatment.</p></caption></fig>\",\n \"<fig id=\\\"Fig6\\\"><label>Figure 6</label><caption><p>Atomic ratio of O and N on the debonded wafer surface of the bonding via both and one side PHPS layers with and without plasma treatment.</p></caption></fig>\",\n \"<fig id=\\\"Fig7\\\"><label>Figure 7</label><caption><p>The room temperature wafer bonding mechanism via (<bold>a</bold>) both side PHPS layers with plasma treatment for one side PHPS layer and (<bold>b</bold>) one side PHPS layer with plasma treatment.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":["<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {O}_{2}$$\\end{document}</tex-math><mml:math id=\"M14\"><mml:msub><mml:mtext>O</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {N}_{2}$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:msub><mml:mtext>N</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${}^{\\circ }\\hbox {C}$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:mrow><mml:msup><mml:mrow/><mml:mo>∘</mml:mo></mml:msup><mml:mtext>C</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${}^{\\circ }\\hbox {C}$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:mrow><mml:msup><mml:mrow/><mml:mo>∘</mml:mo></mml:msup><mml:mtext>C</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq13\"><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M24\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq14\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M26\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq15\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq16\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$((\\hbox {SiH}_{2}\\hbox {NH})_{\\textrm{n}})$$\\end{document}</tex-math><mml:math id=\"M30\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mtext>SiH</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mtext>NH</mml:mtext><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mtext>n</mml:mtext></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq17\"><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M32\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq18\"><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {NH}_{3}$$\\end{document}</tex-math><mml:math id=\"M34\"><mml:msub><mml:mtext>NH</mml:mtext><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq19\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {H}_{2}$$\\end{document}</tex-math><mml:math id=\"M36\"><mml:msub><mml:mtext>H</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq20\"><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiH}_{2}\\hbox {NH}\\,+\\,2\\hbox {H}_{2}\\hbox {O}\\,->\\,\\hbox {SiO}_{2}\\,+\\,\\hbox {NH}_{3}\\,+\\,2\\hbox {H}_{2}$$\\end{document}</tex-math><mml:math id=\"M38\"><mml:mrow><mml:msub><mml:mtext>SiH</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mtext>NH</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mn>2</mml:mn><mml:msub><mml:mtext>H</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mtext>O</mml:mtext><mml:mspace width=\"0.166667em\"/><mml:mo>-</mml:mo><mml:mo>></mml:mo><mml:mspace width=\"0.166667em\"/><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mspace width=\"0.166667em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.166667em\"/><mml:msub><mml:mtext>NH</mml:mtext><mml:mn>3</mml:mn></mml:msub><mml:mspace width=\"0.166667em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mn>2</mml:mn><mml:msub><mml:mtext>H</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq21\"><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M40\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq22\"><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${}^{\\circ }\\hbox {C}$$\\end{document}</tex-math><mml:math id=\"M42\"><mml:mrow><mml:msup><mml:mrow/><mml:mo>∘</mml:mo></mml:msup><mml:mtext>C</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq23\"><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M44\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq24\"><alternatives><tex-math id=\"M45\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M46\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq25\"><alternatives><tex-math id=\"M47\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M48\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq26\"><alternatives><tex-math id=\"M49\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M50\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq27\"><alternatives><tex-math id=\"M51\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {H}_{2}\\hbox {O}$$\\end{document}</tex-math><mml:math id=\"M52\"><mml:mrow><mml:msub><mml:mtext>H</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mtext>O</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq28\"><alternatives><tex-math id=\"M53\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M54\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq29\"><alternatives><tex-math id=\"M55\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M56\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq30\"><alternatives><tex-math id=\"M57\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M58\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq31\"><alternatives><tex-math id=\"M59\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {N}_{2}$$\\end{document}</tex-math><mml:math id=\"M60\"><mml:msub><mml:mtext>N</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq32\"><alternatives><tex-math id=\"M61\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {N}_{2}$$\\end{document}</tex-math><mml:math id=\"M62\"><mml:msub><mml:mtext>N</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq33\"><alternatives><tex-math id=\"M63\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {H}_{2}\\hbox {O}$$\\end{document}</tex-math><mml:math id=\"M64\"><mml:mrow><mml:msub><mml:mtext>H</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mtext>O</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq34\"><alternatives><tex-math id=\"M65\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {O}_{2}$$\\end{document}</tex-math><mml:math id=\"M66\"><mml:msub><mml:mtext>O</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq35\"><alternatives><tex-math id=\"M67\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {N}_{2}$$\\end{document}</tex-math><mml:math id=\"M68\"><mml:msub><mml:mtext>N</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq36\"><alternatives><tex-math id=\"M69\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M70\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq37\"><alternatives><tex-math id=\"M71\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M72\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq38\"><alternatives><tex-math id=\"M73\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M74\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq39\"><alternatives><tex-math id=\"M75\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$^2$$\\end{document}</tex-math><mml:math id=\"M76\"><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq40\"><alternatives><tex-math id=\"M77\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$^2$$\\end{document}</tex-math><mml:math id=\"M78\"><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq41\"><alternatives><tex-math id=\"M79\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M80\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq42\"><alternatives><tex-math id=\"M81\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$^2$$\\end{document}</tex-math><mml:math id=\"M82\"><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq43\"><alternatives><tex-math id=\"M83\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M84\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq44\"><alternatives><tex-math id=\"M85\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$^2$$\\end{document}</tex-math><mml:math id=\"M86\"><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq45\"><alternatives><tex-math id=\"M87\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$^2$$\\end{document}</tex-math><mml:math id=\"M88\"><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq46\"><alternatives><tex-math id=\"M89\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M90\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq47\"><alternatives><tex-math id=\"M91\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\gamma $$\\end{document}</tex-math><mml:math id=\"M92\"><mml:mi>γ</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq48\"><alternatives><tex-math id=\"M93\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\upmu $$\\end{document}</tex-math><mml:math id=\"M94\"><mml:mi mathvariant=\"normal\">μ</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq49\"><alternatives><tex-math id=\"M95\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\upmu $$\\end{document}</tex-math><mml:math id=\"M96\"><mml:mi mathvariant=\"normal\">μ</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq50\"><alternatives><tex-math id=\"M97\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\upmu $$\\end{document}</tex-math><mml:math id=\"M98\"><mml:mi mathvariant=\"normal\">μ</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq51\"><alternatives><tex-math id=\"M99\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M100\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq52\"><alternatives><tex-math id=\"M101\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M102\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq53\"><alternatives><tex-math id=\"M103\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M104\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq54\"><alternatives><tex-math id=\"M105\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {NH}_{3}$$\\end{document}</tex-math><mml:math id=\"M106\"><mml:msub><mml:mtext>NH</mml:mtext><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq55\"><alternatives><tex-math id=\"M107\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {NH}_{3}$$\\end{document}</tex-math><mml:math id=\"M108\"><mml:msub><mml:mtext>NH</mml:mtext><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq56\"><alternatives><tex-math id=\"M109\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {H}_{2}$$\\end{document}</tex-math><mml:math id=\"M110\"><mml:msub><mml:mtext>H</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq57\"><alternatives><tex-math id=\"M111\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M112\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq58\"><alternatives><tex-math id=\"M113\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {NH}_{3}$$\\end{document}</tex-math><mml:math id=\"M114\"><mml:msub><mml:mtext>NH</mml:mtext><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq59\"><alternatives><tex-math id=\"M115\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {H}_{2}$$\\end{document}</tex-math><mml:math id=\"M116\"><mml:msub><mml:mtext>H</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq60\"><alternatives><tex-math id=\"M117\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {H}_{2}\\hbox {O}$$\\end{document}</tex-math><mml:math id=\"M118\"><mml:mrow><mml:msub><mml:mtext>H</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mtext>O</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq61\"><alternatives><tex-math id=\"M119\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math id=\"M120\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq62\"><alternatives><tex-math id=\"M121\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {SiO}_{2}$$\\end{document}</tex-math><mml:math 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id=\"M169\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${}^{\\circ }\\hbox {C}$$\\end{document}</tex-math><mml:math id=\"M170\"><mml:mrow><mml:msup><mml:mrow/><mml:mo>∘</mml:mo></mml:msup><mml:mtext>C</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq87\"><alternatives><tex-math id=\"M171\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {N}_{2}$$\\end{document}</tex-math><mml:math 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\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hbox {N}_{2}$$\\end{document}</tex-math><mml:math id=\"M176\"><mml:msub><mml:mtext>N</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>"],"string":"[\n \"<inline-formula id=\\\"IEq1\\\"><alternatives><tex-math id=\\\"M1\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\hbox {SiO}_{2}$$\\\\end{document}</tex-math><mml:math 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{SiO}_{2}$$\\\\end{document}</tex-math><mml:math id=\\\"M54\\\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq29\\\"><alternatives><tex-math id=\\\"M55\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\hbox {SiO}_{2}$$\\\\end{document}</tex-math><mml:math id=\\\"M56\\\"><mml:msub><mml:mtext>SiO</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq30\\\"><alternatives><tex-math 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Technol.\"], \"year\": [\"2014\"], \"volume\": [\"3\"], \"fpage\": [\"Q42\"], \"lpage\": [\"Q54\"], \"pub-id\": [\"10.1149/2.007403jss\"]}, {\"label\": [\"42.\"], \"surname\": [\"Le Tiec\"], \"given-names\": [\"Y\"], \"article-title\": [\"Water management on semiconductor surfaces\"], \"source\": [\"Microelectron. Eng.\"], \"year\": [\"2011\"], \"volume\": [\"88\"], \"fpage\": [\"3432\"], \"lpage\": [\"3436\"], \"pub-id\": [\"10.1016/j.mee.2010.10.017\"]}, {\"label\": [\"43.\"], \"surname\": [\"Ohishi\", \"Yamazaki\"], \"given-names\": [\"T\", \"Y\"], \"article-title\": [\"Formation and gas barrier characteristics of polysilazane-derived silica coatings formed by excimer light irradiation on pet films with vacuum evaporated silica coatings\"], \"source\": [\"Mater. Sci. Appl.\"], \"year\": [\"2016\"], \"volume\": [\"8\"], \"fpage\": [\"1\"], \"lpage\": [\"14\"]}, {\"label\": [\"44.\"], \"surname\": [\"He\", \"Yamauchi\", \"Suga\"], \"given-names\": [\"R\", \"A\", \"T\"], \"article-title\": [\"Sequential plasma activation methods for hydrophilic direct bonding at sub-200\\n\"], \"tex-math\": [\"\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${}^{\\\\circ }$$\\\\end{document}\"], \"{http://www.w3.org/1998/Math/MathML}mo\": [\"\\u2218\"], \"source\": [\"Jpn. J. Appl. Phys.\"], \"year\": [\"2018\"], \"volume\": [\"57\"], \"fpage\": [\"02BD03\"], \"pub-id\": [\"10.7567/jjap.57.02bd03\"]}, {\"label\": [\"45.\"], \"surname\": [\"Takeuchi\", \"Mu\", \"Yamauchi\", \"Suga\"], \"given-names\": [\"K\", \"F\", \"A\", \"T\"], \"article-title\": [\"Sequential plasma activation for low temperature bonding of aluminosilicate glass\"], \"source\": [\"ECS J. Solid State Sci. Technol.\"], \"year\": [\"2021\"], \"volume\": [\"10\"], \"fpage\": [\"054007\"], \"pub-id\": [\"10.1149/2162-8777/abfd4b\"]}, {\"label\": [\"46.\"], \"surname\": [\"Maszara\", \"Goetz\", \"Caviglia\", \"McKitterick\"], \"given-names\": [\"WP\", \"G\", \"A\", \"JB\"], \"article-title\": [\"Bonding of silicon wafers for silicon-on-insulator\"], \"source\": [\"J. Appl. Phys.\"], \"year\": [\"1988\"], \"volume\": [\"64\"], \"fpage\": [\"4943\"], \"lpage\": [\"4950\"], \"pub-id\": [\"10.1063/1.342443\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":46,"string":"46"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:56"},"citation":{"kind":"string","value":"Sci Rep. 2024 Jan 13; 14:1267"},"package_file":{"kind":"string","value":"oa_package/5f/94/PMC10787747.tar.gz"}}},{"rowIdx":78,"cells":{"accession_id":{"kind":"string","value":"PMC10787748"},"pmid":{"kind":"string","value":"38218950"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par3\">Reproduction in an environment suitable for genome stability is important for the preservation of species. This requirement is more imperative in poikilotherms animals, in which gametes are directly exposed to undesirable temperatures. As a result, they may have evolved a system that rapidly adapts to such changes. In fish, for example, temperature is generally considered to be an important factor in determining the exact timing of gamete maturation and spawning<sup>##UREF##0##1##</sup>.</p>","<p id=\"Par4\">Inhibition of reproduction by high temperature is observed in a wide range of fish species<sup>##REF##20738700##2##</sup>. High-temperature environment inhibits female ovulation and male spermatogenesis of Atlantic salmon (<italic>Salmo salar</italic>), which leads to arrest<sup>##UREF##1##3##</sup>. The suppression of ovulation by the high-temperature environment is due to decreased expression of <italic>20β-hsd</italic>, which in turn inhibits maturation-inducing steroid conversion. However, the mechanism of spermatogenesis arrest with regard to the sensing of temperature in the testis remains unclear.</p>","<p id=\"Par5\">Testes in mammals as well as in fish show clear sensitivities to high-temperature environments. Mammalian testes exposed to temperature >37 °C display abnormal spermatogenesis by way of germ cell apoptosis through various signaling pathways<sup>##UREF##2##4##</sup>. A proposed model posits that high temperature causes damage to meiotic prophase I and damaged pachytene spermatocytes are eliminated at the checkpoints that monitor meiotic progression<sup>##REF##35618762##5##</sup>. Checkpoints in multicellular organisms induce apoptosis in DNA-damaged cells and contribute to genome stability across generations. This mechanism contributes to the lower mutation rate of germ cells than somatic cells, which is important for stronger genome integrity<sup>##REF##28485371##6##</sup>. The mitotic checkpoint functions also in <italic>zebrafish</italic>: high temperature applied to knockouts of <italic>Mps1</italic>, which is required for cell cycle fidelity and genome stability leads to the failure of checkpoint and production of aneuploid sperm<sup>##REF##15231734##7##</sup>.</p>","<p id=\"Par6\">Leydig cells are found in the interstitial tissue of the testis and regulate spermatogenesis and reproductive function through the secretion of steroid hormones such as testosterone<sup>##REF##30980107##8##</sup>. In mammalian models, hyperthermic stimulation of the testis by an artificially induced cryptorchidism leads to a decrease in Leydig cell activity as well as in germ cells<sup>##REF##19484498##9##</sup>. Reduced activity of Leydig cells leads to decreased steroid hormone secretion<sup>##REF##8817242##10##,##UREF##3##11##</sup>. High temperature also induces apoptosis not only in germ cells but also in Leydig cells<sup>##REF##30196893##12##</sup>. However, the molecular mechanism and the significance of apoptosis in Leydig cells induced by high-temperature stimulation remain unexplored. In particular, the causal relationship between reduced Leydig cell activity and abnormal spermatogenesis due to the high-temperature environment is not clear.</p>","<p id=\"Par7\">In this study, we provide evidence that Leydig cells in <italic>zebrafish</italic> undergo apoptosis prior to germ cells when placed in high temperatures, and <italic>Trpv4</italic> is involved in this process. Furthermore, we show that the Leydig cell-specific apoptosis decreased the synthesis of steroid hormones, which in turn impaired the motility of sperm whose genome integrity is compromised.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par3\\\">Reproduction in an environment suitable for genome stability is important for the preservation of species. This requirement is more imperative in poikilotherms animals, in which gametes are directly exposed to undesirable temperatures. As a result, they may have evolved a system that rapidly adapts to such changes. In fish, for example, temperature is generally considered to be an important factor in determining the exact timing of gamete maturation and spawning<sup>##UREF##0##1##</sup>.</p>\",\n \"<p id=\\\"Par4\\\">Inhibition of reproduction by high temperature is observed in a wide range of fish species<sup>##REF##20738700##2##</sup>. High-temperature environment inhibits female ovulation and male spermatogenesis of Atlantic salmon (<italic>Salmo salar</italic>), which leads to arrest<sup>##UREF##1##3##</sup>. The suppression of ovulation by the high-temperature environment is due to decreased expression of <italic>20β-hsd</italic>, which in turn inhibits maturation-inducing steroid conversion. However, the mechanism of spermatogenesis arrest with regard to the sensing of temperature in the testis remains unclear.</p>\",\n \"<p id=\\\"Par5\\\">Testes in mammals as well as in fish show clear sensitivities to high-temperature environments. Mammalian testes exposed to temperature >37 °C display abnormal spermatogenesis by way of germ cell apoptosis through various signaling pathways<sup>##UREF##2##4##</sup>. A proposed model posits that high temperature causes damage to meiotic prophase I and damaged pachytene spermatocytes are eliminated at the checkpoints that monitor meiotic progression<sup>##REF##35618762##5##</sup>. Checkpoints in multicellular organisms induce apoptosis in DNA-damaged cells and contribute to genome stability across generations. This mechanism contributes to the lower mutation rate of germ cells than somatic cells, which is important for stronger genome integrity<sup>##REF##28485371##6##</sup>. The mitotic checkpoint functions also in <italic>zebrafish</italic>: high temperature applied to knockouts of <italic>Mps1</italic>, which is required for cell cycle fidelity and genome stability leads to the failure of checkpoint and production of aneuploid sperm<sup>##REF##15231734##7##</sup>.</p>\",\n \"<p id=\\\"Par6\\\">Leydig cells are found in the interstitial tissue of the testis and regulate spermatogenesis and reproductive function through the secretion of steroid hormones such as testosterone<sup>##REF##30980107##8##</sup>. In mammalian models, hyperthermic stimulation of the testis by an artificially induced cryptorchidism leads to a decrease in Leydig cell activity as well as in germ cells<sup>##REF##19484498##9##</sup>. Reduced activity of Leydig cells leads to decreased steroid hormone secretion<sup>##REF##8817242##10##,##UREF##3##11##</sup>. High temperature also induces apoptosis not only in germ cells but also in Leydig cells<sup>##REF##30196893##12##</sup>. However, the molecular mechanism and the significance of apoptosis in Leydig cells induced by high-temperature stimulation remain unexplored. In particular, the causal relationship between reduced Leydig cell activity and abnormal spermatogenesis due to the high-temperature environment is not clear.</p>\",\n \"<p id=\\\"Par7\\\">In this study, we provide evidence that Leydig cells in <italic>zebrafish</italic> undergo apoptosis prior to germ cells when placed in high temperatures, and <italic>Trpv4</italic> is involved in this process. Furthermore, we show that the Leydig cell-specific apoptosis decreased the synthesis of steroid hormones, which in turn impaired the motility of sperm whose genome integrity is compromised.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Methods</title>","<title>Fish</title>","<p id=\"Par35\">All experiments using animals in this study followed the guidelines of Osaka Medical and Pharmaceutical University. Adult <italic>zebrafish</italic> and <italic>medaka</italic> were maintained at 28.5 °C on a 10 h light:14 h darkness photoperiod. <italic>Zebrafish</italic> were bred from a RIKEN WT background and <italic>medaka</italic> strain was OKcab. Male adults 6–12 months old were used for experiments.</p>","<title>Temperature stimulation</title>","<p id=\"Par36\">Up to four individuals were temperature-stimulated in W22 × D12 × H11 cm tanks with 1.8 l of water, and water changes were performed daily. The tanks were placed in a water bath at 35 °C and oxygen was supplied by aquarium air stones. Temperature stimulation was interrupted for feeding from 12:00 to 13:00 daily. Exposure of isolated sperm was for 20 min (Supplementary Fig. ##SUPPL##1##9##).</p>","<title>Histology</title>","<p id=\"Par37\">Testes were fixed in Bouin solution, and 4 μm plastic sections were prepared using Technobit 8100 (Heraeus Kulzer). Samples were stained with hematoxylin solution for 10 min (Muto pure chemicals, Tokyo, Japan), and stained with 1% eosin (Muto pure chemicals, Tokyo, Japan) for 30 s. The mounted samples were imaged using an Olympus DP74 camera (Evident, Tokyo, Japan).</p>","<title>qRT-PCR</title>","<p id=\"Par38\">RNA samples were extracted from testes. According to the manufacturer’s instructions, 0.5 μg RNA template was used for reverse transcription to synthesize cDNA using a first-strand cDNA synthesis kit (ReverTra Ace, TOYOBO, Tokyo, Japan). The qPCR primers are listed in Supplementary Table ##SUPPL##1##1##. For amplification, the KOD SYBR qPCR/RT (TOYOBO, Tokyo, Japan) and ABI real-time system were used. Statistical significance was evaluated by two-tailed Student’s <italic>t</italic>-test.</p>","<title>In situ hybridization</title>","<p id=\"Par39\">RNA in situ hybridization (WISH) was carried out following a standard protocol<sup>##REF##16682019##47##</sup>. The primers used to amplify the regions used for the probes are shown in Supplementary Table ##SUPPL##1##1##. Briefly, testes were fixed in 4% paraformaldehyde in 1× PBS overnight. On the following day, samples were hybridized at 65 °C overnight. Samples were further incubated with anti-digoxigenin-AP antibody solution (1:2000) overnight at 4 °C and stained with NBT/BCIP. The stained samples were imaged using an Olympus DP74 camera (Evident). Double in situ hybridization was carried out following a standard protocol<sup>##REF##16894600##48##</sup>. The first probe was labeled with DIG and the second probe was labeled with FITC. After the first staining, incubation was performed with 0.1 M glycine–HCl pH 2.2 for 40 min to remove alkaline phosphatase activity.</p>","<title>Immunohistochemistry</title>","<p id=\"Par40\">Immunofluorescence staining was performed using an anti-Cleaved caspase 3 antibody (#9661, Cell Signaling Technology, USA). Alexa 488 was used as the secondary antibody. Nuclear DNA was stained with 4’,6-diamidino-2-phenylindol (DAPI). Stained samples were imaged using Leica SP8 (Leica, Germany).</p>","<title>Targeted genetic disruption of <italic>Trpv4</italic> by CRISPR/Cas9</title>","<p id=\"Par41\">CRISPR/Cas9 target sites in <italic>Trpv4</italic> were searched using CRISPR/Cas9 target online predictor (crispr.direct)<sup>##REF##25414360##49##</sup>. Selected target sites are shown in Supplementary Fig. ##SUPPL##1##5##. Guide RNA was generated using a Guide-it sgRNA In Vitro Transcription kit (TAKARA, Tokyo, Japan). <italic>Cas9</italic> mRNA (50 ng/μl) and sgRNA (25 ng/μl) were injected into one-cell stage embryos. Mutant allele was identified in F1 adult fish using the primer sets shown in Supplementary Table ##SUPPL##1##1## and sequence analysis.</p>","<title>Sperm motility measurement</title>","<p id=\"Par42\"><italic>Zebrafish</italic> and <italic>medaka</italic> testes were diluted in fetal bovine serum (FBS). In <italic>zebrafish</italic>, sperm diluted in FBS were activated by adding an equal volume of breeding water immediately prior to measurement. Sperm motility was recorded as sequential 1024 × 1024-pixel phase-contrast images with a ×10 objective lens for 3 s at 300 frames per second (fps) using cell motion system SI8000 (Sony Corporation, Tokyo, Japan). Images were acquired with SI8000 view Software (Sony Corporation, Tokyo, Japan) and analyzed with SI8000R Analyzer Software. Appropriate amounts of 20β-S were added to the sperm suspension and incubated for 10 min before sperm motility was measured. 20β-S was purchased from Steraloids Inc. Statistical significance was evaluated by two-tailed Student’s <italic>t</italic>-test.</p>","<title>Flow cytometry</title>","<p id=\"Par43\">Testes were isolated from the adult <italic>zebrafish</italic> and dissociated by 0.2% collagenase. The cell suspension was fixated in 2% PFA and was stained with propidium iodide (PI) staining solution (50 μg/ml PI and 20 μg/ml RNase) for at least 10 min at room temperature in the dark. The cells were subsequently filtered through a 40-μm nylon mesh, and the suspension was analyzed using a FACSAria Fusion flow cytometer (BD Biosciences). Statistical significance was evaluated by two-tailed Student’s <italic>t</italic>-test.</p>","<title>Measurement of fertilization rate and survival rates in early embryogenesis</title>","<p id=\"Par44\">Embryos that developed past the epiboly stage were measured as fertilized eggs. Daily survival rates were measured relative to the number of eggs reaching the epiboly stage. Statistical significance was evaluated by two-tailed Student’s <italic>t</italic>-test.</p>","<title>Reporting summary</title>","<p id=\"Par45\">Further information on research design is available in the ##SUPPL##8##Nature Portfolio Reporting Summary## linked to this article.</p>"],"string":"[\n \"<title>Methods</title>\",\n \"<title>Fish</title>\",\n \"<p id=\\\"Par35\\\">All experiments using animals in this study followed the guidelines of Osaka Medical and Pharmaceutical University. Adult <italic>zebrafish</italic> and <italic>medaka</italic> were maintained at 28.5 °C on a 10 h light:14 h darkness photoperiod. <italic>Zebrafish</italic> were bred from a RIKEN WT background and <italic>medaka</italic> strain was OKcab. Male adults 6–12 months old were used for experiments.</p>\",\n \"<title>Temperature stimulation</title>\",\n \"<p id=\\\"Par36\\\">Up to four individuals were temperature-stimulated in W22 × D12 × H11 cm tanks with 1.8 l of water, and water changes were performed daily. The tanks were placed in a water bath at 35 °C and oxygen was supplied by aquarium air stones. Temperature stimulation was interrupted for feeding from 12:00 to 13:00 daily. Exposure of isolated sperm was for 20 min (Supplementary Fig. ##SUPPL##1##9##).</p>\",\n \"<title>Histology</title>\",\n \"<p id=\\\"Par37\\\">Testes were fixed in Bouin solution, and 4 μm plastic sections were prepared using Technobit 8100 (Heraeus Kulzer). Samples were stained with hematoxylin solution for 10 min (Muto pure chemicals, Tokyo, Japan), and stained with 1% eosin (Muto pure chemicals, Tokyo, Japan) for 30 s. The mounted samples were imaged using an Olympus DP74 camera (Evident, Tokyo, Japan).</p>\",\n \"<title>qRT-PCR</title>\",\n \"<p id=\\\"Par38\\\">RNA samples were extracted from testes. According to the manufacturer’s instructions, 0.5 μg RNA template was used for reverse transcription to synthesize cDNA using a first-strand cDNA synthesis kit (ReverTra Ace, TOYOBO, Tokyo, Japan). The qPCR primers are listed in Supplementary Table ##SUPPL##1##1##. For amplification, the KOD SYBR qPCR/RT (TOYOBO, Tokyo, Japan) and ABI real-time system were used. Statistical significance was evaluated by two-tailed Student’s <italic>t</italic>-test.</p>\",\n \"<title>In situ hybridization</title>\",\n \"<p id=\\\"Par39\\\">RNA in situ hybridization (WISH) was carried out following a standard protocol<sup>##REF##16682019##47##</sup>. The primers used to amplify the regions used for the probes are shown in Supplementary Table ##SUPPL##1##1##. Briefly, testes were fixed in 4% paraformaldehyde in 1× PBS overnight. On the following day, samples were hybridized at 65 °C overnight. Samples were further incubated with anti-digoxigenin-AP antibody solution (1:2000) overnight at 4 °C and stained with NBT/BCIP. The stained samples were imaged using an Olympus DP74 camera (Evident). Double in situ hybridization was carried out following a standard protocol<sup>##REF##16894600##48##</sup>. The first probe was labeled with DIG and the second probe was labeled with FITC. After the first staining, incubation was performed with 0.1 M glycine–HCl pH 2.2 for 40 min to remove alkaline phosphatase activity.</p>\",\n \"<title>Immunohistochemistry</title>\",\n \"<p id=\\\"Par40\\\">Immunofluorescence staining was performed using an anti-Cleaved caspase 3 antibody (#9661, Cell Signaling Technology, USA). Alexa 488 was used as the secondary antibody. Nuclear DNA was stained with 4’,6-diamidino-2-phenylindol (DAPI). Stained samples were imaged using Leica SP8 (Leica, Germany).</p>\",\n \"<title>Targeted genetic disruption of <italic>Trpv4</italic> by CRISPR/Cas9</title>\",\n \"<p id=\\\"Par41\\\">CRISPR/Cas9 target sites in <italic>Trpv4</italic> were searched using CRISPR/Cas9 target online predictor (crispr.direct)<sup>##REF##25414360##49##</sup>. Selected target sites are shown in Supplementary Fig. ##SUPPL##1##5##. Guide RNA was generated using a Guide-it sgRNA In Vitro Transcription kit (TAKARA, Tokyo, Japan). <italic>Cas9</italic> mRNA (50 ng/μl) and sgRNA (25 ng/μl) were injected into one-cell stage embryos. Mutant allele was identified in F1 adult fish using the primer sets shown in Supplementary Table ##SUPPL##1##1## and sequence analysis.</p>\",\n \"<title>Sperm motility measurement</title>\",\n \"<p id=\\\"Par42\\\"><italic>Zebrafish</italic> and <italic>medaka</italic> testes were diluted in fetal bovine serum (FBS). In <italic>zebrafish</italic>, sperm diluted in FBS were activated by adding an equal volume of breeding water immediately prior to measurement. Sperm motility was recorded as sequential 1024 × 1024-pixel phase-contrast images with a ×10 objective lens for 3 s at 300 frames per second (fps) using cell motion system SI8000 (Sony Corporation, Tokyo, Japan). Images were acquired with SI8000 view Software (Sony Corporation, Tokyo, Japan) and analyzed with SI8000R Analyzer Software. Appropriate amounts of 20β-S were added to the sperm suspension and incubated for 10 min before sperm motility was measured. 20β-S was purchased from Steraloids Inc. Statistical significance was evaluated by two-tailed Student’s <italic>t</italic>-test.</p>\",\n \"<title>Flow cytometry</title>\",\n \"<p id=\\\"Par43\\\">Testes were isolated from the adult <italic>zebrafish</italic> and dissociated by 0.2% collagenase. The cell suspension was fixated in 2% PFA and was stained with propidium iodide (PI) staining solution (50 μg/ml PI and 20 μg/ml RNase) for at least 10 min at room temperature in the dark. The cells were subsequently filtered through a 40-μm nylon mesh, and the suspension was analyzed using a FACSAria Fusion flow cytometer (BD Biosciences). Statistical significance was evaluated by two-tailed Student’s <italic>t</italic>-test.</p>\",\n \"<title>Measurement of fertilization rate and survival rates in early embryogenesis</title>\",\n \"<p id=\\\"Par44\\\">Embryos that developed past the epiboly stage were measured as fertilized eggs. Daily survival rates were measured relative to the number of eggs reaching the epiboly stage. Statistical significance was evaluated by two-tailed Student’s <italic>t</italic>-test.</p>\",\n \"<title>Reporting summary</title>\",\n \"<p id=\\\"Par45\\\">Further information on research design is available in the ##SUPPL##8##Nature Portfolio Reporting Summary## linked to this article.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<title>High temperature affects Leydig cells prior to germ cells</title>","<p id=\"Par8\">To determine the appropriate condition for high-temperature stimulation in adult <italic>zebrafish</italic> whose optimal temperature is 28–30 °C, we first examined fish survival during 2 weeks of incubation at 33–39 °C. When placed in water <34 °C, mortality was not observed. In contrast, temperature >35 °C caused the death of incubated fish. Based on this observation, 34 °C was chosen as the temperature of stimulus in subsequent analyses (Supplementary Fig. ##SUPPL##1##1##). No abnormalities in spermatogenesis or morphological changes in the testes were observed after 2 weeks of rearing at 29 °C in the identical setup (Supplementary Fig. ##SUPPL##1##2##).</p>","<p id=\"Par9\">To analyze the effect of temperature stimulation on testicular structures and spermatogenesis, sections of the testes were observed by HE staining (Fig. ##FIG##0##1a##, Supplementary Fig. ##SUPPL##1##3a##). There are three developmental stages of spermatids: initial (E1), intermediate (E2), and final (E3)<sup>##REF##19339708##13##</sup>. Temperature stimulation led to a decrease in spermatids, especially at E3 (circled in black lines), and the appearance of abnormal cells (circled in red lines). The abnormal cells had large nuclei relative to the cytoplasm and were different in appearance from any normal types of cells in germ cell development (Supplementary Fig. ##SUPPL##1##4##). Germ cells at pachytene, diplotene, and metaphase stages were present, but the number of spermatids was dramatically reduced in testis exposed to 34 °C. In normal <italic>zebrafish</italic>, germ cells at the identical differentiation stage are confined in a cyst<sup>##REF##19348807##14##</sup>. In contrast, abnormal cells were observed within a single cyst containing meiotic germ cell at metaphase or spermatid after 1 and 3 days of exposure to 34 °C (Supplementary Fig. ##SUPPL##1##4##). After 7 days or 2 weeks at 34 °C, cysts with a mixture of normal and abnormal cells disappeared and only those with abnormal cells were observed (Supplementary Fig. ##SUPPL##1##4##). These results indicate that under high-temperature differentiation from metaphase to spermatid stages became abnormal.</p>","<p id=\"Par10\">In addition, there was a significant decrease in the interstitial tissue, which was observed as gaps between cysts. This tendency became more obvious as the duration of exposure to 34 °C became longer. Specifically, distinct Leydig cells were rarely detected after 2 weeks of exposure to 34 °C (Fig. ##FIG##0##1a##, upper panels).</p>","<p id=\"Par11\">Transfer of fish from 34 °C back to 29 °C resulted in the disappearance of the abnormal cell population and the appearance of interstitial cells in 7 days (Fig. ##FIG##0##1a##, lower panels). E3 spermatid appeared within 10 days of the transition to 29 °C, and spermatogenesis completely recovered to normal in 1 month. In <italic>zebrafish</italic> testes, undifferentiated type-A spermatogonia and differentiated type-B spermatogonia are present<sup>##REF##19348807##14##</sup>. The type-A spermatogonia differentiates from E3 spermatid in 7 days<sup>##REF##19339708##13##</sup>. There were no morphological abnormalities observed in type-A and type-B spermatogonia in testes treated at 34 °C temperature for 2 weeks (Supplementary Fig. ##SUPPL##1##3b##). Therefore, we expected that the transition to 29 °C would induce differentiation to the E3 spermatid within 1 week. However, differentiation into E3 spermatid was observed only after 10 days of recovery, which suggests the involvement of factors other than autonomous differentiation of germ cells.</p>","<p id=\"Par12\">We analyzed the expression of several groups of genes in testes exposed to 34 °C. Among them, genes in the <italic>Heat shock protein</italic> (<italic>Hsp</italic>) group showed significantly upregulated expression within 24 h of temperature stimulation (Fig. ##FIG##0##1b##). <italic>Hsps</italic> are genes that function as molecular chaperones and protect cells when exposed to stress conditions such as heat<sup>##REF##31713482##15##</sup>. Most of the Leydig cell marker genes were significantly downregulated in the testes at 3 days of temperature stimulation. In contrast, the majority of marker genes for germ cells and Sertoli cells showed no change (Fig. ##FIG##0##1b##).</p>","<p id=\"Par13\">Based on the loss of E3 spermatid at 34 °C (Fig. ##FIG##0##1a##), we predicted a decrease in <italic>Odf3b</italic>, a spermatid marker<sup>##REF##31322700##16##</sup>, which contrary to our expectation was not changed (Fig. ##FIG##0##1b##). To investigate why <italic>Odf3b</italic> expression was not decreased at 34 °C, we analyzed the localization of <italic>Odf3b</italic> expression in testes. <italic>Odf3b</italic> was expressed in spermatocytes and early spermatids but not in E3 spermatid at 29 °C (Supplementary Fig. ##SUPPL##1##5##). In contrast, it was expressed in spermatocytes and abnormal cells at 34 °C (Supplementary Fig. ##SUPPL##1##5##). These results suggested that temperature stimulation led to differentiation of O<italic>df3b</italic>-expressing abnormal spermatid, which was consistent with histology (Fig. ##FIG##0##1a##).</p>","<p id=\"Par14\">Since temperature stimulation strongly affected Leydig cells, they were further analyzed by in situ hybridization using its marker <italic>Insl3</italic><sup>##REF##26077926##17##</sup>. The region with positive signal decreased as the duration of 34 °C treatment became longer (Fig. ##FIG##0##1c##), which was consistent with HE staining and the expression analysis (Fig. ##FIG##0##1a and b##). Of note, the expression of <italic>Hspa5</italic>, involved in protein folding and assembly in the endoplasmic reticulum (ER)<sup>##REF##30592132##18##</sup> was induced by high temperature not only in Leydig cells but also in germ cells (Fig. ##FIG##0##1d##). These results suggest that the ER stress response to temperature stimulation occurred in both Leydig cells and germ cells.</p>","<p id=\"Par15\">Suspecting that apoptosis is involved in the different responses between Leydig cells and germ cells, we analyzed the localization of its marker, cleaved caspase 3. The signal was detected in the interstitial cells (circumscribed in white lines) after 1-day exposure to 34 °C and in spermatocytes (in red lines) after 3 days (Fig. ##FIG##0##1e##). These results indicated that apoptosis in response to 34 °C exposure is induced in Leydig cells prior to spermatocytes.</p>","<title>Temperature stimulation led to increased expression of <italic>Trpv4</italic> in Leydig cells</title>","<p id=\"Par16\">To identify molecules involved in temperature sensing, we performed qRT-PCR for a group of <italic>Trp</italic> channel genes, sensor molecules for thermoreception<sup>##UREF##4##19##</sup>. Transient receptor potential (TRP) channels are expressed in sensory organs and play an important role in temperature sensitivity in <italic>zebrafish</italic><sup>##REF##33863444##20##,##REF##21678526##21##</sup>. Expression of <italic>Trpv4, Trpm4b2</italic>, and <italic>Trpm4b3</italic> was significantly increased by temperature stimulation (Fig. ##FIG##1##2a##). Among the three, the change was most evident for <italic>Trpv4</italic>, which was upregulated specifically in interstitial cells as shown by in situ hybridization (Fig. ##FIG##1##2b##). To further characterize these <italic>Trpv4</italic>-expressing cells, we performed double in situ hybridization using Leydig cell marker genes <italic>Insl3</italic> and <italic>Trpv4</italic>. Remarkably, <italic>Trpv4</italic> expression (blue) was limited to <italic>Insl3</italic>-positive cells (brown) in testes treated at 34 °C (Fig. ##FIG##1##2c##). These results show that temperature stimulation led to increased expression of <italic>Trpv4</italic> specifically in Leydig cells.</p>","<title>In <italic>Trpv4</italic> KOs, apoptosis of Leydig cells was not induced by high-temperature</title>","<p id=\"Par17\">To clarify the function of TRPV4, a <italic>Trpv4</italic> mutant was generated using the CRISPR-Cas9 system (Supplementary Fig. ##SUPPL##1##6##). An 8 bp deletion in exon 2 resulted in a frameshift causing a change in amino acid sequence and a premature stop codon. <italic>Trpv4</italic><sup>−/−</sup>\n<italic>zebrafish</italic> were fertile and no abnormalities in spermatogenesis were observed under normal rearing conditions.</p>","<p id=\"Par18\">To analyze the effects of high-temperature treatment on testis structure and spermatogenesis in <italic>Trpv4</italic><sup>−/−</sup>, testis sections were observed with HE staining (Fig. ##FIG##2##3a##, Supplementary Fig. ##SUPPL##1##7##). After 2 weeks at 34 °C, <italic>Trpv4</italic><sup>−/−</sup> as well as <italic>Trpv4</italic><sup>+/+</sup> showed a decrease in E3 spermatid (black lines) and the appearance of abnormal cells (red lines). In contrast, distinct effects were observed in stromal tissue: <italic>Trpv4</italic><sup>+/+</sup> showed reduced interstitial tissue (arrows in Fig. ##FIG##2##3a##), while in <italic>Trpv4</italic><sup>−/−</sup> it remained unaffected. Moreover, recovery of E3 spermatid was observed as early as 7 days after transferring the fish from 34 to 29 °C, earlier in <italic>Trpv4</italic><sup>−/−</sup> than in <italic>Trpv4</italic><sup>+/+</sup>.</p>","<p id=\"Par19\">To examine further the effects of <italic>Trpv4</italic> KO on Leydig cells, the expression of the Leydig cell marker genes was examined. <italic>Star, Insl3</italic>, and <italic>Cyp11c1</italic> were significantly decreased by treatment at 34 °C in <italic>Trpv4</italic><sup>+/+</sup>, while they remained unchanged in <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##2##3b##). Expression of <italic>Cyp17a1</italic> and <italic>Hsd3β</italic> was significantly decreased in <italic>Trpv4</italic><sup>−/−</sup> as well as <italic>Trpv4</italic><sup>+/+</sup> at 34 °C. These results suggested that steroid hormone biosynthesis in <italic>Trpv4</italic><sup>−/−</sup> was affected to a lesser extent by temperature stimulation. Heterogeneity of Leydig cells in <italic>zebrafish</italic>, as was reported in adult mice<sup>##REF##30851938##22##</sup>, may have affected the expression of marker genes in response to high-temperature stimulation. Noticeable difference was not observed for <italic>Hsp</italic>, germ cell markers or Sertoli cell markers (Supplementary Fig. ##SUPPL##1##8##). Correspondingly, <italic>Insl3</italic>(+) area decreased as the duration of treatment at 34 °C increased in <italic>Trpv4</italic>\n<sup>+/+</sup>, while it remained unchanged in <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##2##3c##). Notably, the expression of <italic>Hspa5</italic> showed no difference (Fig. ##FIG##2##3d##). Strong signals of cleaved caspase3 were detected in the interstitial cells (white lines) and spermatocyte (red lines) after 3 days of 34 °C treatment in <italic>Trpv4</italic><sup>+/+</sup>. In <italic>Trpv4</italic><sup>−/−</sup>, in contrast, cleaved caspase3 was restricted to the spermatocytes, and no clear signal in the interstitial cells was observed (Fig. ##FIG##2##3e##). These results indicate that temperature stimulation failed to induce Leydig cell apoptosis in <italic>Trpv4</italic><sup>−/−</sup>.</p>","<title>High temperature impaired sperm motility via <italic>Trpv4</italic> in Leydig cell</title>","<p id=\"Par20\">We analyzed the involvement of <italic>Trpv4</italic> in the motility of mature spermatozoa by exposing adult males to 34 °C for 1 day. When the motility of sperm was analyzed under SI8000, which allows quantification of cell movement, a significant decrease in swimming velocity was observed in <italic>Trpv4</italic><sup>+/+</sup> but not in <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##3##4##a, ##FIG##3##c## and Supplementary Movies ##SUPPL##3##1##–##SUPPL##6##4##, short movies showing representative cases for each genotype). The analysis of trajectories revealed that sperm from <italic>Trpv4</italic><sup>+/+</sup> after 34 °C exposure followed small circular trajectories, traveling shorter distance, which was not observed in <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##3##4b##). Steroid hormone (17α,20β,21-trihydroxy-4-pregnen-3-one (20β-S)) secreted from fish testis work on receptors expressed in sperms and control their motility<sup>##REF##30529310##23##–##REF##19741205##26##</sup>. 20β-S at 100 nM resulted in increased motility and improved trajectory of spermatozoa from <italic>Trpv4</italic><sup>+/+</sup> after 34 °C exposure (Fig. ##FIG##3##4d–f## and Supplementary Fig. ##SUPPL##1##9##).</p>","<p id=\"Par21\">Based on these results, we hypothesized that the decrease of 20β-S secreted from Leydig cells after 34 °C treatment impaired sperm motility. The secretion of 20β-S in the testis was analyzed by LC–MS/MS. Unfortunately, 20β-S in the testes was below the detection limit (data not shown). We therefore used qRT-PCR to examine the expression of the <italic>20β-hsd</italic> by qRT-PCR, an enzyme involved in the synthesis of 20β-S<sup>##REF##24202455##27##</sup>. In <italic>Trpv4</italic><sup>+/+</sup>, the expression was significantly decreased in testes at 3 days of temperature stimulation, while no difference was observed in <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##3##4g##). The result was further confirmed by in situ hybridization. In <italic>Trpv4</italic><sup>+/+</sup>, <italic>20β-hsd</italic> was expressed in the interstitial cells at 29 °C, but its expression disappeared as the incubation at 34 °C lasted longer. In contrast, <italic>Trpv4</italic><sup>−/−</sup> showed clear expression in the interstitial cells even after temperature stimulation (Fig. ##FIG##3##4h##). Based on these results, we propose that the reduction of 20β-S synthesis, caused by decreased expression of <italic>20β-hsd</italic> in Leydig cells, is responsible for the decrease in sperm motility induced by high temperature.</p>","<p id=\"Par22\">Indeed, when sperm isolated from the testis was directly exposed to high temperature, its motility did not decrease even at 40 °C (Supplementary Fig. ##SUPPL##1##10##). Although the duration of exposure to high temperature was shorter than the incubation of the whole fish (Fig. ##FIG##3##4##) due to technical limitations of maintaining isolated sperm in vitro, these results were consistent with the hypothesis that the endocrine regulation of Leydig cells, not autonomous regulation of sperm, determined the sperm motility in response to high temperature.</p>","<title>Sperm matured in a high-temperature environment leads to abnormalities in offspring</title>","<p id=\"Par23\">We hypothesized that <italic>zebrafish</italic> actively inhibit sperm motility in high temperatures so that embryos containing damaged cells, in particular damaged gametes, will not be generated. To test these predictions, we used flow cytometry to analyzed the chromosome content of sperm after 1 day of exposure to 34 °C. Regions of small cell size and low PI content were defined as mature sperm fractions (Supplementary Fig. ##SUPPL##1##11##). Sperm after 1-day exposure to 34 °C displayed a wider range of DNA content than sperm at 29 °C (Fig. ##FIG##4##5a and b##).</p>","<p id=\"Par24\">Males incubated at 34 °C for 1 day were mated with females normally reared and incubated at 29 °C. Fertilization rates were significantly lower for both WT and <italic>Trpv4</italic><sup>−/−</sup> incubated at 34 °C than at 29 °C. However, in the 34 °C group, WT had significantly lower fertilization rates than <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##4##5c##). We propose that the Trpv4<sup>−/−</sup>, whose sperm velocity did not decrease even at 34 °C, had a decrease in fertilization rate smaller than that of WT (Supplementary Fig. ##SUPPL##1##12##). Among the fertilized eggs, there was no difference in survival up to 6 days post fertilization (dpf) (Fig. ##FIG##4##5d##). However, higher developmental abnormalities were observed in offspring from <italic>Trpv4</italic><sup><italic>+/+</italic></sup> and <italic>Trpv4</italic><sup><italic>−/−</italic></sup> males exposed to 34 °C (Fig. ##FIG##4##5e, f##). Because a high frequency of developmental abnormalities occurs in offspring derived from aneuploid sperm in <italic>zebrafish</italic><sup>##REF##15231734##7##,##REF##32470029##28##</sup>, these results suggested that some of the spermatozoa that matured in high-temperature environments have compromised genome stability. They also suggested that <italic>Trpv4</italic> is not involved in the maintenance of sperm quality.</p>","<p id=\"Par25\">If fertilization occurs at high temperatures, generated embryos are also likely to develop in high temperatures. We therefore examined the effect of 34 °C water temperature on developing embryos of <italic>zebrafish</italic>. Embryos incubated at 34 °C resulted in high mortality rates and high developmental anomalies exceeding 70% at 1 dpf (Supplementary Fig. ##SUPPL##1##13##). It was clear that 34 °C is not a favorable temperature for normal embryonic development. However, it is of note that some embryos do develop normally and potentially grow up into fertile adults. These results indicated that <italic>zebrafish</italic> actively reduce sperm motility in order to suppress fertilization in high temperatures and thereby prevent the creation of embryos with compromised genome stability that can potentially be transmitted to offspring.</p>","<title>Temperature-dependent regulation of Leydig cells through <italic>Trp</italic> is species-specific</title>","<p id=\"Par26\">Finally, to examine whether this mechanism of Leydig cell sensitivity to high temperature is universally conserved among fish, we analyzed <italic>medaka</italic> (<italic>Oryzias latipes</italic>), which had an optimal temperature range distinct from that of <italic>zebrafish</italic>\n<sup>##REF##8589427##29##,##REF##15210169##30##</sup>.</p>","<p id=\"Par27\">Similar to <italic>zebrafish</italic>, high-temperature stimulation in <italic>medaka</italic> testes leads to abnormal spermatogenesis<sup>##REF##37126120##31##</sup>. However, the optimal temperature for spermatogenesis was also different between the two species. <italic>Medaka</italic> required exposure to higher temperatures compared to <italic>zebrafish</italic> to show reduced sperm motility (Supplementary Fig. ##SUPPL##1##14a and b##).</p>","<p id=\"Par28\">We performed qRT-PCR analysis of a group of <italic>Trp</italic> channels in the testes of <italic>medaka</italic> exposed to 39 °C. <italic>Hsp</italic> group was significantly upregulated in the temperature stimulation, but none of the genes in the <italic>Trp</italic> group was significantly upregulated (Supplementary Fig. ##SUPPL##1##14c##). Cleaved caspase3 was widely observed in the Leydig cells and germ cells especially spermatocytes after 1 day of exposure to 39 °C (Supplementary Fig. ##SUPPL##1##14d##). These results indicate that the induction of acute apoptosis in Leydig cells via <italic>Trpv4</italic> by temperature stimulation is a phenomenon characteristic to <italic>zebrafish</italic>, and <italic>medaka</italic> may employ different molecules to reduce sperm motility in high temperature<sup>##REF##37126120##31##</sup>.</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>High temperature affects Leydig cells prior to germ cells</title>\",\n \"<p id=\\\"Par8\\\">To determine the appropriate condition for high-temperature stimulation in adult <italic>zebrafish</italic> whose optimal temperature is 28–30 °C, we first examined fish survival during 2 weeks of incubation at 33–39 °C. When placed in water <34 °C, mortality was not observed. In contrast, temperature >35 °C caused the death of incubated fish. Based on this observation, 34 °C was chosen as the temperature of stimulus in subsequent analyses (Supplementary Fig. ##SUPPL##1##1##). No abnormalities in spermatogenesis or morphological changes in the testes were observed after 2 weeks of rearing at 29 °C in the identical setup (Supplementary Fig. ##SUPPL##1##2##).</p>\",\n \"<p id=\\\"Par9\\\">To analyze the effect of temperature stimulation on testicular structures and spermatogenesis, sections of the testes were observed by HE staining (Fig. ##FIG##0##1a##, Supplementary Fig. ##SUPPL##1##3a##). There are three developmental stages of spermatids: initial (E1), intermediate (E2), and final (E3)<sup>##REF##19339708##13##</sup>. Temperature stimulation led to a decrease in spermatids, especially at E3 (circled in black lines), and the appearance of abnormal cells (circled in red lines). The abnormal cells had large nuclei relative to the cytoplasm and were different in appearance from any normal types of cells in germ cell development (Supplementary Fig. ##SUPPL##1##4##). Germ cells at pachytene, diplotene, and metaphase stages were present, but the number of spermatids was dramatically reduced in testis exposed to 34 °C. In normal <italic>zebrafish</italic>, germ cells at the identical differentiation stage are confined in a cyst<sup>##REF##19348807##14##</sup>. In contrast, abnormal cells were observed within a single cyst containing meiotic germ cell at metaphase or spermatid after 1 and 3 days of exposure to 34 °C (Supplementary Fig. ##SUPPL##1##4##). After 7 days or 2 weeks at 34 °C, cysts with a mixture of normal and abnormal cells disappeared and only those with abnormal cells were observed (Supplementary Fig. ##SUPPL##1##4##). These results indicate that under high-temperature differentiation from metaphase to spermatid stages became abnormal.</p>\",\n \"<p id=\\\"Par10\\\">In addition, there was a significant decrease in the interstitial tissue, which was observed as gaps between cysts. This tendency became more obvious as the duration of exposure to 34 °C became longer. Specifically, distinct Leydig cells were rarely detected after 2 weeks of exposure to 34 °C (Fig. ##FIG##0##1a##, upper panels).</p>\",\n \"<p id=\\\"Par11\\\">Transfer of fish from 34 °C back to 29 °C resulted in the disappearance of the abnormal cell population and the appearance of interstitial cells in 7 days (Fig. ##FIG##0##1a##, lower panels). E3 spermatid appeared within 10 days of the transition to 29 °C, and spermatogenesis completely recovered to normal in 1 month. In <italic>zebrafish</italic> testes, undifferentiated type-A spermatogonia and differentiated type-B spermatogonia are present<sup>##REF##19348807##14##</sup>. The type-A spermatogonia differentiates from E3 spermatid in 7 days<sup>##REF##19339708##13##</sup>. There were no morphological abnormalities observed in type-A and type-B spermatogonia in testes treated at 34 °C temperature for 2 weeks (Supplementary Fig. ##SUPPL##1##3b##). Therefore, we expected that the transition to 29 °C would induce differentiation to the E3 spermatid within 1 week. However, differentiation into E3 spermatid was observed only after 10 days of recovery, which suggests the involvement of factors other than autonomous differentiation of germ cells.</p>\",\n \"<p id=\\\"Par12\\\">We analyzed the expression of several groups of genes in testes exposed to 34 °C. Among them, genes in the <italic>Heat shock protein</italic> (<italic>Hsp</italic>) group showed significantly upregulated expression within 24 h of temperature stimulation (Fig. ##FIG##0##1b##). <italic>Hsps</italic> are genes that function as molecular chaperones and protect cells when exposed to stress conditions such as heat<sup>##REF##31713482##15##</sup>. Most of the Leydig cell marker genes were significantly downregulated in the testes at 3 days of temperature stimulation. In contrast, the majority of marker genes for germ cells and Sertoli cells showed no change (Fig. ##FIG##0##1b##).</p>\",\n \"<p id=\\\"Par13\\\">Based on the loss of E3 spermatid at 34 °C (Fig. ##FIG##0##1a##), we predicted a decrease in <italic>Odf3b</italic>, a spermatid marker<sup>##REF##31322700##16##</sup>, which contrary to our expectation was not changed (Fig. ##FIG##0##1b##). To investigate why <italic>Odf3b</italic> expression was not decreased at 34 °C, we analyzed the localization of <italic>Odf3b</italic> expression in testes. <italic>Odf3b</italic> was expressed in spermatocytes and early spermatids but not in E3 spermatid at 29 °C (Supplementary Fig. ##SUPPL##1##5##). In contrast, it was expressed in spermatocytes and abnormal cells at 34 °C (Supplementary Fig. ##SUPPL##1##5##). These results suggested that temperature stimulation led to differentiation of O<italic>df3b</italic>-expressing abnormal spermatid, which was consistent with histology (Fig. ##FIG##0##1a##).</p>\",\n \"<p id=\\\"Par14\\\">Since temperature stimulation strongly affected Leydig cells, they were further analyzed by in situ hybridization using its marker <italic>Insl3</italic><sup>##REF##26077926##17##</sup>. The region with positive signal decreased as the duration of 34 °C treatment became longer (Fig. ##FIG##0##1c##), which was consistent with HE staining and the expression analysis (Fig. ##FIG##0##1a and b##). Of note, the expression of <italic>Hspa5</italic>, involved in protein folding and assembly in the endoplasmic reticulum (ER)<sup>##REF##30592132##18##</sup> was induced by high temperature not only in Leydig cells but also in germ cells (Fig. ##FIG##0##1d##). These results suggest that the ER stress response to temperature stimulation occurred in both Leydig cells and germ cells.</p>\",\n \"<p id=\\\"Par15\\\">Suspecting that apoptosis is involved in the different responses between Leydig cells and germ cells, we analyzed the localization of its marker, cleaved caspase 3. The signal was detected in the interstitial cells (circumscribed in white lines) after 1-day exposure to 34 °C and in spermatocytes (in red lines) after 3 days (Fig. ##FIG##0##1e##). These results indicated that apoptosis in response to 34 °C exposure is induced in Leydig cells prior to spermatocytes.</p>\",\n \"<title>Temperature stimulation led to increased expression of <italic>Trpv4</italic> in Leydig cells</title>\",\n \"<p id=\\\"Par16\\\">To identify molecules involved in temperature sensing, we performed qRT-PCR for a group of <italic>Trp</italic> channel genes, sensor molecules for thermoreception<sup>##UREF##4##19##</sup>. Transient receptor potential (TRP) channels are expressed in sensory organs and play an important role in temperature sensitivity in <italic>zebrafish</italic><sup>##REF##33863444##20##,##REF##21678526##21##</sup>. Expression of <italic>Trpv4, Trpm4b2</italic>, and <italic>Trpm4b3</italic> was significantly increased by temperature stimulation (Fig. ##FIG##1##2a##). Among the three, the change was most evident for <italic>Trpv4</italic>, which was upregulated specifically in interstitial cells as shown by in situ hybridization (Fig. ##FIG##1##2b##). To further characterize these <italic>Trpv4</italic>-expressing cells, we performed double in situ hybridization using Leydig cell marker genes <italic>Insl3</italic> and <italic>Trpv4</italic>. Remarkably, <italic>Trpv4</italic> expression (blue) was limited to <italic>Insl3</italic>-positive cells (brown) in testes treated at 34 °C (Fig. ##FIG##1##2c##). These results show that temperature stimulation led to increased expression of <italic>Trpv4</italic> specifically in Leydig cells.</p>\",\n \"<title>In <italic>Trpv4</italic> KOs, apoptosis of Leydig cells was not induced by high-temperature</title>\",\n \"<p id=\\\"Par17\\\">To clarify the function of TRPV4, a <italic>Trpv4</italic> mutant was generated using the CRISPR-Cas9 system (Supplementary Fig. ##SUPPL##1##6##). An 8 bp deletion in exon 2 resulted in a frameshift causing a change in amino acid sequence and a premature stop codon. <italic>Trpv4</italic><sup>−/−</sup>\\n<italic>zebrafish</italic> were fertile and no abnormalities in spermatogenesis were observed under normal rearing conditions.</p>\",\n \"<p id=\\\"Par18\\\">To analyze the effects of high-temperature treatment on testis structure and spermatogenesis in <italic>Trpv4</italic><sup>−/−</sup>, testis sections were observed with HE staining (Fig. ##FIG##2##3a##, Supplementary Fig. ##SUPPL##1##7##). After 2 weeks at 34 °C, <italic>Trpv4</italic><sup>−/−</sup> as well as <italic>Trpv4</italic><sup>+/+</sup> showed a decrease in E3 spermatid (black lines) and the appearance of abnormal cells (red lines). In contrast, distinct effects were observed in stromal tissue: <italic>Trpv4</italic><sup>+/+</sup> showed reduced interstitial tissue (arrows in Fig. ##FIG##2##3a##), while in <italic>Trpv4</italic><sup>−/−</sup> it remained unaffected. Moreover, recovery of E3 spermatid was observed as early as 7 days after transferring the fish from 34 to 29 °C, earlier in <italic>Trpv4</italic><sup>−/−</sup> than in <italic>Trpv4</italic><sup>+/+</sup>.</p>\",\n \"<p id=\\\"Par19\\\">To examine further the effects of <italic>Trpv4</italic> KO on Leydig cells, the expression of the Leydig cell marker genes was examined. <italic>Star, Insl3</italic>, and <italic>Cyp11c1</italic> were significantly decreased by treatment at 34 °C in <italic>Trpv4</italic><sup>+/+</sup>, while they remained unchanged in <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##2##3b##). Expression of <italic>Cyp17a1</italic> and <italic>Hsd3β</italic> was significantly decreased in <italic>Trpv4</italic><sup>−/−</sup> as well as <italic>Trpv4</italic><sup>+/+</sup> at 34 °C. These results suggested that steroid hormone biosynthesis in <italic>Trpv4</italic><sup>−/−</sup> was affected to a lesser extent by temperature stimulation. Heterogeneity of Leydig cells in <italic>zebrafish</italic>, as was reported in adult mice<sup>##REF##30851938##22##</sup>, may have affected the expression of marker genes in response to high-temperature stimulation. Noticeable difference was not observed for <italic>Hsp</italic>, germ cell markers or Sertoli cell markers (Supplementary Fig. ##SUPPL##1##8##). Correspondingly, <italic>Insl3</italic>(+) area decreased as the duration of treatment at 34 °C increased in <italic>Trpv4</italic>\\n<sup>+/+</sup>, while it remained unchanged in <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##2##3c##). Notably, the expression of <italic>Hspa5</italic> showed no difference (Fig. ##FIG##2##3d##). Strong signals of cleaved caspase3 were detected in the interstitial cells (white lines) and spermatocyte (red lines) after 3 days of 34 °C treatment in <italic>Trpv4</italic><sup>+/+</sup>. In <italic>Trpv4</italic><sup>−/−</sup>, in contrast, cleaved caspase3 was restricted to the spermatocytes, and no clear signal in the interstitial cells was observed (Fig. ##FIG##2##3e##). These results indicate that temperature stimulation failed to induce Leydig cell apoptosis in <italic>Trpv4</italic><sup>−/−</sup>.</p>\",\n \"<title>High temperature impaired sperm motility via <italic>Trpv4</italic> in Leydig cell</title>\",\n \"<p id=\\\"Par20\\\">We analyzed the involvement of <italic>Trpv4</italic> in the motility of mature spermatozoa by exposing adult males to 34 °C for 1 day. When the motility of sperm was analyzed under SI8000, which allows quantification of cell movement, a significant decrease in swimming velocity was observed in <italic>Trpv4</italic><sup>+/+</sup> but not in <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##3##4##a, ##FIG##3##c## and Supplementary Movies ##SUPPL##3##1##–##SUPPL##6##4##, short movies showing representative cases for each genotype). The analysis of trajectories revealed that sperm from <italic>Trpv4</italic><sup>+/+</sup> after 34 °C exposure followed small circular trajectories, traveling shorter distance, which was not observed in <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##3##4b##). Steroid hormone (17α,20β,21-trihydroxy-4-pregnen-3-one (20β-S)) secreted from fish testis work on receptors expressed in sperms and control their motility<sup>##REF##30529310##23##–##REF##19741205##26##</sup>. 20β-S at 100 nM resulted in increased motility and improved trajectory of spermatozoa from <italic>Trpv4</italic><sup>+/+</sup> after 34 °C exposure (Fig. ##FIG##3##4d–f## and Supplementary Fig. ##SUPPL##1##9##).</p>\",\n \"<p id=\\\"Par21\\\">Based on these results, we hypothesized that the decrease of 20β-S secreted from Leydig cells after 34 °C treatment impaired sperm motility. The secretion of 20β-S in the testis was analyzed by LC–MS/MS. Unfortunately, 20β-S in the testes was below the detection limit (data not shown). We therefore used qRT-PCR to examine the expression of the <italic>20β-hsd</italic> by qRT-PCR, an enzyme involved in the synthesis of 20β-S<sup>##REF##24202455##27##</sup>. In <italic>Trpv4</italic><sup>+/+</sup>, the expression was significantly decreased in testes at 3 days of temperature stimulation, while no difference was observed in <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##3##4g##). The result was further confirmed by in situ hybridization. In <italic>Trpv4</italic><sup>+/+</sup>, <italic>20β-hsd</italic> was expressed in the interstitial cells at 29 °C, but its expression disappeared as the incubation at 34 °C lasted longer. In contrast, <italic>Trpv4</italic><sup>−/−</sup> showed clear expression in the interstitial cells even after temperature stimulation (Fig. ##FIG##3##4h##). Based on these results, we propose that the reduction of 20β-S synthesis, caused by decreased expression of <italic>20β-hsd</italic> in Leydig cells, is responsible for the decrease in sperm motility induced by high temperature.</p>\",\n \"<p id=\\\"Par22\\\">Indeed, when sperm isolated from the testis was directly exposed to high temperature, its motility did not decrease even at 40 °C (Supplementary Fig. ##SUPPL##1##10##). Although the duration of exposure to high temperature was shorter than the incubation of the whole fish (Fig. ##FIG##3##4##) due to technical limitations of maintaining isolated sperm in vitro, these results were consistent with the hypothesis that the endocrine regulation of Leydig cells, not autonomous regulation of sperm, determined the sperm motility in response to high temperature.</p>\",\n \"<title>Sperm matured in a high-temperature environment leads to abnormalities in offspring</title>\",\n \"<p id=\\\"Par23\\\">We hypothesized that <italic>zebrafish</italic> actively inhibit sperm motility in high temperatures so that embryos containing damaged cells, in particular damaged gametes, will not be generated. To test these predictions, we used flow cytometry to analyzed the chromosome content of sperm after 1 day of exposure to 34 °C. Regions of small cell size and low PI content were defined as mature sperm fractions (Supplementary Fig. ##SUPPL##1##11##). Sperm after 1-day exposure to 34 °C displayed a wider range of DNA content than sperm at 29 °C (Fig. ##FIG##4##5a and b##).</p>\",\n \"<p id=\\\"Par24\\\">Males incubated at 34 °C for 1 day were mated with females normally reared and incubated at 29 °C. Fertilization rates were significantly lower for both WT and <italic>Trpv4</italic><sup>−/−</sup> incubated at 34 °C than at 29 °C. However, in the 34 °C group, WT had significantly lower fertilization rates than <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##4##5c##). We propose that the Trpv4<sup>−/−</sup>, whose sperm velocity did not decrease even at 34 °C, had a decrease in fertilization rate smaller than that of WT (Supplementary Fig. ##SUPPL##1##12##). Among the fertilized eggs, there was no difference in survival up to 6 days post fertilization (dpf) (Fig. ##FIG##4##5d##). However, higher developmental abnormalities were observed in offspring from <italic>Trpv4</italic><sup><italic>+/+</italic></sup> and <italic>Trpv4</italic><sup><italic>−/−</italic></sup> males exposed to 34 °C (Fig. ##FIG##4##5e, f##). Because a high frequency of developmental abnormalities occurs in offspring derived from aneuploid sperm in <italic>zebrafish</italic><sup>##REF##15231734##7##,##REF##32470029##28##</sup>, these results suggested that some of the spermatozoa that matured in high-temperature environments have compromised genome stability. They also suggested that <italic>Trpv4</italic> is not involved in the maintenance of sperm quality.</p>\",\n \"<p id=\\\"Par25\\\">If fertilization occurs at high temperatures, generated embryos are also likely to develop in high temperatures. We therefore examined the effect of 34 °C water temperature on developing embryos of <italic>zebrafish</italic>. Embryos incubated at 34 °C resulted in high mortality rates and high developmental anomalies exceeding 70% at 1 dpf (Supplementary Fig. ##SUPPL##1##13##). It was clear that 34 °C is not a favorable temperature for normal embryonic development. However, it is of note that some embryos do develop normally and potentially grow up into fertile adults. These results indicated that <italic>zebrafish</italic> actively reduce sperm motility in order to suppress fertilization in high temperatures and thereby prevent the creation of embryos with compromised genome stability that can potentially be transmitted to offspring.</p>\",\n \"<title>Temperature-dependent regulation of Leydig cells through <italic>Trp</italic> is species-specific</title>\",\n \"<p id=\\\"Par26\\\">Finally, to examine whether this mechanism of Leydig cell sensitivity to high temperature is universally conserved among fish, we analyzed <italic>medaka</italic> (<italic>Oryzias latipes</italic>), which had an optimal temperature range distinct from that of <italic>zebrafish</italic>\\n<sup>##REF##8589427##29##,##REF##15210169##30##</sup>.</p>\",\n \"<p id=\\\"Par27\\\">Similar to <italic>zebrafish</italic>, high-temperature stimulation in <italic>medaka</italic> testes leads to abnormal spermatogenesis<sup>##REF##37126120##31##</sup>. However, the optimal temperature for spermatogenesis was also different between the two species. <italic>Medaka</italic> required exposure to higher temperatures compared to <italic>zebrafish</italic> to show reduced sperm motility (Supplementary Fig. ##SUPPL##1##14a and b##).</p>\",\n \"<p id=\\\"Par28\\\">We performed qRT-PCR analysis of a group of <italic>Trp</italic> channels in the testes of <italic>medaka</italic> exposed to 39 °C. <italic>Hsp</italic> group was significantly upregulated in the temperature stimulation, but none of the genes in the <italic>Trp</italic> group was significantly upregulated (Supplementary Fig. ##SUPPL##1##14c##). Cleaved caspase3 was widely observed in the Leydig cells and germ cells especially spermatocytes after 1 day of exposure to 39 °C (Supplementary Fig. ##SUPPL##1##14d##). These results indicate that the induction of acute apoptosis in Leydig cells via <italic>Trpv4</italic> by temperature stimulation is a phenomenon characteristic to <italic>zebrafish</italic>, and <italic>medaka</italic> may employ different molecules to reduce sperm motility in high temperature<sup>##REF##37126120##31##</sup>.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par29\">Multiple reports in mammals showed that germ cells are the main target of detrimental high temperatures in testes<sup>##REF##12943702##32##</sup>. Our study using <italic>zebrafish</italic> revealed that high temperature induced apoptosis in Leydig cells prior to germ cells. Moreover, we showed that Leydig cells and germ cells in <italic>zebrafish</italic> have distinct mechanisms in sensing and responding to high temperatures. <italic>Trpv4</italic> was involved in the Leydig cell-specific high-temperature sensitivity, which caused apoptosis, leading in turn to impaired sperm motility and reduced fertilization.</p>","<p id=\"Par30\">TRP channels were identified as sensor molecules for thermal reception, through which cellular responses to temperature changes are regulated<sup>##REF##33227348##33##</sup>. Each TRP channel has a unique thermoreceptive range. Although TRP proteins are expressed also in the testes<sup>##UREF##5##34##</sup>, <italic>Trp</italic> in the <italic>zebrafish</italic> testis is poorly characterized. <italic>Trpv4</italic> is strongly expressed in stressful environments<sup>##REF##30455633##35##,##REF##28205608##36##</sup> and activated above 27–35 °C<sup>##REF##35728275##37##</sup>, which fits the high-temperature condition used in this study. <italic>Trpv4</italic> activation by endogenous and exogenous stimuli increases Ca<sup>2+</sup> influx, resulting in an excess of intracellular free Ca<sup>2+</sup><sup>##UREF##6##38##</sup>, and causes apoptosis in pathological conditions<sup>##REF##30455633##35##,##REF##28205608##36##,##REF##29293584##39##</sup>. Trpv4 in <italic>zebrafish</italic> was studied by Amato et al. who showed the expression of this protein in sensory organs<sup>##REF##21678526##21##</sup>. The lack of temperature sensing mechanism may also contribute to the phenotypes of <italic>Trpv4</italic><sup><italic>−/−</italic></sup> we observed (Fig. ##FIG##2##3##). Taken together, these characteristics of <italic>Trpv4</italic> fit our hypothesis that high temperature led to upregulation of <italic>Trpv4</italic> in Leydig cells and caused apoptosis.</p>","<p id=\"Par31\">In <italic>zebrafish</italic>, as in mice, high-temperature stimulation caused abnormal spermatogenesis (Fig. ##FIG##0##1a##). Specifically, high-temperature treatment induced apoptosis in spermatocytes and abnormal differentiation from spermatocyte to spermatid, which was consistent with mice. Meiotic checkpoints cause spermatogenesis defects in mice following high-temperature stimulus and eliminate damaged spermatocytes<sup>##UREF##7##40##</sup>. A similar, sperm cell-autonomous mechanism seems to operate also in <italic>zebrafish</italic>. Hormone secretion from Leydig cells is unlikely to be essential for these cell-autonomous apoptoses because spermatogenesis failure was not distinguishable between <italic>Trpv4</italic><sup>+/+</sup> and <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##2##3##, Supplementary Fig. ##SUPPL##1##12##). Indeed, knockouts of steroid synthase genes in <italic>zebrafish</italic> can produce mature spermatozoa<sup>##REF##31322169##41##–##REF##32805708##44##</sup>, which was consistent with our hypothesis. On the other hand, the effect of hormones secreted from Leydig cells on certain aspects of spermatogenesis was suggested from our data. First, the transition from 34 to 29 °C led to earlier E3 spermatid differentiation in <italic>Trpv4</italic><sup>−/−</sup> compared to <italic>Trpv4</italic><sup>+/+</sup>. Second, expression of several genes including <italic>Insl3</italic> did not change in <italic>Trpv4</italic><sup>−/−</sup> exposed to 34 °C. In <italic>zebrafish</italic>, <italic>Insl3</italic> was reported to act as a germline survival factor<sup>##REF##33589679##42##</sup>. In <italic>Trpv4</italic><sup>−/−</sup>, hormone secretion from Leydig cells was likely involved in promoting the rapid differentiation of E3 spermatid or survival of the remaining germ cells.</p>","<p id=\"Par32\">We propose that the Leydig cell-specific temperature-sensing mechanism comprises a system that suppresses fertilization in <italic>zebrafish</italic> under high-temperature conditions. In their natural habitat, <italic>zebrafish</italic> live in shallow tropical water with slow currents that are subject to frequent temperature fluctuations<sup>##REF##18093234##45##</sup>, where water temperatures can rise to 38.6 °C<sup>##UREF##8##46##</sup>. High-temperature environments lead to severe abnormalities in early <italic>zebrafish</italic> development (Supplementary Fig. ##SUPPL##1##13##). Importantly, the maturation of spermatozoa in a high-temperature environment resulted in aneuploid spermatozoa (Fig. ##FIG##4##5a##). In addition, a high-frequency of developmental abnormalities were observed in offspring, even when incubated at 29 °C after fertilization (Fig. ##FIG##4##5e and f##). If gametes with compromised genomic integrity are transmitted to the next generation, the result will be detrimental to the species. Therefore, a mechanism to avoid fertilization in high-temperature environments will be advantageous.</p>","<p id=\"Par33\">Because the meiosis checkpoint controls the differentiation from metaphase to early spermatid, spermatozoa that have matured prior to temperature stimulation cannot be eliminated by the meiosis checkpoint. (Supplementary Figs. ##SUPPL##1##4## and ##SUPPL##1##5##). Indeed, E3 spermatids were abundantly observed in the testis after 1-day incubation at 34 °C (Fig. ##FIG##0##1a## and Supplementary Fig. ##SUPPL##1##3##). Moreover, mature spermatozoa did not lose sperm motility in high temperatures (Supplementary Fig. ##SUPPL##1##10##). Therefore, mature sperm differentiated prior to temperature stimulation are expected to be motile with fertilization capabilities even at 34 °C. However, sperm motility and fertilization rate were significantly reduced by a 1-day exposure to 34 °C (Figs. ##FIG##3##4##a, c and ##FIG##4##5c##). Taken together, we propose that <italic>Trpv4</italic>-mediated reduction of 20β-S synthesis is the mechanism responsible for inhibiting fertilization of mature sperm which has already passed the meiosis checkpoint prior to exposure to high temperature.</p>","<p id=\"Par34\">Neither upregulated expression of <italic>Trp</italic> family genes nor Leydig cell-specific apoptosis at high temperatures was observed in <italic>medaka</italic>. Temperature stimulation at 39 °C resulted in reduced sperm motility and induction of apoptosis, especially in spermatocytes (Supplementary Fig. ##SUPPL##1##14##), while molecular changes identified in <italic>zebrafish</italic> were not observed in <italic>medaka</italic>. These results suggest that factors other than <italic>Trp</italic> and meiotic checkpoints are involved in the apoptotic response to high temperature in <italic>medaka</italic>. We propose that response to high temperature in Leydig cells was established as a system to actively suppress fertilization in <italic>zebrafish</italic>, whose adaptive water temperature range matched the molecular characteristics of <italic>Trpv4</italic>.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par29\\\">Multiple reports in mammals showed that germ cells are the main target of detrimental high temperatures in testes<sup>##REF##12943702##32##</sup>. Our study using <italic>zebrafish</italic> revealed that high temperature induced apoptosis in Leydig cells prior to germ cells. Moreover, we showed that Leydig cells and germ cells in <italic>zebrafish</italic> have distinct mechanisms in sensing and responding to high temperatures. <italic>Trpv4</italic> was involved in the Leydig cell-specific high-temperature sensitivity, which caused apoptosis, leading in turn to impaired sperm motility and reduced fertilization.</p>\",\n \"<p id=\\\"Par30\\\">TRP channels were identified as sensor molecules for thermal reception, through which cellular responses to temperature changes are regulated<sup>##REF##33227348##33##</sup>. Each TRP channel has a unique thermoreceptive range. Although TRP proteins are expressed also in the testes<sup>##UREF##5##34##</sup>, <italic>Trp</italic> in the <italic>zebrafish</italic> testis is poorly characterized. <italic>Trpv4</italic> is strongly expressed in stressful environments<sup>##REF##30455633##35##,##REF##28205608##36##</sup> and activated above 27–35 °C<sup>##REF##35728275##37##</sup>, which fits the high-temperature condition used in this study. <italic>Trpv4</italic> activation by endogenous and exogenous stimuli increases Ca<sup>2+</sup> influx, resulting in an excess of intracellular free Ca<sup>2+</sup><sup>##UREF##6##38##</sup>, and causes apoptosis in pathological conditions<sup>##REF##30455633##35##,##REF##28205608##36##,##REF##29293584##39##</sup>. Trpv4 in <italic>zebrafish</italic> was studied by Amato et al. who showed the expression of this protein in sensory organs<sup>##REF##21678526##21##</sup>. The lack of temperature sensing mechanism may also contribute to the phenotypes of <italic>Trpv4</italic><sup><italic>−/−</italic></sup> we observed (Fig. ##FIG##2##3##). Taken together, these characteristics of <italic>Trpv4</italic> fit our hypothesis that high temperature led to upregulation of <italic>Trpv4</italic> in Leydig cells and caused apoptosis.</p>\",\n \"<p id=\\\"Par31\\\">In <italic>zebrafish</italic>, as in mice, high-temperature stimulation caused abnormal spermatogenesis (Fig. ##FIG##0##1a##). Specifically, high-temperature treatment induced apoptosis in spermatocytes and abnormal differentiation from spermatocyte to spermatid, which was consistent with mice. Meiotic checkpoints cause spermatogenesis defects in mice following high-temperature stimulus and eliminate damaged spermatocytes<sup>##UREF##7##40##</sup>. A similar, sperm cell-autonomous mechanism seems to operate also in <italic>zebrafish</italic>. Hormone secretion from Leydig cells is unlikely to be essential for these cell-autonomous apoptoses because spermatogenesis failure was not distinguishable between <italic>Trpv4</italic><sup>+/+</sup> and <italic>Trpv4</italic><sup>−/−</sup> (Fig. ##FIG##2##3##, Supplementary Fig. ##SUPPL##1##12##). Indeed, knockouts of steroid synthase genes in <italic>zebrafish</italic> can produce mature spermatozoa<sup>##REF##31322169##41##–##REF##32805708##44##</sup>, which was consistent with our hypothesis. On the other hand, the effect of hormones secreted from Leydig cells on certain aspects of spermatogenesis was suggested from our data. First, the transition from 34 to 29 °C led to earlier E3 spermatid differentiation in <italic>Trpv4</italic><sup>−/−</sup> compared to <italic>Trpv4</italic><sup>+/+</sup>. Second, expression of several genes including <italic>Insl3</italic> did not change in <italic>Trpv4</italic><sup>−/−</sup> exposed to 34 °C. In <italic>zebrafish</italic>, <italic>Insl3</italic> was reported to act as a germline survival factor<sup>##REF##33589679##42##</sup>. In <italic>Trpv4</italic><sup>−/−</sup>, hormone secretion from Leydig cells was likely involved in promoting the rapid differentiation of E3 spermatid or survival of the remaining germ cells.</p>\",\n \"<p id=\\\"Par32\\\">We propose that the Leydig cell-specific temperature-sensing mechanism comprises a system that suppresses fertilization in <italic>zebrafish</italic> under high-temperature conditions. In their natural habitat, <italic>zebrafish</italic> live in shallow tropical water with slow currents that are subject to frequent temperature fluctuations<sup>##REF##18093234##45##</sup>, where water temperatures can rise to 38.6 °C<sup>##UREF##8##46##</sup>. High-temperature environments lead to severe abnormalities in early <italic>zebrafish</italic> development (Supplementary Fig. ##SUPPL##1##13##). Importantly, the maturation of spermatozoa in a high-temperature environment resulted in aneuploid spermatozoa (Fig. ##FIG##4##5a##). In addition, a high-frequency of developmental abnormalities were observed in offspring, even when incubated at 29 °C after fertilization (Fig. ##FIG##4##5e and f##). If gametes with compromised genomic integrity are transmitted to the next generation, the result will be detrimental to the species. Therefore, a mechanism to avoid fertilization in high-temperature environments will be advantageous.</p>\",\n \"<p id=\\\"Par33\\\">Because the meiosis checkpoint controls the differentiation from metaphase to early spermatid, spermatozoa that have matured prior to temperature stimulation cannot be eliminated by the meiosis checkpoint. (Supplementary Figs. ##SUPPL##1##4## and ##SUPPL##1##5##). Indeed, E3 spermatids were abundantly observed in the testis after 1-day incubation at 34 °C (Fig. ##FIG##0##1a## and Supplementary Fig. ##SUPPL##1##3##). Moreover, mature spermatozoa did not lose sperm motility in high temperatures (Supplementary Fig. ##SUPPL##1##10##). Therefore, mature sperm differentiated prior to temperature stimulation are expected to be motile with fertilization capabilities even at 34 °C. However, sperm motility and fertilization rate were significantly reduced by a 1-day exposure to 34 °C (Figs. ##FIG##3##4##a, c and ##FIG##4##5c##). Taken together, we propose that <italic>Trpv4</italic>-mediated reduction of 20β-S synthesis is the mechanism responsible for inhibiting fertilization of mature sperm which has already passed the meiosis checkpoint prior to exposure to high temperature.</p>\",\n \"<p id=\\\"Par34\\\">Neither upregulated expression of <italic>Trp</italic> family genes nor Leydig cell-specific apoptosis at high temperatures was observed in <italic>medaka</italic>. Temperature stimulation at 39 °C resulted in reduced sperm motility and induction of apoptosis, especially in spermatocytes (Supplementary Fig. ##SUPPL##1##14##), while molecular changes identified in <italic>zebrafish</italic> were not observed in <italic>medaka</italic>. These results suggest that factors other than <italic>Trp</italic> and meiotic checkpoints are involved in the apoptotic response to high temperature in <italic>medaka</italic>. We propose that response to high temperature in Leydig cells was established as a system to actively suppress fertilization in <italic>zebrafish</italic>, whose adaptive water temperature range matched the molecular characteristics of <italic>Trpv4</italic>.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Exposure of testes to high-temperature environment results in defective spermatogenesis. <italic>Zebrafish</italic> exposed to high temperature exhibited apoptosis not only in germ cells but also in Leydig cells, as expected from studies using mice or salmon. However, the role of testicular somatic cells in spermatogenesis defects remains unclear. We found that in Leydig cells the <italic>Trpv4</italic> gene encoding the temperature sensitive ion channel was specifically upregulated in high temperature. High temperature also reduced hormone synthesis in Leydig cells and led to a prompt downregulation of sperm motility. In the <italic>Trpv4</italic> null mutant, neither Leydig cell-specific apoptosis nor decreased sperm motility was observed under high temperature. These results indicate that Leydig cell specific-apoptosis is induced via <italic>Trpv4</italic> by high temperature. Notably this <italic>Trpv4</italic>-dependent mechanism was specific to Leydig cells and did not operate in germ cells. Because sperm exposed to high temperature exhibited compromised genome stability, we propose that temperature sensing leading to apoptosis in Leydig cells evolved to actively suppress generation of offspring with unstable genome.</p>","<p id=\"Par2\">Under high temperature in zebrafish, the temperature sensitive ion channel <italic>Trpv4</italic> is upregulated in Leydig cells, inducing apoptosis. Leydig cell-specific apoptosis decreases steroid hormone synthesis, impairing the motility of sperm with compromised genome integrity.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Exposure of testes to high-temperature environment results in defective spermatogenesis. <italic>Zebrafish</italic> exposed to high temperature exhibited apoptosis not only in germ cells but also in Leydig cells, as expected from studies using mice or salmon. However, the role of testicular somatic cells in spermatogenesis defects remains unclear. We found that in Leydig cells the <italic>Trpv4</italic> gene encoding the temperature sensitive ion channel was specifically upregulated in high temperature. High temperature also reduced hormone synthesis in Leydig cells and led to a prompt downregulation of sperm motility. In the <italic>Trpv4</italic> null mutant, neither Leydig cell-specific apoptosis nor decreased sperm motility was observed under high temperature. These results indicate that Leydig cell specific-apoptosis is induced via <italic>Trpv4</italic> by high temperature. Notably this <italic>Trpv4</italic>-dependent mechanism was specific to Leydig cells and did not operate in germ cells. Because sperm exposed to high temperature exhibited compromised genome stability, we propose that temperature sensing leading to apoptosis in Leydig cells evolved to actively suppress generation of offspring with unstable genome.</p>\",\n \"<p id=\\\"Par2\\\">Under high temperature in zebrafish, the temperature sensitive ion channel <italic>Trpv4</italic> is upregulated in Leydig cells, inducing apoptosis. Leydig cell-specific apoptosis decreases steroid hormone synthesis, impairing the motility of sperm with compromised genome integrity.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary information</title>","<p>\n\n\n\n\n\n\n\n\n\n</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary information</title>","<p>The online version contains supplementary material available at 10.1038/s42003-023-05740-y.</p>","<title>Acknowledgements</title>","<p>We are grateful to Mrs. Natsuko Okuda at OMPU for her excellent care of <italic>zebrafish</italic> and <italic>medaka</italic> used in this study. We are grateful to medical students at OMPU, Mr. Akihiro Tani, Ms. Yuri Ozaki, Mr. Tomohiro Hirayama, Mr. Takeyoshi Murakami, Mr. Kei Yotsumoto and Mr. Kakeru Terada in particular, for their assistance in some of the experiments. We thank Dr. Toshiya Nishimura for critically reading the manuscript. This research was partially supported by Grant-in-Aid for Scientific Research (19K06460, Y.Y.).</p>","<title>Author contributions</title>","<p>Y.Y. carried out the experiment and analyzed the data. D.H. carried out the LC–MS/MS analysis. Y.Y. designed the study. F.O. supervised the study. Y.Y. and F.O. wrote the paper.</p>","<title>Peer review</title>","<title>Peer review information</title>","<p id=\"Par46\"><italic>Communications Biology</italic> thanks Juan (I) Fernandino and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Frank Avila and Tobias Goris. A peer review file is available.</p>","<title>Data availability</title>","<p>The video data are available from the authors upon request. The source data behind the figures can be found in Supplementary Data ##SUPPL##7##1##.</p>","<title>Competing interests</title>","<p id=\"Par47\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s42003-023-05740-y.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>We are grateful to Mrs. Natsuko Okuda at OMPU for her excellent care of <italic>zebrafish</italic> and <italic>medaka</italic> used in this study. We are grateful to medical students at OMPU, Mr. Akihiro Tani, Ms. Yuri Ozaki, Mr. Tomohiro Hirayama, Mr. Takeyoshi Murakami, Mr. Kei Yotsumoto and Mr. Kakeru Terada in particular, for their assistance in some of the experiments. We thank Dr. Toshiya Nishimura for critically reading the manuscript. This research was partially supported by Grant-in-Aid for Scientific Research (19K06460, Y.Y.).</p>\",\n \"<title>Author contributions</title>\",\n \"<p>Y.Y. carried out the experiment and analyzed the data. D.H. carried out the LC–MS/MS analysis. Y.Y. designed the study. F.O. supervised the study. Y.Y. and F.O. wrote the paper.</p>\",\n \"<title>Peer review</title>\",\n \"<title>Peer review information</title>\",\n \"<p id=\\\"Par46\\\"><italic>Communications Biology</italic> thanks Juan (I) Fernandino and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Frank Avila and Tobias Goris. A peer review file is available.</p>\",\n \"<title>Data availability</title>\",\n \"<p>The video data are available from the authors upon request. The source data behind the figures can be found in Supplementary Data ##SUPPL##7##1##.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par47\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>High-temperature treatment led to significant apoptosis of Leydig cells.</title><p><bold>a</bold> Top row: HE staining of testes. The black lines indicate areas containing E3 spermatid. The red lines indicate areas containing abnormal cell populations. Arrows indicate interstitial tissue. Interstitial areas decreased at 3, 7, and 14 days of temperature stimulation. Bottom row: Recovery of spermatogenesis and interstitial areas from temperature stimulation. The abnormal cell population disappeared, and E3 spermatid and interstitial areas were observed. <bold>b</bold> qRT-PCR analysis in testes. Values are the mean ± SEM (<italic>n</italic> = 4). *<italic>p</italic> < 0.01 compared with the 29 °C testes. <bold>c</bold> In situ hybridization of <italic>Insl3</italic>, a Leydig cell marker, in testes. <bold>d</bold> In situ hybridization of <italic>Hspa5</italic>. Black broken lines indicate interstitial cells and red broken lines indicate spermatocyte cells. <bold>e</bold> Localization of cleaved caspase 3. White broken lines indicate interstitial cells and red broken lines indicate spermatocyte cells. Scale bars are 20 μm.</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Higher temperature led to upregulated expression of <italic>Trpv4</italic> in Leydig cells.</title><p><bold>a</bold> qRT-PCR analysis of Trp family genes in the testes. Values are the mean ± SEM (<italic>n</italic> = 3). **<italic>p</italic> < 0.01 and *<italic>p</italic> < 0.05 compared with the 29 °C testis. <bold>b</bold> In situ hybridization of <italic>Trpv4</italic> in testes. <bold>c</bold> Double in situ hybridization of <italic>Trpv4</italic> (blue) and <italic>Insl3</italic> (yellow). The insets were magnified in the bottom rows. Scale bars are 20 μm (<bold>b</bold> and <bold>c</bold>: high magnification) and 50 μm (<bold>c</bold>: low magnification).</p></caption></fig>","<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Leydig cells in <italic>Trpv4</italic> KOs did not undergo apoptosis in high temperature.</title><p><bold>a</bold> HE staining of testes. The upper panels show <italic>Trpv4</italic><sup>+/+</sup> and the lower panels show <italic>Trpv4</italic><sup>−/−</sup>. Black line indicates areas of E3 spermatid. Red line indicates an abnormal cell population. Arrows indicate interstitial tissue. Interstitial tissue can be detected clearly in <italic>Trpv4</italic><sup>−/−</sup> after 7 and 14 days of temperature stimulation. <bold>b</bold> qRT-PCR analysis of Leydig cell marker genes in testes. Values are the mean ± SEM (<italic>n</italic> = 4). *<italic>p</italic> < 0.01 compared with the 29 °C testis. <bold>c</bold> In situ hybridization in testes of <italic>Isl3</italic>, which is a Leydig cell marker. <bold>d</bold> In situ hybridization of <italic>Hspa5</italic>. Black broken lines indicate interstitial cells and red broken lines indicate spermatocyte cells. <bold>e</bold> Localization of cleaved caspase 3. White broken lines indicate interstitial cells and red broken lines indicate spermatocyte cells. Scale bars are 20 μm.</p></caption></fig>","<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Higher temperature impaired sperm motility in <italic>Trpv4</italic><sup>+/+</sup> but not in <italic>Trpv4</italic><sup>−/−</sup>.</title><p><bold>a</bold> Heat map images of sperm motility. Red indicates high motility. Light blue indicates low motility. <bold>b</bold> Tracking of sperm swimming. The green line shows the trajectory of sperm in 3 s. <bold>c</bold> Pooled data of sperm motility in two groups. Values are the mean ± SEM (<italic>n</italic> = 8). *<italic>p</italic> < 0.01 compared with the 29 °C. <bold>d</bold> Heat map images of sperm motility with the addition of 100 nM 20β-S. Red indicates high motility; light blue indicates low motility. <bold>e</bold> Tracking of sperm swimming with the addition of 100 nM 20β-S. <bold>f</bold> Averaged sperm motility in added 20β-S. Values are the mean ± SEM (29 °C <italic>n</italic> = 7 and 34 °C <italic>n</italic> = 6). **<italic>p</italic> < 0.01 and *<italic>p</italic> < 0.05 compared with the 29 °C. <bold>g</bold> qRT-PCR analysis of the <italic>20β-Hsd</italic> gene in the testes. Values are the mean ± SEM (<italic>n</italic> = 4). *<italic>p</italic> < 0.01 compared with the 29 °C testis. <bold>h</bold> In situ hybridization of <italic>20β</italic><sup><italic>-</italic></sup><italic>hsd</italic>. Scale bars are 50 μm (<bold>a</bold>, <bold>b</bold>, <bold>d</bold>, and <bold>e</bold>) and 20 μm (<bold>h</bold>).</p></caption></fig>","<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Sperm matured at high temperature showed an abnormality.</title><p><bold>a</bold> Flow cytometric analysis of sperm DNA content. PI luminance values (30–42) were set as the P4 gate. <bold>b</bold> Percentage of sperm within the P4 gate. Values are the mean ± SEM (<italic>n</italic> = 6). *<italic>p</italic> < 0.01 compared with the 29 °C sperm. <bold>c</bold> Fertilization rates of 34 °C exposed male. Values are the mean ± SEM (29 °C <italic>n</italic> = 4 and 34 °C <italic>n</italic> = 5). **<italic>p</italic> < 0.01 and *<italic>p</italic> < 0.05 compared two groups. <bold>d</bold> Survival rates of embryos derived from 34 °C exposed males. Values are the mean ± SEM (29 °C <italic>n</italic> = 4 and 34 °C <italic>n</italic> = 5). *<italic>p</italic> < 0.01 compared with the 29 °C sperm. <bold>e</bold> Percentage of morphologically abnormal embryos derived from 34 °C exposed males. Values are the mean ± SEM (29 °C <italic>n</italic> = 4 and 34 °C <italic>n</italic> = 5). *<italic>p</italic> < 0.01 compared with the 29 °C sperm. <bold>f</bold> Images of embryos at 2 dpf and 6 dpf. Scale bars are 500 μm.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><title>High-temperature treatment led to significant apoptosis of Leydig cells.</title><p><bold>a</bold> Top row: HE staining of testes. The black lines indicate areas containing E3 spermatid. The red lines indicate areas containing abnormal cell populations. Arrows indicate interstitial tissue. Interstitial areas decreased at 3, 7, and 14 days of temperature stimulation. Bottom row: Recovery of spermatogenesis and interstitial areas from temperature stimulation. The abnormal cell population disappeared, and E3 spermatid and interstitial areas were observed. <bold>b</bold> qRT-PCR analysis in testes. Values are the mean ± SEM (<italic>n</italic> = 4). *<italic>p</italic> < 0.01 compared with the 29 °C testes. <bold>c</bold> In situ hybridization of <italic>Insl3</italic>, a Leydig cell marker, in testes. <bold>d</bold> In situ hybridization of <italic>Hspa5</italic>. Black broken lines indicate interstitial cells and red broken lines indicate spermatocyte cells. <bold>e</bold> Localization of cleaved caspase 3. White broken lines indicate interstitial cells and red broken lines indicate spermatocyte cells. Scale bars are 20 μm.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><title>Higher temperature led to upregulated expression of <italic>Trpv4</italic> in Leydig cells.</title><p><bold>a</bold> qRT-PCR analysis of Trp family genes in the testes. Values are the mean ± SEM (<italic>n</italic> = 3). **<italic>p</italic> < 0.01 and *<italic>p</italic> < 0.05 compared with the 29 °C testis. <bold>b</bold> In situ hybridization of <italic>Trpv4</italic> in testes. <bold>c</bold> Double in situ hybridization of <italic>Trpv4</italic> (blue) and <italic>Insl3</italic> (yellow). The insets were magnified in the bottom rows. Scale bars are 20 μm (<bold>b</bold> and <bold>c</bold>: high magnification) and 50 μm (<bold>c</bold>: low magnification).</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Fig. 3</label><caption><title>Leydig cells in <italic>Trpv4</italic> KOs did not undergo apoptosis in high temperature.</title><p><bold>a</bold> HE staining of testes. The upper panels show <italic>Trpv4</italic><sup>+/+</sup> and the lower panels show <italic>Trpv4</italic><sup>−/−</sup>. Black line indicates areas of E3 spermatid. Red line indicates an abnormal cell population. Arrows indicate interstitial tissue. Interstitial tissue can be detected clearly in <italic>Trpv4</italic><sup>−/−</sup> after 7 and 14 days of temperature stimulation. <bold>b</bold> qRT-PCR analysis of Leydig cell marker genes in testes. Values are the mean ± SEM (<italic>n</italic> = 4). *<italic>p</italic> < 0.01 compared with the 29 °C testis. <bold>c</bold> In situ hybridization in testes of <italic>Isl3</italic>, which is a Leydig cell marker. <bold>d</bold> In situ hybridization of <italic>Hspa5</italic>. Black broken lines indicate interstitial cells and red broken lines indicate spermatocyte cells. <bold>e</bold> Localization of cleaved caspase 3. White broken lines indicate interstitial cells and red broken lines indicate spermatocyte cells. Scale bars are 20 μm.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Fig. 4</label><caption><title>Higher temperature impaired sperm motility in <italic>Trpv4</italic><sup>+/+</sup> but not in <italic>Trpv4</italic><sup>−/−</sup>.</title><p><bold>a</bold> Heat map images of sperm motility. Red indicates high motility. Light blue indicates low motility. <bold>b</bold> Tracking of sperm swimming. The green line shows the trajectory of sperm in 3 s. <bold>c</bold> Pooled data of sperm motility in two groups. Values are the mean ± SEM (<italic>n</italic> = 8). *<italic>p</italic> < 0.01 compared with the 29 °C. <bold>d</bold> Heat map images of sperm motility with the addition of 100 nM 20β-S. Red indicates high motility; light blue indicates low motility. <bold>e</bold> Tracking of sperm swimming with the addition of 100 nM 20β-S. <bold>f</bold> Averaged sperm motility in added 20β-S. Values are the mean ± SEM (29 °C <italic>n</italic> = 7 and 34 °C <italic>n</italic> = 6). **<italic>p</italic> < 0.01 and *<italic>p</italic> < 0.05 compared with the 29 °C. <bold>g</bold> qRT-PCR analysis of the <italic>20β-Hsd</italic> gene in the testes. Values are the mean ± SEM (<italic>n</italic> = 4). *<italic>p</italic> < 0.01 compared with the 29 °C testis. <bold>h</bold> In situ hybridization of <italic>20β</italic><sup><italic>-</italic></sup><italic>hsd</italic>. Scale bars are 50 μm (<bold>a</bold>, <bold>b</bold>, <bold>d</bold>, and <bold>e</bold>) and 20 μm (<bold>h</bold>).</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Fig. 5</label><caption><title>Sperm matured at high temperature showed an abnormality.</title><p><bold>a</bold> Flow cytometric analysis of sperm DNA content. PI luminance values (30–42) were set as the P4 gate. <bold>b</bold> Percentage of sperm within the P4 gate. Values are the mean ± SEM (<italic>n</italic> = 6). *<italic>p</italic> < 0.01 compared with the 29 °C sperm. <bold>c</bold> Fertilization rates of 34 °C exposed male. Values are the mean ± SEM (29 °C <italic>n</italic> = 4 and 34 °C <italic>n</italic> = 5). **<italic>p</italic> < 0.01 and *<italic>p</italic> < 0.05 compared two groups. <bold>d</bold> Survival rates of embryos derived from 34 °C exposed males. Values are the mean ± SEM (29 °C <italic>n</italic> = 4 and 34 °C <italic>n</italic> = 5). *<italic>p</italic> < 0.01 compared with the 29 °C sperm. <bold>e</bold> Percentage of morphologically abnormal embryos derived from 34 °C exposed males. Values are the mean ± SEM (29 °C <italic>n</italic> = 4 and 34 °C <italic>n</italic> = 5). *<italic>p</italic> < 0.01 compared with the 29 °C sperm. <bold>f</bold> Images of embryos at 2 dpf and 6 dpf. Scale bars are 500 μm.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":["<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>","<supplementary-material 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Many of these diseases—including, for example, asthma<sup>##REF##36280330##7##</sup>, atopic dermatitis<sup>##REF##30597208##8##</sup>, food allergies<sup>##REF##35476967##9##</sup>, systemic lupus erythematosus<sup>##REF##36987604##10##</sup>, and periodontal disease<sup>##REF##31860530##11##</sup>—vary widely in their pathologies but share a common etiological pathway involving immune dysregulation, and they are more common in lower socioeconomic strata than among people with higher SES<sup>##REF##31986347##12##</sup>. Three strands of evidence also document associations between SES and biomarkers of the immune system. First, many studies report associations between childhood SES and pro-inflammatory markers in circulating peripheral blood (such as interleukin-6 and C-reactive protein (CRP)<sup>##REF##32919037##13##</sup>) that, if chronically activated, presage a wide-range of diseases including, for example, diabetes type 2, some cancers, and cardiovascular disease. Second, studies have also examined white blood cell composition, finding that low SES is associated with increased development and circulation of pro-inflammatory immune cells (monocytes and neutrophils<sup>##REF##24062448##14##</sup>), whereas parental education is positively associated with the proportion of lymphocytes and negatively associated with the proportion of neutrophils and, among older adults, that SES is related to shifts in cell composition indicative of immunosenescence<sup>##REF##35696572##15##–##REF##31872259##18##</sup>. And finally, a limited number of studies have examined functional assays of immune response, sometimes ex-vivo, and show that, once again, childhood socioeconomic status is a risk factor for immune dysregulation, possibly more so among boys<sup>##UREF##2##19##–##REF##19617551##22##</sup>. Nevertheless, despite the abundance of evidence connecting socioeconomic inequalities to immune-related diseases and biomarkers, the molecular etiology of SES-mediated immune alterations remains less explored.</p>","<p id=\"Par3\">A growing number of studies have examined SES and transcriptional patterns indicative of immune functioning. Research consistently shows people from low SES backgrounds have greater proinflammatory activity<sup>##REF##24062448##14##,##REF##19617551##22##–##REF##25658624##26##</sup>. Additionally, SES is associated with the expression of genes regulated by the glucocorticoid receptor and interferon response factors, suggesting a suppression of adaptive immunity and innate antiviral immunity<sup>##REF##19617551##22##,##REF##28911009##27##</sup>. This signature pattern—involving the upregulation of proinflammatory genes and the downregulation of type I interferon innate antiviral response genes among the lower strata of status, called the “Conserved Transcriptional Response to Adversity” (CTRA)—has been observed in numerous populations with a range of research designs. Essentially, SES is associated with CTRA activation, which in turn is associated with the molecular underpinnings of immune-related and inflammatory diseases<sup>##REF##19617551##22##,##REF##26166230##28##–##UREF##3##30##</sup>.</p>","<p id=\"Par4\">The current study seeks to expand on these findings by providing a broader mapping of associations between SES and the molecular signaling pathways that regulate immunity. Understanding the impact of socioeconomic status on immune gene expression requires a systems-oriented approach that extends beyond the functional examination of individually differentially expressed genes and includes the network of upstream regulators. Furthermore, traditional transcription factor binding motif enrichment analysis suffers from the lack of tissue specificity. We aim to address these drawbacks in this paper by detailing upstream tissue-specific transcriptional factors and protein-protein interactors as a networked system, along with differentially regulated genes, that responds to SES. Such an approach offers a comprehensive understanding of how SES is associated with the molecular mechanisms that drive biological processes such as gene expression, cell signaling, and cell fate, which ultimately lead to disease in individuals of lower SES<sup>##REF##21414488##31##,##REF##29425488##32##</sup>. Significantly, we leverage the comprehensive resource of human tissue-specific gene regulatory network of Marbach et al.<sup>##UREF##4##33##</sup> to isolate transcription factors that could play a pivotal role in modulating the expression of SES—differentially expressed genes in whole blood. Additionally, by incorporating direct protein-protein interactors, we create a broad, inclusive and holistic set of genes and proteins that act as a networked system in disrupting essential biological processes. Our approach reveals the decisive role of transcription factors in driving SES—associated dysregulation that previously would be attributed to SES—differentially expressed genes.</p>","<p id=\"Par5\">Emerging evidence points to the intricate interplay between low socioeconomic position and high body mass index (BMI)<sup>##REF##34091232##34##,##REF##33160231##35##</sup>. Recent meta-analyses have consistently linked lowered socioeconomic status and elevated inflammatory biomarkers largely via obesity<sup>##REF##31628416##36##,##UREF##5##37##</sup>. Although BMI is an imperfect indicator of obesity as it does not distinguish fat from fat-free mass, Liu et al.<sup>##REF##28490476##38##</sup> observed a likely strong mediation of BMI in the negative relationship between childhood SES and adulthood inflammatory marker C reactive protein (CRP). Additionally, previous systematic reviews have shown that improved indicators of fat and obesity follow a similar pattern of health disparity to those seen with BMI<sup>##UREF##5##37##</sup>. Thus, we examine the potential mediation of BMI, as an indicator of obesity, along with other common social and behavioral mediators of SES, and the entire system of differentially expressed genes, upstream transcription factors and protein-protein interactors that drive immune dysregulation as a result of lowered SES.</p>","<p id=\"Par6\">We focus on American adults in their late 1930s, who are ostensibly healthy but nevertheless at-risk for later health challenges. We leverage the mRNA data from 4543 early adults participating in the National Longitudinal Study of Adolescent Health (Add Health)<sup>##REF##31257425##39##</sup>. First, we identify cell functional pathways and their directionality in SES-related dysregulation of the immune system. To this end, we capitalize on publicly available pathway ontologies to functionally annotate genes that show changes in expression and that cluster together. Second, we identify upstream modulators and regulators of the differentially expressed genes to provide a systems perspective on SES and immunity. Such a view also isolates potential targets for remediation. Finally, we consider the behavioral and health-related factors that may explain associations between SES and the immune cell transcriptome. Results reveal that SES is associated with widespread dysregulation of immunity involving intricately interrelated differentially expressed genes, transcription factors, and protein-protein regulators. Furthermore, BMI is a likely, potent mechanism driving these patterns.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">A considerable body of evidence suggests that disparities in SES—reflecting education, income, occupational prestige, and subjective status—play a critical role in shaping the health trajectories of people, with lower SES associated with elevated morbidity and mortality rates<sup>##UREF##0##1##–##REF##7604919##6##</sup>. Many of these diseases—including, for example, asthma<sup>##REF##36280330##7##</sup>, atopic dermatitis<sup>##REF##30597208##8##</sup>, food allergies<sup>##REF##35476967##9##</sup>, systemic lupus erythematosus<sup>##REF##36987604##10##</sup>, and periodontal disease<sup>##REF##31860530##11##</sup>—vary widely in their pathologies but share a common etiological pathway involving immune dysregulation, and they are more common in lower socioeconomic strata than among people with higher SES<sup>##REF##31986347##12##</sup>. Three strands of evidence also document associations between SES and biomarkers of the immune system. First, many studies report associations between childhood SES and pro-inflammatory markers in circulating peripheral blood (such as interleukin-6 and C-reactive protein (CRP)<sup>##REF##32919037##13##</sup>) that, if chronically activated, presage a wide-range of diseases including, for example, diabetes type 2, some cancers, and cardiovascular disease. Second, studies have also examined white blood cell composition, finding that low SES is associated with increased development and circulation of pro-inflammatory immune cells (monocytes and neutrophils<sup>##REF##24062448##14##</sup>), whereas parental education is positively associated with the proportion of lymphocytes and negatively associated with the proportion of neutrophils and, among older adults, that SES is related to shifts in cell composition indicative of immunosenescence<sup>##REF##35696572##15##–##REF##31872259##18##</sup>. And finally, a limited number of studies have examined functional assays of immune response, sometimes ex-vivo, and show that, once again, childhood socioeconomic status is a risk factor for immune dysregulation, possibly more so among boys<sup>##UREF##2##19##–##REF##19617551##22##</sup>. Nevertheless, despite the abundance of evidence connecting socioeconomic inequalities to immune-related diseases and biomarkers, the molecular etiology of SES-mediated immune alterations remains less explored.</p>\",\n \"<p id=\\\"Par3\\\">A growing number of studies have examined SES and transcriptional patterns indicative of immune functioning. Research consistently shows people from low SES backgrounds have greater proinflammatory activity<sup>##REF##24062448##14##,##REF##19617551##22##–##REF##25658624##26##</sup>. Additionally, SES is associated with the expression of genes regulated by the glucocorticoid receptor and interferon response factors, suggesting a suppression of adaptive immunity and innate antiviral immunity<sup>##REF##19617551##22##,##REF##28911009##27##</sup>. This signature pattern—involving the upregulation of proinflammatory genes and the downregulation of type I interferon innate antiviral response genes among the lower strata of status, called the “Conserved Transcriptional Response to Adversity” (CTRA)—has been observed in numerous populations with a range of research designs. Essentially, SES is associated with CTRA activation, which in turn is associated with the molecular underpinnings of immune-related and inflammatory diseases<sup>##REF##19617551##22##,##REF##26166230##28##–##UREF##3##30##</sup>.</p>\",\n \"<p id=\\\"Par4\\\">The current study seeks to expand on these findings by providing a broader mapping of associations between SES and the molecular signaling pathways that regulate immunity. Understanding the impact of socioeconomic status on immune gene expression requires a systems-oriented approach that extends beyond the functional examination of individually differentially expressed genes and includes the network of upstream regulators. Furthermore, traditional transcription factor binding motif enrichment analysis suffers from the lack of tissue specificity. We aim to address these drawbacks in this paper by detailing upstream tissue-specific transcriptional factors and protein-protein interactors as a networked system, along with differentially regulated genes, that responds to SES. Such an approach offers a comprehensive understanding of how SES is associated with the molecular mechanisms that drive biological processes such as gene expression, cell signaling, and cell fate, which ultimately lead to disease in individuals of lower SES<sup>##REF##21414488##31##,##REF##29425488##32##</sup>. Significantly, we leverage the comprehensive resource of human tissue-specific gene regulatory network of Marbach et al.<sup>##UREF##4##33##</sup> to isolate transcription factors that could play a pivotal role in modulating the expression of SES—differentially expressed genes in whole blood. Additionally, by incorporating direct protein-protein interactors, we create a broad, inclusive and holistic set of genes and proteins that act as a networked system in disrupting essential biological processes. Our approach reveals the decisive role of transcription factors in driving SES—associated dysregulation that previously would be attributed to SES—differentially expressed genes.</p>\",\n \"<p id=\\\"Par5\\\">Emerging evidence points to the intricate interplay between low socioeconomic position and high body mass index (BMI)<sup>##REF##34091232##34##,##REF##33160231##35##</sup>. Recent meta-analyses have consistently linked lowered socioeconomic status and elevated inflammatory biomarkers largely via obesity<sup>##REF##31628416##36##,##UREF##5##37##</sup>. Although BMI is an imperfect indicator of obesity as it does not distinguish fat from fat-free mass, Liu et al.<sup>##REF##28490476##38##</sup> observed a likely strong mediation of BMI in the negative relationship between childhood SES and adulthood inflammatory marker C reactive protein (CRP). Additionally, previous systematic reviews have shown that improved indicators of fat and obesity follow a similar pattern of health disparity to those seen with BMI<sup>##UREF##5##37##</sup>. Thus, we examine the potential mediation of BMI, as an indicator of obesity, along with other common social and behavioral mediators of SES, and the entire system of differentially expressed genes, upstream transcription factors and protein-protein interactors that drive immune dysregulation as a result of lowered SES.</p>\",\n \"<p id=\\\"Par6\\\">We focus on American adults in their late 1930s, who are ostensibly healthy but nevertheless at-risk for later health challenges. We leverage the mRNA data from 4543 early adults participating in the National Longitudinal Study of Adolescent Health (Add Health)<sup>##REF##31257425##39##</sup>. First, we identify cell functional pathways and their directionality in SES-related dysregulation of the immune system. To this end, we capitalize on publicly available pathway ontologies to functionally annotate genes that show changes in expression and that cluster together. Second, we identify upstream modulators and regulators of the differentially expressed genes to provide a systems perspective on SES and immunity. Such a view also isolates potential targets for remediation. Finally, we consider the behavioral and health-related factors that may explain associations between SES and the immune cell transcriptome. Results reveal that SES is associated with widespread dysregulation of immunity involving intricately interrelated differentially expressed genes, transcription factors, and protein-protein regulators. Furthermore, BMI is a likely, potent mechanism driving these patterns.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Methods and materials</title>","<title>Add health and differential gene expression</title>","<p id=\"Par7\">The National Longitudinal Study of Adolescent to Adult Health (Add Health) is a representative study of adolescents in the Unites States who were followed into adulthood over five waves of data collection<sup>##REF##31257425##39##</sup>. Study participants provided informed written consent with respect to all aspects of the Add Health study in accordance with the University of North Carolina School of Public Health Institution Review Board (IRB). Transcriptomic profiles of the consenting participants were collected during Wave V of the Add Health Study (2016–2017) via an intravenous blood draw (age of subjects range from 33 to 43 years). The access to restricted use Add Health transcriptomic data was obtained by completing a contractual and data use agreement. Additional detailed information on the study design, interview procedures, consent procedures, demographic assessments, collection, sequencing and quality control of the blood sample, and derivation of the analytical samples is reported in ##SUPPL##3##Supplementary Methods## and in previous studies<sup>##REF##32041883##40##–##REF##36252037##42##</sup>. Furthermore, the data analysis and all methods presented in this work were carried out in accordance with the relevant ethical guidelines and regulations. We draw on the mRNA-seq data of 4015 subjects with complete information on the models’ variables. Socioeconomic status composite scores were calculated using the sum of standardized indicators of education, income, occupation, and subjective socioeconomic status of the early adult subjects<sup>##REF##36252037##42##–##UREF##7##44##</sup>.</p>","<p id=\"Par8\">Genes with low counts were excluded from the analysis (see ##SUPPL##3##Supplementary Methods##). After normalizing the raw mRNA-seq counts using a weighted trimmed mean of log expression ratios (TMM normalization)<sup>##UREF##8##45##</sup> using the <italic>edgeR</italic><sup>##REF##19910308##46##</sup> package in R, we analyzed genes whose expression varied significantly by the early adulthood socioeconomic composite score using a linear model analysis<sup>##REF##25605792##47##,##UREF##9##48##</sup>. We controlled for covariates that could influence mRNA abundance levels: sex, self-described race, age, pregnancy status, sample analysis plate, number of hours fasting prior to blood sample collection, use of anti-inflammatory medication (e.g., NSAIDS, COX-2 inhibitors, inhaled corticosteroids), instances of common subclinical symptoms (e.g., colds, flu), and common infectious or inflammatory diseases (e.g., infection, allergies) in the 4 weeks prior to blood sample collection. We also corrected for batch effects using the <italic>ComBat</italic> function in the <italic>sva</italic> package<sup>##REF##16632515##49##</sup> in R.</p>","<p id=\"Par9\">Our overall analytic strategy is to (1) estimate clusters of genes across the whole genome and, within these clusters, identify genes that differentially expressed (DE) by SES (hereafter, SES–DEG); (2) characterize the biological function of these DE genes and gene clusters that are likely to have DE genes; (3) identify transcription factors and their protein neighbors that are associated with these DE genes and act as a networked system; (4) determine the relative functional relevance of SES–DEG and upstream regulators in manifesting immune dysregulation, and, finally, (5) identify behavioral mediators that may account for associations between SES and DE genes and their upstream regulators.</p>","<title>Whole-genome clusters and cluster-SES relationship</title>","<p id=\"Par10\">Processed gene expression data from 14,251 transcripts in 4015 individuals were subject to unsupervised clustering using Weighted Gene Coexpression Network Analysis (WGCNA)<sup>##UREF##10##50##</sup>. We identified a total of 19 clusters and the number of genes in each cluster and the clusters’ overlaps with the SES–DEG are shown in Supplementary Fig. ##SUPPL##3##S1##. To identify the clusters that have a significant relationship to SES, we modelled the cluster eigengenes (a summarized expression vector of each cluster) as a linear function of SES as in the differential expression analysis. Additionally, we performed a Fisher exact test to identify clusters that show an enrichment for SES–DEG. Together, the two tests resulted in clusters that, (1) have a significant cluster-SES relationship and (2) are enriched for SES—up or downregulated genes (see Supplementary Fig. ##SUPPL##3##S2##). Four clusters (Cluster 7, 11, 13 and 17) had eigengenes that are significantly differentially expressed by SES. Of the 4 clusters, Cluster 11 showed an overrepresentation for SES—downregulated genes, while Clusters 7, 13 and 17 showed overrepresentation of by SES—upregulated genes. In this context, upregulation refers to a positive association between SES and mRNA abundance levels.</p>","<title>Functional enrichment analysis of the differentially expressed genes and significant clusters</title>","<p id=\"Par11\">Functional enrichment analysis for the SES–DEG (see Supplementary Fig. ##SUPPL##3##S3## and Supplementary Dataset ##SUPPL##0##S1##) and WGCNA identified cluster genes (see Supplementary Fig. ##SUPPL##3##S4## and Supplementary Dataset ##SUPPL##1##S2##) was performed using R Bioconductor package <italic>ReactomePA</italic><sup>##REF##26661513##51##</sup> to identify the biological function of the genes (FDR <italic>p</italic> < 0.05). The Reactome results are organized in a hierarchical structure of biological pathways with each biological pathway being a node that shows parent–child relationships<sup>##REF##29165610##52##</sup>. We relied on this parent–child relational database to pool together multiple pathways under the same parent node in order to better understand the large-scale changes (up to 3 hierarchical levels). The significance of the parent node was determined by its most significant child.</p>","<p id=\"Par12\">Functional enrichment analysis of the SES–DEG and the upstream regulators was performed using the ClueGO<sup>##REF##19237447##53##</sup> plugin in Cytoscape<sup>##REF##14597658##54##</sup>. This plugin allows for the combined analysis of multiple gene lists using a preselected ontology. We analyzed the SES–DEG (up- and downregulated gene lists) along with their upstream regulators (transcription factors and protein neighbors) with the Reactome ontology.</p>","<title>Identifying key controllers of genes exhibiting differential expression by SES</title>","<p id=\"Par13\">Upstream regulators of the SES–DEG (Set A; see Supplementary Fig. ##SUPPL##3##S5##) were categorized into (1) transcription factors that are themselves differentially expressed (Set B; see Supplementary Fig. ##SUPPL##3##S5##), (2) protein neighbors of the differentially expressed transcription factors (Set C; see Supplementary Fig. ##SUPPL##3##S5##), and (3) transcription factors that putatively modulate the expression of differentially expressed genes (Set D; see Supplementary Fig. ##SUPPL##3##S5##). Marbach et al.<sup>##UREF##4##33##</sup> constructed tissue-specific regulatory networks that linked transcription factors and genes with a score based on a curated collection of sequence binding motifs. Those transcription factors that had a medium or greater confidence (> 0.4) of modulating the expression of the differentially expressed genes in blood tissue were included in the set of upstream regulators (Sets B and D). A total of 643 transcription factors were identified in the blood tissue-specific gene regulatory network. 304 transcription factors had gene interactions with at least a medium confidence score. Protein neighbors of differentially regulated transcription factors were obtained using the STRING database<sup>##REF##23203871##55##</sup>. Each protein-protein interaction (PPI) in STRING is annotated with a score that indicates the confidence of the interaction. Only neighbors with scores of at least high confidence (> 0.7) were included in the set of upstream regulators (Set C). Thus, Set A represents the DE genes and Sets B, C and D together constitute their upstream regulators. The 304 tissue-specific transcription factors had interactions with 8543 unique protein-protein neighbors. 1750 neighbors exceeded the confidence threshold for PPI.</p>","<title>Possible mediators of SES and DE genes and upstream regulators</title>","<p id=\"Par14\">We examined behavioral and psychobiological process that might mediate associations between Wave V early adulthood SES and the expression of the genes and upstream regulators using a counterfactual mediational framework<sup>##REF##26917999##56##</sup>. The mediators included Body Mass Index (BMI), perceived stress (based on Cohen’s Perceived Stress Scale<sup>##REF##25031113##57##</sup>), current self-reported smoking status, consumption of alcoholic drinks (days drank over past 30 days; categorized as 0 drinks, 1–2 drinks, 3–5 drinks, and more than 5 drinks per occasion), financial stress (self-reported difficulty in paying bills), and access to health insurance. We also compared the mediation of BMI with the mediation observed for waist circumference, which has been reported to be a more accurate measure of fatness<sup>##REF##18166236##58##</sup>.</p>","<title>Randomization test of differentially expressed genes and upstream regulators</title>","<p id=\"Par15\">We quantified the statistical significance of the observed results by performing randomization tests based on 1000 randomly generated sets of differentially expressed genes. Random samples were drawn from the entire genome to obtain a set of genes equal in number to Set A (see Supplementary Fig. ##SUPPL##3##S5##). Sets B, C, and D were derived from every randomly generated Set A using the same procedure used with the SES–DEG. We then computed the significance (empirical <italic>p</italic>-value) of every actual gene in Set A (DE genes) and Sets B, C, and D (upstream regulators)<sup>##REF##12111669##59##</sup> by comparing them to their respective sets from the 1000 randomly generated sets. We obtained <italic>p</italic>-values for each of the sets of genes by combining the <italic>p</italic>-values of every gene in the set using Fisher’s method.</p>"],"string":"[\n \"<title>Methods and materials</title>\",\n \"<title>Add health and differential gene expression</title>\",\n \"<p id=\\\"Par7\\\">The National Longitudinal Study of Adolescent to Adult Health (Add Health) is a representative study of adolescents in the Unites States who were followed into adulthood over five waves of data collection<sup>##REF##31257425##39##</sup>. Study participants provided informed written consent with respect to all aspects of the Add Health study in accordance with the University of North Carolina School of Public Health Institution Review Board (IRB). Transcriptomic profiles of the consenting participants were collected during Wave V of the Add Health Study (2016–2017) via an intravenous blood draw (age of subjects range from 33 to 43 years). The access to restricted use Add Health transcriptomic data was obtained by completing a contractual and data use agreement. Additional detailed information on the study design, interview procedures, consent procedures, demographic assessments, collection, sequencing and quality control of the blood sample, and derivation of the analytical samples is reported in ##SUPPL##3##Supplementary Methods## and in previous studies<sup>##REF##32041883##40##–##REF##36252037##42##</sup>. Furthermore, the data analysis and all methods presented in this work were carried out in accordance with the relevant ethical guidelines and regulations. We draw on the mRNA-seq data of 4015 subjects with complete information on the models’ variables. Socioeconomic status composite scores were calculated using the sum of standardized indicators of education, income, occupation, and subjective socioeconomic status of the early adult subjects<sup>##REF##36252037##42##–##UREF##7##44##</sup>.</p>\",\n \"<p id=\\\"Par8\\\">Genes with low counts were excluded from the analysis (see ##SUPPL##3##Supplementary Methods##). After normalizing the raw mRNA-seq counts using a weighted trimmed mean of log expression ratios (TMM normalization)<sup>##UREF##8##45##</sup> using the <italic>edgeR</italic><sup>##REF##19910308##46##</sup> package in R, we analyzed genes whose expression varied significantly by the early adulthood socioeconomic composite score using a linear model analysis<sup>##REF##25605792##47##,##UREF##9##48##</sup>. We controlled for covariates that could influence mRNA abundance levels: sex, self-described race, age, pregnancy status, sample analysis plate, number of hours fasting prior to blood sample collection, use of anti-inflammatory medication (e.g., NSAIDS, COX-2 inhibitors, inhaled corticosteroids), instances of common subclinical symptoms (e.g., colds, flu), and common infectious or inflammatory diseases (e.g., infection, allergies) in the 4 weeks prior to blood sample collection. We also corrected for batch effects using the <italic>ComBat</italic> function in the <italic>sva</italic> package<sup>##REF##16632515##49##</sup> in R.</p>\",\n \"<p id=\\\"Par9\\\">Our overall analytic strategy is to (1) estimate clusters of genes across the whole genome and, within these clusters, identify genes that differentially expressed (DE) by SES (hereafter, SES–DEG); (2) characterize the biological function of these DE genes and gene clusters that are likely to have DE genes; (3) identify transcription factors and their protein neighbors that are associated with these DE genes and act as a networked system; (4) determine the relative functional relevance of SES–DEG and upstream regulators in manifesting immune dysregulation, and, finally, (5) identify behavioral mediators that may account for associations between SES and DE genes and their upstream regulators.</p>\",\n \"<title>Whole-genome clusters and cluster-SES relationship</title>\",\n \"<p id=\\\"Par10\\\">Processed gene expression data from 14,251 transcripts in 4015 individuals were subject to unsupervised clustering using Weighted Gene Coexpression Network Analysis (WGCNA)<sup>##UREF##10##50##</sup>. We identified a total of 19 clusters and the number of genes in each cluster and the clusters’ overlaps with the SES–DEG are shown in Supplementary Fig. ##SUPPL##3##S1##. To identify the clusters that have a significant relationship to SES, we modelled the cluster eigengenes (a summarized expression vector of each cluster) as a linear function of SES as in the differential expression analysis. Additionally, we performed a Fisher exact test to identify clusters that show an enrichment for SES–DEG. Together, the two tests resulted in clusters that, (1) have a significant cluster-SES relationship and (2) are enriched for SES—up or downregulated genes (see Supplementary Fig. ##SUPPL##3##S2##). Four clusters (Cluster 7, 11, 13 and 17) had eigengenes that are significantly differentially expressed by SES. Of the 4 clusters, Cluster 11 showed an overrepresentation for SES—downregulated genes, while Clusters 7, 13 and 17 showed overrepresentation of by SES—upregulated genes. In this context, upregulation refers to a positive association between SES and mRNA abundance levels.</p>\",\n \"<title>Functional enrichment analysis of the differentially expressed genes and significant clusters</title>\",\n \"<p id=\\\"Par11\\\">Functional enrichment analysis for the SES–DEG (see Supplementary Fig. ##SUPPL##3##S3## and Supplementary Dataset ##SUPPL##0##S1##) and WGCNA identified cluster genes (see Supplementary Fig. ##SUPPL##3##S4## and Supplementary Dataset ##SUPPL##1##S2##) was performed using R Bioconductor package <italic>ReactomePA</italic><sup>##REF##26661513##51##</sup> to identify the biological function of the genes (FDR <italic>p</italic> < 0.05). The Reactome results are organized in a hierarchical structure of biological pathways with each biological pathway being a node that shows parent–child relationships<sup>##REF##29165610##52##</sup>. We relied on this parent–child relational database to pool together multiple pathways under the same parent node in order to better understand the large-scale changes (up to 3 hierarchical levels). The significance of the parent node was determined by its most significant child.</p>\",\n \"<p id=\\\"Par12\\\">Functional enrichment analysis of the SES–DEG and the upstream regulators was performed using the ClueGO<sup>##REF##19237447##53##</sup> plugin in Cytoscape<sup>##REF##14597658##54##</sup>. This plugin allows for the combined analysis of multiple gene lists using a preselected ontology. We analyzed the SES–DEG (up- and downregulated gene lists) along with their upstream regulators (transcription factors and protein neighbors) with the Reactome ontology.</p>\",\n \"<title>Identifying key controllers of genes exhibiting differential expression by SES</title>\",\n \"<p id=\\\"Par13\\\">Upstream regulators of the SES–DEG (Set A; see Supplementary Fig. ##SUPPL##3##S5##) were categorized into (1) transcription factors that are themselves differentially expressed (Set B; see Supplementary Fig. ##SUPPL##3##S5##), (2) protein neighbors of the differentially expressed transcription factors (Set C; see Supplementary Fig. ##SUPPL##3##S5##), and (3) transcription factors that putatively modulate the expression of differentially expressed genes (Set D; see Supplementary Fig. ##SUPPL##3##S5##). Marbach et al.<sup>##UREF##4##33##</sup> constructed tissue-specific regulatory networks that linked transcription factors and genes with a score based on a curated collection of sequence binding motifs. Those transcription factors that had a medium or greater confidence (> 0.4) of modulating the expression of the differentially expressed genes in blood tissue were included in the set of upstream regulators (Sets B and D). A total of 643 transcription factors were identified in the blood tissue-specific gene regulatory network. 304 transcription factors had gene interactions with at least a medium confidence score. Protein neighbors of differentially regulated transcription factors were obtained using the STRING database<sup>##REF##23203871##55##</sup>. Each protein-protein interaction (PPI) in STRING is annotated with a score that indicates the confidence of the interaction. Only neighbors with scores of at least high confidence (> 0.7) were included in the set of upstream regulators (Set C). Thus, Set A represents the DE genes and Sets B, C and D together constitute their upstream regulators. The 304 tissue-specific transcription factors had interactions with 8543 unique protein-protein neighbors. 1750 neighbors exceeded the confidence threshold for PPI.</p>\",\n \"<title>Possible mediators of SES and DE genes and upstream regulators</title>\",\n \"<p id=\\\"Par14\\\">We examined behavioral and psychobiological process that might mediate associations between Wave V early adulthood SES and the expression of the genes and upstream regulators using a counterfactual mediational framework<sup>##REF##26917999##56##</sup>. The mediators included Body Mass Index (BMI), perceived stress (based on Cohen’s Perceived Stress Scale<sup>##REF##25031113##57##</sup>), current self-reported smoking status, consumption of alcoholic drinks (days drank over past 30 days; categorized as 0 drinks, 1–2 drinks, 3–5 drinks, and more than 5 drinks per occasion), financial stress (self-reported difficulty in paying bills), and access to health insurance. We also compared the mediation of BMI with the mediation observed for waist circumference, which has been reported to be a more accurate measure of fatness<sup>##REF##18166236##58##</sup>.</p>\",\n \"<title>Randomization test of differentially expressed genes and upstream regulators</title>\",\n \"<p id=\\\"Par15\\\">We quantified the statistical significance of the observed results by performing randomization tests based on 1000 randomly generated sets of differentially expressed genes. Random samples were drawn from the entire genome to obtain a set of genes equal in number to Set A (see Supplementary Fig. ##SUPPL##3##S5##). Sets B, C, and D were derived from every randomly generated Set A using the same procedure used with the SES–DEG. We then computed the significance (empirical <italic>p</italic>-value) of every actual gene in Set A (DE genes) and Sets B, C, and D (upstream regulators)<sup>##REF##12111669##59##</sup> by comparing them to their respective sets from the 1000 randomly generated sets. We obtained <italic>p</italic>-values for each of the sets of genes by combining the <italic>p</italic>-values of every gene in the set using Fisher’s method.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<title>Transcriptional alterations with SES are characterized by organism wide dysregulation</title>","<p id=\"Par16\">We performed a differential gene expression analysis followed by an enrichment analysis of the resulting SES–DEG (see Supplementary Dataset ##SUPPL##1##S2## and Supplementary Fig. ##SUPPL##3##S3##). Upregulated genes indicate a significant association between high SES and high expression (i.e., a positive association). Functional enrichment of the SES–DEG (423 upregulated genes and 389 downregulated genes) showed a majority upregulated for pathways involving metabolism, signal transduction and cellular response to stress by a core of ribosomal and translational genes. Interestingly, these cytosolic ribosomal genes (<italic>RPL</italic>- and <italic>RPS</italic>-genes) were found to be downregulated with aging in an analysis of the human peripheral blood and previously linked with SES<sup>##REF##36252037##42##</sup>. Indeed, a combined WGCNA and SES—differential expression analysis (see Fig. ##FIG##0##1##) showed a tight clustering of the ribosomal and transcriptional activity genes (Cluster 11 in Fig. ##FIG##0##1##) that are responsible for the SES-upregulated pathways. One cluster of SES-DEGs (Cluster 7) displayed dysregulation in immune system and response, hemostasis and cell death that were predominantly driven by downregulated genes, while another cluster (Cluster 11), largely comprising upregulated ribosomal genes, affected transcriptional events in several cellular functions. Cluster 13 consisted of genes involved in cell division and cell cycle control dysregulating a relatively small number of pathways in signal transduction and immune system, while Cluster 17 comprised too few genes for a meaningful enrichment interpretation.</p>","<p id=\"Par17\">An inspection of enriched pathways reveals that SES-upregulated pathways include interferon innate immune response and neutrophil degranulation (see Fig. ##FIG##1##2##). Curiously, type II interferon (IFN-γ) signaling is also upregulated. Despite the lack of direct evidence for their involvement, the genes that are tied to the upregulation of type I and type II interferon signaling do share <italic>HLA</italic>-genes that regulate the antiviral immune response, which may explain the overrepresentation of both classes of immunity. Figure ##FIG##1##2## also shows an attenuation of proinflammatory pathways with higher SES (i.e., upregulation of proinflammatory pathways with low SES) via pathways in the proinflammatory nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and proinflammatory toll-like receptors (TLR).</p>","<title>Upstream regulators structure immune dysregulation with socioeconomic disparities</title>","<p id=\"Par18\">The biological pathways and molecular mechanisms associated with socioeconomic disparity were also examined via an analysis of upstream regulators (transcriptional factors and protein partners) of the DE genes. A combined functional enrichment analysis of the resulting upstream regulators (239 upstream regulators of SES—upregulated genes and 87 upstream regulators of SES—downregulated genes) and SES–DEG (see Fig. ##FIG##2##3## and Supplementary Dataset ##SUPPL##2##S3##) indicates a significant role of the upstream regulators in structuring the overrepresented pathways in the immune system. The upstream regulators include tissue-specific transcription factors that are (1) differentially expressed (Set B, see Supplementary Fig. ##SUPPL##3##S5## and Supplementary Table ##SUPPL##3##S1##), (2) potentially regulating the expression of a differentially expressed gene (Set D, see Supplementary Fig. ##SUPPL##3##S5## and Supplementary Table ##SUPPL##3##S1##), and (3) PPI neighbors of differentially expressed transcription factors (Set C, see Supplementary Fig. ##SUPPL##3##S5## and Supplementary Table ##SUPPL##3##S1##). Figure ##FIG##2##3## indicates that the immune pathways that are enriched have a larger proportion of upstream targets. Importantly, many of these pathways were also enriched by SES–DEG signifying that the patterns of immune dysregulation observed in Fig. ##FIG##2##3## do not reflect the inclusion of upstream regulators per se, but rather reflect the significant mechanistic role played by these transcription factors and protein partners. Furthermore, the immune dysregulation observed in functional enrichment analysis of the upstream targets without the inclusion of SES–DEG (see Supplementary Fig. ##SUPPL##3##S6##) mirror those enriched by SES–DEG alone and SES–DEG with upstream targets, suggesting that SES–DEG are not wholly responsible for SES-associated immune dysregulation.</p>","<p id=\"Par19\">Figure ##FIG##3##4## shows the upstream regulators along with the SES–DEG represented as layers (4 in total, where the innermost circle of genes and upstream regulators is labelled “Layer 1”, sequentially to the outermost group of genes and regulators labelled “Layer 4”) based on their interaction scores derived from the STRING database and the number of times each gene or upstream regulator is involved in functional immune pathways that are enriched in Fig. ##FIG##2##3##. The genes that are responsible for the enrichment of each immune pathway were identified. The innermost layer in Fig. ##FIG##3##4## depicts genes and upstream regulators that are involved in more than ten functional pathways, whereas the outermost layer consists of genes and upstream regulators that are only involved in one enriched biological process. The most essential regulators involved in the dysregulation of the immune system related to SES disparities thus occupy the center of the diagram. Significantly, variations in early adult SES are prominently linked to alterations in cytokine signaling in the immune system involving interleukin and interferon gamma signaling, Toll-like receptor signaling cascade, and TNF pathways (Fig. ##FIG##2##3## and innermost layer in Fig. ##FIG##3##4##).</p>","<p id=\"Par20\">Proteins most deeply linked to SES inequalities invariably revolve around the cyclic 3ʹ–5ʹ adenosine monophosphate response element-binding protein (CREB) and NF-κB pathway signaling. Variations in the activity of CREB and NF-κB selectively upregulate the transcription of interferon response factor family while simultaneously inhibiting the activity of proinflammatory interleukins in subjects with high SES. Genes and transcription factors (via the upstream analysis) that are central to the functional response to SES disparities in functional gene regulation (shown in Fig. ##FIG##2##3##) are also shown in Fig. ##FIG##3##4##. Molecules are placed in layers depending on their contribution (instances of enrichment of a pathway) to the functional immune enrichment. The inner most layer consists of transcriptional factors such as <italic>CREB1</italic>, <italic>TP53</italic>, <italic>RELA</italic>, <italic>REL</italic>, <italic>BTRC</italic>, <italic>BTK</italic> and the <italic>MAPK-</italic>family. <italic>CREB</italic> and <italic>REL</italic> proteins, among other important functions, play a crucial role in the activation of the fight-or-flight signaling pathways that is directly responsible in eliciting the CTRA gene expression profiles. Although a large fraction of the proteins is derived from the set of upregulated genes, it is noteworthy that these regulators can have far reaching impact and they are not always in the expected direction. This is particularly true for the proinflammatory toll-like receptors (TLR) (see Fig. ##FIG##2##3##) pathways, which have a larger proportion of downregulated genes than upregulated genes. However, they also functionally interact with the upstream regulators connected to upregulated genes. The greater influence of the upstream regulators connected to upregulated genes suggests that the downregulation of certain genes could transpire as a consequence of inhibitory activity of the transcription factors.</p>","<title>Social/behavioral mediators of SES-transcriptome associations</title>","<p id=\"Par21\">Figure ##FIG##4##5## reports the median percentage mediated ratio for key SES-related social/behavioral processes in every layer of SES-related gene regulation. Intriguingly, the inner most group of genes that are most centrally implicated in SES-associated dysregulation, are also mediated the least (lowest median percentage mediated ratio) by every behavioral risk factor. However, this could reflect the fact that the inner most group of genes are not themselves differentially expressed despite potentially inducing larger mediated changes in the outer layers of genes. BMI presented the strongest explanation of the association between the transcriptional response of the gene groups and SES, followed by smoking tobacco (also see Supplementary Figs. ##SUPPL##3##S7## and ##SUPPL##3##S8##). No significant mediation was observed for financial stress or access to health insurance. Furthermore, we observed no significant differences between BMI and waist circumference in mediating the SES—associated immune transcriptome (see Supplementary Fig. ##SUPPL##3##S9##).</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>Transcriptional alterations with SES are characterized by organism wide dysregulation</title>\",\n \"<p id=\\\"Par16\\\">We performed a differential gene expression analysis followed by an enrichment analysis of the resulting SES–DEG (see Supplementary Dataset ##SUPPL##1##S2## and Supplementary Fig. ##SUPPL##3##S3##). Upregulated genes indicate a significant association between high SES and high expression (i.e., a positive association). Functional enrichment of the SES–DEG (423 upregulated genes and 389 downregulated genes) showed a majority upregulated for pathways involving metabolism, signal transduction and cellular response to stress by a core of ribosomal and translational genes. Interestingly, these cytosolic ribosomal genes (<italic>RPL</italic>- and <italic>RPS</italic>-genes) were found to be downregulated with aging in an analysis of the human peripheral blood and previously linked with SES<sup>##REF##36252037##42##</sup>. Indeed, a combined WGCNA and SES—differential expression analysis (see Fig. ##FIG##0##1##) showed a tight clustering of the ribosomal and transcriptional activity genes (Cluster 11 in Fig. ##FIG##0##1##) that are responsible for the SES-upregulated pathways. One cluster of SES-DEGs (Cluster 7) displayed dysregulation in immune system and response, hemostasis and cell death that were predominantly driven by downregulated genes, while another cluster (Cluster 11), largely comprising upregulated ribosomal genes, affected transcriptional events in several cellular functions. Cluster 13 consisted of genes involved in cell division and cell cycle control dysregulating a relatively small number of pathways in signal transduction and immune system, while Cluster 17 comprised too few genes for a meaningful enrichment interpretation.</p>\",\n \"<p id=\\\"Par17\\\">An inspection of enriched pathways reveals that SES-upregulated pathways include interferon innate immune response and neutrophil degranulation (see Fig. ##FIG##1##2##). Curiously, type II interferon (IFN-γ) signaling is also upregulated. Despite the lack of direct evidence for their involvement, the genes that are tied to the upregulation of type I and type II interferon signaling do share <italic>HLA</italic>-genes that regulate the antiviral immune response, which may explain the overrepresentation of both classes of immunity. Figure ##FIG##1##2## also shows an attenuation of proinflammatory pathways with higher SES (i.e., upregulation of proinflammatory pathways with low SES) via pathways in the proinflammatory nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and proinflammatory toll-like receptors (TLR).</p>\",\n \"<title>Upstream regulators structure immune dysregulation with socioeconomic disparities</title>\",\n \"<p id=\\\"Par18\\\">The biological pathways and molecular mechanisms associated with socioeconomic disparity were also examined via an analysis of upstream regulators (transcriptional factors and protein partners) of the DE genes. A combined functional enrichment analysis of the resulting upstream regulators (239 upstream regulators of SES—upregulated genes and 87 upstream regulators of SES—downregulated genes) and SES–DEG (see Fig. ##FIG##2##3## and Supplementary Dataset ##SUPPL##2##S3##) indicates a significant role of the upstream regulators in structuring the overrepresented pathways in the immune system. The upstream regulators include tissue-specific transcription factors that are (1) differentially expressed (Set B, see Supplementary Fig. ##SUPPL##3##S5## and Supplementary Table ##SUPPL##3##S1##), (2) potentially regulating the expression of a differentially expressed gene (Set D, see Supplementary Fig. ##SUPPL##3##S5## and Supplementary Table ##SUPPL##3##S1##), and (3) PPI neighbors of differentially expressed transcription factors (Set C, see Supplementary Fig. ##SUPPL##3##S5## and Supplementary Table ##SUPPL##3##S1##). Figure ##FIG##2##3## indicates that the immune pathways that are enriched have a larger proportion of upstream targets. Importantly, many of these pathways were also enriched by SES–DEG signifying that the patterns of immune dysregulation observed in Fig. ##FIG##2##3## do not reflect the inclusion of upstream regulators per se, but rather reflect the significant mechanistic role played by these transcription factors and protein partners. Furthermore, the immune dysregulation observed in functional enrichment analysis of the upstream targets without the inclusion of SES–DEG (see Supplementary Fig. ##SUPPL##3##S6##) mirror those enriched by SES–DEG alone and SES–DEG with upstream targets, suggesting that SES–DEG are not wholly responsible for SES-associated immune dysregulation.</p>\",\n \"<p id=\\\"Par19\\\">Figure ##FIG##3##4## shows the upstream regulators along with the SES–DEG represented as layers (4 in total, where the innermost circle of genes and upstream regulators is labelled “Layer 1”, sequentially to the outermost group of genes and regulators labelled “Layer 4”) based on their interaction scores derived from the STRING database and the number of times each gene or upstream regulator is involved in functional immune pathways that are enriched in Fig. ##FIG##2##3##. The genes that are responsible for the enrichment of each immune pathway were identified. The innermost layer in Fig. ##FIG##3##4## depicts genes and upstream regulators that are involved in more than ten functional pathways, whereas the outermost layer consists of genes and upstream regulators that are only involved in one enriched biological process. The most essential regulators involved in the dysregulation of the immune system related to SES disparities thus occupy the center of the diagram. Significantly, variations in early adult SES are prominently linked to alterations in cytokine signaling in the immune system involving interleukin and interferon gamma signaling, Toll-like receptor signaling cascade, and TNF pathways (Fig. ##FIG##2##3## and innermost layer in Fig. ##FIG##3##4##).</p>\",\n \"<p id=\\\"Par20\\\">Proteins most deeply linked to SES inequalities invariably revolve around the cyclic 3ʹ–5ʹ adenosine monophosphate response element-binding protein (CREB) and NF-κB pathway signaling. Variations in the activity of CREB and NF-κB selectively upregulate the transcription of interferon response factor family while simultaneously inhibiting the activity of proinflammatory interleukins in subjects with high SES. Genes and transcription factors (via the upstream analysis) that are central to the functional response to SES disparities in functional gene regulation (shown in Fig. ##FIG##2##3##) are also shown in Fig. ##FIG##3##4##. Molecules are placed in layers depending on their contribution (instances of enrichment of a pathway) to the functional immune enrichment. The inner most layer consists of transcriptional factors such as <italic>CREB1</italic>, <italic>TP53</italic>, <italic>RELA</italic>, <italic>REL</italic>, <italic>BTRC</italic>, <italic>BTK</italic> and the <italic>MAPK-</italic>family. <italic>CREB</italic> and <italic>REL</italic> proteins, among other important functions, play a crucial role in the activation of the fight-or-flight signaling pathways that is directly responsible in eliciting the CTRA gene expression profiles. Although a large fraction of the proteins is derived from the set of upregulated genes, it is noteworthy that these regulators can have far reaching impact and they are not always in the expected direction. This is particularly true for the proinflammatory toll-like receptors (TLR) (see Fig. ##FIG##2##3##) pathways, which have a larger proportion of downregulated genes than upregulated genes. However, they also functionally interact with the upstream regulators connected to upregulated genes. The greater influence of the upstream regulators connected to upregulated genes suggests that the downregulation of certain genes could transpire as a consequence of inhibitory activity of the transcription factors.</p>\",\n \"<title>Social/behavioral mediators of SES-transcriptome associations</title>\",\n \"<p id=\\\"Par21\\\">Figure ##FIG##4##5## reports the median percentage mediated ratio for key SES-related social/behavioral processes in every layer of SES-related gene regulation. Intriguingly, the inner most group of genes that are most centrally implicated in SES-associated dysregulation, are also mediated the least (lowest median percentage mediated ratio) by every behavioral risk factor. However, this could reflect the fact that the inner most group of genes are not themselves differentially expressed despite potentially inducing larger mediated changes in the outer layers of genes. BMI presented the strongest explanation of the association between the transcriptional response of the gene groups and SES, followed by smoking tobacco (also see Supplementary Figs. ##SUPPL##3##S7## and ##SUPPL##3##S8##). No significant mediation was observed for financial stress or access to health insurance. Furthermore, we observed no significant differences between BMI and waist circumference in mediating the SES—associated immune transcriptome (see Supplementary Fig. ##SUPPL##3##S9##).</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussions</title>","<p id=\"Par22\">The present analyses expand the scope of prior studies of SES-related alteration in transcriptomic profiles of human immune response genes by identifying new genomic functional impacts (e.g., ribosomal biology) and new features of the gene regulatory architecture of SES (e.g., <italic>TP53</italic>, <italic>BTRC</italic>, <italic>BTK</italic> and <italic>MAPK</italic> transcriptional control pathways). Consistent with prior research, we find that SES is negatively associated with pro-inflammatory pathways in the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and proinflammatory toll-like receptors (TLR). Our findings also link high SES to elevated type II interferon (IFN-γ) signaling and identify a related upregulation of <italic>HLA</italic>- genes, which may underpin both type I and type II interferon effects.</p>","<p id=\"Par23\">The analyses extend previous research by mapping networks of upstream blood-specific transcriptional factors and protein interactions that could play a vital role in structuring the observed transcriptional landscape. The complexity of the impact of socioeconomic inequalities on the immune system and its association with diseases with widely varying pathologies warrants a systems-oriented approach to comprehensively analyze the SES-related perturbations in the immune transcriptome. To our knowledge, most prior research on SES focuses solely on transcriptomic alterations with a particular focus on pro-inflammatory action<sup>##UREF##3##30##</sup>. Here, we include the upstream regulators to depict an enhanced view of dysregulation with SES, shedding light on a tightly knit group of transcription factors that play a central role in modulating the transcriptomic alterations.</p>","<p id=\"Par24\">Our findings map a central network of upstream regulators that vary as a function of early adult SES (central positions in Fig. ##FIG##3##4##). Given the lack of change in the expression of the genes that encode these transcriptional factors, receptor-mediated post-transcriptional modification of these transcriptional factors (e.g., receptor-mediated phosphorylation of <italic>CREB1</italic>, <italic>TP53</italic>, <italic>RELA</italic>, <italic>REL</italic>, <italic>BTRC</italic>, <italic>BTK</italic> and <italic>MAPK</italic>) might modulate the expression of their downstream targets.</p>","<p id=\"Par25\">Despite the congruence between our results and the CTRA model in terms of dysregulated pathways, the findings show that SES disparities in early adulthood, interestingly, do not alter the same set of genes identified in previous studies of CTRA. For example, the attenuated interferon innate responses in the CTRA model is possible through the direct suppression of <italic>INFA</italic> and <italic>INFB</italic>. Here, we observed upregulated <italic>HLA-</italic>genes that additionally regulate the antiviral innate response. These findings call for a for further study in SES-modulated genes in the immune system beyond the signaling pathways already implicated in structuring the conserved transcriptional response to adversity (CTRA) RNA profile<sup>##REF##34142656##60##</sup>.</p>","<p id=\"Par26\">The observed SES-related transcriptional perturbations are associated with both up- and downregulated functional pathways in the immune system. Social stress-induced gene alterations in humans are associated with diseases that include both upregulation of the immune system as well as suppressed immune responsiveness with lowered social status. This seemingly perplexing pattern has been explained by the selective characteristics of the immune transcriptome, i.e., the increase in expression of certain pro-inflammatory genes and the repression of groups of antiviral immune response genes<sup>##REF##32041883##40##,##REF##25166010##61##–##REF##23853742##63##</sup>. It is, therefore, unsurprising that a similar pattern of dysregulated immune pathways is emergent in the blood transcriptomic landscape of subjects in Add Health with contributions of enrichment from both up- and downregulated gene clusters. Such a finding calls for additional research that moves beyond the initial general finding that stressors upregulate proinflammatory genes and downregulate antiviral genes.</p>","<p id=\"Par27\">Lastly, among the common mediating (or possible explanatory) mechanisms studied here, BMI consistently emerged as a plausible mediator of the SES associations with immune cell gene regulation. Smoking also appears to play a significant role in the SES-related transcriptional alterations. These results underscore the importance of gene regulatory network approach in formulating a comprehensive understanding of psychosocial stressors and their impact on biological mechanisms early in life. Studies have already established that changes caused by socioeconomic disparities in early adulthood could have far-reaching implications for chronic conditions in later adulthood. Identification of novel regulators of such perturbations is an important step in formulating a mitigating strategy.</p>","<p id=\"Par28\">We used tissue-specific regulatory networks to link transcription factors to differentially expressed genes, and subsequently the STRING database to find protein interaction partners. There were 643 transcription factors identified in the gene regulatory network, with an even a smaller number (304) having a confidence score that is above the threshold used (0.4). High confidence (threshold > 0.7) protein-protein neighbors derived from the whole network of 643 transcription factors numbered 3051 (18,875 total protein-protein neighbors), of which 1750 were pruned for the 304 transcription factors. Given the central role of many of the transcription factors and protein partners, one could argue that the set of upstream regulators found from the SES–DEG could be equal to a set of upstream regulators derived from a random set of genes. To examine this possibility, we performed randomized trials by starting with randomly selected sets of “DE” genes to then derive these upstream regulators of the random sets. We subsequently compared the upstream regulators of each random set of “DE” genes to our observed results (see Supplementary Figs. ##SUPPL##3##S10## and ##SUPPL##3##S11##). While some of the individual transcription factors may not reach statistical significance (Supplementary Fig. ##SUPPL##3##S10##), the entire set of the upstream regulators is highly significant (Supplementary Fig. ##SUPPL##3##S11##).</p>"],"string":"[\n \"<title>Discussions</title>\",\n \"<p id=\\\"Par22\\\">The present analyses expand the scope of prior studies of SES-related alteration in transcriptomic profiles of human immune response genes by identifying new genomic functional impacts (e.g., ribosomal biology) and new features of the gene regulatory architecture of SES (e.g., <italic>TP53</italic>, <italic>BTRC</italic>, <italic>BTK</italic> and <italic>MAPK</italic> transcriptional control pathways). Consistent with prior research, we find that SES is negatively associated with pro-inflammatory pathways in the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and proinflammatory toll-like receptors (TLR). Our findings also link high SES to elevated type II interferon (IFN-γ) signaling and identify a related upregulation of <italic>HLA</italic>- genes, which may underpin both type I and type II interferon effects.</p>\",\n \"<p id=\\\"Par23\\\">The analyses extend previous research by mapping networks of upstream blood-specific transcriptional factors and protein interactions that could play a vital role in structuring the observed transcriptional landscape. The complexity of the impact of socioeconomic inequalities on the immune system and its association with diseases with widely varying pathologies warrants a systems-oriented approach to comprehensively analyze the SES-related perturbations in the immune transcriptome. To our knowledge, most prior research on SES focuses solely on transcriptomic alterations with a particular focus on pro-inflammatory action<sup>##UREF##3##30##</sup>. Here, we include the upstream regulators to depict an enhanced view of dysregulation with SES, shedding light on a tightly knit group of transcription factors that play a central role in modulating the transcriptomic alterations.</p>\",\n \"<p id=\\\"Par24\\\">Our findings map a central network of upstream regulators that vary as a function of early adult SES (central positions in Fig. ##FIG##3##4##). Given the lack of change in the expression of the genes that encode these transcriptional factors, receptor-mediated post-transcriptional modification of these transcriptional factors (e.g., receptor-mediated phosphorylation of <italic>CREB1</italic>, <italic>TP53</italic>, <italic>RELA</italic>, <italic>REL</italic>, <italic>BTRC</italic>, <italic>BTK</italic> and <italic>MAPK</italic>) might modulate the expression of their downstream targets.</p>\",\n \"<p id=\\\"Par25\\\">Despite the congruence between our results and the CTRA model in terms of dysregulated pathways, the findings show that SES disparities in early adulthood, interestingly, do not alter the same set of genes identified in previous studies of CTRA. For example, the attenuated interferon innate responses in the CTRA model is possible through the direct suppression of <italic>INFA</italic> and <italic>INFB</italic>. Here, we observed upregulated <italic>HLA-</italic>genes that additionally regulate the antiviral innate response. These findings call for a for further study in SES-modulated genes in the immune system beyond the signaling pathways already implicated in structuring the conserved transcriptional response to adversity (CTRA) RNA profile<sup>##REF##34142656##60##</sup>.</p>\",\n \"<p id=\\\"Par26\\\">The observed SES-related transcriptional perturbations are associated with both up- and downregulated functional pathways in the immune system. Social stress-induced gene alterations in humans are associated with diseases that include both upregulation of the immune system as well as suppressed immune responsiveness with lowered social status. This seemingly perplexing pattern has been explained by the selective characteristics of the immune transcriptome, i.e., the increase in expression of certain pro-inflammatory genes and the repression of groups of antiviral immune response genes<sup>##REF##32041883##40##,##REF##25166010##61##–##REF##23853742##63##</sup>. It is, therefore, unsurprising that a similar pattern of dysregulated immune pathways is emergent in the blood transcriptomic landscape of subjects in Add Health with contributions of enrichment from both up- and downregulated gene clusters. Such a finding calls for additional research that moves beyond the initial general finding that stressors upregulate proinflammatory genes and downregulate antiviral genes.</p>\",\n \"<p id=\\\"Par27\\\">Lastly, among the common mediating (or possible explanatory) mechanisms studied here, BMI consistently emerged as a plausible mediator of the SES associations with immune cell gene regulation. Smoking also appears to play a significant role in the SES-related transcriptional alterations. These results underscore the importance of gene regulatory network approach in formulating a comprehensive understanding of psychosocial stressors and their impact on biological mechanisms early in life. Studies have already established that changes caused by socioeconomic disparities in early adulthood could have far-reaching implications for chronic conditions in later adulthood. Identification of novel regulators of such perturbations is an important step in formulating a mitigating strategy.</p>\",\n \"<p id=\\\"Par28\\\">We used tissue-specific regulatory networks to link transcription factors to differentially expressed genes, and subsequently the STRING database to find protein interaction partners. There were 643 transcription factors identified in the gene regulatory network, with an even a smaller number (304) having a confidence score that is above the threshold used (0.4). High confidence (threshold > 0.7) protein-protein neighbors derived from the whole network of 643 transcription factors numbered 3051 (18,875 total protein-protein neighbors), of which 1750 were pruned for the 304 transcription factors. Given the central role of many of the transcription factors and protein partners, one could argue that the set of upstream regulators found from the SES–DEG could be equal to a set of upstream regulators derived from a random set of genes. To examine this possibility, we performed randomized trials by starting with randomly selected sets of “DE” genes to then derive these upstream regulators of the random sets. We subsequently compared the upstream regulators of each random set of “DE” genes to our observed results (see Supplementary Figs. ##SUPPL##3##S10## and ##SUPPL##3##S11##). While some of the individual transcription factors may not reach statistical significance (Supplementary Fig. ##SUPPL##3##S10##), the entire set of the upstream regulators is highly significant (Supplementary Fig. ##SUPPL##3##S11##).</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Disparities in socio-economic status (SES) predict many immune system-related diseases, and previous research documents relationships between SES and the immune cell transcriptome. Drawing on a bioinformatically-informed network approach, we situate these findings in a broader molecular framework by examining the upstream regulators of SES-associated transcriptional alterations. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative sample of 4543 adults in the United States. Results reveal a network—of differentially expressed genes, transcription factors, and protein neighbors of transcription factors—that shows widespread SES-related dysregulation of the immune system. Mediational models suggest that body mass index (BMI) plays a key role in accounting for many of these associations. Overall, the results reveal the central role of upstream regulators in socioeconomic differences in the molecular basis of immunity, which propagate to increase risk of chronic health conditions in later-life.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Disparities in socio-economic status (SES) predict many immune system-related diseases, and previous research documents relationships between SES and the immune cell transcriptome. Drawing on a bioinformatically-informed network approach, we situate these findings in a broader molecular framework by examining the upstream regulators of SES-associated transcriptional alterations. Data come from the National Longitudinal Study of Adolescent to Adult Health (Add Health), a nationally representative sample of 4543 adults in the United States. Results reveal a network—of differentially expressed genes, transcription factors, and protein neighbors of transcription factors—that shows widespread SES-related dysregulation of the immune system. Mediational models suggest that body mass index (BMI) plays a key role in accounting for many of these associations. Overall, the results reveal the central role of upstream regulators in socioeconomic differences in the molecular basis of immunity, which propagate to increase risk of chronic health conditions in later-life.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Limitations</title>","<p id=\"Par29\">Several limitations are noteworthy. First, the hypotheses and the subsequent results are driven by the mRNA abundance data collected once from every participating subject. The repeated collection of transcriptomic data would be essential to address their highly transient nature, which is likely associated with considerable noise. Second, the identification of upstream transcriptional regulators of gene expression is performed with the aid of tissue-specific gene regulatory networks. These networks link genes to transcription factors based on experimental evidence and assign a confidence score to every identified transcription factor. It is, therefore, possible that transcription factors that play a central role in cell maintenance and cell cycle may be implicated without having a substantive role in the etiology or progression of dysfunction. We tried to account for such effects using a randomization experiment. However, direct measurements of protein abundance are required to concretely determine the role of every transcription factor. In the absence of proteomic assay data, inferring transcription factor abundance from publicly available chromatin immunoprecipitation followed by sequencing (ChIP-seq) data sources that have similar gene expression alterations as those observed with lowered SES, could offer valuable insights and presents an important extension of the current work. Finally, because the design is not experimental, the findings cannot be interpreted as casual relationships. This limitation is especially salient for the casual identification of social and behavioral mediators of SES-related immune dysregulation. Future research could usefully examine them and other mediators in a casual framework to disentangle expected tissue repair response to stress induced by these risk factors (e.g., obesity, smoking) and global immune system dysfunction.</p>","<p id=\"Par30\">Nevertheless, results suggest that a network of transcription factors and protein partners play a pivotal role in modulating the SES-related transcriptional response that precipitates dysregulated immune system response in terms of inflammation and interferon innate immunity. These central actors are important targets for future research connecting health disparities and socioeconomic inequalities. The results highlight the need for system-oriented analyses to comprehensively map the biological impact of SES disparities and they represent an essential step forward in identifying targets for possible prevention and intervention.</p>","<title>Supplementary Information</title>","<p>\n</p>"],"string":"[\n \"<title>Limitations</title>\",\n \"<p id=\\\"Par29\\\">Several limitations are noteworthy. First, the hypotheses and the subsequent results are driven by the mRNA abundance data collected once from every participating subject. The repeated collection of transcriptomic data would be essential to address their highly transient nature, which is likely associated with considerable noise. Second, the identification of upstream transcriptional regulators of gene expression is performed with the aid of tissue-specific gene regulatory networks. These networks link genes to transcription factors based on experimental evidence and assign a confidence score to every identified transcription factor. It is, therefore, possible that transcription factors that play a central role in cell maintenance and cell cycle may be implicated without having a substantive role in the etiology or progression of dysfunction. We tried to account for such effects using a randomization experiment. However, direct measurements of protein abundance are required to concretely determine the role of every transcription factor. In the absence of proteomic assay data, inferring transcription factor abundance from publicly available chromatin immunoprecipitation followed by sequencing (ChIP-seq) data sources that have similar gene expression alterations as those observed with lowered SES, could offer valuable insights and presents an important extension of the current work. Finally, because the design is not experimental, the findings cannot be interpreted as casual relationships. This limitation is especially salient for the casual identification of social and behavioral mediators of SES-related immune dysregulation. Future research could usefully examine them and other mediators in a casual framework to disentangle expected tissue repair response to stress induced by these risk factors (e.g., obesity, smoking) and global immune system dysfunction.</p>\",\n \"<p id=\\\"Par30\\\">Nevertheless, results suggest that a network of transcription factors and protein partners play a pivotal role in modulating the SES-related transcriptional response that precipitates dysregulated immune system response in terms of inflammation and interferon innate immunity. These central actors are important targets for future research connecting health disparities and socioeconomic inequalities. The results highlight the need for system-oriented analyses to comprehensively map the biological impact of SES disparities and they represent an essential step forward in identifying targets for possible prevention and intervention.</p>\",\n \"<title>Supplementary Information</title>\",\n \"<p>\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary Information</title>","<p>The online version contains supplementary material available at 10.1038/s41598-024-51517-6.</p>","<title>Author contributions</title>","<p>S.R. conceived of paper, performed data analysis, and wrote paper. S.C., K.H., and M.S. collected the data. S.C., B.L., and M.S. assisted with interpretation and writing.</p>","<title>Funding</title>","<p>This research was supported by NIH Grants R01-HD087061 (MPIs K.H. Harris and M. J. Shanahan) specifically for the present analyses, as well as P30-AG017265, R01-AG043404, and R01-AG033590; by the Swiss National Science Foundation (10531C-197964, Shanahan PI); and by the Jacobs Center for Productive Youth Development (University of Zürich). This research uses data from Add Health, a program directed by Robert Hummer and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris (University of North Carolina at Chapel Hill). The Add Health program is funded by Grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.cpc.unc.edu/projects/addhealth/about/funders\">https://www.cpc.unc.edu/projects/addhealth/about/funders</ext-link>).</p>","<title>Data availability</title>","<p>Add Health data are available at <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.cpc.unc.edu/projects/addhealth/documentation/\">https://www.cpc.unc.edu/projects/addhealth/documentation/</ext-link>. All the data used in these analyses, except for the transcriptomic data are not restricted. The mRNA-seq data is available via a restricted data contract. Additional information and application for the restricted-use data can be accessed through the Carolina Population Center (CPC) data portal at <ext-link ext-link-type=\"uri\" xlink:href=\"https://data.cpc.unc.edu/projects/2/view\">https://data.cpc.unc.edu/projects/2/view</ext-link>. The Cytoscape sessions, supplemental datasets and R codes used in these analyses are available at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/socialgnome/Immune-SES\">https://github.com/socialgnome/Immune-SES</ext-link>.</p>","<title>Competing interests</title>","<p id=\"Par31\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary Information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41598-024-51517-6.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>S.R. conceived of paper, performed data analysis, and wrote paper. S.C., K.H., and M.S. collected the data. S.C., B.L., and M.S. assisted with interpretation and writing.</p>\",\n \"<title>Funding</title>\",\n \"<p>This research was supported by NIH Grants R01-HD087061 (MPIs K.H. Harris and M. J. Shanahan) specifically for the present analyses, as well as P30-AG017265, R01-AG043404, and R01-AG033590; by the Swiss National Science Foundation (10531C-197964, Shanahan PI); and by the Jacobs Center for Productive Youth Development (University of Zürich). This research uses data from Add Health, a program directed by Robert Hummer and designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris (University of North Carolina at Chapel Hill). The Add Health program is funded by Grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 23 other federal agencies and foundations (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.cpc.unc.edu/projects/addhealth/about/funders\\\">https://www.cpc.unc.edu/projects/addhealth/about/funders</ext-link>).</p>\",\n \"<title>Data availability</title>\",\n \"<p>Add Health data are available at <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.cpc.unc.edu/projects/addhealth/documentation/\\\">https://www.cpc.unc.edu/projects/addhealth/documentation/</ext-link>. All the data used in these analyses, except for the transcriptomic data are not restricted. The mRNA-seq data is available via a restricted data contract. Additional information and application for the restricted-use data can be accessed through the Carolina Population Center (CPC) data portal at <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://data.cpc.unc.edu/projects/2/view\\\">https://data.cpc.unc.edu/projects/2/view</ext-link>. The Cytoscape sessions, supplemental datasets and R codes used in these analyses are available at <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://github.com/socialgnome/Immune-SES\\\">https://github.com/socialgnome/Immune-SES</ext-link>.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par31\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Enrichment analysis of the WGCNA clusters exhibiting significant cluster-SES relationships. Eigengenes from individual clusters were identified using WGCNA and then examined for significant associations with SES (adjusted <italic>p</italic> < 0.05). Enriched biological pathways for significant clusters (adjusted <italic>p</italic> < 0.05) were then examined using Reactome. Here, upregulation refers to a significant positive association between SES and expression. The chord diagram shows the connection between the clusters (on the left half of the chord diagram) and the enriched grouped pathways (right). The width of the arcs and the lines connecting WGCNA identifies clusters to the enriched pathways correspond to the number of pathways that are dysregulated. For example, Cluster 13 (identified as “III” on the left half of the chord diagram) significantly downregulates 5 pathways. 1 pathway each in Developmental Biology and Immune system (“D” and “I” on the right half of the figure) respectively, and, 3 pathways in Signal Transduction (marked “N”).</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Immune pathway enrichment of the differentially expressed genes by early adult SES. Significantly enriched Reactome pathways (with immune parent nodes reported to the right, child nodes to the left) for SES–DEG. The size of the circle signifies the number of genes that contribute to the significant enrichment in a pathway and the color of the circle indicates Cramer’s V, a measure of the magnitude of association. The overrepresentation analysis shows a predominantly upregulated antiviral immune response (Interferon signaling and neutrophil degranulation) and downregulation in proinflammatory pathways (NF-κB and toll-like receptor cascade) with high SES.</p></caption></fig>","<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Combined functional immune enrichment analysis of the SES–DEG and their upstream regulators. Reactome pathways in the immune system are represented here as circular filled nodes. Reactome pathways are hierarchical, and the arrow connects a parent node to its child. The significance of pathways (adjusted <italic>p</italic> < 0.05) for the combined analysis are computed using ClueGO<sup>##REF##19237447##53##</sup> and represented by the size of the circular nodes. These nodes are filled with a pie chart which indicates the contribution of individual gene class. The size of the circular node signifies the adjusted <italic>p</italic>-value (larger nodes are more significant).</p></caption></fig>","<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Relative importance of SES–DEG and upstream regulators to the immune dysregulation. The figure shows the upstream regulators along with the SES–DEG represented as layers (4 in total). The innermost layer represents genes and upstream regulators that are involved in the enrichment of more than 10 functional pathways (in Fig. ##FIG##2##3##) and thus they are pivotal in immune dysregulation, whereas the outermost layer consists of genes and upstream regulators that are only involved in 1 enriched biological process.</p></caption></fig>","<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Mediation analysis for common behavioral risk factors and the layers of upstream regulators of SES-associated immune system dysfunction. The median percent mediated ratio (Average casual mediated effect (ACME)/total effect) is shown for mediational models for the risk factors and the layers of SES–DEG and upstream regulators in Fig. ##FIG##3##4##. All the mediational models were significant (aggregated adjusted <italic>p</italic>-value < 0.05) (also see Supplementary Figs. ##SUPPL##3##S6## and ##SUPPL##3##S7##). Negative ratios suggest a pattern of suppression.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>Enrichment analysis of the WGCNA clusters exhibiting significant cluster-SES relationships. Eigengenes from individual clusters were identified using WGCNA and then examined for significant associations with SES (adjusted <italic>p</italic> < 0.05). Enriched biological pathways for significant clusters (adjusted <italic>p</italic> < 0.05) were then examined using Reactome. Here, upregulation refers to a significant positive association between SES and expression. The chord diagram shows the connection between the clusters (on the left half of the chord diagram) and the enriched grouped pathways (right). The width of the arcs and the lines connecting WGCNA identifies clusters to the enriched pathways correspond to the number of pathways that are dysregulated. For example, Cluster 13 (identified as “III” on the left half of the chord diagram) significantly downregulates 5 pathways. 1 pathway each in Developmental Biology and Immune system (“D” and “I” on the right half of the figure) respectively, and, 3 pathways in Signal Transduction (marked “N”).</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>Immune pathway enrichment of the differentially expressed genes by early adult SES. Significantly enriched Reactome pathways (with immune parent nodes reported to the right, child nodes to the left) for SES–DEG. The size of the circle signifies the number of genes that contribute to the significant enrichment in a pathway and the color of the circle indicates Cramer’s V, a measure of the magnitude of association. The overrepresentation analysis shows a predominantly upregulated antiviral immune response (Interferon signaling and neutrophil degranulation) and downregulation in proinflammatory pathways (NF-κB and toll-like receptor cascade) with high SES.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Figure 3</label><caption><p>Combined functional immune enrichment analysis of the SES–DEG and their upstream regulators. Reactome pathways in the immune system are represented here as circular filled nodes. Reactome pathways are hierarchical, and the arrow connects a parent node to its child. The significance of pathways (adjusted <italic>p</italic> < 0.05) for the combined analysis are computed using ClueGO<sup>##REF##19237447##53##</sup> and represented by the size of the circular nodes. These nodes are filled with a pie chart which indicates the contribution of individual gene class. The size of the circular node signifies the adjusted <italic>p</italic>-value (larger nodes are more significant).</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Figure 4</label><caption><p>Relative importance of SES–DEG and upstream regulators to the immune dysregulation. The figure shows the upstream regulators along with the SES–DEG represented as layers (4 in total). The innermost layer represents genes and upstream regulators that are involved in the enrichment of more than 10 functional pathways (in Fig. ##FIG##2##3##) and thus they are pivotal in immune dysregulation, whereas the outermost layer consists of genes and upstream regulators that are only involved in 1 enriched biological process.</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Figure 5</label><caption><p>Mediation analysis for common behavioral risk factors and the layers of upstream regulators of SES-associated immune system dysfunction. The median percent mediated ratio (Average casual mediated effect (ACME)/total effect) is shown for mediational models for the risk factors and the layers of SES–DEG and upstream regulators in Fig. ##FIG##3##4##. All the mediational models were significant (aggregated adjusted <italic>p</italic>-value < 0.05) (also see Supplementary Figs. ##SUPPL##3##S6## and ##SUPPL##3##S7##). Negative ratios suggest a pattern of suppression.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":["<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>"],"string":"[\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM1\\\"></supplementary-material>\",\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM2\\\"></supplementary-material>\",\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM3\\\"></supplementary-material>\",\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM4\\\"></supplementary-material>\"\n]","isConversational":false},"footnote":{"kind":"list like","value":["<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"],"string":"[\n \"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41598_2024_51517_Fig1_HTML\" id=\"MO1\"/>","<graphic xlink:href=\"41598_2024_51517_Fig2_HTML\" id=\"MO2\"/>","<graphic xlink:href=\"41598_2024_51517_Fig3_HTML\" id=\"MO3\"/>","<graphic xlink:href=\"41598_2024_51517_Fig4_HTML\" id=\"MO4\"/>","<graphic xlink:href=\"41598_2024_51517_Fig5_HTML\" id=\"MO5\"/>"],"string":"[\n \"<graphic xlink:href=\\\"41598_2024_51517_Fig1_HTML\\\" id=\\\"MO1\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51517_Fig2_HTML\\\" id=\\\"MO2\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51517_Fig3_HTML\\\" id=\\\"MO3\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51517_Fig4_HTML\\\" id=\\\"MO4\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51517_Fig5_HTML\\\" id=\\\"MO5\\\"/>\"\n]","isConversational":false},"media":{"kind":"list like","value":["<media xlink:href=\"41598_2024_51517_MOESM1_ESM.xlsx\"><caption><p>Supplementary Information 1.</p></caption></media>","<media xlink:href=\"41598_2024_51517_MOESM2_ESM.xlsx\"><caption><p>Supplementary Information 2.</p></caption></media>","<media xlink:href=\"41598_2024_51517_MOESM3_ESM.xlsx\"><caption><p>Supplementary Information 3.</p></caption></media>","<media xlink:href=\"41598_2024_51517_MOESM4_ESM.pdf\"><caption><p>Supplementary Information 4.</p></caption></media>"],"string":"[\n \"<media xlink:href=\\\"41598_2024_51517_MOESM1_ESM.xlsx\\\"><caption><p>Supplementary Information 1.</p></caption></media>\",\n \"<media xlink:href=\\\"41598_2024_51517_MOESM2_ESM.xlsx\\\"><caption><p>Supplementary Information 2.</p></caption></media>\",\n \"<media xlink:href=\\\"41598_2024_51517_MOESM3_ESM.xlsx\\\"><caption><p>Supplementary Information 3.</p></caption></media>\",\n \"<media xlink:href=\\\"41598_2024_51517_MOESM4_ESM.pdf\\\"><caption><p>Supplementary Information 4.</p></caption></media>\"\n]","isConversational":false},"unknown_pub":{"kind":"string","value":"[{\"label\": [\"1.\"], \"surname\": [\"Mathews\"], \"given-names\": [\"L\"], \"article-title\": [\"Impact of socioeconomic status on mortality and readmission in patients with heart failure with reduced ejection fraction: The ARIC study\"], \"source\": [\"J. Am. Heart Assoc.\"], \"year\": [\"2022\"], \"volume\": [\"11\"], \"fpage\": [\"24057\"], \"pub-id\": [\"10.1161/JAHA.121.024057\"]}, {\"label\": [\"16.\"], \"mixed-citation\": [\"Klopack, E. T. \"], \"italic\": [\"et al.\"]}, {\"label\": [\"19.\"], \"surname\": [\"Azad\"], \"given-names\": [\"MB\"], \"article-title\": [\"Influence of socioeconomic status trajectories on innate immune responsiveness in children\"], \"source\": [\"PLoS One\"], \"year\": [\"2012\"], \"volume\": [\"7\"], \"fpage\": [\"38669\"], \"pub-id\": [\"10.1371/journal.pone.0038669\"]}, {\"label\": [\"30.\"], \"surname\": [\"Robles\"], \"given-names\": [\"TF\"], \"article-title\": [\"Annual Research Review: Social relationships and the immune system during development\"], \"source\": [\"J. Child Psychol. 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Prestige and Socioeconomic Scores for the 2010 Census Codes.\"]}, {\"label\": [\"45.\"], \"surname\": [\"Robinson\", \"Oshlack\"], \"given-names\": [\"MD\", \"A\"], \"article-title\": [\"A scaling normalization method for differential expression analysis of RNA-seq data\"], \"source\": [\"Genome Biol.\"], \"year\": [\"2010\"], \"volume\": [\"11\"], \"fpage\": [\"1\"], \"lpage\": [\"9\"], \"pub-id\": [\"10.1186/gb-2010-11-3-r25\"]}, {\"label\": [\"48.\"], \"surname\": [\"Law\", \"Chen\", \"Shi\", \"Smyth\"], \"given-names\": [\"CW\", \"Y\", \"W\", \"GK\"], \"article-title\": [\"Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts\"], \"source\": [\"Genome Biol.\"], \"year\": [\"2014\"], \"volume\": [\"15\"], \"fpage\": [\"1\"], \"lpage\": [\"17\"], \"pub-id\": [\"10.1186/gb-2014-15-2-r29\"]}, {\"label\": [\"50.\"], \"surname\": [\"Langfelder\", \"Horvath\"], \"given-names\": [\"P\", \"S\"], \"article-title\": [\"WGCNA: An R package for weighted correlation network analysis\"], \"source\": [\"BMC Bioinform.\"], \"year\": [\"2008\"], \"volume\": [\"9\"], \"fpage\": [\"1\"], \"lpage\": [\"13\"], \"pub-id\": [\"10.1186/1471-2105-9-559\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":63,"string":"63"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:56"},"citation":{"kind":"string","value":"Sci Rep. 2024 Jan 13; 14:1255"},"package_file":{"kind":"string","value":"oa_package/7e/bc/PMC10787749.tar.gz"}}},{"rowIdx":80,"cells":{"accession_id":{"kind":"string","value":"PMC10787750"},"pmid":{"kind":"string","value":"38218941"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par10\">Atherosclerosis is a chronic, progressive disease characterized by the accumulation of lipids, inflammatory cells, and fibrous elements in the arterial wall, leading to the formation of atherosclerotic plaques [##REF##33883728##1##, ##REF##31420554##2##]. These plaques can lead to serious clinical consequences, such as myocardial infarction (MI) and stroke, which are major causes of morbidity and mortality worldwide [##REF##26892956##3##]. The pathogenesis of atherosclerosis involves multiple genetic, environmental, and metabolic factors [##REF##35504280##4##].</p>","<p id=\"Par11\">Copper is an essential trace element involved in mitochondrial respiration, antioxidant defense, and neurotransmitter synthesis [##REF##22075424##5##, ##REF##18277979##6##]. Copper homeostasis is tightly regulated. Copper excess or deficiency can lead to pathological alterations, and thus negatively affect human health [##REF##36414625##7##]. Copper dyshomeostasis may contribute to the pathogenesis of atherosclerosis by increasing oxidative stress, inflammation, endothelial dysfunction, and lipid metabolism levels [##REF##36774340##8##–##REF##29874110##10##]. Moreover, a novel type of cell death, which is copper-dependent, has recently been described. This copper-induced cell death, termed cuproptosis, may contribute to atherosclerosis development by causing cell death and impairing mitochondrial function [##REF##35354936##11##, ##REF##35298263##12##].</p>","<p id=\"Par12\">In this review, we explore the role of copper homeostasis and cuproptosis in atherosclerosis. We also discuss potential therapeutic strategies against cuproptosis and provide directions for future research on cuproptosis and atherosclerosis.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par10\\\">Atherosclerosis is a chronic, progressive disease characterized by the accumulation of lipids, inflammatory cells, and fibrous elements in the arterial wall, leading to the formation of atherosclerotic plaques [##REF##33883728##1##, ##REF##31420554##2##]. These plaques can lead to serious clinical consequences, such as myocardial infarction (MI) and stroke, which are major causes of morbidity and mortality worldwide [##REF##26892956##3##]. The pathogenesis of atherosclerosis involves multiple genetic, environmental, and metabolic factors [##REF##35504280##4##].</p>\",\n \"<p id=\\\"Par11\\\">Copper is an essential trace element involved in mitochondrial respiration, antioxidant defense, and neurotransmitter synthesis [##REF##22075424##5##, ##REF##18277979##6##]. Copper homeostasis is tightly regulated. Copper excess or deficiency can lead to pathological alterations, and thus negatively affect human health [##REF##36414625##7##]. Copper dyshomeostasis may contribute to the pathogenesis of atherosclerosis by increasing oxidative stress, inflammation, endothelial dysfunction, and lipid metabolism levels [##REF##36774340##8##–##REF##29874110##10##]. Moreover, a novel type of cell death, which is copper-dependent, has recently been described. This copper-induced cell death, termed cuproptosis, may contribute to atherosclerosis development by causing cell death and impairing mitochondrial function [##REF##35354936##11##, ##REF##35298263##12##].</p>\",\n \"<p id=\\\"Par12\\\">In this review, we explore the role of copper homeostasis and cuproptosis in atherosclerosis. We also discuss potential therapeutic strategies against cuproptosis and provide directions for future research on cuproptosis and atherosclerosis.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":[],"string":"[]","isConversational":false},"results":{"kind":"list like","value":[],"string":"[]","isConversational":false},"discussion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"conclusion":{"kind":"list like","value":["<title>Conclusions and future perspectives</title>","<p id=\"Par59\">In conclusion, the multifaceted role of copper in atherosclerosis involves complex cellular and systemic metabolic processes. Copper deficiency or excess promotes atherosclerosis by inducing oxidative stress, inflammation, endothelial dysfunction, and adverse effects on lipid metabolism. Further research is required to elucidate the interactions between these factors and their role in the development of atherosclerotic lesions.</p>","<p id=\"Par60\">Copper-induced cell death and the subsequent emergence of the concept of cuproptosis have expanded our understanding of the role of copper in atherosclerosis. The involvement of cuproptosis and the associated mitochondrial dysfunction in atherosclerosis highlights the need for further research into the molecular mechanisms underlying these processes. Therapeutic targets and strategies for mitigating the detrimental effects of copper-induced cell death in atherosclerosis have also been identified, including modulating copper levels, inhibiting cuproptosis, or enhancing cellular defenses against cuproptosis. However, potential side effects should also be considered since an excessive reduction in copper levels may impair essential physiological processes or, in itself, lead to atherosclerosis. Additionally, the absence of cuproptosis-specific biomarkers is a significant barrier that hinders further development of clinical applications regarding cuproptosis. Identifying such biomarkers may facilitate risk stratification and the development of personalized therapeutic approaches.</p>"],"string":"[\n \"<title>Conclusions and future perspectives</title>\",\n \"<p id=\\\"Par59\\\">In conclusion, the multifaceted role of copper in atherosclerosis involves complex cellular and systemic metabolic processes. Copper deficiency or excess promotes atherosclerosis by inducing oxidative stress, inflammation, endothelial dysfunction, and adverse effects on lipid metabolism. Further research is required to elucidate the interactions between these factors and their role in the development of atherosclerotic lesions.</p>\",\n \"<p id=\\\"Par60\\\">Copper-induced cell death and the subsequent emergence of the concept of cuproptosis have expanded our understanding of the role of copper in atherosclerosis. The involvement of cuproptosis and the associated mitochondrial dysfunction in atherosclerosis highlights the need for further research into the molecular mechanisms underlying these processes. Therapeutic targets and strategies for mitigating the detrimental effects of copper-induced cell death in atherosclerosis have also been identified, including modulating copper levels, inhibiting cuproptosis, or enhancing cellular defenses against cuproptosis. However, potential side effects should also be considered since an excessive reduction in copper levels may impair essential physiological processes or, in itself, lead to atherosclerosis. Additionally, the absence of cuproptosis-specific biomarkers is a significant barrier that hinders further development of clinical applications regarding cuproptosis. Identifying such biomarkers may facilitate risk stratification and the development of personalized therapeutic approaches.</p>\"\n]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Copper is an essential micronutrient that plays a pivotal role in numerous physiological processes in virtually all cell types. Nevertheless, the dysregulation of copper homeostasis, whether towards excess or deficiency, can lead to pathological alterations, such as atherosclerosis. With the advent of the concept of copper-induced cell death, termed cuproptosis, researchers have increasingly focused on the potential role of copper dyshomeostasis in atherosclerosis. In this review, we provide a broad overview of cellular and systemic copper metabolism. We then summarize the evidence linking copper dyshomeostasis to atherosclerosis and elucidate the potential mechanisms underlying atherosclerosis development in terms of both copper excess and copper deficiency. Furthermore, we discuss the evidence for and mechanisms of cuproptosis, discuss its interactions with other modes of cell death, and highlight the role of cuproptosis-related mitochondrial dysfunction in atherosclerosis. Finally, we explore the therapeutic strategy of targeting this novel form of cell death, aiming to provide some insights for the management of atherosclerosis.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Copper is an essential micronutrient that plays a pivotal role in numerous physiological processes in virtually all cell types. Nevertheless, the dysregulation of copper homeostasis, whether towards excess or deficiency, can lead to pathological alterations, such as atherosclerosis. With the advent of the concept of copper-induced cell death, termed cuproptosis, researchers have increasingly focused on the potential role of copper dyshomeostasis in atherosclerosis. In this review, we provide a broad overview of cellular and systemic copper metabolism. We then summarize the evidence linking copper dyshomeostasis to atherosclerosis and elucidate the potential mechanisms underlying atherosclerosis development in terms of both copper excess and copper deficiency. Furthermore, we discuss the evidence for and mechanisms of cuproptosis, discuss its interactions with other modes of cell death, and highlight the role of cuproptosis-related mitochondrial dysfunction in atherosclerosis. Finally, we explore the therapeutic strategy of targeting this novel form of cell death, aiming to provide some insights for the management of atherosclerosis.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Facts</title>","<p id=\"Par2\">\n<list list-type=\"order\"><list-item><p id=\"Par3\">Copper is an essential trace element required in various physiological processes in the human body.</p></list-item><list-item><p id=\"Par4\">Dysregulation of copper homeostasis, whether towards excess or deficiency, has been implicated in various health problems, including atherosclerosis.</p></list-item><list-item><p id=\"Par5\">Dysregulation of copper homeostasis and copper-induced cell death (cuproptosis) are acknowledged as potential contributors to the pathogenesis of atherosclerosis.</p></list-item></list>\n</p>","<title>Open questions</title>","<p id=\"Par6\">\n<list list-type=\"order\"><list-item><p id=\"Par7\">What is the safe window for copper levels to avoid the development of atherosclerosis? How can the suitable copper concentration be determined for the treatment of atherosclerosis?</p></list-item><list-item><p id=\"Par8\">What are the potential candidate biomarkers that can reliably and sensitively indicate the occurrence of cuproptosis in the context of atherosclerosis?</p></list-item><list-item><p id=\"Par9\">What are the potential undiscovered roles of copper in mitochondrial function, and is there interplay between copper and mitochondrial dynamics or mitophagy?</p></list-item></list>\n</p>","<title>Copper metabolism</title>","<title>Systemic copper homeostasis</title>","<p id=\"Par13\">Copper is an essential trace element required in various physiological processes in the human body. Research shows that Cu levels vary across organs and tissues, with values ranging from 3 mg (kidneys) to 46 mg (bone) [##REF##37500296##13##]. Copper serves as a cofactor for numerous enzymes involved in energy metabolism, neurotransmitter synthesis, and antioxidant defense [##REF##22075424##5##, ##REF##18277979##6##, ##REF##34390520##14##]. Excessive or deficient copper levels can, however, lead to cytotoxicity and pathological alterations [##REF##36414625##7##]. Therefore, it is essential to maintain systemic copper levels within a narrow range <bold>(</bold>Fig. ##FIG##0##1##<bold>)</bold>.</p>","<title>Copper absorption</title>","<p id=\"Par14\">Copper is mainly obtained from dietary sources such as organ meat and shellfish [##REF##27049134##15##]. Copper is primarily absorbed by enterocytes in the small intestine, and its uptake is mediated by copper transporter 1 (CTR1), also known as solute carrier family 31 member 1 (SLC31A1) [##REF##36882769##16##]. CTR1 appears to play a primary role in this uptake, as indicated by research showing greatly reduced copper accumulation in the peripheral tissues of neonatal mice lacking CTR1 [##REF##16950140##17##]. The six-transmembrane epithelial antigen of the prostate (STEAP) facilitates this process by reducing divalent Cu<sup>2+</sup> to Cu<sup>+</sup>.</p>","<title>Copper storage and transportation</title>","<p id=\"Par15\">After uptake by the intestinal epithelial cells, copper is transported and exported into the bloodstream by the ATPase copper transporter 7 A (ATP7A) [##REF##18779306##18##]. Here, copper is transported through the portal system to the liver by binding to soluble chaperones such as human serum albumin (HSA), ceruloplasmin, histidine, and macroglobulin [##UREF##1##19##, ##REF##26934375##20##]. Copper uptake by the hepatocytes in the liver is also mediated by CTR1. The liver is crucial in regulating copper metabolism by storing and excreting copper. Copper storage is mediated by the copper-binding protein metallothionein (MT), a reducing molecule rich in thiol groups with a high affinity for copper ions [##REF##32049413##21##]. MT plays a crucial role in copper homeostasis by storing and releasing excess copper when required. Copper transport is performed by ATPase copper transporter 7B (ATP7B) in hepatocytes, which pumps copper ions from the liver back into the bloodstream. Here, copper ions bind to their soluble partners and are transported to specific tissues and organs [##REF##19922814##22##].</p>","<title>Copper elimination</title>","<p id=\"Par16\">Copper is primarily eliminated via biliary excretion [##REF##36414625##7##]. Excess copper is secreted by the liver into the bile and then excreted through feces [##REF##15099406##23##]. The ATP7B transporter regulates the elimination of copper from the liver to bile canaliculi [##REF##18395099##24##]. In cases of ATP7B dysfunction, such as Wilson’s disease, copper accumulates in the liver, leading to liver damage and subsequent health issues [##REF##22240481##25##].</p>","<p id=\"Par17\">In summary, copper metabolism is a complex process that involves multiple mechanisms to regulate copper absorption, transport, storage, and elimination.</p>","<title>Cellular copper homeostasis</title>","<p id=\"Par18\">The maintenance of intracellular copper homeostasis is a complex and tightly regulated process involving the coordinated action of copper transporters, chaperones, and cuproenzymes <bold>(</bold>Fig. ##FIG##1##2##<bold>)</bold>. Copper concentration is maintained within a narrow range through the collaborative action of these copper-dependent proteins.</p>","<title>Copper uptake</title>","<p id=\"Par19\">In mammalian cells, copper uptake is primarily mediated by CTR1 [##REF##18277979##6##], a transmembrane protein that forms a trimeric channel to facilitate the passage of Cu ions across the plasma membrane [##REF##23658018##26##]. CTR1 expression is regulated by copper levels. Low copper levels upregulate CTR1 expression to increase copper uptake, whereas high levels downregulate CTR1 expression to prevent copper cytotoxicity [##UREF##2##27##, ##REF##16365053##28##]. These findings highlight the importance of CTR1 in copper homeostasis.</p>","<title>Intracellular copper distribution</title>","<p id=\"Par20\">After entering the cell, copper is first delivered to cytoplasmic copper chaperones, which then transport it to intracellular compartments such as the mitochondria, trans-Golgi network (TGN), and nucleus. In mammalian cells, three major copper chaperones have been identified: antioxidant 1 copper chaperone (ATOX1), copper chaperone for superoxide dismutase (CCS), and cytochrome c oxidase copper chaperone 17 (COX17) [##REF##18761103##29##, ##REF##15113935##30##].</p>","<p id=\"Par21\">ATOX1 delivers copper to ATP7A and ATP7B in the TGN [##REF##37017692##31##]. Additionally, ATOX1 has been shown to function as a new type of copper-dependent transcription factor that mediates copper-induced cell proliferation [##REF##18245776##32##].</p>","<p id=\"Par22\">CCS is a soluble cytoplasmic copper-chaperone protein that transfers copper ions to the copper-binding site of superoxide dismutase 1 (SOD1) [##REF##11263870##33##]. SOD1 is a major antioxidant enzyme that catalyzes the conversion of superoxide radicals into oxygen and hydrogen peroxide, thereby maintaining reactive oxygen species (ROS) homeostasis and protecting the cells from oxidative stress damage [##REF##30034766##34##]. This function has been shown in SOD1-knockout mice, where the absence of SOD1 increased oxidative stress and led first to liver cell damage and eventually liver cancer [##REF##15531919##35##].</p>","<p id=\"Par23\">COX17 is responsible for delivering copper ions to the assembly of cytochrome c oxidase (COX), a key component of the electron transport chain involved in cellular respiration [##REF##35691151##36##]. Subsequently, COX11 and SCO1, which are also important components of the COX assembly, donate copper to Cu(B) and Cu(A) sites in COX2 and COX1 core subunits of COX in the mitochondrial inner membrane, respectively. Additionally, COX17 also acts as a copper donor within the mitochondrial intermembrane space (IMS) [##REF##15199057##37##]. COX17 is thus essential for proper COX assembly, with mutations in COX17 shown to further reduce COX activity, resulting in mitochondrial dysfunction and oxidative stress [##REF##12370308##38##].</p>","<title>Intracellular copper storage and export</title>","<p id=\"Par24\">Intracellular copper export is mainly mediated by ATP7A and ATP7B, which are copper transporters located in the TGN that regulate copper delivery to secretory pathways and the plasma membrane [##REF##17615395##39##]. Under normal conditions, ATP7A and ATP7B transport copper ions from the TGN to other cellular compartments for various cellular functions. At excessive intracellular copper levels, ATP7A and ATP7B are activated to export excess copper ions from the TGN and sequester them in copper-binding proteins such as metallothionein [##REF##17531189##40##]. These proteins also regulate copper homeostasis by reducing uptake and increasing efflux. Mutations in ATP7A and ATP7B can lead to copper metabolism disorders such as Menkes disease and Wilson’s disease [##UREF##3##41##, ##UREF##4##42##].</p>","<title>Evidence linking copper dyshomeostasis to atherosclerosis</title>","<p id=\"Par25\">Atherosclerosis is a chronic inflammatory disease characterized by plaque accumulation on the arterial walls, leading to narrowing and hardening of the arteries [##REF##30653442##43##]. This process can result in serious complications, including coronary artery disease (CAD), stroke, and peripheral artery disease [##REF##22064431##44##–##REF##35420915##46##]. Growing evidence from numerous studies has linked copper dyshomeostasis and the resultant excess or deficiency of copper to atherosclerosis.</p>","<title>Evidence linking copper excess and atherosclerosis</title>","<p id=\"Par26\">Extensive research has revealed a correlation between elevated copper levels and cardiovascular disease (Table ##TAB##0##1##). For instance, several prospective cohort studies have shown a significant correlation between elevated serum copper levels and higher mortality rates related to cardiovascular diseases, particularly coronary heart disease [##REF##8862478##47##–##REF##3394701##49##]. Elevated copper levels have also been shown to be an independent risk factor for ischemic heart disease [##REF##1877585##50##]. In addition to evidence from prospective studies, Stadler et al. directly detected and quantified transition metal ions in human atherosclerotic plaques and found increased copper levels in the diseased intima [##REF##15001454##51##]. Studies on populations with acute myocardial infarction (AMI) also support these findings, with patients with AMI exhibiting significantly higher serum copper levels than those without AMI [##UREF##5##52##]. Moreover, an increase in serum copper levels among post-MI patients was found to have considerable diagnostic value for the occurrence of MI [##UREF##6##53##]. Furthermore, altered copper bioavailability is negatively correlated with carotid intima-media thickness (IMT), which may serve as a reliable predictor for early atherosclerosis in patients with obesity [##REF##29405466##54##]. Furthermore, serum copper concentrations differ among patients with different carotid atherosclerotic plaque morphologies. Specifically, patients with hemorrhagic plaques have significantly higher serum copper concentrations than those with calcified plaques [##REF##26554112##55##]. These findings suggest a potential involvement of elevated copper levels in the pathogenesis of atherosclerosis.</p>","<title>Evidence linking copper deficiency and atherosclerosis</title>","<p id=\"Par27\">Copper deficiency is also recognized as a major contributing factor to the development of atherosclerosis. This is evidenced by the benefit of high dietary copper and copper supplementation. The Institute of Medicine recommends a daily dietary allowance of 900 ug of copper for adults, with a tolerable upper limit of 10,000 μg/day to prevent liver damage [##REF##11269606##56##]. Although recommendations vary between national authorities, most recommend an intake of 800 to 2400 ug/day [##REF##27049134##15##]. Substantial research suggests that adequate copper intake reduces the risk of atherosclerosis. For example, Rock et al. found that copper supplementation in middle-aged individuals enhanced the antioxidative capacity of cells, which may help prevent vascular damage and thus reduce the risk of atherosclerosis [##REF##10699742##57##]. Another cohort study showed an association between adequate dietary intake of copper (equal to or above the estimated average requirement) and a reduced risk of all-cause- and cardiovascular disease-related mortality. However, this association was limited to copper intake from food sources [##REF##30959527##58##]. Studies on animal models corroborate these findings with Lamb et al., showing that dietary copper deficiency or excess increases susceptibility to atherosclerosis of the aorta in cholesterol-fed rabbits [##REF##11703538##59##]. Similarly, copper supplementation was found to reverse pathological changes induced by dietary iron overload in mice, partially normalizing cardiac hypertrophy [##REF##29960117##60##] and improving cardiac function in pressure overload-induced dilated cardiomyopathy [##REF##18679830##61##]. Nevertheless, the effects of copper supplementation on the cardiovascular system remain controversial. A study by Diaf et al. conducted on middle-aged women from Algeria showed that there is little association between dietary copper intake and atherosclerosis prevalence in diabetes [##REF##29740584##62##]. These conflicting results may be due to differences in study design and the dose and duration of copper supplementation.</p>","<title>Potential mechanisms of atherosclerosis development due to copper dyshomeostasis</title>","<p id=\"Par28\">The mechanisms of atherosclerosis development due to altered copper levels are not fully understood. Several potential mechanisms include oxidative stress, inflammation, endothelial dysfunction, and lipid metabolism.</p>","<title>Oxidative stress</title>","<p id=\"Par29\">Oxidative stress is a key factor in atherosclerosis development, which typically involves an imbalance in reactive oxygen species (ROS) production and antioxidant defenses [##REF##24300855##63##]. Since copper is a redox-active metal, changes in copper levels can contribute to the generation of ROS and thereby promote oxidative stress [##REF##27049134##15##, ##REF##33316526##64##] <bold>(</bold>Fig. ##FIG##2##3##<bold>)</bold>.</p>","<title>Copper excess and oxidative stress</title>","<p id=\"Par30\">Elevated levels of free copper ions tend to interact more with hydrogen peroxide through Fenton reactions, leading to the production of highly-reactive hydroxyl radicals [##REF##15892631##65##]. These radicals cause lipid peroxidation, protein oxidation, and DNA damage, ultimately contributing to the initiation and progression of atherosclerosis [##REF##10872549##66##]. Lipid peroxidation is a chain reaction initiated by an attack of ROS on polyunsaturated fatty acids in cell membrane lipids, which then leads to the oxidative damage of lipid molecules. Increased copper levels can promote lipid peroxidation and result in the formation of oxidized low-density lipoprotein (ox-LDL), which is an atherosclerosis risk factor [##REF##1398217##67##]. Furthermore, increased copper levels have been shown to reduce the activity of antioxidant enzymes, such as Cu/Zn-SOD, catalase, and glutathione peroxidase, in the red blood cells, whole blood, live tissue, and brain tissue of rats (67, 68, 69). In rat brain tissue, the decrease in SOD activity and glutathione levels arises because copper overload induces lipid peroxidation [##REF##18784908##68##]. Copper is also capable of causing DNA strand breaks and the oxidation of bases via oxygen-derived free radicals [##REF##17914163##69##]. These findings suggest that copper overload causes impairment of the antioxidant defense system in several tissues.</p>","<title>Copper deficiency and oxidative stress</title>","<p id=\"Par31\">Copper deficiency has also been suggested to contribute to the development and progression of atherosclerosis via oxidative stress. Copper is an essential cofactor of various antioxidant enzymes, including Cu/Zn-SOD, ceruloplasmin, and lysyl oxidase [##REF##25789247##70##]. Copper deficiency may thus impair the function of these enzymes, leading to an impaired antioxidant defense system and increased susceptibility to oxidative stress [##REF##25789247##70##, ##REF##8615367##71##]. This suggestion is supported by copper deficiency in rats being found to reduce Cu/Zn-SOD activity and increase oxidative damage to various subunits of the erythrocyte spectrin [##REF##9119252##72##]. Decreased activity of <italic>SOD1</italic>, which encodes Cu/Zn SOD, has also been observed in the liver and red blood cells of copper-deficient rats [##REF##12514262##73##]. The decrease in <italic>SOD1</italic> caused by copper deficiency leads to a reduction in NO levels, which may promote endothelial dysfunction, reduce vascular relaxation, and increase oxidative stress, thus ultimately contributing to atherosclerosis development [##REF##25789247##70##]. In addition to affecting antioxidant enzymes, copper deficiency may also reduce COX activity and lead to oxidative inactivation of complex I (NADH: ubiquinone oxidoreductase). This oxidative inactivation may then lead to elevated production of ROS in copper-deficient cells, thereby exacerbating cellular oxidative stress [##REF##10352126##74##].</p>","<title>Inflammation</title>","<p id=\"Par32\">Copper may also promote atherosclerosis development by modulating inflammatory responses associated with the disease.</p>","<title>Copper excess and inflammation</title>","<p id=\"Par33\">Excess copper has been implicated in atherosclerosis pathogenesis due to its ability to induce inflammation. High copper levels were previously reported to stimulate pro-inflammatory cytokine production and thereby promote inflammation within arterial walls [##REF##32727323##75##]. For example, in primary cardiac cells, Cu<sup>2+</sup> increases the release of interleukin-6 (IL-6) and activates MAP kinases, which are linked to cardiac inflammation and hypertrophy [##REF##19517273##76##]. Copper-induced oxidative stress also contributes to inflammation because excessive copper generates ROS, which causes oxidative damage to lipids, proteins, and DNA and promotes inflammation [##REF##27049134##15##]. Furthermore, increases in ROS levels due to increased copper levels can, in turn, lead to the activation of nuclear factor-κB (NF-κB), a crucial protein involved in regulating the expression of proinflammatory genes [##REF##35985369##77##]. These findings suggest that high copper levels can exacerbate inflammation of the vascular walls and thus promote atherosclerosis development.</p>","<title>Copper deficiency and inflammation</title>","<p id=\"Par34\">Copper deficiency has also been linked to atherosclerosis development through its impact on inflammation. Copper deficiency may result in decreased expression of adhesion molecules, such as ICAM-1 and VCAM-1, which facilitate leukocyte adhesion onto activated endothelial cells [##REF##11274733##78##]. Evidence from animal studies also supports the role of copper deficiency in inflammation. For example, neutrophil accumulation increases due to increased ICAM-1 expression in the livers of copper-deficient rats following ischemia/reperfusion [##REF##20544302##79##]. Further, this elevated ICAM-1 expression has been shown to activate neutrophils and endothelial cells, as evidenced by F-actin polymerization and increased accumulation of neutrophils within the lung microcirculation [##REF##16118404##80##–##REF##15186252##82##]. Pulmonary inflammatory responses are also intensified in copper-deficient animals [##REF##11435213##83##]. Finally, copper deficiency impairs the activity of Cu/Zn-SOD as previously mentioned, leading to an increased accumulation of ROS and exacerbation of oxidative stress, which can further promote inflammation and atherosclerosis development [##REF##225457##84##].</p>","<title>Endothelial dysfunction</title>","<p id=\"Par35\">Copper dyshomeostasis has been linked to atherosclerosis, with both an excess and deficiency of copper contributing to endothelial dysfunction, a critical early step in atherogenesis.</p>","<title>Copper excess and endothelial dysfunction</title>","<p id=\"Par36\">Excessive copper can disrupt the balance between the production and degradation of nitric oxide (NO), a key regulator of vascular tone and endothelial function [##REF##16585403##85##]. Increased copper levels can upregulate inducible NO synthase (iNOS) expression, resulting in excessive NO production and peroxynitrite, a potent oxidant that causes further oxidative damage [##REF##17237348##86##]. Moreover, copper also interacts with atherosclerosis risk factors such as homocysteine and thereby causes increased hydrogen peroxidation and oxidative stress. Specifically, a study shows that incubation with homocysteine and copper for 4 h is able to cause significant damage to endothelial cells [##REF##3514679##87##].</p>","<title>Copper deficiency and endothelial dysfunction</title>","<p id=\"Par37\">Copper deficiency is associated with increased endothelial dysfunction as well. Endothelial dysfunction can result in decreased production of NO, a vasodilator and inhibitor of platelet aggregation, and thereby promote a proatherogenic environment [##REF##19875572##88##]. Copper deficiency can also lead to decreased NO levels by reducing the levels of SOD1. Reduced NO levels can then inhibit endothelial function and vasodilation, increase oxidative stress, and thereby promote atherosclerosis [##REF##25789247##70##].</p>","<title>Lipid metabolism</title>","<title>Copper excess and lipid metabolism</title>","<p id=\"Par38\">Excess copper strongly contributes to atherosclerosis development via its effects on the ox-LDL, which plays a central role in atherosclerosis [##REF##29737246##89##]. Specifically, copper participates in the oxidation of LDL particles, and alterations in copper levels may affect the susceptibility of LDL particles to oxidation. Excess copper levels can increase the oxidation of LDL particles and trigger the production of ox-LDL, which then contributes to atherosclerosis development [##REF##1398217##67##].</p>","<p id=\"Par39\">In addition to its effects on ox-LDL, copper is involved in several forms of lipid metabolism, including fatty acid and cholesterol synthesis and lipoprotein metabolism. Alterations in copper levels may be associated with changes in lipid and lipoprotein concentrations. For example, serum copper and ceruloplasmin levels were found to be positively associated with lipid peroxides, total cholesterol, triglycerides, and apolipoprotein B in healthy individuals [##REF##7773726##90##]. In another study, the serum copper levels of Iranian patients with angiographically defined CAD were found to be positively correlated with fasting serum triglycerides [##REF##17317522##91##]. In vivo, evidence from animal experiments also supports the role of excess copper in lipid metabolism. A study on yellow catfish demonstrated that copper-induced endoplasmic reticulum stress and disrupted calcium homeostasis alter hepatic lipid metabolism, leading to increased lipid accumulation in the liver [##REF##26615493##92##].</p>","<title>Copper deficiency and lipid metabolism</title>","<p id=\"Par40\">Insufficient copper levels may also contribute to atherosclerosis development by affecting lipid metabolism. Since copper is involved in the synthesis of fatty acids and cholesterol, insufficient copper levels may lead to impaired lipid metabolism and, thus, the development of fatty liver disease. Copper deficiency increases total cholesterol levels as well [##REF##36414625##7##]. Insufficient copper levels can affect the activity of sterol regulatory element-binding proteins 1 and 2 (SREBP-1 and SREBP-2), which are transcription factors involved in fatty acid and cholesterol metabolism [##REF##28271632##93##]. Specifically, both SREBP-1 isoforms (SREBP-1a and SREBP-1c) are involved in the regulation of fatty acid synthesis, whereas SREBP-2 is mainly involved in the regulation of cholesterol biosynthesis [##UREF##7##94##]. Low copper diets were found to induce accumulation of the mature form of SREBP-1 in the nuclei of rat liver cells, yet no change was observed in the DNA-binding site of SREBP-1 [##REF##11110846##95##]. Hence, copper may play a role in regulating the subcellular localization of SREBP-1, potentially affecting its activity and, in turn, lipid metabolism. Further research is needed to fully understand the mechanisms through which copper affects the activity of SREBP-1 and other transcription factors involved in lipid metabolism.</p>","<title>Other risk factors</title>","<p id=\"Par41\">Copper levels are also associated with other atherosclerosis risk factors, such as high blood pressure. Copper inhibits the activity of angiotensin-converting enzymes, such that copper deficiency leads to increasing angiotensis levels and, thus, water and sodium retention and hypertension [##REF##1711361##96##]. Moreover, copper is a cofactor of SOD, which is a major player in the antioxidant defense system. Hence, copper deficiency may increase the levels of superoxide free radicals, leading to elevated angiotensin levels and consequent hypertension [##REF##22753205##97##].</p>","<title>Copper-induced cell death and atherosclerosis</title>","<title>From copper-induced cell death to cuproptosis</title>","<p id=\"Par42\">The discovery of copper-induced cell death dates back to the early 1980s [##REF##6326753##98##]. Increased copper levels were found to promote ROS generation and thereby lead to oxidative stress, DNA damage, and, ultimately cell death [##REF##12821289##99##]. These findings have led to further investigations into the molecular mechanisms underlying copper-induced cell death and their potential implications for human diseases. Indeed, conflicting findings suggest that, in addition to ROS accumulation, excess copper may induce cell death through apoptosis or caspase-independent cell death [##REF##21452832##100##, ##REF##22542443##101##]. Overall, the mechanisms responsible for copper-induced cell death were not well understood.</p>","<p id=\"Par43\">However, in March 2022, Tsvetkov et al. published a groundbreaking study unveiling the mechanism of copper-induced cell death, which was termed cuproptosis [##REF##35298263##12##]. Cuproptosis represents a unique form of cell death triggered by an excess of copper ions. In their study, Tsvetkov et al. showed that treatment with the copper ionophore elesclomol induced cell death. Remarkably, only the copper chelator can rescue cells from elesclomol-induced cell death. In contrast, rescue is not possible with any of the inhibitors for apoptosis, necroptosis, oxidative stress, ROS induced cell death, or ferroptosis. These findings unequivocally establish that cuproptosis is distinct from other known cell death modalities, underscoring its unique mechanisms and signaling pathways. Notably, cuproptosis is regulated by mitochondrial respiration, as supported by research indicating that mitochondria-dependent cells exhibit a sensitivity to copper ionophores nearly 1,000 times higher than cells undergoing glycolysis [##REF##35298263##12##]. The importance of mitochondrial respiration in cuproptosis has been highlighted in further research, revealing a close correlation between cuproptosis and the tricarboxylic acid (TCA) cycle. During cuproptosis, intracellular copper binds to the lipoylated components of the TCA cycle. This leads to the aggregation of copper-bound lipoylated mitochondrial proteins, which can disrupt the TCA cycle and, therefore, interfere with cellular energy production. The upstream regulatory factors FDX1 and LIAS are critical in this process. Aggregation of these proteins and the subsequent reduction of Fe–S clusters, which are essential cofactors for various cellular processes, including electron transport and enzymatic reactions [##REF##31935115##102##], promote proteotoxic stress and ultimately lead to cell death <bold>(</bold>Fig. ##FIG##3##4##<bold>)</bold>.</p>","<title>Potential interactions between copper-induced cell death and other cell death pathways</title>","<p id=\"Par44\">Accumulating evidence suggests widespread cross-talk and interactions among the primary initiators, effectors, and executioners involved in pyroptosis, necroptosis, ferroptosis, and cuproptosis.</p>","<title>Pyroptosis</title>","<p id=\"Par45\">Pyroptosis is a pro-inflammatory programmed cell death that is primarily driven by inflammasome assembly, accompanied by GSDMD cleavage and IL-1β and IL-18 release [##REF##33776057##103##–##UREF##8##105##]. NLRP1, NLRP3, NLRC4, AIM2, and pyrin are well-established inflammasome sensors that assemble canonical inflammasomes in a process induced by inflammatory stimuli such as those resulting from microbial infections [##REF##27291964##106##, ##REF##23707339##107##]. Copper ions, which are essential micronutrients for many physiological processes, have been shown to trigger ROS production and activate the NF-κB signaling pathway. This leads to the upregulation of pro-inflammatory genes and cytokines, potentially influencing the progression of atherosclerosis [##REF##33485222##108##–##REF##35312958##110##]. Therefore, there is a plausible suggestion of crosstalk between copper ions and pyroptosis.</p>","<p id=\"Par46\">Evidence from animal studies demonstrates that copper loading in rat hepatocytes leads to a significant increase in the mRNA expression of pyroptosis-related genes (caspase-1, IL-18, IL-1β, and NLRP3) and the protein expression of caspase-1[##REF##30822667##111##]. Similarly, copper exposure in mouse microglial cells triggers an inflammatory response, resulting in the upregulation of NLRP3/caspase 1/GSDMD axis proteins and subsequent pyroptosis. These effects are likely mediated by the early activation of the ROS/NF-κB pathway and subsequent disruption of mitophagy [##REF##35985369##77##]. Moreover, comparable outcomes were observed in murine macrophages treated with copper oxide nanoparticles. Copper oxide nanoparticle exposure induces oxidative stress and activates NLRP3 inflammasomes, leading to the expression of pro-IL-1β through the MyD88-dependent TLR4 signaling pathway, followed by NF-κB activation in murine macrophages [##REF##33485222##108##].</p>","<p id=\"Par47\">In summary, these findings demonstrate the presence of crosstalk between copper-induced cell death and pyroptosis. Copper exposure influences gene and protein expression associated with pyroptosis in various cell types, with the underlying mechanisms being ROS/NF-κB pathway activation and the inflammatory response. Further research is needed to elucidate the precise underlying mechanisms and explore the implications of this interaction.</p>","<title>Necroptosis</title>","<p id=\"Par48\">Necroptosis is a form of programmed necrosis linked to atherosclerosis via its potential involvement in plaque destabilization and rupture [##REF##35809652##112##, ##REF##35585052##113##]. Necroptosis was recently shown to activate the NLRP3 inflammasome by causing potassium efflux through MLKL pores in macrophages [##REF##28130493##114##]. Bioinformatics analysis further indicated an association between ZBP1 and cuproptosis as well as necroptosis [##REF##36186436##115##]. ZBP1 activation led to the recruitment of RIPK3 and caspase-8 to activate the NLRP3 inflammasome, which in turn triggers necroptosis and pyroptosis [##UREF##9##116##, ##REF##32322058##117##].</p>","<title>Ferroptosis</title>","<p id=\"Par49\">Ferroptosis, a form of iron-dependent cell death triggered by lipid peroxidation and accumulation of lipid-based reactive oxygen species [##REF##22632970##118##–##REF##26653790##120##], appears to be influenced by copper levels due to the redox-active properties of copper ions [##UREF##10##121##, ##REF##34482117##122##]. Cuproptosis can modulate the expression of key genes involved in ferroptosis regulation, such as glutathione peroxidase 4 (GPX4), by eliminating phospholipid hydroperoxides [##REF##31634900##123##, ##REF##24439385##124##] and acylcoenzyme A synthetase long-chain family member 4 (ACSL4) [##REF##27842070##125##], thereby regulating the sensitivity of cells to ferroptosis inducers. Notably, copper chelators can reduce sensitivity to ferroptosis specifically while leaving other forms of cell death unaffected. In a study by Xue et al., copper was found to play a novel role in promoting ferroptotisis through the degradation of GPX4 via macroautophagy/autophagy [##REF##36622894##126##]. Furthermore, research by Gao et al. reveals an interaction between copper-induced cell death and necroptosis. Their findings indicate that elesclomol administration to colorectal cancer cells increased Cu(II) levels in the mitochondria, downregulated ATP7A expression, and increased ROS accumulation. This process triggered SLC7A11 degradation, intensifying oxidative stress and resulting in ferroptosis [##REF##34390123##127##]. Additionally, copper depletion greatly enhanced ferroptosis through mitochondrial perturbation and the disruption of antioxidant mechanisms [##UREF##11##128##]. Specifically, copper depletion limits GPX4 protein expression and reduces cellular sensitivity to ferroptosis inducers, establishing a direct link between copper levels and ferroptosis [##UREF##11##128##].</p>","<p id=\"Par50\">Based on the aforementioned studies, it is evident that copper is closely linked to necroptosis, pyroptosis, and ferroptosis, suggesting a significant cross-talk between these different cell death pathways. Understanding the underlying mechanisms that connect these modes of cell death is of paramount importance for the development of novel atherosclerotic therapeutic strategies that can target multiple pathways simultaneously.</p>","<title>Cuproptosis-related mitochondrial dysfunction and atherosclerosis</title>","<p id=\"Par51\">Mitochondria are vulnerable to copper-induced damage, which causes oxidative damage to its membrane [##REF##15629136##129##, ##REF##32979421##130##]. Excessive intracellular copper may also disrupt mitochondrial function by altering the activity of several key enzymes, such as those involved in the tricarboxylic acid (TCA) cycle and oxidative phosphorylation [##REF##15122704##131##]. Severe mitochondrial dysfunction and decreases in the activities of several liver enzymes, including complex I, complex II, complex III, complex IV, and aconitase, were observed in patients with copper overload. These effects are potentially mediated by the accumulation of copper in the mitochondria [##REF##10981891##132##]. Furthermore, copper treatment induces selective changes in metabolic enzymes through lipoylation, which is a highly conserved lysine post-translational modification. Protein lipoylation occurs only on Dihydrolipoamide S-Acetyltransferase (DLAT), Dihydrolipoamide S-Succinyltransferase (DLST), Dihydrolipoamide Branched Chain Transacylase E2 (DBT), and Glycine Cleavage System Protein H (GCSH), all of which are involved in metabolic complexes that regulate carbon entry points to the TCA cycle [##REF##29191830##133##, ##REF##29169048##134##]. Additionally, copper overload may trigger the opening of the mitochondrial permeability transition pore and cause the release of pro-apoptotic factors, ultimately resulting in cell death [##REF##21484409##135##].</p>","<p id=\"Par52\">Mitochondrial dysfunction is implicated in atherosclerosis development and progression [##REF##29237304##136##]. Impaired mitochondrial function promotes lipid accumulation, oxidative stress, inflammatory responses, and proliferation of vascular smooth muscle cells, all of which contribute to plaque formation and destabilization [##UREF##12##137##, ##REF##34743301##138##]. Considering its impact on mitochondrial function, cuproptosis may contribute to the progression of atherosclerosis by aggravating mitochondrial dysfunction.</p>","<title>Potential therapeutic strategies targeting cuproptosis in atherosclerosis</title>","<title>Copper chelators</title>","<p id=\"Par53\">Copper chelators bind and sequester copper ions. Several copper chelators have shown promise in animal studies and clinical trials for atherosclerosis treatment.</p>","<p id=\"Par54\">Tetrathiomolybdate (TTM) has been shown to be an effective copper chelator with potential therapeutic implications in attenuating atherosclerosis progression, as demonstrated in animal models [##REF##22770994##139##, ##REF##21724870##140##]. In a study using ApoE<sup>-/-</sup> mice, TTM treatment for 10 weeks significantly reduced aortic lesion development, indicating its potential as an anti-atherosclerotic agent [##REF##22770994##139##]. The beneficial effects of TTM were attributed to its ability to reduce bioavailable copper levels and inhibit vascular inflammation [##REF##21724870##140##]. Copper is involved in vascular inflammation as an etiologic factor of atherosclerotic vascular disease, and by chelating copper, TTM may modulate copper-related pathways involved in atherosclerosis and inflammation. Wei et al. demonstrated that TTM copper chelation inhibited lipopolysaccharide (LPS)-induced inflammatory responses in mice aorta and other tissues. This inhibition may occur by suppressing redox-sensitive transcription factors NF-κB and AP-1, which play crucial roles in inflammation [##REF##21724870##140##]. By targeting copper-related pathways and modulating inflammatory processes, TTM exhibits potential as an anti-atherosclerotic agent.</p>","<p id=\"Par55\">Ethylenediaminetetraacetic acid disodium salt (EDTA) is a broad-spectrum metal-chelating agent that has also been investigated for its potential to treat atherosclerosis [##REF##13372537##141##]. For instance, a double-blind placebo-controlled trial (<italic>N</italic> = 1708) shows that chelation therapy with disodium EDTA reduced the risk of adverse cardiovascular outcomes in stable patients with a history of myocardial infarction (MI). The primary endpoint occurred less frequently in the chelation group than in the placebo group (26% vs. 30%) [##REF##23532240##142##]. These findings offer preliminary evidence to direct further studies but do not in themselves provide sufficient evidence to justify the routine use of chelation therapy in patients with MI. Another meta-analysis of five studies covering a total of 1,993 randomized participants failed to identify sufficient evidence to determine the effectiveness of chelation therapy in the treatment of atherosclerotic cardiovascular disease [##REF##32367513##143##]. The contradictory results among these studies may be attributed to variations in research design and/or characteristics of the study populations. Therefore, further research is needed before routine use of chelation therapy can be recommended.</p>","<title>Regulation of copper chaperone protein expression</title>","<p id=\"Par56\">Copper chaperones play a crucial role in maintaining cellular copper homeostasis by facilitating the transfer of copper ions to target proteins and organelles. Modulating the expression of copper chaperone proteins potentially offers an alternative approach to regulate copper levels and limit the contribution of copper-induced cell death to atherosclerosis.</p>","<p id=\"Par57\">ATOX1 is a copper chaperone protein that is of major importance in mammalian cells. ATOX1 has been shown to translocate to the nucleus in response to inflammatory cytokines or exogenous copper. Furthermore, ATOX1 is localized in the nucleus of endothelial cells in the inflamed atherosclerotic aorta [##UREF##13##144##]. The migration of VSMCs is crucial for neointimal formation following vascular injury and atherosclerotic lesion formation. ATOX1 was found to promote VSMC migration and inflammatory cell recruitment to injured vessels [##REF##23349186##145##]. Furthermore, copper-dependent binding of ATOX1 to TRAF4 is required to facilitate nuclear translocation of ATOX1 and ROS-dependent inflammatory responses in TNF-α-stimulated endothelial cells (ECs) [##REF##31553645##146##]. This highlights the potential of targeting the ATOX1-TRAF4 axis as a novel therapeutic strategy for the treatment of atherosclerosis. In summary, ATOX1 represents a promising therapeutic target for inflammation-related vascular diseases such as atherosclerosis.</p>","<title>Copper ionophores</title>","<p id=\"Par58\">Copper ionophores transport copper into the cells, leading to an increase in intracellular copper levels and subsequent cell death. However, copper chelators can inhibit this process. Several drugs can act as copper ionophores, including disulfiram, pyrithione, chloroquine, and elesclomol [##UREF##14##147##]. Among these, elesclomol has received the most attention and has been subjected to several clinical trials for use in cancer treatment. Although the majority of these trials have not shown promising results regarding further development of elesclomol as a drug, they have verified its safety [##REF##23401447##148##]. Nanomedicines combining copper ions with copper ionophores are also currently being widely investigated. Recently, the researchers successfully designed multifunctional nanoparticles with pH-responsive and CD44-targeted properties. The utilization of dendritic mesoporous silica nanoparticles capped with copper sulfide and coated with hyaluronic acid enables precise drug delivery and controlled release in the acidic microenvironment of atherosclerotic inflammation [##REF##34982089##149##]. These findings highlight the potential of copper-based nanomedicines in developing innovative approaches for targeted atherosclerosis therapy. Notably, an important aspect to consider in the advancement of copper ionophores for clinical therapeutic applications is the notable impact that slight structural changes can exert on their properties and functions [##REF##36882769##16##]. In addition, copper ionophore-mediated cell death is strongly correlated with mitochondrial metabolism and closely associated with atherosclerosis development. Based on these findings, copper ionophores may represent a novel therapeutic strategy for targeting copper-induced cell death in atherosclerosis.</p>"],"string":"[\n \"<title>Facts</title>\",\n \"<p id=\\\"Par2\\\">\\n<list list-type=\\\"order\\\"><list-item><p id=\\\"Par3\\\">Copper is an essential trace element required in various physiological processes in the human body.</p></list-item><list-item><p id=\\\"Par4\\\">Dysregulation of copper homeostasis, whether towards excess or deficiency, has been implicated in various health problems, including atherosclerosis.</p></list-item><list-item><p id=\\\"Par5\\\">Dysregulation of copper homeostasis and copper-induced cell death (cuproptosis) are acknowledged as potential contributors to the pathogenesis of atherosclerosis.</p></list-item></list>\\n</p>\",\n \"<title>Open questions</title>\",\n \"<p id=\\\"Par6\\\">\\n<list list-type=\\\"order\\\"><list-item><p id=\\\"Par7\\\">What is the safe window for copper levels to avoid the development of atherosclerosis? How can the suitable copper concentration be determined for the treatment of atherosclerosis?</p></list-item><list-item><p id=\\\"Par8\\\">What are the potential candidate biomarkers that can reliably and sensitively indicate the occurrence of cuproptosis in the context of atherosclerosis?</p></list-item><list-item><p id=\\\"Par9\\\">What are the potential undiscovered roles of copper in mitochondrial function, and is there interplay between copper and mitochondrial dynamics or mitophagy?</p></list-item></list>\\n</p>\",\n \"<title>Copper metabolism</title>\",\n \"<title>Systemic copper homeostasis</title>\",\n \"<p id=\\\"Par13\\\">Copper is an essential trace element required in various physiological processes in the human body. Research shows that Cu levels vary across organs and tissues, with values ranging from 3 mg (kidneys) to 46 mg (bone) [##REF##37500296##13##]. Copper serves as a cofactor for numerous enzymes involved in energy metabolism, neurotransmitter synthesis, and antioxidant defense [##REF##22075424##5##, ##REF##18277979##6##, ##REF##34390520##14##]. Excessive or deficient copper levels can, however, lead to cytotoxicity and pathological alterations [##REF##36414625##7##]. Therefore, it is essential to maintain systemic copper levels within a narrow range <bold>(</bold>Fig. ##FIG##0##1##<bold>)</bold>.</p>\",\n \"<title>Copper absorption</title>\",\n \"<p id=\\\"Par14\\\">Copper is mainly obtained from dietary sources such as organ meat and shellfish [##REF##27049134##15##]. Copper is primarily absorbed by enterocytes in the small intestine, and its uptake is mediated by copper transporter 1 (CTR1), also known as solute carrier family 31 member 1 (SLC31A1) [##REF##36882769##16##]. CTR1 appears to play a primary role in this uptake, as indicated by research showing greatly reduced copper accumulation in the peripheral tissues of neonatal mice lacking CTR1 [##REF##16950140##17##]. The six-transmembrane epithelial antigen of the prostate (STEAP) facilitates this process by reducing divalent Cu<sup>2+</sup> to Cu<sup>+</sup>.</p>\",\n \"<title>Copper storage and transportation</title>\",\n \"<p id=\\\"Par15\\\">After uptake by the intestinal epithelial cells, copper is transported and exported into the bloodstream by the ATPase copper transporter 7 A (ATP7A) [##REF##18779306##18##]. Here, copper is transported through the portal system to the liver by binding to soluble chaperones such as human serum albumin (HSA), ceruloplasmin, histidine, and macroglobulin [##UREF##1##19##, ##REF##26934375##20##]. Copper uptake by the hepatocytes in the liver is also mediated by CTR1. The liver is crucial in regulating copper metabolism by storing and excreting copper. Copper storage is mediated by the copper-binding protein metallothionein (MT), a reducing molecule rich in thiol groups with a high affinity for copper ions [##REF##32049413##21##]. MT plays a crucial role in copper homeostasis by storing and releasing excess copper when required. Copper transport is performed by ATPase copper transporter 7B (ATP7B) in hepatocytes, which pumps copper ions from the liver back into the bloodstream. Here, copper ions bind to their soluble partners and are transported to specific tissues and organs [##REF##19922814##22##].</p>\",\n \"<title>Copper elimination</title>\",\n \"<p id=\\\"Par16\\\">Copper is primarily eliminated via biliary excretion [##REF##36414625##7##]. Excess copper is secreted by the liver into the bile and then excreted through feces [##REF##15099406##23##]. The ATP7B transporter regulates the elimination of copper from the liver to bile canaliculi [##REF##18395099##24##]. In cases of ATP7B dysfunction, such as Wilson’s disease, copper accumulates in the liver, leading to liver damage and subsequent health issues [##REF##22240481##25##].</p>\",\n \"<p id=\\\"Par17\\\">In summary, copper metabolism is a complex process that involves multiple mechanisms to regulate copper absorption, transport, storage, and elimination.</p>\",\n \"<title>Cellular copper homeostasis</title>\",\n \"<p id=\\\"Par18\\\">The maintenance of intracellular copper homeostasis is a complex and tightly regulated process involving the coordinated action of copper transporters, chaperones, and cuproenzymes <bold>(</bold>Fig. ##FIG##1##2##<bold>)</bold>. Copper concentration is maintained within a narrow range through the collaborative action of these copper-dependent proteins.</p>\",\n \"<title>Copper uptake</title>\",\n \"<p id=\\\"Par19\\\">In mammalian cells, copper uptake is primarily mediated by CTR1 [##REF##18277979##6##], a transmembrane protein that forms a trimeric channel to facilitate the passage of Cu ions across the plasma membrane [##REF##23658018##26##]. CTR1 expression is regulated by copper levels. Low copper levels upregulate CTR1 expression to increase copper uptake, whereas high levels downregulate CTR1 expression to prevent copper cytotoxicity [##UREF##2##27##, ##REF##16365053##28##]. These findings highlight the importance of CTR1 in copper homeostasis.</p>\",\n \"<title>Intracellular copper distribution</title>\",\n \"<p id=\\\"Par20\\\">After entering the cell, copper is first delivered to cytoplasmic copper chaperones, which then transport it to intracellular compartments such as the mitochondria, trans-Golgi network (TGN), and nucleus. In mammalian cells, three major copper chaperones have been identified: antioxidant 1 copper chaperone (ATOX1), copper chaperone for superoxide dismutase (CCS), and cytochrome c oxidase copper chaperone 17 (COX17) [##REF##18761103##29##, ##REF##15113935##30##].</p>\",\n \"<p id=\\\"Par21\\\">ATOX1 delivers copper to ATP7A and ATP7B in the TGN [##REF##37017692##31##]. Additionally, ATOX1 has been shown to function as a new type of copper-dependent transcription factor that mediates copper-induced cell proliferation [##REF##18245776##32##].</p>\",\n \"<p id=\\\"Par22\\\">CCS is a soluble cytoplasmic copper-chaperone protein that transfers copper ions to the copper-binding site of superoxide dismutase 1 (SOD1) [##REF##11263870##33##]. SOD1 is a major antioxidant enzyme that catalyzes the conversion of superoxide radicals into oxygen and hydrogen peroxide, thereby maintaining reactive oxygen species (ROS) homeostasis and protecting the cells from oxidative stress damage [##REF##30034766##34##]. This function has been shown in SOD1-knockout mice, where the absence of SOD1 increased oxidative stress and led first to liver cell damage and eventually liver cancer [##REF##15531919##35##].</p>\",\n \"<p id=\\\"Par23\\\">COX17 is responsible for delivering copper ions to the assembly of cytochrome c oxidase (COX), a key component of the electron transport chain involved in cellular respiration [##REF##35691151##36##]. Subsequently, COX11 and SCO1, which are also important components of the COX assembly, donate copper to Cu(B) and Cu(A) sites in COX2 and COX1 core subunits of COX in the mitochondrial inner membrane, respectively. Additionally, COX17 also acts as a copper donor within the mitochondrial intermembrane space (IMS) [##REF##15199057##37##]. COX17 is thus essential for proper COX assembly, with mutations in COX17 shown to further reduce COX activity, resulting in mitochondrial dysfunction and oxidative stress [##REF##12370308##38##].</p>\",\n \"<title>Intracellular copper storage and export</title>\",\n \"<p id=\\\"Par24\\\">Intracellular copper export is mainly mediated by ATP7A and ATP7B, which are copper transporters located in the TGN that regulate copper delivery to secretory pathways and the plasma membrane [##REF##17615395##39##]. Under normal conditions, ATP7A and ATP7B transport copper ions from the TGN to other cellular compartments for various cellular functions. At excessive intracellular copper levels, ATP7A and ATP7B are activated to export excess copper ions from the TGN and sequester them in copper-binding proteins such as metallothionein [##REF##17531189##40##]. These proteins also regulate copper homeostasis by reducing uptake and increasing efflux. Mutations in ATP7A and ATP7B can lead to copper metabolism disorders such as Menkes disease and Wilson’s disease [##UREF##3##41##, ##UREF##4##42##].</p>\",\n \"<title>Evidence linking copper dyshomeostasis to atherosclerosis</title>\",\n \"<p id=\\\"Par25\\\">Atherosclerosis is a chronic inflammatory disease characterized by plaque accumulation on the arterial walls, leading to narrowing and hardening of the arteries [##REF##30653442##43##]. This process can result in serious complications, including coronary artery disease (CAD), stroke, and peripheral artery disease [##REF##22064431##44##–##REF##35420915##46##]. Growing evidence from numerous studies has linked copper dyshomeostasis and the resultant excess or deficiency of copper to atherosclerosis.</p>\",\n \"<title>Evidence linking copper excess and atherosclerosis</title>\",\n \"<p id=\\\"Par26\\\">Extensive research has revealed a correlation between elevated copper levels and cardiovascular disease (Table ##TAB##0##1##). For instance, several prospective cohort studies have shown a significant correlation between elevated serum copper levels and higher mortality rates related to cardiovascular diseases, particularly coronary heart disease [##REF##8862478##47##–##REF##3394701##49##]. Elevated copper levels have also been shown to be an independent risk factor for ischemic heart disease [##REF##1877585##50##]. In addition to evidence from prospective studies, Stadler et al. directly detected and quantified transition metal ions in human atherosclerotic plaques and found increased copper levels in the diseased intima [##REF##15001454##51##]. Studies on populations with acute myocardial infarction (AMI) also support these findings, with patients with AMI exhibiting significantly higher serum copper levels than those without AMI [##UREF##5##52##]. Moreover, an increase in serum copper levels among post-MI patients was found to have considerable diagnostic value for the occurrence of MI [##UREF##6##53##]. Furthermore, altered copper bioavailability is negatively correlated with carotid intima-media thickness (IMT), which may serve as a reliable predictor for early atherosclerosis in patients with obesity [##REF##29405466##54##]. Furthermore, serum copper concentrations differ among patients with different carotid atherosclerotic plaque morphologies. Specifically, patients with hemorrhagic plaques have significantly higher serum copper concentrations than those with calcified plaques [##REF##26554112##55##]. These findings suggest a potential involvement of elevated copper levels in the pathogenesis of atherosclerosis.</p>\",\n \"<title>Evidence linking copper deficiency and atherosclerosis</title>\",\n \"<p id=\\\"Par27\\\">Copper deficiency is also recognized as a major contributing factor to the development of atherosclerosis. This is evidenced by the benefit of high dietary copper and copper supplementation. The Institute of Medicine recommends a daily dietary allowance of 900 ug of copper for adults, with a tolerable upper limit of 10,000 μg/day to prevent liver damage [##REF##11269606##56##]. Although recommendations vary between national authorities, most recommend an intake of 800 to 2400 ug/day [##REF##27049134##15##]. Substantial research suggests that adequate copper intake reduces the risk of atherosclerosis. For example, Rock et al. found that copper supplementation in middle-aged individuals enhanced the antioxidative capacity of cells, which may help prevent vascular damage and thus reduce the risk of atherosclerosis [##REF##10699742##57##]. Another cohort study showed an association between adequate dietary intake of copper (equal to or above the estimated average requirement) and a reduced risk of all-cause- and cardiovascular disease-related mortality. However, this association was limited to copper intake from food sources [##REF##30959527##58##]. Studies on animal models corroborate these findings with Lamb et al., showing that dietary copper deficiency or excess increases susceptibility to atherosclerosis of the aorta in cholesterol-fed rabbits [##REF##11703538##59##]. Similarly, copper supplementation was found to reverse pathological changes induced by dietary iron overload in mice, partially normalizing cardiac hypertrophy [##REF##29960117##60##] and improving cardiac function in pressure overload-induced dilated cardiomyopathy [##REF##18679830##61##]. Nevertheless, the effects of copper supplementation on the cardiovascular system remain controversial. A study by Diaf et al. conducted on middle-aged women from Algeria showed that there is little association between dietary copper intake and atherosclerosis prevalence in diabetes [##REF##29740584##62##]. These conflicting results may be due to differences in study design and the dose and duration of copper supplementation.</p>\",\n \"<title>Potential mechanisms of atherosclerosis development due to copper dyshomeostasis</title>\",\n \"<p id=\\\"Par28\\\">The mechanisms of atherosclerosis development due to altered copper levels are not fully understood. Several potential mechanisms include oxidative stress, inflammation, endothelial dysfunction, and lipid metabolism.</p>\",\n \"<title>Oxidative stress</title>\",\n \"<p id=\\\"Par29\\\">Oxidative stress is a key factor in atherosclerosis development, which typically involves an imbalance in reactive oxygen species (ROS) production and antioxidant defenses [##REF##24300855##63##]. Since copper is a redox-active metal, changes in copper levels can contribute to the generation of ROS and thereby promote oxidative stress [##REF##27049134##15##, ##REF##33316526##64##] <bold>(</bold>Fig. ##FIG##2##3##<bold>)</bold>.</p>\",\n \"<title>Copper excess and oxidative stress</title>\",\n \"<p id=\\\"Par30\\\">Elevated levels of free copper ions tend to interact more with hydrogen peroxide through Fenton reactions, leading to the production of highly-reactive hydroxyl radicals [##REF##15892631##65##]. These radicals cause lipid peroxidation, protein oxidation, and DNA damage, ultimately contributing to the initiation and progression of atherosclerosis [##REF##10872549##66##]. Lipid peroxidation is a chain reaction initiated by an attack of ROS on polyunsaturated fatty acids in cell membrane lipids, which then leads to the oxidative damage of lipid molecules. Increased copper levels can promote lipid peroxidation and result in the formation of oxidized low-density lipoprotein (ox-LDL), which is an atherosclerosis risk factor [##REF##1398217##67##]. Furthermore, increased copper levels have been shown to reduce the activity of antioxidant enzymes, such as Cu/Zn-SOD, catalase, and glutathione peroxidase, in the red blood cells, whole blood, live tissue, and brain tissue of rats (67, 68, 69). In rat brain tissue, the decrease in SOD activity and glutathione levels arises because copper overload induces lipid peroxidation [##REF##18784908##68##]. Copper is also capable of causing DNA strand breaks and the oxidation of bases via oxygen-derived free radicals [##REF##17914163##69##]. These findings suggest that copper overload causes impairment of the antioxidant defense system in several tissues.</p>\",\n \"<title>Copper deficiency and oxidative stress</title>\",\n \"<p id=\\\"Par31\\\">Copper deficiency has also been suggested to contribute to the development and progression of atherosclerosis via oxidative stress. Copper is an essential cofactor of various antioxidant enzymes, including Cu/Zn-SOD, ceruloplasmin, and lysyl oxidase [##REF##25789247##70##]. Copper deficiency may thus impair the function of these enzymes, leading to an impaired antioxidant defense system and increased susceptibility to oxidative stress [##REF##25789247##70##, ##REF##8615367##71##]. This suggestion is supported by copper deficiency in rats being found to reduce Cu/Zn-SOD activity and increase oxidative damage to various subunits of the erythrocyte spectrin [##REF##9119252##72##]. Decreased activity of <italic>SOD1</italic>, which encodes Cu/Zn SOD, has also been observed in the liver and red blood cells of copper-deficient rats [##REF##12514262##73##]. The decrease in <italic>SOD1</italic> caused by copper deficiency leads to a reduction in NO levels, which may promote endothelial dysfunction, reduce vascular relaxation, and increase oxidative stress, thus ultimately contributing to atherosclerosis development [##REF##25789247##70##]. In addition to affecting antioxidant enzymes, copper deficiency may also reduce COX activity and lead to oxidative inactivation of complex I (NADH: ubiquinone oxidoreductase). This oxidative inactivation may then lead to elevated production of ROS in copper-deficient cells, thereby exacerbating cellular oxidative stress [##REF##10352126##74##].</p>\",\n \"<title>Inflammation</title>\",\n \"<p id=\\\"Par32\\\">Copper may also promote atherosclerosis development by modulating inflammatory responses associated with the disease.</p>\",\n \"<title>Copper excess and inflammation</title>\",\n \"<p id=\\\"Par33\\\">Excess copper has been implicated in atherosclerosis pathogenesis due to its ability to induce inflammation. High copper levels were previously reported to stimulate pro-inflammatory cytokine production and thereby promote inflammation within arterial walls [##REF##32727323##75##]. For example, in primary cardiac cells, Cu<sup>2+</sup> increases the release of interleukin-6 (IL-6) and activates MAP kinases, which are linked to cardiac inflammation and hypertrophy [##REF##19517273##76##]. Copper-induced oxidative stress also contributes to inflammation because excessive copper generates ROS, which causes oxidative damage to lipids, proteins, and DNA and promotes inflammation [##REF##27049134##15##]. Furthermore, increases in ROS levels due to increased copper levels can, in turn, lead to the activation of nuclear factor-κB (NF-κB), a crucial protein involved in regulating the expression of proinflammatory genes [##REF##35985369##77##]. These findings suggest that high copper levels can exacerbate inflammation of the vascular walls and thus promote atherosclerosis development.</p>\",\n \"<title>Copper deficiency and inflammation</title>\",\n \"<p id=\\\"Par34\\\">Copper deficiency has also been linked to atherosclerosis development through its impact on inflammation. Copper deficiency may result in decreased expression of adhesion molecules, such as ICAM-1 and VCAM-1, which facilitate leukocyte adhesion onto activated endothelial cells [##REF##11274733##78##]. Evidence from animal studies also supports the role of copper deficiency in inflammation. For example, neutrophil accumulation increases due to increased ICAM-1 expression in the livers of copper-deficient rats following ischemia/reperfusion [##REF##20544302##79##]. Further, this elevated ICAM-1 expression has been shown to activate neutrophils and endothelial cells, as evidenced by F-actin polymerization and increased accumulation of neutrophils within the lung microcirculation [##REF##16118404##80##–##REF##15186252##82##]. Pulmonary inflammatory responses are also intensified in copper-deficient animals [##REF##11435213##83##]. Finally, copper deficiency impairs the activity of Cu/Zn-SOD as previously mentioned, leading to an increased accumulation of ROS and exacerbation of oxidative stress, which can further promote inflammation and atherosclerosis development [##REF##225457##84##].</p>\",\n \"<title>Endothelial dysfunction</title>\",\n \"<p id=\\\"Par35\\\">Copper dyshomeostasis has been linked to atherosclerosis, with both an excess and deficiency of copper contributing to endothelial dysfunction, a critical early step in atherogenesis.</p>\",\n \"<title>Copper excess and endothelial dysfunction</title>\",\n \"<p id=\\\"Par36\\\">Excessive copper can disrupt the balance between the production and degradation of nitric oxide (NO), a key regulator of vascular tone and endothelial function [##REF##16585403##85##]. Increased copper levels can upregulate inducible NO synthase (iNOS) expression, resulting in excessive NO production and peroxynitrite, a potent oxidant that causes further oxidative damage [##REF##17237348##86##]. Moreover, copper also interacts with atherosclerosis risk factors such as homocysteine and thereby causes increased hydrogen peroxidation and oxidative stress. Specifically, a study shows that incubation with homocysteine and copper for 4 h is able to cause significant damage to endothelial cells [##REF##3514679##87##].</p>\",\n \"<title>Copper deficiency and endothelial dysfunction</title>\",\n \"<p id=\\\"Par37\\\">Copper deficiency is associated with increased endothelial dysfunction as well. Endothelial dysfunction can result in decreased production of NO, a vasodilator and inhibitor of platelet aggregation, and thereby promote a proatherogenic environment [##REF##19875572##88##]. Copper deficiency can also lead to decreased NO levels by reducing the levels of SOD1. Reduced NO levels can then inhibit endothelial function and vasodilation, increase oxidative stress, and thereby promote atherosclerosis [##REF##25789247##70##].</p>\",\n \"<title>Lipid metabolism</title>\",\n \"<title>Copper excess and lipid metabolism</title>\",\n \"<p id=\\\"Par38\\\">Excess copper strongly contributes to atherosclerosis development via its effects on the ox-LDL, which plays a central role in atherosclerosis [##REF##29737246##89##]. Specifically, copper participates in the oxidation of LDL particles, and alterations in copper levels may affect the susceptibility of LDL particles to oxidation. Excess copper levels can increase the oxidation of LDL particles and trigger the production of ox-LDL, which then contributes to atherosclerosis development [##REF##1398217##67##].</p>\",\n \"<p id=\\\"Par39\\\">In addition to its effects on ox-LDL, copper is involved in several forms of lipid metabolism, including fatty acid and cholesterol synthesis and lipoprotein metabolism. Alterations in copper levels may be associated with changes in lipid and lipoprotein concentrations. For example, serum copper and ceruloplasmin levels were found to be positively associated with lipid peroxides, total cholesterol, triglycerides, and apolipoprotein B in healthy individuals [##REF##7773726##90##]. In another study, the serum copper levels of Iranian patients with angiographically defined CAD were found to be positively correlated with fasting serum triglycerides [##REF##17317522##91##]. In vivo, evidence from animal experiments also supports the role of excess copper in lipid metabolism. A study on yellow catfish demonstrated that copper-induced endoplasmic reticulum stress and disrupted calcium homeostasis alter hepatic lipid metabolism, leading to increased lipid accumulation in the liver [##REF##26615493##92##].</p>\",\n \"<title>Copper deficiency and lipid metabolism</title>\",\n \"<p id=\\\"Par40\\\">Insufficient copper levels may also contribute to atherosclerosis development by affecting lipid metabolism. Since copper is involved in the synthesis of fatty acids and cholesterol, insufficient copper levels may lead to impaired lipid metabolism and, thus, the development of fatty liver disease. Copper deficiency increases total cholesterol levels as well [##REF##36414625##7##]. Insufficient copper levels can affect the activity of sterol regulatory element-binding proteins 1 and 2 (SREBP-1 and SREBP-2), which are transcription factors involved in fatty acid and cholesterol metabolism [##REF##28271632##93##]. Specifically, both SREBP-1 isoforms (SREBP-1a and SREBP-1c) are involved in the regulation of fatty acid synthesis, whereas SREBP-2 is mainly involved in the regulation of cholesterol biosynthesis [##UREF##7##94##]. Low copper diets were found to induce accumulation of the mature form of SREBP-1 in the nuclei of rat liver cells, yet no change was observed in the DNA-binding site of SREBP-1 [##REF##11110846##95##]. Hence, copper may play a role in regulating the subcellular localization of SREBP-1, potentially affecting its activity and, in turn, lipid metabolism. Further research is needed to fully understand the mechanisms through which copper affects the activity of SREBP-1 and other transcription factors involved in lipid metabolism.</p>\",\n \"<title>Other risk factors</title>\",\n \"<p id=\\\"Par41\\\">Copper levels are also associated with other atherosclerosis risk factors, such as high blood pressure. Copper inhibits the activity of angiotensin-converting enzymes, such that copper deficiency leads to increasing angiotensis levels and, thus, water and sodium retention and hypertension [##REF##1711361##96##]. Moreover, copper is a cofactor of SOD, which is a major player in the antioxidant defense system. Hence, copper deficiency may increase the levels of superoxide free radicals, leading to elevated angiotensin levels and consequent hypertension [##REF##22753205##97##].</p>\",\n \"<title>Copper-induced cell death and atherosclerosis</title>\",\n \"<title>From copper-induced cell death to cuproptosis</title>\",\n \"<p id=\\\"Par42\\\">The discovery of copper-induced cell death dates back to the early 1980s [##REF##6326753##98##]. Increased copper levels were found to promote ROS generation and thereby lead to oxidative stress, DNA damage, and, ultimately cell death [##REF##12821289##99##]. These findings have led to further investigations into the molecular mechanisms underlying copper-induced cell death and their potential implications for human diseases. Indeed, conflicting findings suggest that, in addition to ROS accumulation, excess copper may induce cell death through apoptosis or caspase-independent cell death [##REF##21452832##100##, ##REF##22542443##101##]. Overall, the mechanisms responsible for copper-induced cell death were not well understood.</p>\",\n \"<p id=\\\"Par43\\\">However, in March 2022, Tsvetkov et al. published a groundbreaking study unveiling the mechanism of copper-induced cell death, which was termed cuproptosis [##REF##35298263##12##]. Cuproptosis represents a unique form of cell death triggered by an excess of copper ions. In their study, Tsvetkov et al. showed that treatment with the copper ionophore elesclomol induced cell death. Remarkably, only the copper chelator can rescue cells from elesclomol-induced cell death. In contrast, rescue is not possible with any of the inhibitors for apoptosis, necroptosis, oxidative stress, ROS induced cell death, or ferroptosis. These findings unequivocally establish that cuproptosis is distinct from other known cell death modalities, underscoring its unique mechanisms and signaling pathways. Notably, cuproptosis is regulated by mitochondrial respiration, as supported by research indicating that mitochondria-dependent cells exhibit a sensitivity to copper ionophores nearly 1,000 times higher than cells undergoing glycolysis [##REF##35298263##12##]. The importance of mitochondrial respiration in cuproptosis has been highlighted in further research, revealing a close correlation between cuproptosis and the tricarboxylic acid (TCA) cycle. During cuproptosis, intracellular copper binds to the lipoylated components of the TCA cycle. This leads to the aggregation of copper-bound lipoylated mitochondrial proteins, which can disrupt the TCA cycle and, therefore, interfere with cellular energy production. The upstream regulatory factors FDX1 and LIAS are critical in this process. Aggregation of these proteins and the subsequent reduction of Fe–S clusters, which are essential cofactors for various cellular processes, including electron transport and enzymatic reactions [##REF##31935115##102##], promote proteotoxic stress and ultimately lead to cell death <bold>(</bold>Fig. ##FIG##3##4##<bold>)</bold>.</p>\",\n \"<title>Potential interactions between copper-induced cell death and other cell death pathways</title>\",\n \"<p id=\\\"Par44\\\">Accumulating evidence suggests widespread cross-talk and interactions among the primary initiators, effectors, and executioners involved in pyroptosis, necroptosis, ferroptosis, and cuproptosis.</p>\",\n \"<title>Pyroptosis</title>\",\n \"<p id=\\\"Par45\\\">Pyroptosis is a pro-inflammatory programmed cell death that is primarily driven by inflammasome assembly, accompanied by GSDMD cleavage and IL-1β and IL-18 release [##REF##33776057##103##–##UREF##8##105##]. NLRP1, NLRP3, NLRC4, AIM2, and pyrin are well-established inflammasome sensors that assemble canonical inflammasomes in a process induced by inflammatory stimuli such as those resulting from microbial infections [##REF##27291964##106##, ##REF##23707339##107##]. Copper ions, which are essential micronutrients for many physiological processes, have been shown to trigger ROS production and activate the NF-κB signaling pathway. This leads to the upregulation of pro-inflammatory genes and cytokines, potentially influencing the progression of atherosclerosis [##REF##33485222##108##–##REF##35312958##110##]. Therefore, there is a plausible suggestion of crosstalk between copper ions and pyroptosis.</p>\",\n \"<p id=\\\"Par46\\\">Evidence from animal studies demonstrates that copper loading in rat hepatocytes leads to a significant increase in the mRNA expression of pyroptosis-related genes (caspase-1, IL-18, IL-1β, and NLRP3) and the protein expression of caspase-1[##REF##30822667##111##]. Similarly, copper exposure in mouse microglial cells triggers an inflammatory response, resulting in the upregulation of NLRP3/caspase 1/GSDMD axis proteins and subsequent pyroptosis. These effects are likely mediated by the early activation of the ROS/NF-κB pathway and subsequent disruption of mitophagy [##REF##35985369##77##]. Moreover, comparable outcomes were observed in murine macrophages treated with copper oxide nanoparticles. Copper oxide nanoparticle exposure induces oxidative stress and activates NLRP3 inflammasomes, leading to the expression of pro-IL-1β through the MyD88-dependent TLR4 signaling pathway, followed by NF-κB activation in murine macrophages [##REF##33485222##108##].</p>\",\n \"<p id=\\\"Par47\\\">In summary, these findings demonstrate the presence of crosstalk between copper-induced cell death and pyroptosis. Copper exposure influences gene and protein expression associated with pyroptosis in various cell types, with the underlying mechanisms being ROS/NF-κB pathway activation and the inflammatory response. Further research is needed to elucidate the precise underlying mechanisms and explore the implications of this interaction.</p>\",\n \"<title>Necroptosis</title>\",\n \"<p id=\\\"Par48\\\">Necroptosis is a form of programmed necrosis linked to atherosclerosis via its potential involvement in plaque destabilization and rupture [##REF##35809652##112##, ##REF##35585052##113##]. Necroptosis was recently shown to activate the NLRP3 inflammasome by causing potassium efflux through MLKL pores in macrophages [##REF##28130493##114##]. Bioinformatics analysis further indicated an association between ZBP1 and cuproptosis as well as necroptosis [##REF##36186436##115##]. ZBP1 activation led to the recruitment of RIPK3 and caspase-8 to activate the NLRP3 inflammasome, which in turn triggers necroptosis and pyroptosis [##UREF##9##116##, ##REF##32322058##117##].</p>\",\n \"<title>Ferroptosis</title>\",\n \"<p id=\\\"Par49\\\">Ferroptosis, a form of iron-dependent cell death triggered by lipid peroxidation and accumulation of lipid-based reactive oxygen species [##REF##22632970##118##–##REF##26653790##120##], appears to be influenced by copper levels due to the redox-active properties of copper ions [##UREF##10##121##, ##REF##34482117##122##]. Cuproptosis can modulate the expression of key genes involved in ferroptosis regulation, such as glutathione peroxidase 4 (GPX4), by eliminating phospholipid hydroperoxides [##REF##31634900##123##, ##REF##24439385##124##] and acylcoenzyme A synthetase long-chain family member 4 (ACSL4) [##REF##27842070##125##], thereby regulating the sensitivity of cells to ferroptosis inducers. Notably, copper chelators can reduce sensitivity to ferroptosis specifically while leaving other forms of cell death unaffected. In a study by Xue et al., copper was found to play a novel role in promoting ferroptotisis through the degradation of GPX4 via macroautophagy/autophagy [##REF##36622894##126##]. Furthermore, research by Gao et al. reveals an interaction between copper-induced cell death and necroptosis. Their findings indicate that elesclomol administration to colorectal cancer cells increased Cu(II) levels in the mitochondria, downregulated ATP7A expression, and increased ROS accumulation. This process triggered SLC7A11 degradation, intensifying oxidative stress and resulting in ferroptosis [##REF##34390123##127##]. Additionally, copper depletion greatly enhanced ferroptosis through mitochondrial perturbation and the disruption of antioxidant mechanisms [##UREF##11##128##]. Specifically, copper depletion limits GPX4 protein expression and reduces cellular sensitivity to ferroptosis inducers, establishing a direct link between copper levels and ferroptosis [##UREF##11##128##].</p>\",\n \"<p id=\\\"Par50\\\">Based on the aforementioned studies, it is evident that copper is closely linked to necroptosis, pyroptosis, and ferroptosis, suggesting a significant cross-talk between these different cell death pathways. Understanding the underlying mechanisms that connect these modes of cell death is of paramount importance for the development of novel atherosclerotic therapeutic strategies that can target multiple pathways simultaneously.</p>\",\n \"<title>Cuproptosis-related mitochondrial dysfunction and atherosclerosis</title>\",\n \"<p id=\\\"Par51\\\">Mitochondria are vulnerable to copper-induced damage, which causes oxidative damage to its membrane [##REF##15629136##129##, ##REF##32979421##130##]. Excessive intracellular copper may also disrupt mitochondrial function by altering the activity of several key enzymes, such as those involved in the tricarboxylic acid (TCA) cycle and oxidative phosphorylation [##REF##15122704##131##]. Severe mitochondrial dysfunction and decreases in the activities of several liver enzymes, including complex I, complex II, complex III, complex IV, and aconitase, were observed in patients with copper overload. These effects are potentially mediated by the accumulation of copper in the mitochondria [##REF##10981891##132##]. Furthermore, copper treatment induces selective changes in metabolic enzymes through lipoylation, which is a highly conserved lysine post-translational modification. Protein lipoylation occurs only on Dihydrolipoamide S-Acetyltransferase (DLAT), Dihydrolipoamide S-Succinyltransferase (DLST), Dihydrolipoamide Branched Chain Transacylase E2 (DBT), and Glycine Cleavage System Protein H (GCSH), all of which are involved in metabolic complexes that regulate carbon entry points to the TCA cycle [##REF##29191830##133##, ##REF##29169048##134##]. Additionally, copper overload may trigger the opening of the mitochondrial permeability transition pore and cause the release of pro-apoptotic factors, ultimately resulting in cell death [##REF##21484409##135##].</p>\",\n \"<p id=\\\"Par52\\\">Mitochondrial dysfunction is implicated in atherosclerosis development and progression [##REF##29237304##136##]. Impaired mitochondrial function promotes lipid accumulation, oxidative stress, inflammatory responses, and proliferation of vascular smooth muscle cells, all of which contribute to plaque formation and destabilization [##UREF##12##137##, ##REF##34743301##138##]. Considering its impact on mitochondrial function, cuproptosis may contribute to the progression of atherosclerosis by aggravating mitochondrial dysfunction.</p>\",\n \"<title>Potential therapeutic strategies targeting cuproptosis in atherosclerosis</title>\",\n \"<title>Copper chelators</title>\",\n \"<p id=\\\"Par53\\\">Copper chelators bind and sequester copper ions. Several copper chelators have shown promise in animal studies and clinical trials for atherosclerosis treatment.</p>\",\n \"<p id=\\\"Par54\\\">Tetrathiomolybdate (TTM) has been shown to be an effective copper chelator with potential therapeutic implications in attenuating atherosclerosis progression, as demonstrated in animal models [##REF##22770994##139##, ##REF##21724870##140##]. In a study using ApoE<sup>-/-</sup> mice, TTM treatment for 10 weeks significantly reduced aortic lesion development, indicating its potential as an anti-atherosclerotic agent [##REF##22770994##139##]. The beneficial effects of TTM were attributed to its ability to reduce bioavailable copper levels and inhibit vascular inflammation [##REF##21724870##140##]. Copper is involved in vascular inflammation as an etiologic factor of atherosclerotic vascular disease, and by chelating copper, TTM may modulate copper-related pathways involved in atherosclerosis and inflammation. Wei et al. demonstrated that TTM copper chelation inhibited lipopolysaccharide (LPS)-induced inflammatory responses in mice aorta and other tissues. This inhibition may occur by suppressing redox-sensitive transcription factors NF-κB and AP-1, which play crucial roles in inflammation [##REF##21724870##140##]. By targeting copper-related pathways and modulating inflammatory processes, TTM exhibits potential as an anti-atherosclerotic agent.</p>\",\n \"<p id=\\\"Par55\\\">Ethylenediaminetetraacetic acid disodium salt (EDTA) is a broad-spectrum metal-chelating agent that has also been investigated for its potential to treat atherosclerosis [##REF##13372537##141##]. For instance, a double-blind placebo-controlled trial (<italic>N</italic> = 1708) shows that chelation therapy with disodium EDTA reduced the risk of adverse cardiovascular outcomes in stable patients with a history of myocardial infarction (MI). The primary endpoint occurred less frequently in the chelation group than in the placebo group (26% vs. 30%) [##REF##23532240##142##]. These findings offer preliminary evidence to direct further studies but do not in themselves provide sufficient evidence to justify the routine use of chelation therapy in patients with MI. Another meta-analysis of five studies covering a total of 1,993 randomized participants failed to identify sufficient evidence to determine the effectiveness of chelation therapy in the treatment of atherosclerotic cardiovascular disease [##REF##32367513##143##]. The contradictory results among these studies may be attributed to variations in research design and/or characteristics of the study populations. Therefore, further research is needed before routine use of chelation therapy can be recommended.</p>\",\n \"<title>Regulation of copper chaperone protein expression</title>\",\n \"<p id=\\\"Par56\\\">Copper chaperones play a crucial role in maintaining cellular copper homeostasis by facilitating the transfer of copper ions to target proteins and organelles. Modulating the expression of copper chaperone proteins potentially offers an alternative approach to regulate copper levels and limit the contribution of copper-induced cell death to atherosclerosis.</p>\",\n \"<p id=\\\"Par57\\\">ATOX1 is a copper chaperone protein that is of major importance in mammalian cells. ATOX1 has been shown to translocate to the nucleus in response to inflammatory cytokines or exogenous copper. Furthermore, ATOX1 is localized in the nucleus of endothelial cells in the inflamed atherosclerotic aorta [##UREF##13##144##]. The migration of VSMCs is crucial for neointimal formation following vascular injury and atherosclerotic lesion formation. ATOX1 was found to promote VSMC migration and inflammatory cell recruitment to injured vessels [##REF##23349186##145##]. Furthermore, copper-dependent binding of ATOX1 to TRAF4 is required to facilitate nuclear translocation of ATOX1 and ROS-dependent inflammatory responses in TNF-α-stimulated endothelial cells (ECs) [##REF##31553645##146##]. This highlights the potential of targeting the ATOX1-TRAF4 axis as a novel therapeutic strategy for the treatment of atherosclerosis. In summary, ATOX1 represents a promising therapeutic target for inflammation-related vascular diseases such as atherosclerosis.</p>\",\n \"<title>Copper ionophores</title>\",\n \"<p id=\\\"Par58\\\">Copper ionophores transport copper into the cells, leading to an increase in intracellular copper levels and subsequent cell death. However, copper chelators can inhibit this process. Several drugs can act as copper ionophores, including disulfiram, pyrithione, chloroquine, and elesclomol [##UREF##14##147##]. Among these, elesclomol has received the most attention and has been subjected to several clinical trials for use in cancer treatment. Although the majority of these trials have not shown promising results regarding further development of elesclomol as a drug, they have verified its safety [##REF##23401447##148##]. Nanomedicines combining copper ions with copper ionophores are also currently being widely investigated. Recently, the researchers successfully designed multifunctional nanoparticles with pH-responsive and CD44-targeted properties. The utilization of dendritic mesoporous silica nanoparticles capped with copper sulfide and coated with hyaluronic acid enables precise drug delivery and controlled release in the acidic microenvironment of atherosclerotic inflammation [##REF##34982089##149##]. These findings highlight the potential of copper-based nanomedicines in developing innovative approaches for targeted atherosclerosis therapy. Notably, an important aspect to consider in the advancement of copper ionophores for clinical therapeutic applications is the notable impact that slight structural changes can exert on their properties and functions [##REF##36882769##16##]. In addition, copper ionophore-mediated cell death is strongly correlated with mitochondrial metabolism and closely associated with atherosclerosis development. Based on these findings, copper ionophores may represent a novel therapeutic strategy for targeting copper-induced cell death in atherosclerosis.</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Author contributions</title>","<p>MW and LL provided the guidance for this study. SY prepared the original manuscript and the illustrations. YL, LZ, and XW assisted with manuscript review and revision. All the authors have read and approved the final version of the manuscript.</p>","<title>Funding</title>","<p>This study was supported by the National Natural Science Foundation of China (Nos. 81202805, 82074254, 82374281), the Beijing Natural Science Foundation (No. 7172185), and the Science and Technology Innovation Project of China Academy of Chinese Medical Science (C12021A01413).</p>","<title>Competing interests</title>","<p id=\"Par61\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Author contributions</title>\",\n \"<p>MW and LL provided the guidance for this study. SY prepared the original manuscript and the illustrations. YL, LZ, and XW assisted with manuscript review and revision. All the authors have read and approved the final version of the manuscript.</p>\",\n \"<title>Funding</title>\",\n \"<p>This study was supported by the National Natural Science Foundation of China (Nos. 81202805, 82074254, 82374281), the Beijing Natural Science Foundation (No. 7172185), and the Science and Technology Innovation Project of China Academy of Chinese Medical Science (C12021A01413).</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par61\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Schematic diagram of systemic copper metabolism process.</title><p>Copper absorption occurs primarily in the small intestine, a process mediated by SLC31A1. Copper is then transported and exported to the bloodstream through the action of ATP7A, combined with soluble chaperones, and transported through the portal system to the liver, where it is stored and further transported. Excess copper is excreted by the liver into the bile. Figure created with BioRender. SLC31A1 solute carrier family 31 member 1, ATP7A and ATP7B ATPase copper transporters 7 A and 7B, STEAP six-transmembrane epithelial antigen of the prostate, HSA human serum albumin, CP ceruloplasmin, MT metallothionein, HIS histidine, MG macroglobulin.</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Schematic diagram of cellular copper metabolism.</title><p>The maintenance of intracellular copper homeostasis is a complex and tightly regulated process. Copper ions are primarily taken up by cells via SLC31A1, whereas their export is facilitated by ATP7A/B. Once inside the cell, copper is transported to interact with cytoplasmic copper chaperones, including COX17, CCS, and ATOX1. These chaperones transport copper to specific cellular compartments, such as the mitochondria, TGN, and nucleus. Figure created with BioRender. SLC31A1 solute carrier family 31 member 1, ATP7A and ATP7B ATPase copper transporter 7A and 7B, ATOX1 antioxidant 1 copper chaperone, CCS copper chaperone for superoxide dismutase, COX17 cytochrome c oxidase copper chaperone 17, COX11 cytochrome c oxidase copper chaperone 11, COX cytochrome c oxidase, ROS reactive oxygen species, SCO1 synthesis of cytochrome c oxidase 1, SOD1 superoxide dismutase 1, TGN trans-Golgi network, GSH glutathione, MT1/2 metallothionein 1/2, and STEAP six-transmembrane epithelial antigen of the prostate.</p></caption></fig>","<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Copper dyshomeostasis promotes atherosclerosis through oxidative stress.</title><p>Excessive copper interacts with H<sub>2</sub>O<sub>2</sub> via the Fenton reaction, generating highly reactive hydroxyl radicals. These radicals induce lipid peroxidation, DNA strand breaks, and base oxidation and impair the function of antioxidant enzymes, ultimately leading to increased oxidative stress and potential damage to cells. On the other hand, copper deficiency also impairs the function of certain antioxidant enzymes, causing a decrease in SOD1 and COX activity. This reduced activity results in lowered NO levels, inactivation of complex I, and increased production of ROS, thereby exacerbating oxidative stress within cells. Together, these processes promote the development of atherosclerosis. Figure created with BioRender. SLC31A1 solute carrier family 31 member 1, STEAP six-transmembrane epithelial antigen of the prostate, H<sub>2</sub>O<sub>2</sub> hydrogen peroxide, <sup>•</sup>OH reactive hydroxyl radicals, ROS reactive oxygen species, ATP7A and ATP7B ATPase copper transporter 7A and 7B, CAT catalase, GSH-Px glutathione peroxidase, CP ceruloplasmin, LOX lysyl oxidase, SOD1 superoxide dismutase 1, COX cytochrome c oxidase, and AS atherosclerosis.</p></caption></fig>","<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Schematic diagram of the mechanisms of cuproptosis.</title><p>The copper ionophore ES transports extracellular copper into the cell. Subsequently, intracellular copper binds to lipoylated mitochondrial enzymes (such as DLAT) involved in the TCA cycle. This binding leads to the aggregation of these proteins, which can disrupt the TCA cycle and, therefore, interfere with cellular energy production. The upstream regulatory factors FDX1 and LIAS play a critical role in this process. The aggregation of lipoylated mitochondrial proteins and loss of Fe-S clusters promote proteotoxic stress and ultimately lead to cell death. Figure created with BioRender. SLC31A1 solute carrier family 31 member 1, STEAP the six-transmembrane epithelial antigen of the prostate, ATP7A and ATP7B ATPase copper transporter 7A and 7B, ES elesclomol, α-KG α-ketoglutarate, DLAT dihydrolipoamide S-acetyltransferase, FDX1 ferredoxin-1, Fe–S iron–sulfur, LIAS lipoic acid synthetase, TCA tricarboxylic acid, and GSH glutathione.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><title>Schematic diagram of systemic copper metabolism process.</title><p>Copper absorption occurs primarily in the small intestine, a process mediated by SLC31A1. Copper is then transported and exported to the bloodstream through the action of ATP7A, combined with soluble chaperones, and transported through the portal system to the liver, where it is stored and further transported. Excess copper is excreted by the liver into the bile. Figure created with BioRender. SLC31A1 solute carrier family 31 member 1, ATP7A and ATP7B ATPase copper transporters 7 A and 7B, STEAP six-transmembrane epithelial antigen of the prostate, HSA human serum albumin, CP ceruloplasmin, MT metallothionein, HIS histidine, MG macroglobulin.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><title>Schematic diagram of cellular copper metabolism.</title><p>The maintenance of intracellular copper homeostasis is a complex and tightly regulated process. Copper ions are primarily taken up by cells via SLC31A1, whereas their export is facilitated by ATP7A/B. Once inside the cell, copper is transported to interact with cytoplasmic copper chaperones, including COX17, CCS, and ATOX1. These chaperones transport copper to specific cellular compartments, such as the mitochondria, TGN, and nucleus. Figure created with BioRender. SLC31A1 solute carrier family 31 member 1, ATP7A and ATP7B ATPase copper transporter 7A and 7B, ATOX1 antioxidant 1 copper chaperone, CCS copper chaperone for superoxide dismutase, COX17 cytochrome c oxidase copper chaperone 17, COX11 cytochrome c oxidase copper chaperone 11, COX cytochrome c oxidase, ROS reactive oxygen species, SCO1 synthesis of cytochrome c oxidase 1, SOD1 superoxide dismutase 1, TGN trans-Golgi network, GSH glutathione, MT1/2 metallothionein 1/2, and STEAP six-transmembrane epithelial antigen of the prostate.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Fig. 3</label><caption><title>Copper dyshomeostasis promotes atherosclerosis through oxidative stress.</title><p>Excessive copper interacts with H<sub>2</sub>O<sub>2</sub> via the Fenton reaction, generating highly reactive hydroxyl radicals. These radicals induce lipid peroxidation, DNA strand breaks, and base oxidation and impair the function of antioxidant enzymes, ultimately leading to increased oxidative stress and potential damage to cells. On the other hand, copper deficiency also impairs the function of certain antioxidant enzymes, causing a decrease in SOD1 and COX activity. This reduced activity results in lowered NO levels, inactivation of complex I, and increased production of ROS, thereby exacerbating oxidative stress within cells. Together, these processes promote the development of atherosclerosis. Figure created with BioRender. SLC31A1 solute carrier family 31 member 1, STEAP six-transmembrane epithelial antigen of the prostate, H<sub>2</sub>O<sub>2</sub> hydrogen peroxide, <sup>•</sup>OH reactive hydroxyl radicals, ROS reactive oxygen species, ATP7A and ATP7B ATPase copper transporter 7A and 7B, CAT catalase, GSH-Px glutathione peroxidase, CP ceruloplasmin, LOX lysyl oxidase, SOD1 superoxide dismutase 1, COX cytochrome c oxidase, and AS atherosclerosis.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Fig. 4</label><caption><title>Schematic diagram of the mechanisms of cuproptosis.</title><p>The copper ionophore ES transports extracellular copper into the cell. Subsequently, intracellular copper binds to lipoylated mitochondrial enzymes (such as DLAT) involved in the TCA cycle. This binding leads to the aggregation of these proteins, which can disrupt the TCA cycle and, therefore, interfere with cellular energy production. The upstream regulatory factors FDX1 and LIAS play a critical role in this process. The aggregation of lipoylated mitochondrial proteins and loss of Fe-S clusters promote proteotoxic stress and ultimately lead to cell death. Figure created with BioRender. SLC31A1 solute carrier family 31 member 1, STEAP the six-transmembrane epithelial antigen of the prostate, ATP7A and ATP7B ATPase copper transporter 7A and 7B, ES elesclomol, α-KG α-ketoglutarate, DLAT dihydrolipoamide S-acetyltransferase, FDX1 ferredoxin-1, Fe–S iron–sulfur, LIAS lipoic acid synthetase, TCA tricarboxylic acid, and GSH glutathione.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":["<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Evidence linking copper excess and atherosclerosis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Country, author, year</th><th>Methods and study population</th><th>Result</th></tr></thead><tbody><tr><td>Finland, Reunanen et al. [##REF##8862478##47##]</td><td>230 men who died from CVDs and 298 matched controls; 10 years follow up.</td><td>The adjusted relative risk of CHD mortality between the highest and lowest tertiles of serum copper were 2.86 (<italic>P</italic> = 0.03). Elevated serum copper levels were significantly associated with an increased risk of mortality from all CVDs, especially from CHD.</td></tr><tr><td>United States, Ford, et al. [##REF##10905530##48##]</td><td>4,574 participants aged ≥ 30 years; 151 died from CHD during 16-year follow-up.</td><td>There was an ~5% increase in serum copper levels among individuals who died from CHD compared to those who did not (<italic>p</italic> = 0.072). Elevated serum copper concentrations were associated with an increased risk of mortality from CHD.</td></tr><tr><td>Dutch, Kok et al. [##REF##3394701##49##]</td><td>Cancer (<italic>n</italic> = 64) and CVD (<italic>n</italic> = 62) deaths, together with their respective matched controls, follow-up of six to 9 years.</td><td>Individuals with elevated serum copper levels (above 1.43 mg/liter) exhibited a roughly four times higher adjusted risk of mortality from cancer and CVD compared to those with serum copper levels within the normal range.</td></tr><tr><td>Eastern Finland, Salonen et al. [##REF##1877585##50##]</td><td>1666 randomly selected men aged 42, 48, 54, or 60 years with no symptomatic IHD.</td><td>Elevated serum copper level is a risk factor for IHD that acts independently, as shown by a 3.5-fold to 4.0-fold increased risk of AMI associated with high serum copper levels (1.02-1.16 mg/liter and 1.17 mg/liter or more).</td></tr><tr><td>Nadina, Robyn et al. [##REF##15001454##51##]</td><td>Atherosclerotic patients aged 67.9 ± 8.2 years and healthy controls.</td><td>Copper levels in the intima of lesions were notably higher in atherosclerotic patients compared to those in healthy controls (7.51 pmol/mg vs 2.01 pmol/mg tissue, <italic>P</italic> < 0.05).</td></tr><tr><td>Bangladesh, Begum et al. [##UREF##5##52##]</td><td>60 patients diagnosed with AMI and 60 healthy controls.</td><td>The mean serum copper level was significantly higher in AMI patients compared to that in controls (146.49 ± 23.52 μg/dl vs 105.44 ± 24.15 μg/dl, <italic>p</italic> < 0.05).</td></tr><tr><td>Grzegorz, Barbara et al. [##UREF##6##53##]</td><td>74 participants of MI and 72 healthy controls.</td><td>Higher serum copper level was significantly associated with MI (<italic>P</italic> < 0.001).</td></tr><tr><td>Italy, Tarantino et al. [##REF##29405466##54##]</td><td>100 obese patients who had a low prevalence of comorbidities.</td><td>Altered copper bioavailability was negatively associated with carotid IMT (<italic>t</italic> = −2.23, <italic>P</italic> = 0.028) and may predict early atherosclerosis in obese patients.</td></tr><tr><td>Serbia, Tasić et al. [##REF##26554112##55##]</td><td>91 patients (mean age 64 ± 7) with carotid atherosclerosis and 27 patients (mean age 58 ± 9) without carotid atherosclerosis.</td><td>Patients with hemorrhagic plaque had significantly higher serum copper levels compared to those with calcified plaque; high copper concentrations may contribute to atherosclerosis.</td></tr></tbody></table></table-wrap>"],"string":"[\n \"<table-wrap id=\\\"Tab1\\\"><label>Table 1</label><caption><p>Evidence linking copper excess and atherosclerosis.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th>Country, author, year</th><th>Methods and study population</th><th>Result</th></tr></thead><tbody><tr><td>Finland, Reunanen et al. [##REF##8862478##47##]</td><td>230 men who died from CVDs and 298 matched controls; 10 years follow up.</td><td>The adjusted relative risk of CHD mortality between the highest and lowest tertiles of serum copper were 2.86 (<italic>P</italic> = 0.03). Elevated serum copper levels were significantly associated with an increased risk of mortality from all CVDs, especially from CHD.</td></tr><tr><td>United States, Ford, et al. [##REF##10905530##48##]</td><td>4,574 participants aged ≥ 30 years; 151 died from CHD during 16-year follow-up.</td><td>There was an ~5% increase in serum copper levels among individuals who died from CHD compared to those who did not (<italic>p</italic> = 0.072). Elevated serum copper concentrations were associated with an increased risk of mortality from CHD.</td></tr><tr><td>Dutch, Kok et al. [##REF##3394701##49##]</td><td>Cancer (<italic>n</italic> = 64) and CVD (<italic>n</italic> = 62) deaths, together with their respective matched controls, follow-up of six to 9 years.</td><td>Individuals with elevated serum copper levels (above 1.43 mg/liter) exhibited a roughly four times higher adjusted risk of mortality from cancer and CVD compared to those with serum copper levels within the normal range.</td></tr><tr><td>Eastern Finland, Salonen et al. [##REF##1877585##50##]</td><td>1666 randomly selected men aged 42, 48, 54, or 60 years with no symptomatic IHD.</td><td>Elevated serum copper level is a risk factor for IHD that acts independently, as shown by a 3.5-fold to 4.0-fold increased risk of AMI associated with high serum copper levels (1.02-1.16 mg/liter and 1.17 mg/liter or more).</td></tr><tr><td>Nadina, Robyn et al. [##REF##15001454##51##]</td><td>Atherosclerotic patients aged 67.9 ± 8.2 years and healthy controls.</td><td>Copper levels in the intima of lesions were notably higher in atherosclerotic patients compared to those in healthy controls (7.51 pmol/mg vs 2.01 pmol/mg tissue, <italic>P</italic> < 0.05).</td></tr><tr><td>Bangladesh, Begum et al. [##UREF##5##52##]</td><td>60 patients diagnosed with AMI and 60 healthy controls.</td><td>The mean serum copper level was significantly higher in AMI patients compared to that in controls (146.49 ± 23.52 μg/dl vs 105.44 ± 24.15 μg/dl, <italic>p</italic> < 0.05).</td></tr><tr><td>Grzegorz, Barbara et al. [##UREF##6##53##]</td><td>74 participants of MI and 72 healthy controls.</td><td>Higher serum copper level was significantly associated with MI (<italic>P</italic> < 0.001).</td></tr><tr><td>Italy, Tarantino et al. [##REF##29405466##54##]</td><td>100 obese patients who had a low prevalence of comorbidities.</td><td>Altered copper bioavailability was negatively associated with carotid IMT (<italic>t</italic> = −2.23, <italic>P</italic> = 0.028) and may predict early atherosclerosis in obese patients.</td></tr><tr><td>Serbia, Tasić et al. [##REF##26554112##55##]</td><td>91 patients (mean age 64 ± 7) with carotid atherosclerosis and 27 patients (mean age 58 ± 9) without carotid atherosclerosis.</td><td>Patients with hemorrhagic plaque had significantly higher serum copper levels compared to those with calcified plaque; high copper concentrations may contribute to atherosclerosis.</td></tr></tbody></table></table-wrap>\"\n]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":[],"string":"[]","isConversational":false},"footnote":{"kind":"list like","value":["<table-wrap-foot><p>CVDs, cardiovascular disease; CHD, coronary heart disease; AMI, acute myocardial infarction; IHD, ischemic heart disease; IMT, intima-media thickness.</p></table-wrap-foot>","<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"],"string":"[\n \"<table-wrap-foot><p>CVDs, cardiovascular disease; CHD, coronary heart disease; AMI, acute myocardial infarction; IHD, ischemic heart disease; IMT, intima-media thickness.</p></table-wrap-foot>\",\n \"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41420_2023_1796_Fig1_HTML\" id=\"d32e319\"/>","<graphic xlink:href=\"41420_2023_1796_Fig2_HTML\" id=\"d32e393\"/>","<graphic xlink:href=\"41420_2023_1796_Fig3_HTML\" id=\"d32e737\"/>","<graphic xlink:href=\"41420_2023_1796_Fig4_HTML\" id=\"d32e955\"/>"],"string":"[\n \"<graphic xlink:href=\\\"41420_2023_1796_Fig1_HTML\\\" id=\\\"d32e319\\\"/>\",\n \"<graphic xlink:href=\\\"41420_2023_1796_Fig2_HTML\\\" id=\\\"d32e393\\\"/>\",\n \"<graphic xlink:href=\\\"41420_2023_1796_Fig3_HTML\\\" id=\\\"d32e737\\\"/>\",\n \"<graphic xlink:href=\\\"41420_2023_1796_Fig4_HTML\\\" id=\\\"d32e955\\\"/>\"\n]","isConversational":false},"media":{"kind":"list like","value":[],"string":"[]","isConversational":false},"unknown_pub":{"kind":"string","value":"[{\"label\": [\"9.\"], \"surname\": [\"Uriu-Adams\", \"Keen\"], \"given-names\": [\"JY\", \"CL\"], \"article-title\": [\"Copper, oxidative stress, and human health\"], \"source\": [\"Mol Asp Med\"], \"year\": [\"2005\"], \"volume\": [\"26\"], \"fpage\": [\"268\"], \"lpage\": [\"98\"], \"pub-id\": [\"10.1016/j.mam.2005.07.015\"]}, {\"label\": [\"19.\"], \"mixed-citation\": [\"Lutsenko S. Dynamic and cell-specific transport networks for intracellular copper ions. J Cell Sci. 2021;134:96\\u2013114.\"]}, {\"label\": [\"27.\"], \"surname\": [\"Liang\", \"Tsai\", \"Lee\", \"Savaraj\", \"Kuo\"], \"given-names\": [\"ZD\", \"WB\", \"MY\", \"N\", \"MT\"], \"article-title\": [\"Specificity protein 1 (sp1) oscillation is involved in copper homeostasis maintenance by regulating human high-affinity copper transporter 1 expression\"], \"source\": [\"Mol Pharm\"], \"year\": [\"2012\"], \"volume\": [\"81\"], \"fpage\": [\"455\"], \"lpage\": [\"64\"], \"pub-id\": [\"10.1124/mol.111.076422\"]}, {\"label\": [\"41.\"], \"mixed-citation\": [\"Horn N, Wittung SP. et al. ATP7A-regulated enzyme metalation and trafficking in the menkes disease puzzle. 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Sci Immunol. 2016;1:52\\u201368.\"]}, {\"label\": [\"121.\"], \"surname\": [\"Yang\", \"Pei\", \"Zhang\", \"Liao\", \"Yu\", \"Qiao\"], \"given-names\": [\"F\", \"R\", \"Z\", \"J\", \"W\", \"N\"], \"article-title\": [\"Copper induces oxidative stress and apoptosis through mitochondria-mediated pathway in chicken hepatocytes\"], \"source\": [\"Toxicol Vitr\"], \"year\": [\"2019\"], \"volume\": [\"54\"], \"fpage\": [\"310\"], \"lpage\": [\"6\"], \"pub-id\": [\"10.1016/j.tiv.2018.10.017\"]}, {\"label\": [\"128.\"], \"mixed-citation\": [\"Li F, Wu X, Liu H, Liu M, Yue Z, Wu Z, et al. Copper depletion strongly enhances ferroptosis via mitochondrial perturbation and reduction in antioxidative mechanisms. Antioxid Basel. 2022;11:652\\u201398.\"]}, {\"label\": [\"137.\"], \"mixed-citation\": [\"Ciccarelli G, Conte S, Cimmino G, Maiorano P, Morrione A, Giordano A, et al. Mitochondrial dysfunction: the hidden player in the pathogenesis of atherosclerosis? Int J Mol Sci. 2023;24:56\\u201368.\"]}, {\"label\": [\"144.\"], \"mixed-citation\": [\"Sudhahar V, Shi Y, Kaplan JH, Ushio-Fukai M, Fukai T, et al. Whole-transcriptome sequencing analyses of nuclear antixoxidant-1 in endothelial cells: role in inflammation and atherosclerosis. Cells. 2022;11:881\\u201396.\"]}, {\"label\": [\"147.\"], \"mixed-citation\": [\"Xue Q, Kang R, Klionsky DJ, Tang D, Liu J, Chen X, et al. Copper metabolism in cell death and autophagy. Autophagy. 2023;12:65\\u2013104.\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":149,"string":"149"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:56"},"citation":{"kind":"string","value":"Cell Death Discov. 2024 Jan 13; 10:25"},"package_file":{"kind":"string","value":"oa_package/75/58/PMC10787750.tar.gz"}}},{"rowIdx":81,"cells":{"accession_id":{"kind":"string","value":"PMC10787751"},"pmid":{"kind":"string","value":"38218988"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par2\">Fish skin has been used as a biological dressing in the management of burns<sup>##UREF##0##1##–##REF##34332229##9##</sup> and wounds<sup>##REF##33426019##10##</sup>. It has also been used as a biological graft in neovaginoplasty in cases of vaginal agenesis<sup>##REF##31546063##11##,##REF##31103284##12##</sup>. The histological characteristics of fish skin and human skin are similar<sup>##REF##29380095##13##</sup>. The fish skin contains a high amount of collagen, making it a suitable biomaterial for tissue engineering<sup>##REF##34332229##9##,##REF##29380095##13##,##REF##26798644##14##</sup>.</p>","<p id=\"Par3\">Fish skin perishability still poses the most enormous preservation difficulties<sup>##REF##32466909##15##</sup>. It must be kept chilled or frozen, and even then, it has a very short shelf life<sup>##UREF##3##16##</sup>. Several diverse damage mechanisms, including microbiological spoilage, autolytic degradation, and lipid oxidation, are responsible for the depreciating of fresh fish skin during storage<sup>##UREF##4##17##</sup>. Therefore, most clinical practices have used fish skin grafts in fresh form for wound and burn management<sup>##UREF##0##1##,##REF##32603408##3##,##REF##31900475##5##–##REF##33426019##10##</sup>.</p>","<p id=\"Par4\">Lyophilization, known also as freeze drying or cryodesiccation, is a low-temperature dehydration process involving freezing the product, lowering the pressure, then removing the ice by sublimation. This is in contrast to dehydration by most conventional methods use heat to evaporate water<sup>##UREF##5##18##</sup>.</p>","<p id=\"Par5\">Lyophilization can be a potential method for the long-term storage of fish skin grafts. In a previous study, lyophilization was addressed as a method for preserving fish skin grafts<sup>##REF##33052795##2##</sup>. However, applying several detergents and sterilizing agents (e.g., chlorhexidine) to fish skin before lyophilization raises concerns about the integrity of collagen and the targeted material in the fish skin<sup>##REF##33052795##2##,##REF##34332229##9##,##REF##33426019##10##,##REF##29380095##13##</sup>. Moreover, the histopathology and microbiology of these lyophilized fish skin grafts were not evaluated before being used clinically.</p>","<p id=\"Par6\">The microbial colonization of the wound is an essential factor that affects the healing process of wounds<sup>##REF##29649179##19##</sup>. Different sterilization methods have been described for biological dressings. These include physical methods such as irradiation and chemical techniques including treatment with chlorhexidine, povidone iodine, ethylene oxide gas, silver nanoparticles, and ozone<sup>##REF##33426019##10##,##REF##29380095##13##,##REF##16023938##20##</sup>. Gamma irradiation represents an effective sterilization method as it has direct and indirect effects on microbial DNA<sup>##REF##15916651##21##</sup>. Different lengths of Gamma irradiation have been used to sterilize fish skin (25, 30, and 50 kGy) without reference to its impact on collagen content<sup>##UREF##0##1##,##REF##33052795##2##,##REF##29380095##13##</sup> or antimicrobial efficiency<sup>##REF##33052795##2##,##REF##34332229##9##</sup>.</p>","<p id=\"Par7\">The current study’s objectives are (1): to describe the optimized method of Tilapia skin lyophilization and (2): to define the standard length of gamma irradiation for sterilization of lyophilized Tilapia skin with special consideration to collagen integrity and microbial count of fish skin.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">Fish skin has been used as a biological dressing in the management of burns<sup>##UREF##0##1##–##REF##34332229##9##</sup> and wounds<sup>##REF##33426019##10##</sup>. It has also been used as a biological graft in neovaginoplasty in cases of vaginal agenesis<sup>##REF##31546063##11##,##REF##31103284##12##</sup>. The histological characteristics of fish skin and human skin are similar<sup>##REF##29380095##13##</sup>. The fish skin contains a high amount of collagen, making it a suitable biomaterial for tissue engineering<sup>##REF##34332229##9##,##REF##29380095##13##,##REF##26798644##14##</sup>.</p>\",\n \"<p id=\\\"Par3\\\">Fish skin perishability still poses the most enormous preservation difficulties<sup>##REF##32466909##15##</sup>. It must be kept chilled or frozen, and even then, it has a very short shelf life<sup>##UREF##3##16##</sup>. Several diverse damage mechanisms, including microbiological spoilage, autolytic degradation, and lipid oxidation, are responsible for the depreciating of fresh fish skin during storage<sup>##UREF##4##17##</sup>. Therefore, most clinical practices have used fish skin grafts in fresh form for wound and burn management<sup>##UREF##0##1##,##REF##32603408##3##,##REF##31900475##5##–##REF##33426019##10##</sup>.</p>\",\n \"<p id=\\\"Par4\\\">Lyophilization, known also as freeze drying or cryodesiccation, is a low-temperature dehydration process involving freezing the product, lowering the pressure, then removing the ice by sublimation. This is in contrast to dehydration by most conventional methods use heat to evaporate water<sup>##UREF##5##18##</sup>.</p>\",\n \"<p id=\\\"Par5\\\">Lyophilization can be a potential method for the long-term storage of fish skin grafts. In a previous study, lyophilization was addressed as a method for preserving fish skin grafts<sup>##REF##33052795##2##</sup>. However, applying several detergents and sterilizing agents (e.g., chlorhexidine) to fish skin before lyophilization raises concerns about the integrity of collagen and the targeted material in the fish skin<sup>##REF##33052795##2##,##REF##34332229##9##,##REF##33426019##10##,##REF##29380095##13##</sup>. Moreover, the histopathology and microbiology of these lyophilized fish skin grafts were not evaluated before being used clinically.</p>\",\n \"<p id=\\\"Par6\\\">The microbial colonization of the wound is an essential factor that affects the healing process of wounds<sup>##REF##29649179##19##</sup>. Different sterilization methods have been described for biological dressings. These include physical methods such as irradiation and chemical techniques including treatment with chlorhexidine, povidone iodine, ethylene oxide gas, silver nanoparticles, and ozone<sup>##REF##33426019##10##,##REF##29380095##13##,##REF##16023938##20##</sup>. Gamma irradiation represents an effective sterilization method as it has direct and indirect effects on microbial DNA<sup>##REF##15916651##21##</sup>. Different lengths of Gamma irradiation have been used to sterilize fish skin (25, 30, and 50 kGy) without reference to its impact on collagen content<sup>##UREF##0##1##,##REF##33052795##2##,##REF##29380095##13##</sup> or antimicrobial efficiency<sup>##REF##33052795##2##,##REF##34332229##9##</sup>.</p>\",\n \"<p id=\\\"Par7\\\">The current study’s objectives are (1): to describe the optimized method of Tilapia skin lyophilization and (2): to define the standard length of gamma irradiation for sterilization of lyophilized Tilapia skin with special consideration to collagen integrity and microbial count of fish skin.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Materials and methods</title>","<title>Ethics statement</title>","<p id=\"Par8\">The Research Ethics Committee (REC) of the Faculty of Veterinary Medicine, Assiut University, Assiut, Egypt, has approved all the procedures in this study in accordance with the Egyptian bylaws and OIE animal welfare standards for animal care and use in research and education. All methods were performed in accordance with relevant guidelines and regulations.</p>","<title>Fish skin sampling</title>","<p id=\"Par9\">The fish skin was collected from fresh Nile tilapia <italic>(Oreochromis niloticus)</italic> (weigh: 620 ± 35 gm; standard length: 20 ± 3 cm), obtained from The Aquatic Medicine Unit, Faculty of Veterinary Medicine, Assiut University, Assiut, Egypt<italic>.</italic> Fish were euthanized physically by decapitation. After removal the fish scales, the skin was dissected from the underlying tissue, cut into strips (6 × 2 cm), and then washed in sterile normal saline. Lyophilization was performed on skin strips.</p>","<title>Fish skin lyophilization</title>","<p id=\"Par10\">Lyophilization was carried out in a freeze dryer (LYOQUEST, SPAIN, Serial No.: 1812, manufacture company: Telstar). Fresh fish skin strips were placed in the flasks of the freeze dryer apparatus at − 80 °C for 24 h. Skin strips were then subjected to a cold vacuum for 5–6 h. Lyophilized skin strips were then vacuum-packed (Fig. ##FIG##0##1##A–C).</p>","<title>Gama (γ) irradiation sterilization of lyophilized fish skin</title>","<p id=\"Par11\">Gamma irradiation was performed at a gamma station (<sup>60</sup>Co gamma cell (2000 Ci), 30 ± 5 °C, 1.5 Gy/s, 150 rad/s), the National Center for Radiation Research and Technology (NCRRT) of the Egyptian Atomic Energy Authority, Cairo, Egypt. The vacuum-packed lyophilized Tilapia skin was subjected to Cobalt 60 γ sterilization at 5, 10, and 25 kGy<sup>##REF##33052795##2##</sup>. Sterilized skin strips subjected to microbiological and histopathological evaluations after each treatment 15- and 30-days post-sterilization (3 skin strips each).</p>","<title>Microbiological evaluation</title>","<p id=\"Par12\">Following Morton et al.<sup>##UREF##6##22##</sup>, the dilution plate method was employed to count the lyophilized fish skin microbiology under different lengths of gamma irradiation (0, 5, 10, and 25 KGy) after 15 and 30 days of irradiation. Fish skin strips were swabbed with a sterilized swab needle and suspended in 10 mL of sterile saline solution. For microbiological counting and identification, various media types were employed; nutrient agar and MacConkey agar media to count all aerobic bacteria, yeast malt extract medium (YME) to count the aerobic yeasts, and potato dextrose agar medium (PDA) to count the fungi<sup>##UREF##7##23##–##UREF##9##25##</sup>. One milliliter (1 mL) of the saline-swabbed solution was added to sterilized Petri dishes before the dishes were filled with sterilized isolation medium, three replicates were performed for each medium, and left to solidify. For bacteria, plates were incubated for 24 h at 35 °C, for yeasts, for 72 h at 28 °C, and for fungi, 7 days at 28 °C. The developed colony count was estimated as CFU/cm<sup>2</sup>. Using the same media, developed colonies with variations in their morphological characteristics, such as size, color, colony edge, and pigmentation, were sub-cultured and purified for identification using Bergey’s Manual of Systematic Bacteriology<sup>##UREF##10##26##</sup>, Yeasts: characteristics and identification<sup>##UREF##8##24##</sup> and fungal identification<sup>##UREF##11##27##</sup>.</p>","<title>Histological evaluation</title>","<p id=\"Par13\">Fish skin strips (0.5 × 0.5 cm) were fixed in 10% neutral buffered formalin, routinely processed, and subsequently embedded in paraffin. Afterwards, they were sectioned into 5 μm thick sections and stained with Mayer’s hematoxylin (Merck, Darmstadt, Germany) and eosin (Sigma, Missouri, USA). After microscopically examining the slides, histological evaluations were performed blindly on coded samples, with a comparison to control group. Collagen fibers integrity and organization were assessed histologically based on 0–3 scale<sup>##REF##33426019##10##,##REF##21495863##28##,##REF##28255657##29##</sup>. Collagen fibers integrity scores: 0 = continue, long fiber, 1 = slightly fragmented, 2 = moderately fragmented, 3 = severely fragmented. Collagen fibers organization scores: 0 = compact and parallel, 1 = slightly loose and wave, 2 = moderately loose, wavy and cross to each other, 3 = no identifiable pattern.</p>","<title>Histochemical evaluation</title>","<p id=\"Par14\">The collagen content evaluation was carried out by the Gomori’s trichrome stain<sup>##REF##15432364##30##</sup>. After deparaffinization in xylene, paraffin-embedded sections were and rehydrated in a graded series of ethanol solutions (into 0.1 M phosphate-buffered saline (PBS), pH 7.2) to distilled water. Sections were stained with Gomori’s trichrome according to the manufacturer’s protocol, dehydrated in graded alcohol, made transparent with xylene, and mounted. Slides were then microscopically examined to verify collagen staining with green. The collagen content was evaluated based on the depth of the green staining.</p>","<p id=\"Par15\">The percentage of collagen-positive area was calculated by ImageJ (1.48v) using threshold area fraction determination. The amount of collagen was expressed as a percentage from the total number of pixels in the optical view as a percentage and expressed as mean ± SEM.</p>","<title>Statistical analysis</title>","<p id=\"Par16\">One-way ANOVA followed by Tukey's post hoc test was performed using GraphPad Prism software version 8.0.1 (GraphPad Software Inc., La Jolla, CA, USA). <italic>P</italic> values < 0.05 were considered statistically significant.</p>"],"string":"[\n \"<title>Materials and methods</title>\",\n \"<title>Ethics statement</title>\",\n \"<p id=\\\"Par8\\\">The Research Ethics Committee (REC) of the Faculty of Veterinary Medicine, Assiut University, Assiut, Egypt, has approved all the procedures in this study in accordance with the Egyptian bylaws and OIE animal welfare standards for animal care and use in research and education. All methods were performed in accordance with relevant guidelines and regulations.</p>\",\n \"<title>Fish skin sampling</title>\",\n \"<p id=\\\"Par9\\\">The fish skin was collected from fresh Nile tilapia <italic>(Oreochromis niloticus)</italic> (weigh: 620 ± 35 gm; standard length: 20 ± 3 cm), obtained from The Aquatic Medicine Unit, Faculty of Veterinary Medicine, Assiut University, Assiut, Egypt<italic>.</italic> Fish were euthanized physically by decapitation. After removal the fish scales, the skin was dissected from the underlying tissue, cut into strips (6 × 2 cm), and then washed in sterile normal saline. Lyophilization was performed on skin strips.</p>\",\n \"<title>Fish skin lyophilization</title>\",\n \"<p id=\\\"Par10\\\">Lyophilization was carried out in a freeze dryer (LYOQUEST, SPAIN, Serial No.: 1812, manufacture company: Telstar). Fresh fish skin strips were placed in the flasks of the freeze dryer apparatus at − 80 °C for 24 h. Skin strips were then subjected to a cold vacuum for 5–6 h. Lyophilized skin strips were then vacuum-packed (Fig. ##FIG##0##1##A–C).</p>\",\n \"<title>Gama (γ) irradiation sterilization of lyophilized fish skin</title>\",\n \"<p id=\\\"Par11\\\">Gamma irradiation was performed at a gamma station (<sup>60</sup>Co gamma cell (2000 Ci), 30 ± 5 °C, 1.5 Gy/s, 150 rad/s), the National Center for Radiation Research and Technology (NCRRT) of the Egyptian Atomic Energy Authority, Cairo, Egypt. The vacuum-packed lyophilized Tilapia skin was subjected to Cobalt 60 γ sterilization at 5, 10, and 25 kGy<sup>##REF##33052795##2##</sup>. Sterilized skin strips subjected to microbiological and histopathological evaluations after each treatment 15- and 30-days post-sterilization (3 skin strips each).</p>\",\n \"<title>Microbiological evaluation</title>\",\n \"<p id=\\\"Par12\\\">Following Morton et al.<sup>##UREF##6##22##</sup>, the dilution plate method was employed to count the lyophilized fish skin microbiology under different lengths of gamma irradiation (0, 5, 10, and 25 KGy) after 15 and 30 days of irradiation. Fish skin strips were swabbed with a sterilized swab needle and suspended in 10 mL of sterile saline solution. For microbiological counting and identification, various media types were employed; nutrient agar and MacConkey agar media to count all aerobic bacteria, yeast malt extract medium (YME) to count the aerobic yeasts, and potato dextrose agar medium (PDA) to count the fungi<sup>##UREF##7##23##–##UREF##9##25##</sup>. One milliliter (1 mL) of the saline-swabbed solution was added to sterilized Petri dishes before the dishes were filled with sterilized isolation medium, three replicates were performed for each medium, and left to solidify. For bacteria, plates were incubated for 24 h at 35 °C, for yeasts, for 72 h at 28 °C, and for fungi, 7 days at 28 °C. The developed colony count was estimated as CFU/cm<sup>2</sup>. Using the same media, developed colonies with variations in their morphological characteristics, such as size, color, colony edge, and pigmentation, were sub-cultured and purified for identification using Bergey’s Manual of Systematic Bacteriology<sup>##UREF##10##26##</sup>, Yeasts: characteristics and identification<sup>##UREF##8##24##</sup> and fungal identification<sup>##UREF##11##27##</sup>.</p>\",\n \"<title>Histological evaluation</title>\",\n \"<p id=\\\"Par13\\\">Fish skin strips (0.5 × 0.5 cm) were fixed in 10% neutral buffered formalin, routinely processed, and subsequently embedded in paraffin. Afterwards, they were sectioned into 5 μm thick sections and stained with Mayer’s hematoxylin (Merck, Darmstadt, Germany) and eosin (Sigma, Missouri, USA). After microscopically examining the slides, histological evaluations were performed blindly on coded samples, with a comparison to control group. Collagen fibers integrity and organization were assessed histologically based on 0–3 scale<sup>##REF##33426019##10##,##REF##21495863##28##,##REF##28255657##29##</sup>. Collagen fibers integrity scores: 0 = continue, long fiber, 1 = slightly fragmented, 2 = moderately fragmented, 3 = severely fragmented. Collagen fibers organization scores: 0 = compact and parallel, 1 = slightly loose and wave, 2 = moderately loose, wavy and cross to each other, 3 = no identifiable pattern.</p>\",\n \"<title>Histochemical evaluation</title>\",\n \"<p id=\\\"Par14\\\">The collagen content evaluation was carried out by the Gomori’s trichrome stain<sup>##REF##15432364##30##</sup>. After deparaffinization in xylene, paraffin-embedded sections were and rehydrated in a graded series of ethanol solutions (into 0.1 M phosphate-buffered saline (PBS), pH 7.2) to distilled water. Sections were stained with Gomori’s trichrome according to the manufacturer’s protocol, dehydrated in graded alcohol, made transparent with xylene, and mounted. Slides were then microscopically examined to verify collagen staining with green. The collagen content was evaluated based on the depth of the green staining.</p>\",\n \"<p id=\\\"Par15\\\">The percentage of collagen-positive area was calculated by ImageJ (1.48v) using threshold area fraction determination. The amount of collagen was expressed as a percentage from the total number of pixels in the optical view as a percentage and expressed as mean ± SEM.</p>\",\n \"<title>Statistical analysis</title>\",\n \"<p id=\\\"Par16\\\">One-way ANOVA followed by Tukey's post hoc test was performed using GraphPad Prism software version 8.0.1 (GraphPad Software Inc., La Jolla, CA, USA). <italic>P</italic> values < 0.05 were considered statistically significant.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<title>Microbiological evaluation of the lyophilized fish skin after gamma irradiation</title>","<p id=\"Par17\">Gamma irradiation exhibited an efficient sterilizing effect on fish skin surface microbiota. Different lengths of gamma irradiation (5, 10, and 25 KGy) were applied to the lyophilized fish skin. The microbial counts of aerobic bacteria, aerobic yeasts, and fungi were detected 15- and 30- days after the irradiation as cleared in Fig. ##FIG##1##2##A–C. It was clear that gamma irradiation is a great microbial sterilizer, especially with the high length of 25 KGy that inhibited the aerobic bacterial counts significantly by 98.53% and 98.96%, aerobic yeast counts by 99.2% and 99.8%, and fungal counts by 98.48% and 99.25% 15- and 30- days after irradiation, respectively, (<italic>P</italic> < 0.05). Gamma irradiation also maintained the low microbial skin counts for a longer period (30 days) and remarkably at the low length of 5 KGy, gamma irradiation was effective against aerobic bacteria.</p>","<p id=\"Par18\">By increasing the gamma irradiation, the total counts of aerobic bacteria decrease dramatically giving 92.67 ± 2.62 (88.36% inhibition), 39.33 ± 4.92 (95.06% inhibition), and 11.67 ± 1.7 (98.53% inhibition) at 5, 10, and 25 KGy 15 days post-irradiation, respectively, (<italic>P</italic> < 0.05). At 30 days post-irradiation, the bacterial counts were 74.33 ± 2.49 (88.93% inhibition), 32 ± 5.7 (95.24% inhibition), and 7 ± 1.6 (98.96% inhibition) at 5, 10, and 25 KGy, respectively, compared to the untreated sample (796 ± 9.93 and 671.7 ± 7.4) 15- and 30-days after irradiation, respectively, (<italic>P</italic> < 0.05).</p>","<p id=\"Par19\">For yeasts, it was clear that by increasing the length of gamma irradiation, the total counts of yeasts decreased significantly after 15- and 30-days irradiation giving 208.67 ± 6.13 (55.25% inhibition), 19 ± 1.63 (95.93% inhibition), and 3.67 ± 0.47 (99.2% inhibition) at 5, 10, and 25 KGy, respectively, at 15 days (<italic>P</italic> < 0.05). At 30 days, the yeast counts were 104.67 ± 4.9 (76.21% inhibition), 8.7 ± 1.2 (98.03% inhibition), and 1 ± 0 (99.77% inhibition) at 5, 10, and 25 KGy, respectively, compared to the untreated sample (466.3 ± 2.87 and 440 ± 5.72) 15- and 30-days after irradiation, respectively, (<italic>P</italic> < 0.05).</p>","<p id=\"Par20\">Filamentous fungi had the same criteria as bacteria and yeasts, the total counts of fungi decreased significantly (<italic>P</italic> < 0.05) with increasing the length of gamma irradiation giving 95.7 ± 4.9 (45.54% inhibition), 34.7 ± 3.7 (80.27% inhibition), and 2.67 ± 0.4, (98.48% inhibition) at 5, 10, and 25 KGy, respectively, after 15 days. After 30 days, the yeast counts were 55.67 ± 4.9 (68.49% inhibition), 15.7 ± 3.3 (91.13% inhibition), and 1.3 ± 0.1 (99.24% inhibition) at 5, 10, and 25 KGy, respectively, compared to the untreated sample (175.7 ± 3.7 and 176.67 ± 3.8) 15- and 30-days after irradiation, respectively.</p>","<p id=\"Par21\">By investigating the microbial species present on the fish skin, <italic>Bacillus</italic> sp., <italic>Escherichia coli, Micrococcus luteus,</italic> and <italic>Serratia marcescens</italic> were the dominant aerobic bacteria, <italic>Candida</italic> sp., <italic>Saccharomyces</italic> sp. and <italic>Rhodotorula</italic> sp. were the dominant aerobic yeasts, whereas <italic>Aspergillus niger, A. flavus, A. fumigatus,</italic> and <italic>Rhizopus stolonifer</italic> were the dominant aerobic fungi<bold>.</bold></p>","<title>Impact of lyophilization on fish skin</title>","<p id=\"Par22\">In the control group, fresh fish skin was examined histologically, and the collagen fibers were tightly packed, well-organized, parallel-distributed, and no evidence of disaggregation (Fig. ##FIG##2##3##A,B). In addition, the lyophilized fish skin rehydrated in normal saline for 15 min showed the preservation of the collagen fibers in a well-organized pattern with no signs of disaggregation (Fig. ##FIG##2##3##C,D).</p>","<title>Histological evaluation of the lyophilized fish skin after gamma irradiation</title>","<p id=\"Par23\">In the lyophilized fish skin subjected to gamma irradiation at 5, 10, and 25 kGy, histological examination of the fish skin 15-day post-sterilization revealed slightly to moderately disorganized and disaggregated in irradiated skin at 5 and 25 kGy (Figs. ##FIG##3##4##A,E, ##FIG##5##6##A,B) (<italic>P</italic> < 0.05). However, the collagen fibers were well organized in a parallel pattern at 10 kGy (Fig. ##FIG##3##4##C). At 30-days post-sterilization, the collagen fiber structure did not change in the irradiated skin at 5 and 10 kGy (Figs. ##FIG##4##5##A,C, ##FIG##5##6##A,B) (<italic>P</italic> < 0.05), with disorganization and disaggregation were more prominent at 25 kGy (Figs. ##FIG##4##5##E, ##FIG##5##6##A,B) (<italic>P</italic> < 0.05).</p>","<title>Histochemical evaluation of the fish skin</title>","<p id=\"Par24\">The collagen fibers were also stained with Gomori’s trichrome stain and the collagen intensity in the skin was measured using ImageJ (1.48v). At 15- and 30-days post-sterilization, the collagen disposition and intensity in skin samples irradiated at 5 and 10 kGy did not show significant differences compared with the control (Figs. ##FIG##3##4##B,D, ##FIG##4##5##B,D, ##FIG##5##6##C). However, collagen deposition and intensity were significantly decreased in skin samples exposed to 25 kGy after 15 and 30 days of irradiation (Figs. ##FIG##3##4##F, ##FIG##4##5##F, ##FIG##5##6##C) (<italic>P</italic> < 0.0001).</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>Microbiological evaluation of the lyophilized fish skin after gamma irradiation</title>\",\n \"<p id=\\\"Par17\\\">Gamma irradiation exhibited an efficient sterilizing effect on fish skin surface microbiota. Different lengths of gamma irradiation (5, 10, and 25 KGy) were applied to the lyophilized fish skin. The microbial counts of aerobic bacteria, aerobic yeasts, and fungi were detected 15- and 30- days after the irradiation as cleared in Fig. ##FIG##1##2##A–C. It was clear that gamma irradiation is a great microbial sterilizer, especially with the high length of 25 KGy that inhibited the aerobic bacterial counts significantly by 98.53% and 98.96%, aerobic yeast counts by 99.2% and 99.8%, and fungal counts by 98.48% and 99.25% 15- and 30- days after irradiation, respectively, (<italic>P</italic> < 0.05). Gamma irradiation also maintained the low microbial skin counts for a longer period (30 days) and remarkably at the low length of 5 KGy, gamma irradiation was effective against aerobic bacteria.</p>\",\n \"<p id=\\\"Par18\\\">By increasing the gamma irradiation, the total counts of aerobic bacteria decrease dramatically giving 92.67 ± 2.62 (88.36% inhibition), 39.33 ± 4.92 (95.06% inhibition), and 11.67 ± 1.7 (98.53% inhibition) at 5, 10, and 25 KGy 15 days post-irradiation, respectively, (<italic>P</italic> < 0.05). At 30 days post-irradiation, the bacterial counts were 74.33 ± 2.49 (88.93% inhibition), 32 ± 5.7 (95.24% inhibition), and 7 ± 1.6 (98.96% inhibition) at 5, 10, and 25 KGy, respectively, compared to the untreated sample (796 ± 9.93 and 671.7 ± 7.4) 15- and 30-days after irradiation, respectively, (<italic>P</italic> < 0.05).</p>\",\n \"<p id=\\\"Par19\\\">For yeasts, it was clear that by increasing the length of gamma irradiation, the total counts of yeasts decreased significantly after 15- and 30-days irradiation giving 208.67 ± 6.13 (55.25% inhibition), 19 ± 1.63 (95.93% inhibition), and 3.67 ± 0.47 (99.2% inhibition) at 5, 10, and 25 KGy, respectively, at 15 days (<italic>P</italic> < 0.05). At 30 days, the yeast counts were 104.67 ± 4.9 (76.21% inhibition), 8.7 ± 1.2 (98.03% inhibition), and 1 ± 0 (99.77% inhibition) at 5, 10, and 25 KGy, respectively, compared to the untreated sample (466.3 ± 2.87 and 440 ± 5.72) 15- and 30-days after irradiation, respectively, (<italic>P</italic> < 0.05).</p>\",\n \"<p id=\\\"Par20\\\">Filamentous fungi had the same criteria as bacteria and yeasts, the total counts of fungi decreased significantly (<italic>P</italic> < 0.05) with increasing the length of gamma irradiation giving 95.7 ± 4.9 (45.54% inhibition), 34.7 ± 3.7 (80.27% inhibition), and 2.67 ± 0.4, (98.48% inhibition) at 5, 10, and 25 KGy, respectively, after 15 days. After 30 days, the yeast counts were 55.67 ± 4.9 (68.49% inhibition), 15.7 ± 3.3 (91.13% inhibition), and 1.3 ± 0.1 (99.24% inhibition) at 5, 10, and 25 KGy, respectively, compared to the untreated sample (175.7 ± 3.7 and 176.67 ± 3.8) 15- and 30-days after irradiation, respectively.</p>\",\n \"<p id=\\\"Par21\\\">By investigating the microbial species present on the fish skin, <italic>Bacillus</italic> sp., <italic>Escherichia coli, Micrococcus luteus,</italic> and <italic>Serratia marcescens</italic> were the dominant aerobic bacteria, <italic>Candida</italic> sp., <italic>Saccharomyces</italic> sp. and <italic>Rhodotorula</italic> sp. were the dominant aerobic yeasts, whereas <italic>Aspergillus niger, A. flavus, A. fumigatus,</italic> and <italic>Rhizopus stolonifer</italic> were the dominant aerobic fungi<bold>.</bold></p>\",\n \"<title>Impact of lyophilization on fish skin</title>\",\n \"<p id=\\\"Par22\\\">In the control group, fresh fish skin was examined histologically, and the collagen fibers were tightly packed, well-organized, parallel-distributed, and no evidence of disaggregation (Fig. ##FIG##2##3##A,B). In addition, the lyophilized fish skin rehydrated in normal saline for 15 min showed the preservation of the collagen fibers in a well-organized pattern with no signs of disaggregation (Fig. ##FIG##2##3##C,D).</p>\",\n \"<title>Histological evaluation of the lyophilized fish skin after gamma irradiation</title>\",\n \"<p id=\\\"Par23\\\">In the lyophilized fish skin subjected to gamma irradiation at 5, 10, and 25 kGy, histological examination of the fish skin 15-day post-sterilization revealed slightly to moderately disorganized and disaggregated in irradiated skin at 5 and 25 kGy (Figs. ##FIG##3##4##A,E, ##FIG##5##6##A,B) (<italic>P</italic> < 0.05). However, the collagen fibers were well organized in a parallel pattern at 10 kGy (Fig. ##FIG##3##4##C). At 30-days post-sterilization, the collagen fiber structure did not change in the irradiated skin at 5 and 10 kGy (Figs. ##FIG##4##5##A,C, ##FIG##5##6##A,B) (<italic>P</italic> < 0.05), with disorganization and disaggregation were more prominent at 25 kGy (Figs. ##FIG##4##5##E, ##FIG##5##6##A,B) (<italic>P</italic> < 0.05).</p>\",\n \"<title>Histochemical evaluation of the fish skin</title>\",\n \"<p id=\\\"Par24\\\">The collagen fibers were also stained with Gomori’s trichrome stain and the collagen intensity in the skin was measured using ImageJ (1.48v). At 15- and 30-days post-sterilization, the collagen disposition and intensity in skin samples irradiated at 5 and 10 kGy did not show significant differences compared with the control (Figs. ##FIG##3##4##B,D, ##FIG##4##5##B,D, ##FIG##5##6##C). However, collagen deposition and intensity were significantly decreased in skin samples exposed to 25 kGy after 15 and 30 days of irradiation (Figs. ##FIG##3##4##F, ##FIG##4##5##F, ##FIG##5##6##C) (<italic>P</italic> < 0.0001).</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par25\">Recently, biological dressings have become indispensable in modern strategies of burn and wound management. The current study investigated the use of lyophilization as a method for the long-term preservation of fish skin grafts in a manner that maintains the integrity of fish skin collagen. Moreover, the study revealed that gamma irradiation at a length of 10 KGy is optimal for sterilizing the lyophilized fish skin grafts without disrupting the collagen content.</p>","<p id=\"Par26\">Autologous skin grafts have a limited availability and add an additional scaring<sup>##REF##15241002##31##</sup>. Allografts have many limitations such as the finding of suitable donors and the risk of infection transmission, especially, viral infections<sup>##REF##16023938##20##</sup>. Therefore, various xenografts have been described as biological wound dressings in humans. This includes the bovine embryo skin<sup>##UREF##12##32##</sup>, bovine amnion<sup>##UREF##13##33##</sup>, canine skin<sup>##UREF##14##34##</sup>, frog skin<sup>##UREF##15##35##</sup>, and porcine skin<sup>##REF##11996864##36##–##REF##33946298##38##</sup>. However, there are concerns about their clinical application due to immunological causes, zoonotic risk, and religious beliefs<sup>##REF##16023938##20##,##REF##34834962##39##,##REF##10484730##40##</sup>.</p>","<p id=\"Par27\">The fish skin was found to be consistent with human skin<sup>##UREF##2##8##,##REF##29380095##13##</sup> and has a higher collagen content than other skins<sup>##REF##34332229##9##,##REF##29380095##13##,##REF##26798644##14##</sup>, which is a potential wound healing promotor<sup>##UREF##16##41##</sup>. Moreover, fish skin has advantages such as low antigenicity, a high potential adherent to the skin, a lower risk of transmitting diseases, and a degree of moisture symbolizing the human skin<sup>##UREF##2##8##,##UREF##17##42##</sup>. Fish skin graft is highly porous. Since the pore size is within the range of typical cell size, fish skin is well suited to support cell ingrowth<sup>##REF##28291503##43##</sup>. Therefore, the fish skin has recently been suggested as a potential xenograft<sup>##UREF##0##1##–##UREF##1##7##,##REF##34332229##9##–##REF##31103284##12##</sup>.</p>","<p id=\"Par28\">Although the tilapia skin contains a normal non-infectious microbiota, it may be exposed to microbial contaminants from a contaminated water environment<sup>##UREF##1##7##,##REF##28757200##44##</sup>. Bacteria, yeasts, fungi, and viruses represent serious fish contaminants<sup>##REF##32781103##45##</sup>. Enterobacteriaceae, yeast, and aerobic spoilers are the main spoilage microorganisms of fish during the storage<sup>##REF##8913813##46##</sup>. Due to the high-water activity, and low acidity in the fish skin, bacteria proliferate quickly causing its spoilage<sup>##REF##33916441##47##</sup>. Therefore, in most studies, Tilapia skin was used as a biological dressing graft for burn or wound treatment shortly after its processing<sup>##UREF##0##1##,##REF##32603408##3##–##UREF##1##7##,##REF##34332229##9##,##REF##33426019##10##</sup>. This creates an urgent need to find a way for long-term preservation and storage of the fish skin grafts.</p>","<p id=\"Par29\">Various methods have been used for the preservation of different biological dressings, such as cryopreservation<sup>##REF##24018216##48##,##REF##9568334##49##</sup> and glycerolization<sup>##REF##33890902##4##,##REF##31900475##5##,##UREF##1##7##,##REF##34332229##9##,##REF##28196742##50##–##REF##24070848##52##</sup>. Although these methods could prolong the shelf life of biological dressings, they may also affect their effectiveness<sup>##REF##9063797##53##</sup>.</p>","<p id=\"Par30\">Lyophilization or freeze-drying is a specific dehydration process, which provides benefits to the final product through improved composition stability and reduced water content to low levels, which allow the cell to survive during long storage<sup>##UREF##5##18##,##UREF##18##54##</sup>. Microbial cells need high stabilization processes to survive on the lyophilized parts. Otherwise, they will be damaged and/or die<sup>##REF##28446905##55##</sup>.</p>","<p id=\"Par31\">Lyophilization has been addressed in previous studies for fish skin preservation<sup>##REF##33052795##2##,##REF##32603408##3##</sup>. However, using a rigorous process of chemical sterilization of the fish skin before the lyophilization raises concerns about collagen integrity and other biological components potential for healing<sup>##REF##28291503##43##</sup>, especially, since these studies were not followed by histological evaluation before clinical application<sup>##UREF##0##1##,##REF##33052795##2##,##UREF##1##7##</sup>. Moreover, using several sterilizing steps with several chemicals make such procedures cost-effective and impractical for industrial application. Chlorhexidine is commonly used to sterilize and disinfect grafting procedures. However, many bacterial spores or mycobacteria are chlorhexidine-resistant<sup>##REF##11556507##56##,##REF##17908945##57##</sup>. It also has a low activity against viruses. A potential limitation of chlorhexidine is its cytotoxicity and alteration in the biochemical properties of collagen<sup>##REF##33426019##10##,##REF##22523372##58##</sup>.</p>","<p id=\"Par32\">Here, this study describes the lyophilization of fish skin without using any disinfectants during the processing of fish skin grafts to ensure the collagen safety and subsequently its effectiveness. This was confirmed by histological and histochemical evaluations of the lyophilized fish skin after rehydration in normal saline for 15 min that there was no change in the arrangement and content of the collagen fibers of the fish skin.</p>","<p id=\"Par33\">Sterilization of fish skin with gamma irradiation represents an efficient sterilizing method for the fish skin microbiota. High-energy gamma irradiations are released by several radioisotopes, including the comparatively cheap byproducts of nuclear fission Caesium-137 (137Cs) and Cobalt-60 (60Co). By subjecting the plentiful, non-radioactive Co-59 isotope to neutron irradiation inside a nuclear reactor, radioactive Co is created. Then, after emitting one electron and two gamma rays with energies of 1.17 MeV and 1.33 MeV, Co-60 atoms decompose into nonradioactive Ni-60 atoms. Gamma rays don't have enough energy to cause radioactivity in other materials because they are released isotropically. Similar to x-rays, gamma rays have a shorter wavelength and higher energy. Thus, Gamma rays are appealing for industrial sterilization of materials with a significant thickness or volume, such as packaged food, medical equipment, or medical supplies<sup>##UREF##19##59##</sup>.</p>","<p id=\"Par34\">The combination of lyophilization and gamma irradiation inhibited the microbial growth by 99.8%. Gram-negative and Gram-positive bacteria, as proteobacteria and the Lactobacillales, are more vulnerable to Gamma irradiation than spore-forming bacteria, such as Bacilli and Clostridia<sup>##REF##31121676##60##</sup>. This is in accordance with our findings that <italic>Bacillus</italic> sp., <italic>Escherichia coli, Micrococcus luteus,</italic> and <italic>Serratia marcescens</italic> were the dominant aerobic bacteria. Similar findings were recorded by Dharmarha et al.<sup>##REF##31024491##61##</sup>, who found that Gamma irradiation decreased <italic>Pseudomonas</italic> sp., <italic>Escherichia coli</italic>, and Yersinia sp. total counts highly than spore-forming bacteria. Gamma irradiation was found to have an effect on the microbial DNA by altering its composition<sup>##REF##15916651##21##</sup>. It reacts with water molecules forming free radicals damage DNA and breakage the DNA double strand with non-repaired effects causing microbial cell death<sup>##UREF##20##62##</sup>.</p>","<p id=\"Par35\">An infection is deemed to exist when there are more than 10<sup>5</sup> CFU of bacteria per gram of skin graft<sup>##REF##11292638##63##</sup>. On wound beds with more than 10<sup>5</sup> bacteria/g of tissue, the absorption of a skin graft is decreased<sup>##UREF##21##64##</sup>. In our results, the total bacterial counts were less than 35 CFU/cm<sup>2</sup> at 10 and 25 kGy after 30 days of sterilization, indicating the high sterilizing efficiency of these irradiations; however, 10 kGy is preferred as it maintains the collagen integrity and content. Also, the total counts of yeasts and fungi were less than 10 and 16 CFU/cm<sup>2</sup>, respectively, at 10 and 25 kGy after 30 days. In accordance with our results, clinical investigations found that skin transplant failure occurred in wounds that were significantly infected with 10<sup>7</sup> pathogens<sup>##REF##1098503##65##,##UREF##22##66##</sup>. Also, Nsaful et al.<sup>##UREF##23##67##</sup> found that wound graft failure mainly occurs by the microbial contamination, especially bacterial contamination with the bacteria counts ranged from 3.7 × 10<sup>5</sup> to 9 × 10<sup>5</sup> CFU/cm<sup>2</sup>.</p>","<p id=\"Par36\">In accordance, histological evaluation of the fish skin grafts revealed that the exposure to gamma irradiation at 25 kGy caused the collagen fibers to dissociate and disintegrate with a reduction in the collagen intensity, whereas exposure at 5 kGy altered collagen fiber arrangement and integrity. However, the exposure to gamma irradiation at 10 kGy showed the preferred intensity since it preserved the collagen fiber content and intensity in the skin graft.</p>","<p id=\"Par37\">The limitation of this study is the lack of histological and microbiological assessment of the sterilized vacuum-packed lyophilized fish skin grafts on further long period (6 and 12 months). Therefore, future studies should be conducted to address this concern.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par25\\\">Recently, biological dressings have become indispensable in modern strategies of burn and wound management. The current study investigated the use of lyophilization as a method for the long-term preservation of fish skin grafts in a manner that maintains the integrity of fish skin collagen. Moreover, the study revealed that gamma irradiation at a length of 10 KGy is optimal for sterilizing the lyophilized fish skin grafts without disrupting the collagen content.</p>\",\n \"<p id=\\\"Par26\\\">Autologous skin grafts have a limited availability and add an additional scaring<sup>##REF##15241002##31##</sup>. Allografts have many limitations such as the finding of suitable donors and the risk of infection transmission, especially, viral infections<sup>##REF##16023938##20##</sup>. Therefore, various xenografts have been described as biological wound dressings in humans. This includes the bovine embryo skin<sup>##UREF##12##32##</sup>, bovine amnion<sup>##UREF##13##33##</sup>, canine skin<sup>##UREF##14##34##</sup>, frog skin<sup>##UREF##15##35##</sup>, and porcine skin<sup>##REF##11996864##36##–##REF##33946298##38##</sup>. However, there are concerns about their clinical application due to immunological causes, zoonotic risk, and religious beliefs<sup>##REF##16023938##20##,##REF##34834962##39##,##REF##10484730##40##</sup>.</p>\",\n \"<p id=\\\"Par27\\\">The fish skin was found to be consistent with human skin<sup>##UREF##2##8##,##REF##29380095##13##</sup> and has a higher collagen content than other skins<sup>##REF##34332229##9##,##REF##29380095##13##,##REF##26798644##14##</sup>, which is a potential wound healing promotor<sup>##UREF##16##41##</sup>. Moreover, fish skin has advantages such as low antigenicity, a high potential adherent to the skin, a lower risk of transmitting diseases, and a degree of moisture symbolizing the human skin<sup>##UREF##2##8##,##UREF##17##42##</sup>. Fish skin graft is highly porous. Since the pore size is within the range of typical cell size, fish skin is well suited to support cell ingrowth<sup>##REF##28291503##43##</sup>. Therefore, the fish skin has recently been suggested as a potential xenograft<sup>##UREF##0##1##–##UREF##1##7##,##REF##34332229##9##–##REF##31103284##12##</sup>.</p>\",\n \"<p id=\\\"Par28\\\">Although the tilapia skin contains a normal non-infectious microbiota, it may be exposed to microbial contaminants from a contaminated water environment<sup>##UREF##1##7##,##REF##28757200##44##</sup>. Bacteria, yeasts, fungi, and viruses represent serious fish contaminants<sup>##REF##32781103##45##</sup>. Enterobacteriaceae, yeast, and aerobic spoilers are the main spoilage microorganisms of fish during the storage<sup>##REF##8913813##46##</sup>. Due to the high-water activity, and low acidity in the fish skin, bacteria proliferate quickly causing its spoilage<sup>##REF##33916441##47##</sup>. Therefore, in most studies, Tilapia skin was used as a biological dressing graft for burn or wound treatment shortly after its processing<sup>##UREF##0##1##,##REF##32603408##3##–##UREF##1##7##,##REF##34332229##9##,##REF##33426019##10##</sup>. This creates an urgent need to find a way for long-term preservation and storage of the fish skin grafts.</p>\",\n \"<p id=\\\"Par29\\\">Various methods have been used for the preservation of different biological dressings, such as cryopreservation<sup>##REF##24018216##48##,##REF##9568334##49##</sup> and glycerolization<sup>##REF##33890902##4##,##REF##31900475##5##,##UREF##1##7##,##REF##34332229##9##,##REF##28196742##50##–##REF##24070848##52##</sup>. Although these methods could prolong the shelf life of biological dressings, they may also affect their effectiveness<sup>##REF##9063797##53##</sup>.</p>\",\n \"<p id=\\\"Par30\\\">Lyophilization or freeze-drying is a specific dehydration process, which provides benefits to the final product through improved composition stability and reduced water content to low levels, which allow the cell to survive during long storage<sup>##UREF##5##18##,##UREF##18##54##</sup>. Microbial cells need high stabilization processes to survive on the lyophilized parts. Otherwise, they will be damaged and/or die<sup>##REF##28446905##55##</sup>.</p>\",\n \"<p id=\\\"Par31\\\">Lyophilization has been addressed in previous studies for fish skin preservation<sup>##REF##33052795##2##,##REF##32603408##3##</sup>. However, using a rigorous process of chemical sterilization of the fish skin before the lyophilization raises concerns about collagen integrity and other biological components potential for healing<sup>##REF##28291503##43##</sup>, especially, since these studies were not followed by histological evaluation before clinical application<sup>##UREF##0##1##,##REF##33052795##2##,##UREF##1##7##</sup>. Moreover, using several sterilizing steps with several chemicals make such procedures cost-effective and impractical for industrial application. Chlorhexidine is commonly used to sterilize and disinfect grafting procedures. However, many bacterial spores or mycobacteria are chlorhexidine-resistant<sup>##REF##11556507##56##,##REF##17908945##57##</sup>. It also has a low activity against viruses. A potential limitation of chlorhexidine is its cytotoxicity and alteration in the biochemical properties of collagen<sup>##REF##33426019##10##,##REF##22523372##58##</sup>.</p>\",\n \"<p id=\\\"Par32\\\">Here, this study describes the lyophilization of fish skin without using any disinfectants during the processing of fish skin grafts to ensure the collagen safety and subsequently its effectiveness. This was confirmed by histological and histochemical evaluations of the lyophilized fish skin after rehydration in normal saline for 15 min that there was no change in the arrangement and content of the collagen fibers of the fish skin.</p>\",\n \"<p id=\\\"Par33\\\">Sterilization of fish skin with gamma irradiation represents an efficient sterilizing method for the fish skin microbiota. High-energy gamma irradiations are released by several radioisotopes, including the comparatively cheap byproducts of nuclear fission Caesium-137 (137Cs) and Cobalt-60 (60Co). By subjecting the plentiful, non-radioactive Co-59 isotope to neutron irradiation inside a nuclear reactor, radioactive Co is created. Then, after emitting one electron and two gamma rays with energies of 1.17 MeV and 1.33 MeV, Co-60 atoms decompose into nonradioactive Ni-60 atoms. Gamma rays don't have enough energy to cause radioactivity in other materials because they are released isotropically. Similar to x-rays, gamma rays have a shorter wavelength and higher energy. Thus, Gamma rays are appealing for industrial sterilization of materials with a significant thickness or volume, such as packaged food, medical equipment, or medical supplies<sup>##UREF##19##59##</sup>.</p>\",\n \"<p id=\\\"Par34\\\">The combination of lyophilization and gamma irradiation inhibited the microbial growth by 99.8%. Gram-negative and Gram-positive bacteria, as proteobacteria and the Lactobacillales, are more vulnerable to Gamma irradiation than spore-forming bacteria, such as Bacilli and Clostridia<sup>##REF##31121676##60##</sup>. This is in accordance with our findings that <italic>Bacillus</italic> sp., <italic>Escherichia coli, Micrococcus luteus,</italic> and <italic>Serratia marcescens</italic> were the dominant aerobic bacteria. Similar findings were recorded by Dharmarha et al.<sup>##REF##31024491##61##</sup>, who found that Gamma irradiation decreased <italic>Pseudomonas</italic> sp., <italic>Escherichia coli</italic>, and Yersinia sp. total counts highly than spore-forming bacteria. Gamma irradiation was found to have an effect on the microbial DNA by altering its composition<sup>##REF##15916651##21##</sup>. It reacts with water molecules forming free radicals damage DNA and breakage the DNA double strand with non-repaired effects causing microbial cell death<sup>##UREF##20##62##</sup>.</p>\",\n \"<p id=\\\"Par35\\\">An infection is deemed to exist when there are more than 10<sup>5</sup> CFU of bacteria per gram of skin graft<sup>##REF##11292638##63##</sup>. On wound beds with more than 10<sup>5</sup> bacteria/g of tissue, the absorption of a skin graft is decreased<sup>##UREF##21##64##</sup>. In our results, the total bacterial counts were less than 35 CFU/cm<sup>2</sup> at 10 and 25 kGy after 30 days of sterilization, indicating the high sterilizing efficiency of these irradiations; however, 10 kGy is preferred as it maintains the collagen integrity and content. Also, the total counts of yeasts and fungi were less than 10 and 16 CFU/cm<sup>2</sup>, respectively, at 10 and 25 kGy after 30 days. In accordance with our results, clinical investigations found that skin transplant failure occurred in wounds that were significantly infected with 10<sup>7</sup> pathogens<sup>##REF##1098503##65##,##UREF##22##66##</sup>. Also, Nsaful et al.<sup>##UREF##23##67##</sup> found that wound graft failure mainly occurs by the microbial contamination, especially bacterial contamination with the bacteria counts ranged from 3.7 × 10<sup>5</sup> to 9 × 10<sup>5</sup> CFU/cm<sup>2</sup>.</p>\",\n \"<p id=\\\"Par36\\\">In accordance, histological evaluation of the fish skin grafts revealed that the exposure to gamma irradiation at 25 kGy caused the collagen fibers to dissociate and disintegrate with a reduction in the collagen intensity, whereas exposure at 5 kGy altered collagen fiber arrangement and integrity. However, the exposure to gamma irradiation at 10 kGy showed the preferred intensity since it preserved the collagen fiber content and intensity in the skin graft.</p>\",\n \"<p id=\\\"Par37\\\">The limitation of this study is the lack of histological and microbiological assessment of the sterilized vacuum-packed lyophilized fish skin grafts on further long period (6 and 12 months). Therefore, future studies should be conducted to address this concern.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":["<title>Conclusion</title>","<p id=\"Par38\">This study established an optimized method for tilapia skin lyophilization. It defined the standard length of gamma irradiation for sterilizing lyophilized tilapia skin, considering the collagen integrity and microbial count of fish skin. These processed, sterilized, vacuum-packed, lyophilized fish skin patches could be suitable for long-term storage for burn and wound management in hospitals and medical centers. Further clinical studies are still being conducted on this product.</p>"],"string":"[\n \"<title>Conclusion</title>\",\n \"<p id=\\\"Par38\\\">This study established an optimized method for tilapia skin lyophilization. It defined the standard length of gamma irradiation for sterilizing lyophilized tilapia skin, considering the collagen integrity and microbial count of fish skin. These processed, sterilized, vacuum-packed, lyophilized fish skin patches could be suitable for long-term storage for burn and wound management in hospitals and medical centers. Further clinical studies are still being conducted on this product.</p>\"\n]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">The introduction of fish skin as a biological dressing for treating burns and wounds holds great promise, offering an alternative to existing management strategies. However, the risk of disease transmission is a significant concern. Therefore, this study aimed to examine how established sterilization and preservation procedures affected fish skin grafts' microbiological and histological properties for long-term usage. Lyophilization of the fish skin graft followed by rehydration in normal saline for 15 min did not change the collagen content. Furthermore, gamma irradiation of the lyophilized fish skin graft at different lengths 5, 10, and 25 KGy showed a significant reduction in microbial growth (aerobic bacteria, aerobic yeasts, and fungi) at 15- and 30 days after the irradiation. However, exposure to 10 KGy was found to be the most effective intensity among the different gamma irradiation lengths since it preserved the collagen fiber content and intensity in the lyophilized fish skin grafts at 15- and 30 days after the irradiation. These findings provide efficient preservation and sterilization methods for long-term usage of the fresh Tilapia skin grafts used for biological dressings.</p>","<title>Subject terms</title>","<p>Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).</p>"],"string":"[\n \"<p id=\\\"Par1\\\">The introduction of fish skin as a biological dressing for treating burns and wounds holds great promise, offering an alternative to existing management strategies. However, the risk of disease transmission is a significant concern. Therefore, this study aimed to examine how established sterilization and preservation procedures affected fish skin grafts' microbiological and histological properties for long-term usage. Lyophilization of the fish skin graft followed by rehydration in normal saline for 15 min did not change the collagen content. Furthermore, gamma irradiation of the lyophilized fish skin graft at different lengths 5, 10, and 25 KGy showed a significant reduction in microbial growth (aerobic bacteria, aerobic yeasts, and fungi) at 15- and 30 days after the irradiation. However, exposure to 10 KGy was found to be the most effective intensity among the different gamma irradiation lengths since it preserved the collagen fiber content and intensity in the lyophilized fish skin grafts at 15- and 30 days after the irradiation. These findings provide efficient preservation and sterilization methods for long-term usage of the fresh Tilapia skin grafts used for biological dressings.</p>\",\n \"<title>Subject terms</title>\",\n \"<p>Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).</p>\"\n]","isConversational":false},"body":{"kind":"list like","value":[],"string":"[]","isConversational":false},"back":{"kind":"list like","value":["<title>Author contributions</title>","<p>A.I.: Conceptualization, Methodology, Validation, Data curation, Visualization, Investigation, Supervision, Writing- Original draft preparation, Writing- Reviewing and Editing. H.F.: Conceptualization, Methodology, Data curation, Investigation, Writing- Original draft preparation. G.A.-E.M.: Methodology, Visualization, Investigation, Writing- Original draft preparation. A.E.: Methodology, Validation, Data curation, Investigation, Writing- Original draft preparation. M.S.: Methodology, Validation, Data curation, Software, Visualization, Investigation, Supervision, Writing- Original draft preparation, Writing- Reviewing and Editing.</p>","<title>Funding</title>","<p>Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).</p>","<title>Data availability </title>","<p>All data generated or analyzed during this study are included in this published article.</p>","<title>Competing interests</title>","<p id=\"Par39\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Author contributions</title>\",\n \"<p>A.I.: Conceptualization, Methodology, Validation, Data curation, Visualization, Investigation, Supervision, Writing- Original draft preparation, Writing- Reviewing and Editing. H.F.: Conceptualization, Methodology, Data curation, Investigation, Writing- Original draft preparation. G.A.-E.M.: Methodology, Visualization, Investigation, Writing- Original draft preparation. A.E.: Methodology, Validation, Data curation, Investigation, Writing- Original draft preparation. M.S.: Methodology, Validation, Data curation, Software, Visualization, Investigation, Supervision, Writing- Original draft preparation, Writing- Reviewing and Editing.</p>\",\n \"<title>Funding</title>\",\n \"<p>Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).</p>\",\n \"<title>Data availability </title>\",\n \"<p>All data generated or analyzed during this study are included in this published article.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par39\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>(<bold>A</bold>) and (<bold>B</bold>) Gross micrographs of the fish skin after the lyophilization, (<bold>C</bold>) vacuum- packaging.</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Total accounts (CFU/cm<sup>2</sup> ± S.D) of aerobic bacteria (<bold>A</bold>), aerobic yeasts (<bold>B</bold>), and aerobic filamentous fungi (<bold>A</bold>) on lyophilized fish skin sterilized with gamma irradiation (0, 5, 10, 25).</p></caption></fig>","<fig id=\"Fig3\"><label>Figure 3</label><caption><p>(<bold>A</bold>) and (<bold>B</bold>) Histological and histochemical evaluations of the fresh Tilapia skin, (<bold>C</bold>) and (D) the lyophilized Tilapia skin. (<bold>A</bold>, <bold>C</bold>) Hematoxylin and eosin-stained sections. (<bold>B</bold>, <bold>D</bold>) Gomori’s trichrome-stained sections for collagen. The scale bars = 100 μm.</p></caption></fig>","<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Histological and histochemical evaluations of the lyophilized Tilapia fish skin submitted to different irradiation dosages 15-days post-sterilization. Hematoxylin and eosin stained sections were irradiated at 5 (<bold>A</bold>), 10 (<bold>C</bold>), and 25 (<bold>E</bold>) kGy. (<bold>B</bold>), (<bold>D</bold>), and (<bold>F</bold>) Gomori’s trichrome stained sections for collagen. The scale bars = 100 μm.</p></caption></fig>","<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Histological and histochemical evaluations of the lyophilized Tilapia fish skin submitted to different irradiation dosages 30-days post-sterilization. Hematoxylin and eosin-stained sections were irradiated at 5 (<bold>A</bold>), 10 (<bold>C</bold>), and 25 (<bold>E</bold>) kGy. (<bold>B</bold>), (<bold>D</bold>), and (<bold>F</bold>) Gomori’s trichrome stained sections for collagen. The scale bars = 100 μm.</p></caption></fig>","<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Evaluation of the collagen integrity, organization, and intensity in the lyophilized Tilapia fish skin submitted to different irradiation dosages. Collagen integrity and organization were evaluated based on a 0–3 scale as described in the materials and methods. The collagen intensity was quantified using ImageJ. The results are expressed as a percentage of the total number of pixels and are normalized to the control-treated group. Differences were evaluated using one<italic>-</italic>way ANOVA. *<italic>p</italic> < 0.05; **<italic>p</italic> < 0.001; ***<italic>p</italic> < 0.0001.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>(<bold>A</bold>) and (<bold>B</bold>) Gross micrographs of the fish skin after the lyophilization, (<bold>C</bold>) vacuum- packaging.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>Total accounts (CFU/cm<sup>2</sup> ± S.D) of aerobic bacteria (<bold>A</bold>), aerobic yeasts (<bold>B</bold>), and aerobic filamentous fungi (<bold>A</bold>) on lyophilized fish skin sterilized with gamma irradiation (0, 5, 10, 25).</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Figure 3</label><caption><p>(<bold>A</bold>) and (<bold>B</bold>) Histological and histochemical evaluations of the fresh Tilapia skin, (<bold>C</bold>) and (D) the lyophilized Tilapia skin. (<bold>A</bold>, <bold>C</bold>) Hematoxylin and eosin-stained sections. (<bold>B</bold>, <bold>D</bold>) Gomori’s trichrome-stained sections for collagen. The scale bars = 100 μm.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Figure 4</label><caption><p>Histological and histochemical evaluations of the lyophilized Tilapia fish skin submitted to different irradiation dosages 15-days post-sterilization. Hematoxylin and eosin stained sections were irradiated at 5 (<bold>A</bold>), 10 (<bold>C</bold>), and 25 (<bold>E</bold>) kGy. (<bold>B</bold>), (<bold>D</bold>), and (<bold>F</bold>) Gomori’s trichrome stained sections for collagen. The scale bars = 100 μm.</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Figure 5</label><caption><p>Histological and histochemical evaluations of the lyophilized Tilapia fish skin submitted to different irradiation dosages 30-days post-sterilization. Hematoxylin and eosin-stained sections were irradiated at 5 (<bold>A</bold>), 10 (<bold>C</bold>), and 25 (<bold>E</bold>) kGy. (<bold>B</bold>), (<bold>D</bold>), and (<bold>F</bold>) Gomori’s trichrome stained sections for collagen. The scale bars = 100 μm.</p></caption></fig>\",\n \"<fig id=\\\"Fig6\\\"><label>Figure 6</label><caption><p>Evaluation of the collagen integrity, organization, and intensity in the lyophilized Tilapia fish skin submitted to different irradiation dosages. Collagen integrity and organization were evaluated based on a 0–3 scale as described in the materials and methods. The collagen intensity was quantified using ImageJ. The results are expressed as a percentage of the total number of pixels and are normalized to the control-treated group. Differences were evaluated using one<italic>-</italic>way ANOVA. *<italic>p</italic> < 0.05; **<italic>p</italic> < 0.001; ***<italic>p</italic> < 0.0001.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":[],"string":"[]","isConversational":false},"footnote":{"kind":"list like","value":["<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"],"string":"[\n \"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41598_2024_51608_Fig1_HTML\" id=\"MO1\"/>","<graphic xlink:href=\"41598_2024_51608_Fig2_HTML\" id=\"MO2\"/>","<graphic xlink:href=\"41598_2024_51608_Fig3_HTML\" id=\"MO3\"/>","<graphic xlink:href=\"41598_2024_51608_Fig4_HTML\" id=\"MO4\"/>","<graphic xlink:href=\"41598_2024_51608_Fig5_HTML\" id=\"MO6\"/>","<graphic xlink:href=\"41598_2024_51608_Fig6_HTML\" id=\"MO5\"/>"],"string":"[\n \"<graphic xlink:href=\\\"41598_2024_51608_Fig1_HTML\\\" id=\\\"MO1\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51608_Fig2_HTML\\\" id=\\\"MO2\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51608_Fig3_HTML\\\" id=\\\"MO3\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51608_Fig4_HTML\\\" id=\\\"MO4\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51608_Fig5_HTML\\\" id=\\\"MO6\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51608_Fig6_HTML\\\" id=\\\"MO5\\\"/>\"\n]","isConversational":false},"media":{"kind":"list like","value":[],"string":"[]","isConversational":false},"unknown_pub":{"kind":"string","value":"[{\"label\": [\"1.\"], \"surname\": [\"Costa\", \"Lima J\\u00fanior\", \"de Moraes Filho\", \"Fechine\", \"de Moraes\", \"Silva J\\u00fanior\", \"do Nascimento Soares\", \"Rocha\"], \"given-names\": [\"BA\", \"EM\", \"MO\", \"FV\", \"MEA\", \"FR\", \"MFA\", \"MBS\"], \"article-title\": [\"Use of Tilapia skin as a Xenograft for pediatric burn treatment: A case report\"], \"source\": [\"J. Burn Care Res. Off. Publ. Am. Burn Assoc.\"], \"year\": [\"2019\"], \"volume\": [\"40\"], \"issue\": [\"5\"], \"fpage\": [\"714\"], \"lpage\": [\"717\"], \"pub-id\": [\"10.1093/jbcr/irz085\"]}, {\"label\": [\"7.\"], \"surname\": [\"Lima-Junior\", \"de Moraes Filho\", \"Costa\", \"Fechine\", \"de Moraes\", \"Silva-Junior\", \"Soares\", \"Rocha\", \"Leontsinis\"], \"given-names\": [\"EM\", \"MO\", \"BA\", \"FV\", \"MEA\", \"FR\", \"MFAN\", \"MBS\", \"CMP\"], \"article-title\": [\"Innovative treatment using tilapia skin as a xenograft for partial thickness burns after a gunpowder explosion\"], \"source\": [\"J. Surg. Rep.\"], \"year\": [\"2019\"], \"volume\": [\"6\"], \"fpage\": [\"181\"], \"pub-id\": [\"10.1093/jscr/rjz181\"]}, {\"label\": [\"8.\"], \"surname\": [\"Lordello\", \"Bianco\", \"Ribas\"], \"given-names\": [\"A\", \"A\", \"A\"], \"article-title\": [\"Leather of the tilapia, how healing in the treatment of burns\"], \"source\": [\"Nurse Care Open Access J.\"], \"year\": [\"2019\"], \"volume\": [\"6\"], \"issue\": [\"4\"], \"fpage\": [\"147\"], \"lpage\": [\"153\"], \"pub-id\": [\"10.15406/ncoaj.2019.06.00199\"]}, {\"label\": [\"16.\"], \"surname\": [\"Je\\u017eek\", \"Buchtov\\u00e1\"], \"given-names\": [\"F\", \"H\"], \"article-title\": [\"Physical and chemical changes in fresh chilled muscle tissue of common carp (\"], \"italic\": [\"Cyprinus carpio\"], \"source\": [\"Acta Vet. Brno\"], \"year\": [\"2007\"], \"volume\": [\"76\"], \"issue\": [\"8\"], \"fpage\": [\"83\"], \"lpage\": [\"92\"], \"pub-id\": [\"10.2754/avb200776S8S083\"]}, {\"label\": [\"17.\"], \"surname\": [\"Ghaly\", \"Dave\", \"Budge\", \"Brooks\"], \"given-names\": [\"AE\", \"D\", \"S\", \"M\"], \"article-title\": [\"Fish spoilage mechanisms and preservation techniques\"], \"source\": [\"Am. J. Appl. Sci.\"], \"year\": [\"2010\"], \"volume\": [\"7\"], \"issue\": [\"7\"], \"fpage\": [\"859\"], \"pub-id\": [\"10.3844/ajassp.2010.859.877\"]}, {\"label\": [\"18.\"], \"surname\": [\"Haseley\", \"Oetjen\"], \"given-names\": [\"P\", \"G-W\"], \"source\": [\"Freeze-Drying\"], \"year\": [\"2017\"], \"publisher-name\": [\"Wiley\"]}, {\"label\": [\"22.\"], \"mixed-citation\": [\"Morton, R. D. Aerobic plate count, compendium of methods for the microbiological examination of foods \"], \"bold\": [\"4\"]}, {\"label\": [\"23.\"], \"surname\": [\"Atlas\"], \"given-names\": [\"R\"], \"source\": [\"Handbook of Microbiology Media\"], \"year\": [\"1993\"], \"publisher-name\": [\"CRC Press\"]}, {\"label\": [\"24.\"], \"surname\": [\"Barnett\", \"Payne\", \"Yarrow\"], \"given-names\": [\"JA\", \"RW\", \"D\"], \"source\": [\"Yeasts: Characteristics and Identification\"], \"year\": [\"1990\"], \"publisher-name\": [\"Cambridge University Press\"]}, {\"label\": [\"25.\"], \"surname\": [\"Leslie\", \"Summerell\"], \"given-names\": [\"JF\", \"BA\"], \"source\": [\"The Fusarium Laboratory Manual\"], \"year\": [\"2008\"], \"publisher-name\": [\"Wiley\"]}, {\"label\": [\"26.\"], \"surname\": [\"Bergey\"], \"given-names\": [\"DH\"], \"source\": [\"Bergey's Manual of Determinative Bacteriology\"], \"year\": [\"1994\"], \"publisher-name\": [\"Lippincott Williams & Wilkins\"]}, {\"label\": [\"27.\"], \"surname\": [\"Moubasher\"], \"given-names\": [\"A\"], \"source\": [\"Soil Fungi in Qatar and Other Arab Countries\"], \"year\": [\"1993\"], \"publisher-name\": [\"University of Qatar\"]}, {\"label\": [\"32.\"], \"surname\": [\"Rogers\", \"Converse\"], \"given-names\": [\"BO\", \"JM\"], \"article-title\": [\"Bovine embryo skin zoografts as temporary biologic dressings for burns and other skin defects\"], \"source\": [\"Plast. Reconstr. Surg.\"], \"year\": [\"1958\"], \"volume\": [\"22\"], \"issue\": [\"5\"], \"fpage\": [\"471\"], \"lpage\": [\"485\"], \"pub-id\": [\"10.1097/00006534-195811000-00007\"]}, {\"label\": [\"33.\"], \"surname\": [\"Zhu\", \"Xu\", \"Zhao\"], \"given-names\": [\"Z\", \"G\", \"J\"], \"article-title\": [\"Clinical study on application of bovine amnion on burn wounds\"], \"source\": [\"Chin. J. Repar. Reconstr. Surg.\"], \"year\": [\"2006\"], \"volume\": [\"20\"], \"issue\": [\"7\"], \"fpage\": [\"735\"], \"lpage\": [\"738\"]}, {\"label\": [\"34.\"], \"surname\": [\"Switzek\", \"Moncrief\", \"Mills\", \"Lindberg\"], \"given-names\": [\"WE\", \"JA\", \"W\", \"RB\"], \"suffix\": [\"Jr\"], \"article-title\": [\"The use of canine heterografts in the therapy of thermal injury\"], \"source\": [\"J. Trauma Acute Care Surg.\"], \"year\": [\"1966\"], \"volume\": [\"6\"], \"issue\": [\"3\"], \"fpage\": [\"391\"], \"lpage\": [\"398\"], \"pub-id\": [\"10.1097/00005373-196605000-00008\"]}, {\"label\": [\"35.\"], \"surname\": [\"Sarto Piccolo\", \"Sarto Piccolo\", \"Sarto Piccolo\"], \"given-names\": [\"N\", \"M\", \"M\"], \"source\": [\"The Use of Frogskin as a Biological Dressing for Temporary Cover of Burn Wounds. 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Int.\"], \"year\": [\"2014\"], \"volume\": [\"1\"], \"fpage\": [\"1\"], \"pub-id\": [\"10.1155/2014/630757\"]}, {\"label\": [\"54.\"], \"surname\": [\"Fissore\", \"Pisano\"], \"given-names\": [\"D\", \"R\"], \"article-title\": [\"Computer-aided framework for the design of freeze-drying cycles: Optimization of the operating conditions of the primary drying stage\"], \"source\": [\"Processes\"], \"year\": [\"2015\"], \"volume\": [\"3\"], \"issue\": [\"2\"], \"fpage\": [\"406\"], \"lpage\": [\"421\"], \"pub-id\": [\"10.3390/pr3020406\"]}, {\"label\": [\"59.\"], \"surname\": [\"Willey\", \"Sherwood\", \"Woolverton\"], \"given-names\": [\"JM\", \"L\", \"CJ\"], \"source\": [\"Prescott's Microbiology\"], \"year\": [\"2011\"], \"publisher-name\": [\"McGraw-Hill\"]}, {\"label\": [\"62.\"], \"surname\": [\"Hall\", \"Giaccia\"], \"given-names\": [\"EJ\", \"AJ\"], \"source\": [\"Radiobiology for the Radiologist\"], \"year\": [\"2006\"], \"publisher-name\": [\"Lippincott Williams and Wilkins\"]}, {\"label\": [\"64.\"], \"surname\": [\"Rubin\", \"Neligan\"], \"given-names\": [\"JP\", \"PC\"], \"source\": [\"Plastic Surgery. Aesthetic Surgery\"], \"year\": [\"2017\"], \"publisher-name\": [\"Elsevier\"]}, {\"label\": [\"66.\"], \"surname\": [\"Geethabanu\", \"Vanaja\"], \"given-names\": [\"S\", \"R\"], \"article-title\": [\"A study to analyse the influence of bacterial bio-burden on the success rate of split thickness skin grafting\"], \"source\": [\"J. Clin. Diagn. Res.\"], \"year\": [\"2018\"], \"volume\": [\"12\"], \"issue\": [\"6\"], \"fpage\": [\"23\"]}, {\"label\": [\"67.\"], \"mixed-citation\": [\"Nsaful, K. O., Paintsil, A. B., Dakubo, J. C., Nsaful, J., Appiah-Labi, K., Nartey, E. An evaluation of bacterial infection of split thickness skin grafts: At the Korle Bu Teaching Hospital. LAP LAMBERT Academic Publishing (2020).\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":67,"string":"67"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:56"},"citation":{"kind":"string","value":"Sci Rep. 2024 Jan 13; 14:1253"},"package_file":{"kind":"string","value":"oa_package/80/57/PMC10787751.tar.gz"}}},{"rowIdx":82,"cells":{"accession_id":{"kind":"string","value":"PMC10787752"},"pmid":{"kind":"string","value":"38218958"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par2\">When matter vibrates, it transmits energy waves into our ears and produces sound. The sound changes depending on two factors: the material of the object and how slow or fast the object vibrates, making different sound waves. In other words, the object’s material affects the timbre, while the speed of object vibration changes the pitch. Sound event classification is a technique used to distinguish the actions involved in the audio. The action generated from our environment, for example: music genre, human speech, water running, animal sound, etc. Nowadays, sound event classification is a common issue, and is usually addressed in the following pipeline: (1) audio pre-processing: the audio data needs to be transfer to the proper signal that can be recognized by learning algorithm. (2) feature extraction: to make the classification algorithm more efficient and accurate, the high level represented features are extracted from the raw signal. For example, the Short-time Fourier Transform (STFT), and Constant-Q transform are famous feature extraction techniques. (3) Sound event algorithm: once the extracted feature is prepared, the algorithm is developed to predict the label of the sound event with the input features. In the early 20 s, experts focused on generating powerful audio descriptors<sup>##UREF##0##1##</sup>. These are usually divided into three sets: low-level features, score features, and mid-level features. Some hand-created music features are involved in these sets, for example: spectral centroid<sup>##UREF##1##2##</sup>, and pitch salience<sup>##REF##30949547##3##</sup>. Score features, such as mode<sup>##UREF##2##4##</sup> and pitch density<sup>##UREF##3##5##</sup>, are directly calculated from the musical score. In general, mid-level features<sup>##UREF##4##6##</sup> are intuitively understandable by the typical learner. Nowadays, the deep learning algorithm has made tremendous progress by integrating feature extraction and decision-making algorithms for classification. With many trainable parameters, the network can directly learn to map the extracted features from input to target labels. Also, as the number of labeled samples increases, the network becomes more robust and reliable. Reference<sup>##UREF##5##7##</sup> built a sound event classification system by adapting the traditional convolutional neural network from the image domain to the audio domain. The sound event classification system performs well, even in a noisy environment. In common, it is challenging to recognize non-stationary sound under the interference of ambient noise. Reference<sup>##UREF##6##8##</sup> proposed a two-stream convolutional neural network. One stream directly processes the clip of the raw audio sequence, while the other tries to learn rich representation features from the log-Mel spectrogram. Recently, the transformer network has been receiving more attention. Reference<sup>##UREF##7##9##</sup> explores the transformer structure to detect the sound event based on audio tagging and temporal location information.</p>","<p id=\"Par3\">However, in addition to such known sound events, there exist many unknown sound events in our daily lives, which include rhythm, scene, instrument sound, etc. So, the question, “what is that sound?” has frequently come up. Usually, the machine learning algorithm tries to learn categories on a closed set, where the training and test data share the same feature embedding spaces and labels. The training data includes samples with target labels (A, B, C), while the test data restricts the labels to (A, B, C). The classes like D, F, etc. cannot appear on the test. However, in realistic scenarios, it is inevitable to introduce unknown observations D, F, or others into the dataset. The task to learn only with classes (A, B, C) and predict to categorize (A, B, C) or other unexpected D, F, or other classes is called open set recognition. In the open set recognition task, there are several categories (Fig. ##FIG##0##1##) assumed to characterize the learning models<sup>##UREF##8##10##</sup>: (1) Known Known classes (KKCs): the samples are annotated by humans and given with the corresponding labels. (2) Known Unknown classes (KUCs)<sup>##UREF##9##11##</sup>: The KUC is an umbrella term for different events, objects or concepts. It is hard to define the attribute of the KUC, which is usually a particular product incidental to the description of the subject of the thing. (3) Unknown Known classes<sup>##UREF##10##12##</sup>: Classes for which no samples are available during training, but side information, such as semantic/attribute information, is available. The algorithm to detect unknown known classes is called few-shot open set recognition, a subset scope<sup>##UREF##11##13##–##UREF##14##16##</sup> of open set recognition. (4) Unknown Unknown classes (UUCs)<sup>##UREF##15##17##</sup>: The sample of category does not exist during the training, as either the corresponding attribution or semantic information is not known. Our task focuses on unknown classes. The algorithm only learns in labeled known classes, and is able to detect unknown classes D, F, or others, even though we do not know, and cannot describe, what they are.</p>","<p id=\"Par4\">In this paper, we deal with classifier unknown sound events or their related patterns. In summary, our work makes the flowing key contributions:<list list-type=\"bullet\"><list-item><p id=\"Par5\">We proposed an open set sound events classification network. The encoder receives the 2-D log-Mel temporal spectrogram as input, and the decision head determines the probabilities of the known sound events. In our scheme, we can finally identify unknown sound events from the probabilities.</p></list-item><list-item><p id=\"Par6\">It is universally acknowledged that labeling audio data is a challenging task. Despite the neural network requiring a mass of labeled samples to learn, more data usually performs better. We explored a self-supervised learning approach in the open set sound events classification problem. The network was first learned in the unlabeled MagnaTagATune/DCASE2019 Subtask 1C dataset, utilizing the self-supervised contrastive loss to capture the rich representative features of sound events. Then, the learned network fine-tunes for the downstream dataset.</p></list-item><list-item><p id=\"Par7\">The best performance of known classes is still effective for unknown detection because the compact cluster structure provides sample space to detect unknown samples in the embedded feature space<sup>##UREF##16##18##</sup>. For compact cluster structure, the open set sound events classification network has been optimized by center<sup>##UREF##17##19##</sup>, and supervised contrastive losses<sup>##UREF##18##20##</sup>, as well as cross-entropy loss.</p></list-item><list-item><p id=\"Par8\">We evaluate our proposed method in various datasets. The experimental results reveal that both the self-supervised approach and the proposed losses improve the performance of open set classification of sound events.</p></list-item></list></p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">When matter vibrates, it transmits energy waves into our ears and produces sound. The sound changes depending on two factors: the material of the object and how slow or fast the object vibrates, making different sound waves. In other words, the object’s material affects the timbre, while the speed of object vibration changes the pitch. Sound event classification is a technique used to distinguish the actions involved in the audio. The action generated from our environment, for example: music genre, human speech, water running, animal sound, etc. Nowadays, sound event classification is a common issue, and is usually addressed in the following pipeline: (1) audio pre-processing: the audio data needs to be transfer to the proper signal that can be recognized by learning algorithm. (2) feature extraction: to make the classification algorithm more efficient and accurate, the high level represented features are extracted from the raw signal. For example, the Short-time Fourier Transform (STFT), and Constant-Q transform are famous feature extraction techniques. (3) Sound event algorithm: once the extracted feature is prepared, the algorithm is developed to predict the label of the sound event with the input features. In the early 20 s, experts focused on generating powerful audio descriptors<sup>##UREF##0##1##</sup>. These are usually divided into three sets: low-level features, score features, and mid-level features. Some hand-created music features are involved in these sets, for example: spectral centroid<sup>##UREF##1##2##</sup>, and pitch salience<sup>##REF##30949547##3##</sup>. Score features, such as mode<sup>##UREF##2##4##</sup> and pitch density<sup>##UREF##3##5##</sup>, are directly calculated from the musical score. In general, mid-level features<sup>##UREF##4##6##</sup> are intuitively understandable by the typical learner. Nowadays, the deep learning algorithm has made tremendous progress by integrating feature extraction and decision-making algorithms for classification. With many trainable parameters, the network can directly learn to map the extracted features from input to target labels. Also, as the number of labeled samples increases, the network becomes more robust and reliable. Reference<sup>##UREF##5##7##</sup> built a sound event classification system by adapting the traditional convolutional neural network from the image domain to the audio domain. The sound event classification system performs well, even in a noisy environment. In common, it is challenging to recognize non-stationary sound under the interference of ambient noise. Reference<sup>##UREF##6##8##</sup> proposed a two-stream convolutional neural network. One stream directly processes the clip of the raw audio sequence, while the other tries to learn rich representation features from the log-Mel spectrogram. Recently, the transformer network has been receiving more attention. Reference<sup>##UREF##7##9##</sup> explores the transformer structure to detect the sound event based on audio tagging and temporal location information.</p>\",\n \"<p id=\\\"Par3\\\">However, in addition to such known sound events, there exist many unknown sound events in our daily lives, which include rhythm, scene, instrument sound, etc. So, the question, “what is that sound?” has frequently come up. Usually, the machine learning algorithm tries to learn categories on a closed set, where the training and test data share the same feature embedding spaces and labels. The training data includes samples with target labels (A, B, C), while the test data restricts the labels to (A, B, C). The classes like D, F, etc. cannot appear on the test. However, in realistic scenarios, it is inevitable to introduce unknown observations D, F, or others into the dataset. The task to learn only with classes (A, B, C) and predict to categorize (A, B, C) or other unexpected D, F, or other classes is called open set recognition. In the open set recognition task, there are several categories (Fig. ##FIG##0##1##) assumed to characterize the learning models<sup>##UREF##8##10##</sup>: (1) Known Known classes (KKCs): the samples are annotated by humans and given with the corresponding labels. (2) Known Unknown classes (KUCs)<sup>##UREF##9##11##</sup>: The KUC is an umbrella term for different events, objects or concepts. It is hard to define the attribute of the KUC, which is usually a particular product incidental to the description of the subject of the thing. (3) Unknown Known classes<sup>##UREF##10##12##</sup>: Classes for which no samples are available during training, but side information, such as semantic/attribute information, is available. The algorithm to detect unknown known classes is called few-shot open set recognition, a subset scope<sup>##UREF##11##13##–##UREF##14##16##</sup> of open set recognition. (4) Unknown Unknown classes (UUCs)<sup>##UREF##15##17##</sup>: The sample of category does not exist during the training, as either the corresponding attribution or semantic information is not known. Our task focuses on unknown classes. The algorithm only learns in labeled known classes, and is able to detect unknown classes D, F, or others, even though we do not know, and cannot describe, what they are.</p>\",\n \"<p id=\\\"Par4\\\">In this paper, we deal with classifier unknown sound events or their related patterns. In summary, our work makes the flowing key contributions:<list list-type=\\\"bullet\\\"><list-item><p id=\\\"Par5\\\">We proposed an open set sound events classification network. The encoder receives the 2-D log-Mel temporal spectrogram as input, and the decision head determines the probabilities of the known sound events. In our scheme, we can finally identify unknown sound events from the probabilities.</p></list-item><list-item><p id=\\\"Par6\\\">It is universally acknowledged that labeling audio data is a challenging task. Despite the neural network requiring a mass of labeled samples to learn, more data usually performs better. We explored a self-supervised learning approach in the open set sound events classification problem. The network was first learned in the unlabeled MagnaTagATune/DCASE2019 Subtask 1C dataset, utilizing the self-supervised contrastive loss to capture the rich representative features of sound events. Then, the learned network fine-tunes for the downstream dataset.</p></list-item><list-item><p id=\\\"Par7\\\">The best performance of known classes is still effective for unknown detection because the compact cluster structure provides sample space to detect unknown samples in the embedded feature space<sup>##UREF##16##18##</sup>. For compact cluster structure, the open set sound events classification network has been optimized by center<sup>##UREF##17##19##</sup>, and supervised contrastive losses<sup>##UREF##18##20##</sup>, as well as cross-entropy loss.</p></list-item><list-item><p id=\\\"Par8\\\">We evaluate our proposed method in various datasets. The experimental results reveal that both the self-supervised approach and the proposed losses improve the performance of open set classification of sound events.</p></list-item></list></p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Methodology</title>","<title>Pipeline overview</title>","<p id=\"Par14\">Figure ##FIG##1##2## shows the pipeline of the classifier of unknown events. Our system can divide into two stages, the self-supervised training stage, and the fine-tuning stage.</p>","<p id=\"Par15\">In the self-supervised training stage, the raw audio sequences first transform into different audio sequences, but the intra meaning of audio is not modified. Some audio transformation techniques can be applied to increase the number of training samples, such as pitch variation, and noise injection. The raw audio and transformed audio are scale invariant. Then, we generate the log-Mel spectrogram () from the raw and augmented sequence. The encoder network obtains the discriminate features (). Next, we construct the projection head to obtain representative features (). The self-supervised contrastive loss projects the features () into the clustering embedding space. The distance between different audio sequences becomes as far apart as possible, while the identical audio sequences become closer together.</p>","<p id=\"Par16\">Once the encoder part has been trained, we fine-tune the network parameters in the downstream task. In this stage, the network jointly learns both clustering losses and fundamental classification loss (cross-entropy loss). The clustering losses are center loss and supervised contrastive loss, where the center loss builds a compact clustered feature space by clumping the intra-class data samples. Conversely, the supervised contrastive loss projects objects into the well-structured high-dimensional embedding space, where the inter-class frames are scattered by pushing each other and intra-class ones are compacted by pulling each other.</p>","<title>Self-supervised training</title>","<title>Effective data augmentation</title>","<p id=\"Par17\">For effective contrastive representation learning, we propose various audio data augmentation techniques<sup>##UREF##55##57##</sup> for generating positive/negative audio segment samples:<list list-type=\"order\"><list-item><p id=\"Par18\">Polarity Inversion: the audio is an analogue signal, which simulates the response of the change in the sound source (e.g., the violin strings struck or the drum vibrating). The polarity often shows positive and negative with the given median line, which refers to waveform alignment. We augment the audio by flipping the polarity.</p></list-item><list-item><p id=\"Par19\">Noise injection: In the most real word, the poor quality of audio may be affected by the old recording device or signal transmission. We add some white noise to simulate this phenomenon.</p></list-item><list-item><p id=\"Par20\">Delay: We randomly play the audio back after a period. Sometimes it may create an echo-like effect, but this does not affect the prediction of sound events.</p></list-item><list-item><p id=\"Par21\">High/Low pass filters: We apply high/low pass filters for the waveform. The pass filters can be characterized by cut-off frequency. The ambient sounds are usually a mix of different sound sources. We might use the high/low pass filters to strengthen the specific sound source.</p></list-item><list-item><p id=\"Par22\">Gain: The modification of gain often reflects the change in the volume of auditory sensors. The gain controls the microphone preamp by changing the voltage going in. Even though the gain control may sometimes cause distortion or grit for audio, the gain augmentation is helpful to increase the number of samples in some cases. For example, the audio recordings in different public places have different audio characteristics.</p></list-item></list></p>","<p id=\"Par23\">We generate positive and negative sample instances by randomly combining the augmentation methods. Specifically, we select the inversion polarity, noise injection, and delay, high/low pass filters, and gain with probabilities of 0.8, 0.3, 0.5, 0.2, and 0.8, respectively. Noise is injected randomly with a signal-to-noise ratio between 0.3 and 0.5, and other settings are default values from the torchaudio_augmentations library.</p>","<title>Self-supervised representation loss</title>","<p id=\"Par24\">We choose the InfoNCE<sup>##UREF##52##54##,##UREF##54##56##</sup> to address inter-class dispersion and intra-class compactness<sup>##UREF##56##58##</sup> to learn the useful representation in audio:where, is the collection of arbitrary augmented samples from the same audio sequence. is the temperature parameter, and and are the positive and negative embedding for anchor (), respectively. is the set of negative pairs for the anchor. The positive and negative pairs () are extracted from the augmented patches of the log-Mel spectrogram. For example, the pair of augmented spectrogram patches (, ) or the genuine pair of are the negative pairs, while (<italic>A, A</italic>) and , are the positive pairs, even if we do not know the label of the A and B patches. The function of measures the cosine similarity between two input embeddings. During the training, the loss minimizes the distance of all positive pairs, and disperses others (negative pairs).</p>","<title>Supervised learning and fine-tuning</title>","<p id=\"Par25\">Training of the proposed model (Fig. ##FIG##1##2##b) minimizes the three types of losses: cross-entropy loss, center loss, and supervised contrastive loss:</p>","<p id=\"Par26\">In this section, we describe the implementation of the three types of losses in detail.</p>","<title>Cross-entropy</title>","<p id=\"Par27\">For the labeled data in a batch , the cross-entropy loss is defined by:where, <italic>N</italic> spans the mini-batch dimension, and is the logit value of the -th class for the log-Mel spectrogram . The dataset has <italic>K</italic> classes. This cross-entropy loss tries to make the correct classification of labeled inputs.</p>","<title>Center loss</title>","<p id=\"Par28\">The center loss<sup>##UREF##17##19##</sup> gains inspiration from the clustering algorithm, and was first used to solve the face identification task. Intuitively, the key is to reduce the intra-class variation. The center loss builds a center of each category , where denotes to the class center of each feature in terms of class . The center loss learns to minimize the distance between the embedding space of the audio the input and class center. For the classification task, the center should be updated by calculating the mean value of the entire training set, which sacrifices computation efficiency. To address this problem, center loss<sup>##UREF##17##19##</sup> uses the gradient descent method to update the center in the mini-batch and obtain the average centroid for all samples.</p>","<title>Supervised contrastive loss</title>","<p id=\"Par29\">The contrastive loss for supervised learning is defined as:</p>","<p id=\"Par30\">The sim function uses cosine similarity: , where and denote the sets of the positive and negative samples of the anchor (the first) data in a batch. Different from the self-supervised contrastive loss, the supervised approach uses the data label efficiently by exploring the correlation between the same categories. The positive sample of anchors can be the sample of the same label; meanwhile, the negative anchor is the sample from different categories. Specifically, we randomly select a few samples as anchor data () from the mini-batch, ensuring each category has at least one anchor. Anchor samples within the same category in the mini-batch are categorized as positive samples, while those from different categories are designated as negative samples.</p>","<title>Identify unknowns in open set</title>","<p id=\"Par31\">The straightforward approach for unknown decision is to use a threshold. In our work, we applied the MSP method<sup>##UREF##20##22##</sup> to find the unknown samples:where, the is the softmax normalization probability for the input sample in terms of the <italic>k</italic>-th class. If the max value of all the softmax probabilities is smaller than the threshold , the unknown class has been successfully detected. Otherwise, the class label is the indices of the maximum softmax probability value.</p>"],"string":"[\n \"<title>Methodology</title>\",\n \"<title>Pipeline overview</title>\",\n \"<p id=\\\"Par14\\\">Figure ##FIG##1##2## shows the pipeline of the classifier of unknown events. Our system can divide into two stages, the self-supervised training stage, and the fine-tuning stage.</p>\",\n \"<p id=\\\"Par15\\\">In the self-supervised training stage, the raw audio sequences first transform into different audio sequences, but the intra meaning of audio is not modified. Some audio transformation techniques can be applied to increase the number of training samples, such as pitch variation, and noise injection. The raw audio and transformed audio are scale invariant. Then, we generate the log-Mel spectrogram () from the raw and augmented sequence. The encoder network obtains the discriminate features (). Next, we construct the projection head to obtain representative features (). The self-supervised contrastive loss projects the features () into the clustering embedding space. The distance between different audio sequences becomes as far apart as possible, while the identical audio sequences become closer together.</p>\",\n \"<p id=\\\"Par16\\\">Once the encoder part has been trained, we fine-tune the network parameters in the downstream task. In this stage, the network jointly learns both clustering losses and fundamental classification loss (cross-entropy loss). The clustering losses are center loss and supervised contrastive loss, where the center loss builds a compact clustered feature space by clumping the intra-class data samples. Conversely, the supervised contrastive loss projects objects into the well-structured high-dimensional embedding space, where the inter-class frames are scattered by pushing each other and intra-class ones are compacted by pulling each other.</p>\",\n \"<title>Self-supervised training</title>\",\n \"<title>Effective data augmentation</title>\",\n \"<p id=\\\"Par17\\\">For effective contrastive representation learning, we propose various audio data augmentation techniques<sup>##UREF##55##57##</sup> for generating positive/negative audio segment samples:<list list-type=\\\"order\\\"><list-item><p id=\\\"Par18\\\">Polarity Inversion: the audio is an analogue signal, which simulates the response of the change in the sound source (e.g., the violin strings struck or the drum vibrating). The polarity often shows positive and negative with the given median line, which refers to waveform alignment. We augment the audio by flipping the polarity.</p></list-item><list-item><p id=\\\"Par19\\\">Noise injection: In the most real word, the poor quality of audio may be affected by the old recording device or signal transmission. We add some white noise to simulate this phenomenon.</p></list-item><list-item><p id=\\\"Par20\\\">Delay: We randomly play the audio back after a period. Sometimes it may create an echo-like effect, but this does not affect the prediction of sound events.</p></list-item><list-item><p id=\\\"Par21\\\">High/Low pass filters: We apply high/low pass filters for the waveform. The pass filters can be characterized by cut-off frequency. The ambient sounds are usually a mix of different sound sources. We might use the high/low pass filters to strengthen the specific sound source.</p></list-item><list-item><p id=\\\"Par22\\\">Gain: The modification of gain often reflects the change in the volume of auditory sensors. The gain controls the microphone preamp by changing the voltage going in. Even though the gain control may sometimes cause distortion or grit for audio, the gain augmentation is helpful to increase the number of samples in some cases. For example, the audio recordings in different public places have different audio characteristics.</p></list-item></list></p>\",\n \"<p id=\\\"Par23\\\">We generate positive and negative sample instances by randomly combining the augmentation methods. Specifically, we select the inversion polarity, noise injection, and delay, high/low pass filters, and gain with probabilities of 0.8, 0.3, 0.5, 0.2, and 0.8, respectively. Noise is injected randomly with a signal-to-noise ratio between 0.3 and 0.5, and other settings are default values from the torchaudio_augmentations library.</p>\",\n \"<title>Self-supervised representation loss</title>\",\n \"<p id=\\\"Par24\\\">We choose the InfoNCE<sup>##UREF##52##54##,##UREF##54##56##</sup> to address inter-class dispersion and intra-class compactness<sup>##UREF##56##58##</sup> to learn the useful representation in audio:where, is the collection of arbitrary augmented samples from the same audio sequence. is the temperature parameter, and and are the positive and negative embedding for anchor (), respectively. is the set of negative pairs for the anchor. The positive and negative pairs () are extracted from the augmented patches of the log-Mel spectrogram. For example, the pair of augmented spectrogram patches (, ) or the genuine pair of are the negative pairs, while (<italic>A, A</italic>) and , are the positive pairs, even if we do not know the label of the A and B patches. The function of measures the cosine similarity between two input embeddings. During the training, the loss minimizes the distance of all positive pairs, and disperses others (negative pairs).</p>\",\n \"<title>Supervised learning and fine-tuning</title>\",\n \"<p id=\\\"Par25\\\">Training of the proposed model (Fig. ##FIG##1##2##b) minimizes the three types of losses: cross-entropy loss, center loss, and supervised contrastive loss:</p>\",\n \"<p id=\\\"Par26\\\">In this section, we describe the implementation of the three types of losses in detail.</p>\",\n \"<title>Cross-entropy</title>\",\n \"<p id=\\\"Par27\\\">For the labeled data in a batch , the cross-entropy loss is defined by:where, <italic>N</italic> spans the mini-batch dimension, and is the logit value of the -th class for the log-Mel spectrogram . The dataset has <italic>K</italic> classes. This cross-entropy loss tries to make the correct classification of labeled inputs.</p>\",\n \"<title>Center loss</title>\",\n \"<p id=\\\"Par28\\\">The center loss<sup>##UREF##17##19##</sup> gains inspiration from the clustering algorithm, and was first used to solve the face identification task. Intuitively, the key is to reduce the intra-class variation. The center loss builds a center of each category , where denotes to the class center of each feature in terms of class . The center loss learns to minimize the distance between the embedding space of the audio the input and class center. For the classification task, the center should be updated by calculating the mean value of the entire training set, which sacrifices computation efficiency. To address this problem, center loss<sup>##UREF##17##19##</sup> uses the gradient descent method to update the center in the mini-batch and obtain the average centroid for all samples.</p>\",\n \"<title>Supervised contrastive loss</title>\",\n \"<p id=\\\"Par29\\\">The contrastive loss for supervised learning is defined as:</p>\",\n \"<p id=\\\"Par30\\\">The sim function uses cosine similarity: , where and denote the sets of the positive and negative samples of the anchor (the first) data in a batch. Different from the self-supervised contrastive loss, the supervised approach uses the data label efficiently by exploring the correlation between the same categories. The positive sample of anchors can be the sample of the same label; meanwhile, the negative anchor is the sample from different categories. Specifically, we randomly select a few samples as anchor data () from the mini-batch, ensuring each category has at least one anchor. Anchor samples within the same category in the mini-batch are categorized as positive samples, while those from different categories are designated as negative samples.</p>\",\n \"<title>Identify unknowns in open set</title>\",\n \"<p id=\\\"Par31\\\">The straightforward approach for unknown decision is to use a threshold. In our work, we applied the MSP method<sup>##UREF##20##22##</sup> to find the unknown samples:where, the is the softmax normalization probability for the input sample in terms of the <italic>k</italic>-th class. If the max value of all the softmax probabilities is smaller than the threshold , the unknown class has been successfully detected. Otherwise, the class label is the indices of the maximum softmax probability value.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Experiment and result</title>","<title>Evaluation metrics</title>","<p id=\"Par39\">We evaluate the performance of the open-set sound event recognition model by two evaluation metrics:</p>","<title>AUROC (area under the receiver operating characteristic)</title>","<p id=\"Par40\">It is a threshold-independent evaluation metric from the receiver operating characteristic curve (ROC), which shows the true positive rate, and false positive rate of the model’s predictions for different thresholds. AUROC is used to evaluate the performance of a model to identify unknown classes.</p>","<title>ACC</title>","<p id=\"Par41\">We have used the official evaluation toolbox to report result<sup>##UREF##62##64##</sup>, where the valuation metrics of the DCASE2019 Task 1C challenge is the weighted average of the known classes and unknown classes. The ACC is the degree of closeness to true value and shows how much the predicted result is correctly predicted as the ground truth label, which is calculated by , where <italic>TP</italic> is true positive, <italic>TN</italic> true negative, <italic>FP</italic> false positive, and <italic>FN</italic> false negative, respectively. We follow the evaluation toolbox in the open set acoustic scene classification task and use the weighted average of unknown and known classes.where, is the mean accuracy of known classes,and is the unknown class accuracy.</p>","<p id=\"Par42\">Our classifier reports the softmax probability distribution of each category, and filters out unknown by threshold 0.5<sup>##UREF##57##59##</sup>, as in Eq. (##FORMU##35##5##). Here, we found the performance has large gap, depending on the different threshold. That is why we have also provided AUROC metrics to evaluate the model.</p>","<title>Open set acoustic scene classification with proposed losses</title>","<p id=\"Par43\">First, we evaluate our proposed network with clustering loss: center, supervised contrastive loss. For comparison, we used the same encoder structure (Fig. ##FIG##2##3##)<sup>##UREF##57##59##</sup>, where the network consists of 5 convolutional, batch normalization and ReLU layers, and the feature map is down-sampled 2 times with stride 2. Finally, the global average pooling and fully connected layer are applied to map the discriminative feature into feature vectors with the size of 10. We train the classification model in 200 epochs with the batch size 32, and Adam optimizer<sup>##UREF##63##65##</sup>. The learning rate is set to 0.001 and is multiplied by 0.1 in 100, and 150 epochs. The hyper parameters , , and in Eq. (##FORMU##4##1##) are set to 1, 0.1, and 0.1, respectively.</p>","<p id=\"Par44\">Table ##TAB##0##1## shows the performance of DCASE2019 Task 1C. Using center and contrastive learning loss has improved the performance incrementally, compared to the threshold method<sup>##UREF##57##59##</sup>. Table ##TAB##0##1## illustrates that adopting the losses leads to the best 74.6% in AUROC with 6% of performance gains. The contrastive loss improves the robustness of detecting the known and unknown classes by utilizing the distances and relationships between inter/intra-classes.</p>","<title>Self-supervised contrastive learning in open set scene classification dataset</title>","<p id=\"Par45\">Self-supervised learning is a kind of machine learning approach that tries to extract the inherent structure in the data by predicting the output of its own input. This is beneficial for learning rich data representations and improving the generalization ability in downstream tasks. In self-supervised training, to facilitate convergence, we used a large batch size of 1600 to create sufficient positive and negative pairs during the training. We follow the audio augmentation method in “<xref rid=\"Sec8\" ref-type=\"sec\">Effective data augmentation</xref>” section to create positive/negative samples from train/leaderboard/evaluation in DCASE2019 Subtask 1C (no label given). The performance improves with the increase in training epochs<sup>##UREF##37##39##</sup>. Based on that, we trained the network with 5000 epochs. The temperature of contrastive loss is set at 0.1. The optimizer was Adam<sup>##UREF##63##65##</sup> with a learning rate of 0.01 and successively multiplied by 0.1 in 1000, 2500, and 3500 epochs.</p>","<p id=\"Par46\">In the second phase, we chose the best pretrained weights for the slightest loss on test data to fine-tune with training data (with label)<sup>##UREF##57##59##</sup> by supervised approach. The learning rate warming-up strategy was employed to reduce the impact effect in the early training. Therefore, we started with a small learning rate of 1e–6 for the initial 3 epochs. After that, the network continued to train with the model and center loss at learning rate of (0.0001, 0.0005). The weight of both center and contrastive loss was 10. We used the same encoder architecture in the open set acoustic classification task (Fig. ##FIG##2##3##). We froze the first and second Conv2d + ReLU + BatchNorm blocks, and retrained the rest. The network converged within 200 epochs.</p>","<p id=\"Par47\">Figure ##FIG##3##4## shows the experiment result, and we use the same threshold 0.5<sup>##UREF##57##59##</sup> to detect unknown samples. The “baseline” to denote the weight of the network is randomly initialized and optimized by cross-entropy loss. “w.o. self-supervised” means the model is optimized by using our proposed loss, where even when the weight initializes randomly. We can conclude that the self-supervised backbone (w. self-supervised) works better to learn rich representations by contrasting positive and negative pairs. With the guidance of self-supervised contrastive loss, performance improvement in known classes and more accurate unknown detection accuracy (around 3%) could be achieved. The AUROC accuracy increased to 78.2% and 10% above the baseline.</p>","<p id=\"Par48\">In addition, Fig. ##FIG##4##5## plots the confusion matrix of the classification result. It is evident that our proposed method works well in known scenes and is reasonable to find the unknown scenes. In Fig. ##FIG##4##5##, confusion matrix shows that the model detected 2783 out of 3293 known scenes have been detected (Supplementary material Table ##SUPPL##0##2##), and obtained 42.3% recall, even though the training did not include the unknown scene. With the help of the self-supervised pre-trained weight, the model constructs a better structure to address intra-class compactness and inter-class dispersion.</p>","<title>Result of the GTZAN dataset</title>","<p id=\"Par49\">We conducted experiments on the GTZAN dataset. For self-supervised learning, we select the MagnaTagATune dataset, and augmented the audio chunks with a random combination of five types of transforms (“<xref rid=\"Sec8\" ref-type=\"sec\">Effective data augmentation</xref>” section). We used the same encoder network as in Fig. ##FIG##2##3##. The trained network with the Adam optimizer had a learning rate of 0.005, and the training was performed for 10,000 epochs with an automatically decaying learning rate by multiplying by 0.5 in each of 5000, 8000, and 9000 epochs. The batch size was set to 1024 to obtain more negative samples.</p>","<p id=\"Par50\">In the supervised fine-tuning phase, we used the GTZAN data with the self-supervised pre-trained weights. The warming up strategy with a related small learning rate of 0.0005 was applied to reduce the primacy effect of the early training samples. Adam optimizer optimized the network with a learning rate of 0.005. For the experiment of center and supervised contrastive loss in Eqs. (##FORMU##20##3##) and (##FORMU##29##4##), the weight parameters , , and are 1, 0.1, and 0.1, respectively. We applied random temporal axis crop augmentation to increase the number of samples, and we conducted training with 500 epochs.</p>","<p id=\"Par51\">Table ##TAB##1##2## (rows 3 and 4) illustrates the performance with the self-supervised pre-trained weights. We obtained competitive results: the performance achieved significant improvement and achieves 82.7% of AUROC with the help of the proposed losses and self-supervised pretrained weight. The self-supervised approach closely imitates human behavior and automatically learns to distinguish between objects. When a small number of labels is inevitable, the model can immediately learn the discriminative features from the unlabeled data to produce reasonable results.</p>","<p id=\"Par52\">Figure ##FIG##5##6## shows the similar matrix phenomena to the open set acoustic dataset. Our method recognizes the unknown genre, as well as classifying the known genres, and achieves an average 71.3% of precision, and 69.0% recall for unknown identification (Supplementary material Table ##SUPPL##0##3##).</p>","<title>Experiment result of labeling efficiency in the GTZAN dataset</title>","<p id=\"Par53\">The experiment result for the GTZAN dataset shows a similar effect (Fig. ##FIG##6##7##): the model trained with only a 90% subset of the data outperformed the model with 100% labeled data with random weight initialization to identify unknown samples. Self-supervised learning has the potential to improve performance and does not require excessive expenditure of labeling data. Figure ##FIG##7##8## in the context of a self-supervised pre-trained model and shows TSNE visualization results after applying the model. It is obvious that the model can successfully build a clear boundary to classify various genres.</p>","<title>Experimental results of the Tori dataset</title>","<p id=\"Par54\">Finally, we performed the experiment on the Tori dataset (Table ##TAB##2##3##). It is obvious that the self-supervised pretraining efficiently learns the discriminatory features and obtains more than 10% improvement in terms of AUROC. Furthermore, the ACC results show great boosting with the help of self-supervised weight. Moreover, our proposed method achieves better performance in detecting the unknown samples and produces high performance.</p>","<p id=\"Par55\">Figure ##FIG##8##9## has drawn the confusion matrix in terms of result (self-supervised + cross-entropy + center + contrast loss). The result demonstrates that the model performs well for known classes (Gyeong, Menali, Yukjabaeki Tori), and obtain a reasonable result to distinguish the unknown samples even without any semantic/attribute information (Fig. ##FIG##0##1##) for unknown class during the training. Note that the unknown Susimga Tori was misclassified into learned Gyeong Tori due to the similar pitch appearance, even though they have different characteristics in pitch progress. Because our model does not well capture the sequential behavior, confusion might be inevitable.</p>"],"string":"[\n \"<title>Experiment and result</title>\",\n \"<title>Evaluation metrics</title>\",\n \"<p id=\\\"Par39\\\">We evaluate the performance of the open-set sound event recognition model by two evaluation metrics:</p>\",\n \"<title>AUROC (area under the receiver operating characteristic)</title>\",\n \"<p id=\\\"Par40\\\">It is a threshold-independent evaluation metric from the receiver operating characteristic curve (ROC), which shows the true positive rate, and false positive rate of the model’s predictions for different thresholds. AUROC is used to evaluate the performance of a model to identify unknown classes.</p>\",\n \"<title>ACC</title>\",\n \"<p id=\\\"Par41\\\">We have used the official evaluation toolbox to report result<sup>##UREF##62##64##</sup>, where the valuation metrics of the DCASE2019 Task 1C challenge is the weighted average of the known classes and unknown classes. The ACC is the degree of closeness to true value and shows how much the predicted result is correctly predicted as the ground truth label, which is calculated by , where <italic>TP</italic> is true positive, <italic>TN</italic> true negative, <italic>FP</italic> false positive, and <italic>FN</italic> false negative, respectively. We follow the evaluation toolbox in the open set acoustic scene classification task and use the weighted average of unknown and known classes.where, is the mean accuracy of known classes,and is the unknown class accuracy.</p>\",\n \"<p id=\\\"Par42\\\">Our classifier reports the softmax probability distribution of each category, and filters out unknown by threshold 0.5<sup>##UREF##57##59##</sup>, as in Eq. (##FORMU##35##5##). Here, we found the performance has large gap, depending on the different threshold. That is why we have also provided AUROC metrics to evaluate the model.</p>\",\n \"<title>Open set acoustic scene classification with proposed losses</title>\",\n \"<p id=\\\"Par43\\\">First, we evaluate our proposed network with clustering loss: center, supervised contrastive loss. For comparison, we used the same encoder structure (Fig. ##FIG##2##3##)<sup>##UREF##57##59##</sup>, where the network consists of 5 convolutional, batch normalization and ReLU layers, and the feature map is down-sampled 2 times with stride 2. Finally, the global average pooling and fully connected layer are applied to map the discriminative feature into feature vectors with the size of 10. We train the classification model in 200 epochs with the batch size 32, and Adam optimizer<sup>##UREF##63##65##</sup>. The learning rate is set to 0.001 and is multiplied by 0.1 in 100, and 150 epochs. The hyper parameters , , and in Eq. (##FORMU##4##1##) are set to 1, 0.1, and 0.1, respectively.</p>\",\n \"<p id=\\\"Par44\\\">Table ##TAB##0##1## shows the performance of DCASE2019 Task 1C. Using center and contrastive learning loss has improved the performance incrementally, compared to the threshold method<sup>##UREF##57##59##</sup>. Table ##TAB##0##1## illustrates that adopting the losses leads to the best 74.6% in AUROC with 6% of performance gains. The contrastive loss improves the robustness of detecting the known and unknown classes by utilizing the distances and relationships between inter/intra-classes.</p>\",\n \"<title>Self-supervised contrastive learning in open set scene classification dataset</title>\",\n \"<p id=\\\"Par45\\\">Self-supervised learning is a kind of machine learning approach that tries to extract the inherent structure in the data by predicting the output of its own input. This is beneficial for learning rich data representations and improving the generalization ability in downstream tasks. In self-supervised training, to facilitate convergence, we used a large batch size of 1600 to create sufficient positive and negative pairs during the training. We follow the audio augmentation method in “<xref rid=\\\"Sec8\\\" ref-type=\\\"sec\\\">Effective data augmentation</xref>” section to create positive/negative samples from train/leaderboard/evaluation in DCASE2019 Subtask 1C (no label given). The performance improves with the increase in training epochs<sup>##UREF##37##39##</sup>. Based on that, we trained the network with 5000 epochs. The temperature of contrastive loss is set at 0.1. The optimizer was Adam<sup>##UREF##63##65##</sup> with a learning rate of 0.01 and successively multiplied by 0.1 in 1000, 2500, and 3500 epochs.</p>\",\n \"<p id=\\\"Par46\\\">In the second phase, we chose the best pretrained weights for the slightest loss on test data to fine-tune with training data (with label)<sup>##UREF##57##59##</sup> by supervised approach. The learning rate warming-up strategy was employed to reduce the impact effect in the early training. Therefore, we started with a small learning rate of 1e–6 for the initial 3 epochs. After that, the network continued to train with the model and center loss at learning rate of (0.0001, 0.0005). The weight of both center and contrastive loss was 10. We used the same encoder architecture in the open set acoustic classification task (Fig. ##FIG##2##3##). We froze the first and second Conv2d + ReLU + BatchNorm blocks, and retrained the rest. The network converged within 200 epochs.</p>\",\n \"<p id=\\\"Par47\\\">Figure ##FIG##3##4## shows the experiment result, and we use the same threshold 0.5<sup>##UREF##57##59##</sup> to detect unknown samples. The “baseline” to denote the weight of the network is randomly initialized and optimized by cross-entropy loss. “w.o. self-supervised” means the model is optimized by using our proposed loss, where even when the weight initializes randomly. We can conclude that the self-supervised backbone (w. self-supervised) works better to learn rich representations by contrasting positive and negative pairs. With the guidance of self-supervised contrastive loss, performance improvement in known classes and more accurate unknown detection accuracy (around 3%) could be achieved. The AUROC accuracy increased to 78.2% and 10% above the baseline.</p>\",\n \"<p id=\\\"Par48\\\">In addition, Fig. ##FIG##4##5## plots the confusion matrix of the classification result. It is evident that our proposed method works well in known scenes and is reasonable to find the unknown scenes. In Fig. ##FIG##4##5##, confusion matrix shows that the model detected 2783 out of 3293 known scenes have been detected (Supplementary material Table ##SUPPL##0##2##), and obtained 42.3% recall, even though the training did not include the unknown scene. With the help of the self-supervised pre-trained weight, the model constructs a better structure to address intra-class compactness and inter-class dispersion.</p>\",\n \"<title>Result of the GTZAN dataset</title>\",\n \"<p id=\\\"Par49\\\">We conducted experiments on the GTZAN dataset. For self-supervised learning, we select the MagnaTagATune dataset, and augmented the audio chunks with a random combination of five types of transforms (“<xref rid=\\\"Sec8\\\" ref-type=\\\"sec\\\">Effective data augmentation</xref>” section). We used the same encoder network as in Fig. ##FIG##2##3##. The trained network with the Adam optimizer had a learning rate of 0.005, and the training was performed for 10,000 epochs with an automatically decaying learning rate by multiplying by 0.5 in each of 5000, 8000, and 9000 epochs. The batch size was set to 1024 to obtain more negative samples.</p>\",\n \"<p id=\\\"Par50\\\">In the supervised fine-tuning phase, we used the GTZAN data with the self-supervised pre-trained weights. The warming up strategy with a related small learning rate of 0.0005 was applied to reduce the primacy effect of the early training samples. Adam optimizer optimized the network with a learning rate of 0.005. For the experiment of center and supervised contrastive loss in Eqs. (##FORMU##20##3##) and (##FORMU##29##4##), the weight parameters , , and are 1, 0.1, and 0.1, respectively. We applied random temporal axis crop augmentation to increase the number of samples, and we conducted training with 500 epochs.</p>\",\n \"<p id=\\\"Par51\\\">Table ##TAB##1##2## (rows 3 and 4) illustrates the performance with the self-supervised pre-trained weights. We obtained competitive results: the performance achieved significant improvement and achieves 82.7% of AUROC with the help of the proposed losses and self-supervised pretrained weight. The self-supervised approach closely imitates human behavior and automatically learns to distinguish between objects. When a small number of labels is inevitable, the model can immediately learn the discriminative features from the unlabeled data to produce reasonable results.</p>\",\n \"<p id=\\\"Par52\\\">Figure ##FIG##5##6## shows the similar matrix phenomena to the open set acoustic dataset. Our method recognizes the unknown genre, as well as classifying the known genres, and achieves an average 71.3% of precision, and 69.0% recall for unknown identification (Supplementary material Table ##SUPPL##0##3##).</p>\",\n \"<title>Experiment result of labeling efficiency in the GTZAN dataset</title>\",\n \"<p id=\\\"Par53\\\">The experiment result for the GTZAN dataset shows a similar effect (Fig. ##FIG##6##7##): the model trained with only a 90% subset of the data outperformed the model with 100% labeled data with random weight initialization to identify unknown samples. Self-supervised learning has the potential to improve performance and does not require excessive expenditure of labeling data. Figure ##FIG##7##8## in the context of a self-supervised pre-trained model and shows TSNE visualization results after applying the model. It is obvious that the model can successfully build a clear boundary to classify various genres.</p>\",\n \"<title>Experimental results of the Tori dataset</title>\",\n \"<p id=\\\"Par54\\\">Finally, we performed the experiment on the Tori dataset (Table ##TAB##2##3##). It is obvious that the self-supervised pretraining efficiently learns the discriminatory features and obtains more than 10% improvement in terms of AUROC. Furthermore, the ACC results show great boosting with the help of self-supervised weight. Moreover, our proposed method achieves better performance in detecting the unknown samples and produces high performance.</p>\",\n \"<p id=\\\"Par55\\\">Figure ##FIG##8##9## has drawn the confusion matrix in terms of result (self-supervised + cross-entropy + center + contrast loss). The result demonstrates that the model performs well for known classes (Gyeong, Menali, Yukjabaeki Tori), and obtain a reasonable result to distinguish the unknown samples even without any semantic/attribute information (Fig. ##FIG##0##1##) for unknown class during the training. Note that the unknown Susimga Tori was misclassified into learned Gyeong Tori due to the similar pitch appearance, even though they have different characteristics in pitch progress. Because our model does not well capture the sequential behavior, confusion might be inevitable.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Conclusion and discussion</title>","<p id=\"Par56\">In this paper, we investigated the self-supervised learning approach to open-set sound event classification. The experiment result on DCASE2019 Subtask 1C, GTZAN and Tori dataset demonstrated that the self-supervised pre-trained model improves the robustness, and the model’s capability to detect unknown samples. The natural basic notion of open set recognition is that a compact cluster structure in the feature space for known classes facilitates the recognition of unknown classes by allowing a large room to locate unknown samples in the embedded feature space. Based on this concept, we proposed centrality and supervised contrastive losses, where the center loss tries to minimize the intra-class distance by pulling the embedded feature into the cluster center, while the contrastive loss disperses the inter-class members from each other. The experimental database is derived from a range of audio processing tasks, including encompassing genre classification, traditional vocal style differentiation, and acoustic scene classification. This highlights the ability of the method to generalize across diverse tasks. Moving forward, we aim to investigate various music information retrieval tasks to demonstrate the universality of our approach.</p>","<p id=\"Par57\">However, addressing some issues is still necessary: (1) The proper threshold is crucial to obtain good performance in unknown detection, but finding the optimal threshold is challenging. (2) There is no semantic information on unknown samples in the model training process (Fig. ##FIG##0##1## unknown known class). The performance of unknown detection is complex to meet the requirements for industrial deployment. In the future, the semantic information of the unknown might be introduced during the training to improve the accuracy of identifying well the unknown samples. For example<sup>##UREF##14##16##</sup>, researchers applied a Siamese network to achieve open-set face recognition, including known and unknown samples (id label for each image is excluded) during the training. The network focused on learning the feature discrepancy between known and unknown samples rather than changing the meaning (id label) of the image itself.</p>"],"string":"[\n \"<title>Conclusion and discussion</title>\",\n \"<p id=\\\"Par56\\\">In this paper, we investigated the self-supervised learning approach to open-set sound event classification. The experiment result on DCASE2019 Subtask 1C, GTZAN and Tori dataset demonstrated that the self-supervised pre-trained model improves the robustness, and the model’s capability to detect unknown samples. The natural basic notion of open set recognition is that a compact cluster structure in the feature space for known classes facilitates the recognition of unknown classes by allowing a large room to locate unknown samples in the embedded feature space. Based on this concept, we proposed centrality and supervised contrastive losses, where the center loss tries to minimize the intra-class distance by pulling the embedded feature into the cluster center, while the contrastive loss disperses the inter-class members from each other. The experimental database is derived from a range of audio processing tasks, including encompassing genre classification, traditional vocal style differentiation, and acoustic scene classification. This highlights the ability of the method to generalize across diverse tasks. Moving forward, we aim to investigate various music information retrieval tasks to demonstrate the universality of our approach.</p>\",\n \"<p id=\\\"Par57\\\">However, addressing some issues is still necessary: (1) The proper threshold is crucial to obtain good performance in unknown detection, but finding the optimal threshold is challenging. (2) There is no semantic information on unknown samples in the model training process (Fig. ##FIG##0##1## unknown known class). The performance of unknown detection is complex to meet the requirements for industrial deployment. In the future, the semantic information of the unknown might be introduced during the training to improve the accuracy of identifying well the unknown samples. For example<sup>##UREF##14##16##</sup>, researchers applied a Siamese network to achieve open-set face recognition, including known and unknown samples (id label for each image is excluded) during the training. The network focused on learning the feature discrepancy between known and unknown samples rather than changing the meaning (id label) of the image itself.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":["<title>Conclusion and discussion</title>","<p id=\"Par56\">In this paper, we investigated the self-supervised learning approach to open-set sound event classification. The experiment result on DCASE2019 Subtask 1C, GTZAN and Tori dataset demonstrated that the self-supervised pre-trained model improves the robustness, and the model’s capability to detect unknown samples. The natural basic notion of open set recognition is that a compact cluster structure in the feature space for known classes facilitates the recognition of unknown classes by allowing a large room to locate unknown samples in the embedded feature space. Based on this concept, we proposed centrality and supervised contrastive losses, where the center loss tries to minimize the intra-class distance by pulling the embedded feature into the cluster center, while the contrastive loss disperses the inter-class members from each other. The experimental database is derived from a range of audio processing tasks, including encompassing genre classification, traditional vocal style differentiation, and acoustic scene classification. This highlights the ability of the method to generalize across diverse tasks. Moving forward, we aim to investigate various music information retrieval tasks to demonstrate the universality of our approach.</p>","<p id=\"Par57\">However, addressing some issues is still necessary: (1) The proper threshold is crucial to obtain good performance in unknown detection, but finding the optimal threshold is challenging. (2) There is no semantic information on unknown samples in the model training process (Fig. ##FIG##0##1## unknown known class). The performance of unknown detection is complex to meet the requirements for industrial deployment. In the future, the semantic information of the unknown might be introduced during the training to improve the accuracy of identifying well the unknown samples. For example<sup>##UREF##14##16##</sup>, researchers applied a Siamese network to achieve open-set face recognition, including known and unknown samples (id label for each image is excluded) during the training. The network focused on learning the feature discrepancy between known and unknown samples rather than changing the meaning (id label) of the image itself.</p>"],"string":"[\n \"<title>Conclusion and discussion</title>\",\n \"<p id=\\\"Par56\\\">In this paper, we investigated the self-supervised learning approach to open-set sound event classification. The experiment result on DCASE2019 Subtask 1C, GTZAN and Tori dataset demonstrated that the self-supervised pre-trained model improves the robustness, and the model’s capability to detect unknown samples. The natural basic notion of open set recognition is that a compact cluster structure in the feature space for known classes facilitates the recognition of unknown classes by allowing a large room to locate unknown samples in the embedded feature space. Based on this concept, we proposed centrality and supervised contrastive losses, where the center loss tries to minimize the intra-class distance by pulling the embedded feature into the cluster center, while the contrastive loss disperses the inter-class members from each other. The experimental database is derived from a range of audio processing tasks, including encompassing genre classification, traditional vocal style differentiation, and acoustic scene classification. This highlights the ability of the method to generalize across diverse tasks. Moving forward, we aim to investigate various music information retrieval tasks to demonstrate the universality of our approach.</p>\",\n \"<p id=\\\"Par57\\\">However, addressing some issues is still necessary: (1) The proper threshold is crucial to obtain good performance in unknown detection, but finding the optimal threshold is challenging. (2) There is no semantic information on unknown samples in the model training process (Fig. ##FIG##0##1## unknown known class). The performance of unknown detection is complex to meet the requirements for industrial deployment. In the future, the semantic information of the unknown might be introduced during the training to improve the accuracy of identifying well the unknown samples. For example<sup>##UREF##14##16##</sup>, researchers applied a Siamese network to achieve open-set face recognition, including known and unknown samples (id label for each image is excluded) during the training. The network focused on learning the feature discrepancy between known and unknown samples rather than changing the meaning (id label) of the image itself.</p>\"\n]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Sound is one of the primary forms of sensory information that we use to perceive our surroundings. Usually, a sound event is a sequence of an audio clip obtained from an action. The action can be rhythm patterns, music genre, people speaking for a few seconds, etc. The sound event classification address distinguishes what kind of audio clip it is from the given audio sequence. Nowadays, it is a common issue to solve in the following pipeline: audio pre-processing→perceptual feature extraction→classification algorithm. In this paper, we improve the traditional sound event classification algorithm to identify unknown sound events by using the deep learning method. The compact cluster structure in the feature space for known classes helps recognize unknown classes by allowing large room to locate unknown samples in the embedded feature space. Based on this concept, we applied center loss and supervised contrastive loss to optimize the model. The center loss tries to minimize the intra- class distance by pulling the embedded feature into the cluster center, while the contrastive loss disperses the inter-class features from one another. In addition, we explored the performance of self-supervised learning in detecting unknown sound events. The experimental results demonstrate that our proposed open-set sound event classification algorithm and self-supervised learning approach achieve sustained performance improvements in various datasets.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Sound is one of the primary forms of sensory information that we use to perceive our surroundings. Usually, a sound event is a sequence of an audio clip obtained from an action. The action can be rhythm patterns, music genre, people speaking for a few seconds, etc. The sound event classification address distinguishes what kind of audio clip it is from the given audio sequence. Nowadays, it is a common issue to solve in the following pipeline: audio pre-processing→perceptual feature extraction→classification algorithm. In this paper, we improve the traditional sound event classification algorithm to identify unknown sound events by using the deep learning method. The compact cluster structure in the feature space for known classes helps recognize unknown classes by allowing large room to locate unknown samples in the embedded feature space. Based on this concept, we applied center loss and supervised contrastive loss to optimize the model. The center loss tries to minimize the intra- class distance by pulling the embedded feature into the cluster center, while the contrastive loss disperses the inter-class features from one another. In addition, we explored the performance of self-supervised learning in detecting unknown sound events. The experimental results demonstrate that our proposed open-set sound event classification algorithm and self-supervised learning approach achieve sustained performance improvements in various datasets.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Related work</title>","<title>Open set recognition</title>","<p id=\"Par9\">The open set recognition task aims to detect an unexpected sample but learns only among known samples. It has primarily been applied to the image classification problem<sup>##UREF##19##21##</sup>, and extended to other fields. In this section, we explain the open-set recognition problem in two application areas:</p>","<p id=\"Par10\"><italic>Open set recognition in computer vision</italic>: In real-world recognition problems, it is inevitable to introduce unknown points, events, or observations in a collected dataset. Human error, low-precision machine operation, or the natural environment usually cause those unknowns (vibration, geomagnetic activity, illumination, etc.) and can indicate critical incidents. To address this problem, they<sup>##UREF##20##22##</sup> considers the relationship between unknown or out-of-distribution classes. In their observations, the correctly detected samples tend to present higher softmax probabilities than unknown samples once they have learned the practical model. So, the proper threshold can filter the unknown samples from the known—the OpenMax algorithm<sup>##UREF##21##23##</sup> to classify unexpected errors in an open set. The OpenMax algorithm exploits the activation vector before the softmax layer, in which the related classes are often responding to each other in the image but confused in the fake one. They<sup>##UREF##22##24##</sup> built the Classification–Reconstruction learning for Open-Set Recognition (CROSR) by utilizing the reconstructed latent representations and achieved robust unknown detection without compromising the classification accuracy of known categories. Reference<sup>##UREF##23##25##</sup> proposed the distance-based loss based on the assumption that the closer the known objects are to each other, the easier it is to find the unknown objects. Semantic segmentation is a more challenging task than classification because the algorithm has both to understand the representative information of the different pixels and to learn the interrelationships to build a clear object boundary. Reference<sup>##UREF##24##26##</sup> aggregated the abnormal pixels by leveraging the softmax prediction at the bottom of the traditional fully convolutional network. References<sup>##UREF##25##27##,##UREF##26##28##</sup> used a contrastive approach to construct the closer known object clustering and improved the ability to segment unknown pixels. Reference<sup>##UREF##27##29##</sup> created a memory bank to store contrastive features and solved the bottleneck that the contrastive loss requires significant computation and memory to obtain semi-hard negative pairs.</p>","<p id=\"Par11\"><italic>Open set recognition in the audio domain</italic>: Currently, many sociologists are emphasizing the computer vision field, yet very few works exist in the audio domain. A key challenge in open-set audio recognition is the collection of the dataset. Some remarkable research, such as rhythm pattern recognition, requires a professional expert to annotate the audio. Reference<sup>##UREF##28##30##</sup> designs an open set classifier using a support vector machine to map the data into high-level dimension features. Reference<sup>##UREF##29##31##</sup> applied the CNN architecture for a close-set classification set and other deep convolutional autoencoders (DCAES) for unknown detection. Reference<sup>##UREF##30##32##</sup> utilizes the support vector data description (SVDD) model to construct the adequate description of the known data boundary in the feature space and rejects the out-of-distribution samples. Reference<sup>##UREF##31##33##</sup> adopted a class-conditioned autoencoder to detect the unknown, assuming that the unknown has more significant reconstruction errors than the known samples.</p>","<title>Self-supervised representation learning</title>","<p id=\"Par12\">Data labeling is a challenging task that takes time and manual effort to complete correctly. The goal of self-supervised representation is to learn the rich represented features without vast amounts of labeled data<sup>##UREF##32##34##</sup>. In self-supervised learning, we should carefully design label-free pretext tasks and learn the internal features of the data through the supervised approach. The initial attempts started in the field of computer vision. Reference<sup>##UREF##33##35##</sup> designed the architecture for pair classification, where the input data is augmented and split into several patches, and the architecture is forced to predict the correct spatial configuration for the two pairs of patches. Reference<sup>##UREF##34##36##</sup> demonstrated that the learned features can generalize across samples and are suitable for various tasks. References<sup>##UREF##34##36##–##UREF##37##39##</sup> explored the efficiency of combining multiple pretext tasks to learn the deep neural network. However, maintaining consistency during the training takes time and effort. In summary, the algorithm needs to pick up valuable samples to efficiently train the network skillfully. To solve this problem<sup>##UREF##38##40##,##UREF##39##41##</sup>, built a dynamic dictionary with a queue and a moving-averaged encoder and achieved competitive results by reusing the “hard” samples from the dynamic dictionary. Reference<sup>##UREF##40##42##</sup> creates a Cross Quantized Contrastive learning technique that simultaneously learns codewords and deep visual descriptors in different images.</p>","<p id=\"Par13\">In music information retrieval (MIR)<sup>##UREF##41##43##</sup>, experts attempted to learn acoustic features by utilizing the natural synchronization between video and sound signal<sup>##UREF##42##44##</sup>. However, video represents 3-dimensional features, while sound needs to transform into 2-D embedded features. Designing the model carefully to learn both video and sound simultaneously is a complex and time-consuming task. Inspired by the improvement of computer vision, References<sup>##UREF##43##45##,##UREF##44##46##</sup> exploited the contrastive loss to extract the acoustic representation from the log-Mel spectrogram, and obtained a 30% performance gain in speech recognition. References<sup>##UREF##45##47##,##UREF##46##48##</sup> obtained inspiration from Reference<sup>##UREF##47##49##</sup> to adapt contrastive predictive coding in speech recognition. HuBERT<sup>##UREF##48##50##</sup> applied the clustering method to provide labels for the losses and learn the acoustic/linguistic model from the speech data itself. Reference<sup>##UREF##49##51##</sup> explored the advantage of a diverse set of feature representations by jointly training the conventional ResNet101<sup>##UREF##50##52##</sup> architecture with four pretexts. Other studies applied the learned representative information to various downstream tasks, such as sound classification<sup>##UREF##51##53##,##UREF##52##54##</sup>, and music generation<sup>##UREF##53##55##,##UREF##54##56##</sup>. Our study is also related to self-supervised representative learning. Firstly, we design a simple network structure with five convolutional layers and two down-sampling operations to learn representative features from a large number of unlabeled acoustic data by minimizing the self-supervised contrastive loss. Then, the learned network model is fine-tuned for classification task to evaluate the efficiency of representative features in detecting unknown audio events or their related patterns.</p>","<title>Dataset acquisition</title>","<p id=\"Par32\">To demonstrate the efficiency of identifying unknown classes, we conducted experiments on various acoustic datasets. We trained the network using a self-supervised approach on the DCASE2019 Subtask 1C or MagnTagATune dataset. In the supervised approach, each log-mel spectrogram patches produces one class label. Then, the open set acoustic scenes, GTZAN dataset, as well as Tori dataset, are used to evaluate the performance of our network in the open set task. In the supervised approach, each log-Mel spectrogram patch produces one class label. The input to the model is the log-Mel spectrogram patch, while the output is a classification label.</p>","<title>DCASE2019 subtask 1C</title>","<p id=\"Par33\">The dataset open set acoustic scene classification (DCASE2019 Subtask 1C) incudes ten major classes (Supplementary material Table ##SUPPL##0##1##) and other minor classes of audio records. The minor classes are environment sound events, which are labeled as “unknown”. The dataset was collected in 12 large European cities, in which the length of each record is 5–6 min, which is split into 10-s segments. The overall length of the dataset is 44 h with 15,850 segments (1450 unknown classes + 14,400 of 10 major scene classes). Each segment is recoded in a mono channel and sampled with a 44.1 kHz sampling rate. The limited number of training samples causes the difficulty of this task without sufficient variety, so it may produce poor generalization in trained models.</p>","<p id=\"Par34\">We followed the same approach as in<sup>##UREF##57##59##</sup> to process the DCASE2019 Subtask 1C and did not use any other external data resource during the training. The classifier was only trained with known categories, and tested with known + unknown data. The open set acoustic dataset (DCASE2019 Subtask 1C) has train/leaderboard/evaluation folders, where only the train folder provides a corresponding label for each sound. For a fair comparison, we split it into train/validation/test as Reference<sup>##UREF##57##59##</sup>. We obtain the log-Mel spectrogram of 10-s segments with a window size of 2048 after STFT and a hop length of 460. To facilitate the calculation, the pad log-Mel spectrogram features with zeros, and the resultant feature size is 64 × 960, where the first dimension is the number of Mel-scale bins, and the second is the number of frames on the temporal axis. We calculate the mean and standard deviation for each bin across all the data samples and normalize the 2-D feature by the mean and standard deviation for the training, which is a very significant step for successfully training the network. During the training, the model takes the log-Mel spectrogram as input, and produces the probability of scene class. During the test stage, the unknown was filtered by the manually decided threshold, as in Eq. (##FORMU##35##5##).</p>","<title>MagnTagATune dataset</title>","<p id=\"Par35\">We select the MagnTagATune to create a self-supervised training dataset for learning the pre-train model in the task of GTZAN and Tori experiment. The MagnaTagATune dataset<sup>##UREF##58##60##</sup> contains 25,863 audio chunks extracted from 5223 songs, 445 albums, and 230 artists. Each chunk of the music file is mono-recorded with a 16 kHz sampling rate. It spans a broad range of genres, making it suitable for pretraining. First, ensure the each chunk has the same length of 30 s by using replication padding. Then, the transforms of the raw audio using various augmentation methods (“<xref rid=\"Sec8\" ref-type=\"sec\">Effective data augmentation</xref>” section) to create the positive pairs. To convert the audio sequence into log-Mel spectrogram with the same setting of DCASE2019 Subtask 1C. During the training, the algorithm automatically extracts the positive and negative samples from the augmented data, and the model minimizes the self-supervised contrastive loss (Fig. ##FIG##1##2##a) to learn the rich representation of those samples.</p>","<title>GTZAN dataset</title>","<p id=\"Par36\">We evaluate the GTZAN dataset in the fine-tuning phase. The well-known GTZAN dataset is deserving of the MNIST<sup>##UREF##59##61##</sup> for music. The researchers utilized the dataset<sup>##UREF##60##62##</sup> in over 100 articles in 2013<sup>##UREF##59##61##</sup>. Each song records the mono channel for over 30 s. It is popular because the concept of music genres and single-label classification is easy, simple, and straightforward. However, there are too many issues with this dataset. (1) First, the audio quality varies by track. (2) Heavy artist repetition is often ignored during dataset split. (3) The label is not 100% correct. To solve this problem and make fair comparison<sup>##UREF##58##60##</sup>, we use a cleaned version and split – “fault-filtered”<sup>##UREF##61##63##</sup>, which divides the dataset into train, validation, and test.</p>","<p id=\"Par37\">The network (Fig. ##FIG##1##2##a) learns rich represented features from augmented data extracted from the MagnaTagATune dataset. In supervised learning, the model is fed with the training set of the “fault-filtered” GTZAN dataset and predicts the probability of the genre. The unknown class is assumed to be metal. We removed the samples of metal class from the training data and evaluated both known and unknown in the test data.</p>","<title>Tori dataset</title>","<p id=\"Par38\">In the traditional Korean folk song, the singing style is different from province to province, which is called Tori. Each Tori has its scale and musical tone. The famous Tori include Gyeong Tori (경토리), Menali Tori (메나리토리), and Susimga Tori (수심가토리). For example, the Gyeong Tori mainly comprises the five-note system (sol, la, do, re, mi) and gives a bright and light impression to the audience. We collect the four types of Tori (Gyeong Tori, Menali Tori, Susimga Tori, Yukjabaeki Tori) with a total of 146 chunks with a 22,050 Hz sampling rate. The length of each chunk varies from 1 to 10 min. We clip each chunk into small uniform segments with a length of 10 s, which results in Yukjabaeki Tori 1428 segments, Menali Tori 1073 segments, Gyeong Tori 985 segments, and Susimga tori 131 segments. We chose the Susimga Tori as the unknown samples, and the other three types of Tori are split into 4:1 for train and testing. The window size of STFT is 2048 for each segment to generate the log-Mel spectrogram with the size of 128 × 960. In the training, the model takes the spectrogram feature as input and approximates the label (Gyeong, Yukjabaeki, or Menali Tori).</p>","<title>Supplementary Information</title>","<p>\n</p>"],"string":"[\n \"<title>Related work</title>\",\n \"<title>Open set recognition</title>\",\n \"<p id=\\\"Par9\\\">The open set recognition task aims to detect an unexpected sample but learns only among known samples. It has primarily been applied to the image classification problem<sup>##UREF##19##21##</sup>, and extended to other fields. In this section, we explain the open-set recognition problem in two application areas:</p>\",\n \"<p id=\\\"Par10\\\"><italic>Open set recognition in computer vision</italic>: In real-world recognition problems, it is inevitable to introduce unknown points, events, or observations in a collected dataset. Human error, low-precision machine operation, or the natural environment usually cause those unknowns (vibration, geomagnetic activity, illumination, etc.) and can indicate critical incidents. To address this problem, they<sup>##UREF##20##22##</sup> considers the relationship between unknown or out-of-distribution classes. In their observations, the correctly detected samples tend to present higher softmax probabilities than unknown samples once they have learned the practical model. So, the proper threshold can filter the unknown samples from the known—the OpenMax algorithm<sup>##UREF##21##23##</sup> to classify unexpected errors in an open set. The OpenMax algorithm exploits the activation vector before the softmax layer, in which the related classes are often responding to each other in the image but confused in the fake one. They<sup>##UREF##22##24##</sup> built the Classification–Reconstruction learning for Open-Set Recognition (CROSR) by utilizing the reconstructed latent representations and achieved robust unknown detection without compromising the classification accuracy of known categories. Reference<sup>##UREF##23##25##</sup> proposed the distance-based loss based on the assumption that the closer the known objects are to each other, the easier it is to find the unknown objects. Semantic segmentation is a more challenging task than classification because the algorithm has both to understand the representative information of the different pixels and to learn the interrelationships to build a clear object boundary. Reference<sup>##UREF##24##26##</sup> aggregated the abnormal pixels by leveraging the softmax prediction at the bottom of the traditional fully convolutional network. References<sup>##UREF##25##27##,##UREF##26##28##</sup> used a contrastive approach to construct the closer known object clustering and improved the ability to segment unknown pixels. Reference<sup>##UREF##27##29##</sup> created a memory bank to store contrastive features and solved the bottleneck that the contrastive loss requires significant computation and memory to obtain semi-hard negative pairs.</p>\",\n \"<p id=\\\"Par11\\\"><italic>Open set recognition in the audio domain</italic>: Currently, many sociologists are emphasizing the computer vision field, yet very few works exist in the audio domain. A key challenge in open-set audio recognition is the collection of the dataset. Some remarkable research, such as rhythm pattern recognition, requires a professional expert to annotate the audio. Reference<sup>##UREF##28##30##</sup> designs an open set classifier using a support vector machine to map the data into high-level dimension features. Reference<sup>##UREF##29##31##</sup> applied the CNN architecture for a close-set classification set and other deep convolutional autoencoders (DCAES) for unknown detection. Reference<sup>##UREF##30##32##</sup> utilizes the support vector data description (SVDD) model to construct the adequate description of the known data boundary in the feature space and rejects the out-of-distribution samples. Reference<sup>##UREF##31##33##</sup> adopted a class-conditioned autoencoder to detect the unknown, assuming that the unknown has more significant reconstruction errors than the known samples.</p>\",\n \"<title>Self-supervised representation learning</title>\",\n \"<p id=\\\"Par12\\\">Data labeling is a challenging task that takes time and manual effort to complete correctly. The goal of self-supervised representation is to learn the rich represented features without vast amounts of labeled data<sup>##UREF##32##34##</sup>. In self-supervised learning, we should carefully design label-free pretext tasks and learn the internal features of the data through the supervised approach. The initial attempts started in the field of computer vision. Reference<sup>##UREF##33##35##</sup> designed the architecture for pair classification, where the input data is augmented and split into several patches, and the architecture is forced to predict the correct spatial configuration for the two pairs of patches. Reference<sup>##UREF##34##36##</sup> demonstrated that the learned features can generalize across samples and are suitable for various tasks. References<sup>##UREF##34##36##–##UREF##37##39##</sup> explored the efficiency of combining multiple pretext tasks to learn the deep neural network. However, maintaining consistency during the training takes time and effort. In summary, the algorithm needs to pick up valuable samples to efficiently train the network skillfully. To solve this problem<sup>##UREF##38##40##,##UREF##39##41##</sup>, built a dynamic dictionary with a queue and a moving-averaged encoder and achieved competitive results by reusing the “hard” samples from the dynamic dictionary. Reference<sup>##UREF##40##42##</sup> creates a Cross Quantized Contrastive learning technique that simultaneously learns codewords and deep visual descriptors in different images.</p>\",\n \"<p id=\\\"Par13\\\">In music information retrieval (MIR)<sup>##UREF##41##43##</sup>, experts attempted to learn acoustic features by utilizing the natural synchronization between video and sound signal<sup>##UREF##42##44##</sup>. However, video represents 3-dimensional features, while sound needs to transform into 2-D embedded features. Designing the model carefully to learn both video and sound simultaneously is a complex and time-consuming task. Inspired by the improvement of computer vision, References<sup>##UREF##43##45##,##UREF##44##46##</sup> exploited the contrastive loss to extract the acoustic representation from the log-Mel spectrogram, and obtained a 30% performance gain in speech recognition. References<sup>##UREF##45##47##,##UREF##46##48##</sup> obtained inspiration from Reference<sup>##UREF##47##49##</sup> to adapt contrastive predictive coding in speech recognition. HuBERT<sup>##UREF##48##50##</sup> applied the clustering method to provide labels for the losses and learn the acoustic/linguistic model from the speech data itself. Reference<sup>##UREF##49##51##</sup> explored the advantage of a diverse set of feature representations by jointly training the conventional ResNet101<sup>##UREF##50##52##</sup> architecture with four pretexts. Other studies applied the learned representative information to various downstream tasks, such as sound classification<sup>##UREF##51##53##,##UREF##52##54##</sup>, and music generation<sup>##UREF##53##55##,##UREF##54##56##</sup>. Our study is also related to self-supervised representative learning. Firstly, we design a simple network structure with five convolutional layers and two down-sampling operations to learn representative features from a large number of unlabeled acoustic data by minimizing the self-supervised contrastive loss. Then, the learned network model is fine-tuned for classification task to evaluate the efficiency of representative features in detecting unknown audio events or their related patterns.</p>\",\n \"<title>Dataset acquisition</title>\",\n \"<p id=\\\"Par32\\\">To demonstrate the efficiency of identifying unknown classes, we conducted experiments on various acoustic datasets. We trained the network using a self-supervised approach on the DCASE2019 Subtask 1C or MagnTagATune dataset. In the supervised approach, each log-mel spectrogram patches produces one class label. Then, the open set acoustic scenes, GTZAN dataset, as well as Tori dataset, are used to evaluate the performance of our network in the open set task. In the supervised approach, each log-Mel spectrogram patch produces one class label. The input to the model is the log-Mel spectrogram patch, while the output is a classification label.</p>\",\n \"<title>DCASE2019 subtask 1C</title>\",\n \"<p id=\\\"Par33\\\">The dataset open set acoustic scene classification (DCASE2019 Subtask 1C) incudes ten major classes (Supplementary material Table ##SUPPL##0##1##) and other minor classes of audio records. The minor classes are environment sound events, which are labeled as “unknown”. The dataset was collected in 12 large European cities, in which the length of each record is 5–6 min, which is split into 10-s segments. The overall length of the dataset is 44 h with 15,850 segments (1450 unknown classes + 14,400 of 10 major scene classes). Each segment is recoded in a mono channel and sampled with a 44.1 kHz sampling rate. The limited number of training samples causes the difficulty of this task without sufficient variety, so it may produce poor generalization in trained models.</p>\",\n \"<p id=\\\"Par34\\\">We followed the same approach as in<sup>##UREF##57##59##</sup> to process the DCASE2019 Subtask 1C and did not use any other external data resource during the training. The classifier was only trained with known categories, and tested with known + unknown data. The open set acoustic dataset (DCASE2019 Subtask 1C) has train/leaderboard/evaluation folders, where only the train folder provides a corresponding label for each sound. For a fair comparison, we split it into train/validation/test as Reference<sup>##UREF##57##59##</sup>. We obtain the log-Mel spectrogram of 10-s segments with a window size of 2048 after STFT and a hop length of 460. To facilitate the calculation, the pad log-Mel spectrogram features with zeros, and the resultant feature size is 64 × 960, where the first dimension is the number of Mel-scale bins, and the second is the number of frames on the temporal axis. We calculate the mean and standard deviation for each bin across all the data samples and normalize the 2-D feature by the mean and standard deviation for the training, which is a very significant step for successfully training the network. During the training, the model takes the log-Mel spectrogram as input, and produces the probability of scene class. During the test stage, the unknown was filtered by the manually decided threshold, as in Eq. (##FORMU##35##5##).</p>\",\n \"<title>MagnTagATune dataset</title>\",\n \"<p id=\\\"Par35\\\">We select the MagnTagATune to create a self-supervised training dataset for learning the pre-train model in the task of GTZAN and Tori experiment. The MagnaTagATune dataset<sup>##UREF##58##60##</sup> contains 25,863 audio chunks extracted from 5223 songs, 445 albums, and 230 artists. Each chunk of the music file is mono-recorded with a 16 kHz sampling rate. It spans a broad range of genres, making it suitable for pretraining. First, ensure the each chunk has the same length of 30 s by using replication padding. Then, the transforms of the raw audio using various augmentation methods (“<xref rid=\\\"Sec8\\\" ref-type=\\\"sec\\\">Effective data augmentation</xref>” section) to create the positive pairs. To convert the audio sequence into log-Mel spectrogram with the same setting of DCASE2019 Subtask 1C. During the training, the algorithm automatically extracts the positive and negative samples from the augmented data, and the model minimizes the self-supervised contrastive loss (Fig. ##FIG##1##2##a) to learn the rich representation of those samples.</p>\",\n \"<title>GTZAN dataset</title>\",\n \"<p id=\\\"Par36\\\">We evaluate the GTZAN dataset in the fine-tuning phase. The well-known GTZAN dataset is deserving of the MNIST<sup>##UREF##59##61##</sup> for music. The researchers utilized the dataset<sup>##UREF##60##62##</sup> in over 100 articles in 2013<sup>##UREF##59##61##</sup>. Each song records the mono channel for over 30 s. It is popular because the concept of music genres and single-label classification is easy, simple, and straightforward. However, there are too many issues with this dataset. (1) First, the audio quality varies by track. (2) Heavy artist repetition is often ignored during dataset split. (3) The label is not 100% correct. To solve this problem and make fair comparison<sup>##UREF##58##60##</sup>, we use a cleaned version and split – “fault-filtered”<sup>##UREF##61##63##</sup>, which divides the dataset into train, validation, and test.</p>\",\n \"<p id=\\\"Par37\\\">The network (Fig. ##FIG##1##2##a) learns rich represented features from augmented data extracted from the MagnaTagATune dataset. In supervised learning, the model is fed with the training set of the “fault-filtered” GTZAN dataset and predicts the probability of the genre. The unknown class is assumed to be metal. We removed the samples of metal class from the training data and evaluated both known and unknown in the test data.</p>\",\n \"<title>Tori dataset</title>\",\n \"<p id=\\\"Par38\\\">In the traditional Korean folk song, the singing style is different from province to province, which is called Tori. Each Tori has its scale and musical tone. The famous Tori include Gyeong Tori (경토리), Menali Tori (메나리토리), and Susimga Tori (수심가토리). For example, the Gyeong Tori mainly comprises the five-note system (sol, la, do, re, mi) and gives a bright and light impression to the audience. We collect the four types of Tori (Gyeong Tori, Menali Tori, Susimga Tori, Yukjabaeki Tori) with a total of 146 chunks with a 22,050 Hz sampling rate. The length of each chunk varies from 1 to 10 min. We clip each chunk into small uniform segments with a length of 10 s, which results in Yukjabaeki Tori 1428 segments, Menali Tori 1073 segments, Gyeong Tori 985 segments, and Susimga tori 131 segments. We chose the Susimga Tori as the unknown samples, and the other three types of Tori are split into 4:1 for train and testing. The window size of STFT is 2048 for each segment to generate the log-Mel spectrogram with the size of 128 × 960. In the training, the model takes the spectrogram feature as input and approximates the label (Gyeong, Yukjabaeki, or Menali Tori).</p>\",\n \"<title>Supplementary Information</title>\",\n \"<p>\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary Information</title>","<p>The online version contains supplementary material available at 10.1038/s41598-023-50639-7.</p>","<title>Acknowledgements</title>","<p>This work was funded by the National Research Foundation of Korea (NRF) under the Development of AI for Analysis and Synthesis of Korean Pansori Project (NRF-2021R1A2C2006895).</p>","<title>Author contributions</title>","<p>J.Y. designed the study, conducted the experiments, and prepared the original manuscript. W.W. prepared the dataset. J.L. supervised the research and edited the manuscript. All authors have read and agreed to the published version of the manuscript.</p>","<title>Data availability</title>","<p>The authors declare that all training and testing data and codes supporting this study are available from the first author upon reasonable request. All other data supporting this study are available within the article. The dataset of DCASE2019 Subtask 1C, GTZAN, MagnaTagATune is available in (<ext-link ext-link-type=\"uri\" xlink:href=\"https://dcase.community/challenge2019/task-acoustic-scene-classification\">https://dcase.community/challenge2019/task-acoustic-scene-classification</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification\">https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://mirg.city.ac.uk/codeapps/the-magnatagatune-dataset\">https://mirg.city.ac.uk/codeapps/the-magnatagatune-dataset</ext-link>), processed dataset of tori is available at <ext-link ext-link-type=\"uri\" xlink:href=\"https://drive.google.com/drive/folders/1QFP2IEtQU3i5be1tvVKLBeSCEbYMr91I?usp=share_link\">https://drive.google.com/drive/folders/1QFP2IEtQU3i5be1tvVKLBeSCEbYMr91I?usp=share_link</ext-link>.</p>","<title>Competing interests</title>","<p id=\"Par58\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary Information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41598-023-50639-7.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>This work was funded by the National Research Foundation of Korea (NRF) under the Development of AI for Analysis and Synthesis of Korean Pansori Project (NRF-2021R1A2C2006895).</p>\",\n \"<title>Author contributions</title>\",\n \"<p>J.Y. designed the study, conducted the experiments, and prepared the original manuscript. W.W. prepared the dataset. J.L. supervised the research and edited the manuscript. All authors have read and agreed to the published version of the manuscript.</p>\",\n \"<title>Data availability</title>\",\n \"<p>The authors declare that all training and testing data and codes supporting this study are available from the first author upon reasonable request. All other data supporting this study are available within the article. The dataset of DCASE2019 Subtask 1C, GTZAN, MagnaTagATune is available in (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://dcase.community/challenge2019/task-acoustic-scene-classification\\\">https://dcase.community/challenge2019/task-acoustic-scene-classification</ext-link>, <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification\\\">https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification</ext-link>, <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://mirg.city.ac.uk/codeapps/the-magnatagatune-dataset\\\">https://mirg.city.ac.uk/codeapps/the-magnatagatune-dataset</ext-link>), processed dataset of tori is available at <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://drive.google.com/drive/folders/1QFP2IEtQU3i5be1tvVKLBeSCEbYMr91I?usp=share_link\\\">https://drive.google.com/drive/folders/1QFP2IEtQU3i5be1tvVKLBeSCEbYMr91I?usp=share_link</ext-link>.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par58\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>The category definition of the open set recognition task.</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>The pipeline of the open set sound event detection model.</p></caption></fig>","<fig id=\"Fig3\"><label>Figure 3</label><caption><p>The structure of the encoder network for comparison in<sup>##UREF##57##59##</sup>.</p></caption></fig>","<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Performance comparison of the network with/without self-supervised pretrained weight in the open set acoustic scene classification dataset.</p></caption></fig>","<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Confusion matrix of the open set acoustic scene classification dataset.</p></caption></fig>","<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Confusion matrix of the GTZAN dataset.</p></caption></fig>","<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Comparison of the evaluation results with different proportions of labeled data on the GTZAN dataset.</p></caption></fig>","<fig id=\"Fig8\"><label>Figure 8</label><caption><p>TSNE visualization of the GTZAN dataset before/after self-supervised learning.</p></caption></fig>","<fig id=\"Fig9\"><label>Figure 9</label><caption><p>Confusion matrix of tori dataset.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>The category definition of the open set recognition task.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>The pipeline of the open set sound event detection model.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Figure 3</label><caption><p>The structure of the encoder network for comparison in<sup>##UREF##57##59##</sup>.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Figure 4</label><caption><p>Performance comparison of the network with/without self-supervised pretrained weight in the open set acoustic scene classification dataset.</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Figure 5</label><caption><p>Confusion matrix of the open set acoustic scene classification dataset.</p></caption></fig>\",\n \"<fig id=\\\"Fig6\\\"><label>Figure 6</label><caption><p>Confusion matrix of the GTZAN dataset.</p></caption></fig>\",\n \"<fig id=\\\"Fig7\\\"><label>Figure 7</label><caption><p>Comparison of the evaluation results with different proportions of labeled data on the GTZAN dataset.</p></caption></fig>\",\n \"<fig id=\\\"Fig8\\\"><label>Figure 8</label><caption><p>TSNE visualization of the GTZAN dataset before/after self-supervised learning.</p></caption></fig>\",\n \"<fig id=\\\"Fig9\\\"><label>Figure 9</label><caption><p>Confusion matrix of tori dataset.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":["<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>The performance result of the open set acoustic scene classification dataset.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Model</th><th align=\"left\">ACC<sub>k</sub></th><th align=\"left\">ACC<sub>u</sub></th><th align=\"left\">ACC</th><th align=\"left\">AUROC</th></tr></thead><tbody><tr><td align=\"left\">Cross-entropy<sup>##UREF##57##59##</sup></td><td char=\".\" align=\"char\">0.638</td><td char=\".\" align=\"char\">0.359</td><td char=\".\" align=\"char\">0.499</td><td char=\".\" align=\"char\">0.687</td></tr><tr><td align=\"left\">Center</td><td char=\".\" align=\"char\">0.613</td><td char=\".\" align=\"char\">0.389</td><td char=\".\" align=\"char\">0.501</td><td char=\".\" align=\"char\">0.722</td></tr><tr><td align=\"left\">Center + contrast</td><td char=\".\" align=\"char\">0.649</td><td char=\".\" align=\"char\">0.369</td><td char=\".\" align=\"char\">0.509</td><td char=\".\" align=\"char\">0.746</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Performance comparison of the classification network with/without self-supervised pretrained weight in the GTZAN dataset.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Loss</th><th align=\"left\">Self-supervised</th><th align=\"left\">ACC<sub>k</sub></th><th align=\"left\">ACC<sub>u</sub></th><th align=\"left\">ACC</th><th align=\"left\">AUROC</th></tr></thead><tbody><tr><td align=\"left\">Cross-entropy</td><td align=\"left\">w.o.</td><td char=\".\" align=\"char\">0.643</td><td char=\".\" align=\"char\">0.222</td><td char=\".\" align=\"char\">0.432</td><td char=\".\" align=\"char\">0.656</td></tr><tr><td align=\"left\">Cross-entropy + center + contrast</td><td align=\"left\">w.o.</td><td char=\".\" align=\"char\">0.652</td><td char=\".\" align=\"char\">0.259</td><td char=\".\" align=\"char\">0.455</td><td char=\".\" align=\"char\">0.700</td></tr><tr><td align=\"left\">Cross-entropy</td><td align=\"left\">w</td><td char=\".\" align=\"char\">0.657</td><td char=\".\" align=\"char\">0.333</td><td char=\".\" align=\"char\">0.495</td><td char=\".\" align=\"char\">0.790</td></tr><tr><td align=\"left\">Cross-entropy + center + contrast</td><td align=\"left\">w</td><td char=\".\" align=\"char\">0.680</td><td char=\".\" align=\"char\">0.444</td><td char=\".\" align=\"char\">0.521</td><td char=\".\" align=\"char\">0.827</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>The performance result of the Tori dataset.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Loss</th><th align=\"left\">Self-supervised</th><th align=\"left\">ACC<sub>k</sub></th><th align=\"left\">ACC<sub>u</sub></th><th align=\"left\">ACC</th><th align=\"left\">AUROC</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Cross-entropy</td><td align=\"left\">w</td><td char=\".\" align=\"char\">0.860</td><td char=\".\" align=\"char\">0.356</td><td char=\".\" align=\"char\">0.606</td><td char=\".\" align=\"char\">0.589</td></tr><tr><td align=\"left\">w.o.</td><td char=\".\" align=\"char\">0.794</td><td char=\".\" align=\"char\">0.143</td><td char=\".\" align=\"char\">0.468</td><td char=\".\" align=\"char\">0.454</td></tr><tr><td align=\"left\" rowspan=\"2\">Cross-entropy + center</td><td align=\"left\">w</td><td char=\".\" align=\"char\">0.850</td><td char=\".\" align=\"char\">0.340</td><td char=\".\" align=\"char\">0.599</td><td char=\".\" align=\"char\">0.590</td></tr><tr><td align=\"left\">w.o.</td><td char=\".\" align=\"char\">0.790</td><td char=\".\" align=\"char\">0.162</td><td char=\".\" align=\"char\">0.476</td><td char=\".\" align=\"char\">0.471</td></tr><tr><td align=\"left\" rowspan=\"2\">Cross-entropy + center + contrast</td><td align=\"left\">w</td><td char=\".\" align=\"char\">0.859</td><td char=\".\" align=\"char\">0.367</td><td char=\".\" align=\"char\">0.613</td><td char=\".\" align=\"char\">0.603</td></tr><tr><td align=\"left\">w.o.</td><td char=\".\" align=\"char\">0.827</td><td char=\".\" align=\"char\">0.192</td><td char=\".\" align=\"char\">0.509</td><td char=\".\" align=\"char\">0.461</td></tr></tbody></table></table-wrap>"],"string":"[\n \"<table-wrap id=\\\"Tab1\\\"><label>Table 1</label><caption><p>The performance result of the open set acoustic scene classification dataset.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Model</th><th align=\\\"left\\\">ACC<sub>k</sub></th><th align=\\\"left\\\">ACC<sub>u</sub></th><th align=\\\"left\\\">ACC</th><th align=\\\"left\\\">AUROC</th></tr></thead><tbody><tr><td align=\\\"left\\\">Cross-entropy<sup>##UREF##57##59##</sup></td><td char=\\\".\\\" align=\\\"char\\\">0.638</td><td char=\\\".\\\" align=\\\"char\\\">0.359</td><td char=\\\".\\\" align=\\\"char\\\">0.499</td><td char=\\\".\\\" align=\\\"char\\\">0.687</td></tr><tr><td align=\\\"left\\\">Center</td><td char=\\\".\\\" align=\\\"char\\\">0.613</td><td char=\\\".\\\" align=\\\"char\\\">0.389</td><td char=\\\".\\\" align=\\\"char\\\">0.501</td><td char=\\\".\\\" align=\\\"char\\\">0.722</td></tr><tr><td align=\\\"left\\\">Center + contrast</td><td char=\\\".\\\" align=\\\"char\\\">0.649</td><td char=\\\".\\\" align=\\\"char\\\">0.369</td><td char=\\\".\\\" align=\\\"char\\\">0.509</td><td char=\\\".\\\" align=\\\"char\\\">0.746</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab2\\\"><label>Table 2</label><caption><p>Performance comparison of the classification network with/without self-supervised pretrained weight in the GTZAN dataset.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Loss</th><th align=\\\"left\\\">Self-supervised</th><th align=\\\"left\\\">ACC<sub>k</sub></th><th align=\\\"left\\\">ACC<sub>u</sub></th><th align=\\\"left\\\">ACC</th><th align=\\\"left\\\">AUROC</th></tr></thead><tbody><tr><td align=\\\"left\\\">Cross-entropy</td><td align=\\\"left\\\">w.o.</td><td char=\\\".\\\" align=\\\"char\\\">0.643</td><td char=\\\".\\\" align=\\\"char\\\">0.222</td><td char=\\\".\\\" align=\\\"char\\\">0.432</td><td char=\\\".\\\" align=\\\"char\\\">0.656</td></tr><tr><td align=\\\"left\\\">Cross-entropy + center + contrast</td><td align=\\\"left\\\">w.o.</td><td char=\\\".\\\" align=\\\"char\\\">0.652</td><td char=\\\".\\\" align=\\\"char\\\">0.259</td><td char=\\\".\\\" align=\\\"char\\\">0.455</td><td char=\\\".\\\" align=\\\"char\\\">0.700</td></tr><tr><td align=\\\"left\\\">Cross-entropy</td><td align=\\\"left\\\">w</td><td char=\\\".\\\" align=\\\"char\\\">0.657</td><td char=\\\".\\\" align=\\\"char\\\">0.333</td><td char=\\\".\\\" align=\\\"char\\\">0.495</td><td char=\\\".\\\" align=\\\"char\\\">0.790</td></tr><tr><td align=\\\"left\\\">Cross-entropy + center + contrast</td><td align=\\\"left\\\">w</td><td char=\\\".\\\" align=\\\"char\\\">0.680</td><td char=\\\".\\\" align=\\\"char\\\">0.444</td><td char=\\\".\\\" align=\\\"char\\\">0.521</td><td char=\\\".\\\" align=\\\"char\\\">0.827</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab3\\\"><label>Table 3</label><caption><p>The performance result of the Tori dataset.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Loss</th><th align=\\\"left\\\">Self-supervised</th><th align=\\\"left\\\">ACC<sub>k</sub></th><th align=\\\"left\\\">ACC<sub>u</sub></th><th align=\\\"left\\\">ACC</th><th align=\\\"left\\\">AUROC</th></tr></thead><tbody><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Cross-entropy</td><td align=\\\"left\\\">w</td><td char=\\\".\\\" align=\\\"char\\\">0.860</td><td char=\\\".\\\" align=\\\"char\\\">0.356</td><td char=\\\".\\\" align=\\\"char\\\">0.606</td><td char=\\\".\\\" align=\\\"char\\\">0.589</td></tr><tr><td align=\\\"left\\\">w.o.</td><td char=\\\".\\\" align=\\\"char\\\">0.794</td><td char=\\\".\\\" align=\\\"char\\\">0.143</td><td char=\\\".\\\" align=\\\"char\\\">0.468</td><td char=\\\".\\\" align=\\\"char\\\">0.454</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Cross-entropy + center</td><td align=\\\"left\\\">w</td><td char=\\\".\\\" align=\\\"char\\\">0.850</td><td char=\\\".\\\" align=\\\"char\\\">0.340</td><td char=\\\".\\\" align=\\\"char\\\">0.599</td><td char=\\\".\\\" align=\\\"char\\\">0.590</td></tr><tr><td align=\\\"left\\\">w.o.</td><td char=\\\".\\\" align=\\\"char\\\">0.790</td><td char=\\\".\\\" align=\\\"char\\\">0.162</td><td char=\\\".\\\" align=\\\"char\\\">0.476</td><td char=\\\".\\\" align=\\\"char\\\">0.471</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Cross-entropy + center + contrast</td><td align=\\\"left\\\">w</td><td char=\\\".\\\" align=\\\"char\\\">0.859</td><td char=\\\".\\\" align=\\\"char\\\">0.367</td><td char=\\\".\\\" align=\\\"char\\\">0.613</td><td char=\\\".\\\" align=\\\"char\\\">0.603</td></tr><tr><td align=\\\"left\\\">w.o.</td><td char=\\\".\\\" align=\\\"char\\\">0.827</td><td char=\\\".\\\" align=\\\"char\\\">0.192</td><td char=\\\".\\\" align=\\\"char\\\">0.509</td><td char=\\\".\\\" align=\\\"char\\\">0.461</td></tr></tbody></table></table-wrap>\"\n]","isConversational":false},"formula":{"kind":"list like","value":["<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} 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id=\"M24\"><mml:mrow><mml:msup><mml:mrow><mml:mi>υ</mml:mi></mml:mrow><mml:mo>+</mml:mo></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>υ</mml:mi></mml:mrow><mml:mo>-</mml:mo></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${A}^{\\mathrm{^{\\prime}}}$$\\end{document}</tex-math><mml:math id=\"M26\"><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:msup></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq13\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${B}^{\\mathrm{^{\\prime}}}$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:msup><mml:mrow><mml:mi>B</mml:mi></mml:mrow><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:msup></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq14\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$(A,B)$$\\end{document}</tex-math><mml:math id=\"M30\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>A</mml:mi><mml:mo>,</mml:mo><mml:mi>B</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq15\"><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$({B}^{\\mathrm{^{\\prime}}}$$\\end{document}</tex-math><mml:math id=\"M32\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msup><mml:mrow><mml:mi>B</mml:mi></mml:mrow><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq16\"><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${B}^{\\mathrm{^{\\prime}}})$$\\end{document}</tex-math><mml:math id=\"M34\"><mml:mrow><mml:msup><mml:mrow><mml:mi>B</mml:mi></mml:mrow><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:msup><mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq17\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$sim\\left(m, n\\right)={m}^{T}n / \\Vert m\\Vert \\Vert n\\Vert $$\\end{document}</tex-math><mml:math id=\"M36\"><mml:mrow><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mi>T</mml:mi></mml:msup><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">/</mml:mo><mml:mrow><mml:mo stretchy=\"false\">‖</mml:mo><mml:mi>m</mml:mi><mml:mo stretchy=\"false\">‖</mml:mo><mml:mo stretchy=\"false\">‖</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">‖</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${L}_{seg} = {\\alpha L}_{ce} + \\beta {L}_{center} + \\gamma {L}_{cont}^{sup}$$\\end{document}</tex-math><mml:math id=\"M38\" display=\"block\"><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">seg</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>α</mml:mi><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">ce</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mi>β</mml:mi><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">center</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mi>γ</mml:mi><mml:msubsup><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">cont</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">sup</mml:mi></mml:mrow></mml:msubsup></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq18\"><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$({x}_{n}, {y}_{n})$$\\end{document}</tex-math><mml:math id=\"M40\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${L}_{ce} =- \\frac{1}{N}\\sum_{n = 1}^{N}log\\left(\\frac{\\mathit{exp}\\left({f(x}_{n, {y}_{n}})\\right)}{{\\sum }_{k= 1}^{K}exp(f({x}_{n,k}))}\\right)$$\\end{document}</tex-math><mml:math id=\"M42\" display=\"block\"><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ce</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>N</mml:mi></mml:mfrac><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>N</mml:mi></mml:munderover><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>g</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mfrac><mml:mrow><mml:mi mathvariant=\"italic\">exp</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:msub><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>K</mml:mi></mml:msubsup><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>p</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>f</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:mfrac></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq19\"><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f({x}_{n,k})$$\\end{document}</tex-math><mml:math id=\"M44\"><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq20\"><alternatives><tex-math id=\"M45\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$k$$\\end{document}</tex-math><mml:math id=\"M46\"><mml:mi>k</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq21\"><alternatives><tex-math id=\"M47\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$x$$\\end{document}</tex-math><mml:math id=\"M48\"><mml:mi>x</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq22\"><alternatives><tex-math id=\"M49\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${c}_{i}$$\\end{document}</tex-math><mml:math id=\"M50\"><mml:msub><mml:mi>c</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq23\"><alternatives><tex-math id=\"M51\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${L}_{center}=\\frac{1}{2}\\sum_{n=1}^{N}{\\Vert {f(x}_{n})-{c}_{{y}_{n}}\\Vert }^{2}$$\\end{document}</tex-math><mml:math id=\"M52\"><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">center</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>N</mml:mi></mml:msubsup><mml:msup><mml:mrow><mml:mrow><mml:mo stretchy=\"false\">‖</mml:mo></mml:mrow><mml:msub><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>x</mml:mi></mml:mrow><mml:mi>n</mml:mi></mml:msub><mml:mrow><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>-</mml:mo><mml:msub><mml:mi>c</mml:mi><mml:msub><mml:mi>y</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:msub><mml:mo stretchy=\"false\">‖</mml:mo></mml:mrow></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq24\"><alternatives><tex-math id=\"M53\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${c}_{{y}_{n}}$$\\end{document}</tex-math><mml:math id=\"M54\"><mml:msub><mml:mi>c</mml:mi><mml:msub><mml:mi>y</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq25\"><alternatives><tex-math id=\"M55\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${y}_{n}$$\\end{document}</tex-math><mml:math id=\"M56\"><mml:msub><mml:mi>y</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq26\"><alternatives><tex-math id=\"M57\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${c}_{{y}_{n}}$$\\end{document}</tex-math><mml:math id=\"M58\"><mml:msub><mml:mi>c</mml:mi><mml:msub><mml:mi>y</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:msub></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M59\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${L}_{cont}^{sup}=-\\frac{1}{\\left|{P}_{1}\\right|}{\\sum }_{{x}^{1+}\\in {p}_{1}}log\\frac{\\mathit{exp}\\left(sim\\left({x}^{1},{x}^{1+}\\right)/\\tau \\right)}{\\mathit{exp}\\left(sim\\left({x}^{1},{x}^{1+}\\right)/\\tau \\right)+\\sum_{{x}^{1-}\\in {N}_{1} }\\mathit{exp}\\left(sim\\left({x}^{1},{x}^{1-}\\right)/\\tau \\right)}$$\\end{document}</tex-math><mml:math id=\"M60\" display=\"block\"><mml:mrow><mml:msubsup><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">cont</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">sup</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mfenced close=\"|\" open=\"|\"><mml:msub><mml:mi>P</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mfenced></mml:mfrac><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo></mml:mrow></mml:msup><mml:mo>∈</mml:mo><mml:msub><mml:mi>p</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>g</mml:mi><mml:mfrac><mml:mrow><mml:mi mathvariant=\"italic\">exp</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mn>1</mml:mn></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:mfenced><mml:mo stretchy=\"false\">/</mml:mo><mml:mi>τ</mml:mi></mml:mfenced></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">exp</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mn>1</mml:mn></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:mfenced><mml:mo stretchy=\"false\">/</mml:mo><mml:mi>τ</mml:mi></mml:mfenced><mml:mo>+</mml:mo><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>-</mml:mo></mml:mrow></mml:msup><mml:mo>∈</mml:mo><mml:msub><mml:mi>N</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:mi mathvariant=\"italic\">exp</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mn>1</mml:mn></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>-</mml:mo></mml:mrow></mml:msup></mml:mfenced><mml:mo stretchy=\"false\">/</mml:mo><mml:mi>τ</mml:mi></mml:mfenced></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq27\"><alternatives><tex-math id=\"M61\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$sim\\left(m,n\\right)\\triangleq \\frac{{m}^{t}n}{\\Vert m\\Vert \\Vert n\\Vert }$$\\end{document}</tex-math><mml:math id=\"M62\"><mml:mrow><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mfenced><mml:mo>≜</mml:mo><mml:mfrac><mml:mrow><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mi>t</mml:mi></mml:msup><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">‖</mml:mo><mml:mi>m</mml:mi><mml:mo stretchy=\"false\">‖</mml:mo><mml:mo stretchy=\"false\">‖</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">‖</mml:mo></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq28\"><alternatives><tex-math id=\"M63\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${P}_{1}$$\\end{document}</tex-math><mml:math id=\"M64\"><mml:msub><mml:mi>P</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq29\"><alternatives><tex-math id=\"M65\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${N}_{1}$$\\end{document}</tex-math><mml:math id=\"M66\"><mml:msub><mml:mi>N</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq30\"><alternatives><tex-math id=\"M67\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${x}^{1}$$\\end{document}</tex-math><mml:math id=\"M68\"><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mn>1</mml:mn></mml:msup></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq31\"><alternatives><tex-math id=\"M69\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${x}^{1}$$\\end{document}</tex-math><mml:math id=\"M70\"><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mn>1</mml:mn></mml:msup></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ5\"><label>5</label><alternatives><tex-math id=\"M71\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\\{\\begin{array}{ll}class\\_label= unknown, & if\\; {{\\max}}_{k}\\left(p\\left(k|{x}_{n}\\right)\\right)<\\epsilon \\\\ class\\_label=argma{x}_{k}\\left(p\\left(k|{x}_{n}\\right)\\right),& otherwise\\end{array}\\right.$$\\end{document}</tex-math><mml:math id=\"M72\" display=\"block\"><mml:mfenced open=\"{\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mi>_</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>b</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mo>=</mml:mo><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>k</mml:mi><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mi>w</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width=\"0.277778em\"/><mml:msub><mml:mo movablelimits=\"true\">max</mml:mo><mml:mi>k</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>p</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mfenced></mml:mfenced><mml:mo><</mml:mo><mml:mi>ϵ</mml:mi></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"left\"><mml:mrow><mml:mrow/><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mi>_</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>b</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mo>=</mml:mo><mml:mi>a</mml:mi><mml:mi>r</mml:mi><mml:mi>g</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:msub><mml:mi>x</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>p</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mfenced></mml:mfenced><mml:mo>,</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi mathvariant=\"italic\">otherwise</mml:mi></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mfenced></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq32\"><alternatives><tex-math id=\"M73\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p\\left(k|{x}_{n}\\right)$$\\end{document}</tex-math><mml:math id=\"M74\"><mml:mrow><mml:mi>p</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">|</mml:mo></mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq33\"><alternatives><tex-math id=\"M75\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${x}_{n}$$\\end{document}</tex-math><mml:math id=\"M76\"><mml:msub><mml:mi>x</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq34\"><alternatives><tex-math id=\"M77\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon $$\\end{document}</tex-math><mml:math id=\"M78\"><mml:mi>ϵ</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq35\"><alternatives><tex-math id=\"M79\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$tpr=tp/(tp+fn)$$\\end{document}</tex-math><mml:math id=\"M80\"><mml:mrow><mml:mi>t</mml:mi><mml:mi>p</mml:mi><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mi>t</mml:mi><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">/</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mi>p</mml:mi><mml:mo>+</mml:mo><mml:mi>f</mml:mi><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq36\"><alternatives><tex-math id=\"M81\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$fpr=fp/(fp+tn)$$\\end{document}</tex-math><mml:math id=\"M82\"><mml:mrow><mml:mi>f</mml:mi><mml:mi>p</mml:mi><mml:mi>r</mml:mi><mml:mo>=</mml:mo><mml:mi>f</mml:mi><mml:mi>p</mml:mi><mml:mo stretchy=\"false\">/</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>f</mml:mi><mml:mi>p</mml:mi><mml:mo>+</mml:mo><mml:mi>t</mml:mi><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq37\"><alternatives><tex-math id=\"M83\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$(TP+TN)/(TP+TN+FP+FN)$$\\end{document}</tex-math><mml:math id=\"M84\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>T</mml:mi><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mi>T</mml:mi><mml:mi>N</mml:mi><mml:mo stretchy=\"false\">)</mml:mo><mml:mo stretchy=\"false\">/</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>T</mml:mi><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mi>T</mml:mi><mml:mi>N</mml:mi><mml:mo>+</mml:mo><mml:mi>F</mml:mi><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mi>F</mml:mi><mml:mi>N</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ6\"><label>6</label><alternatives><tex-math id=\"M85\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$ACC=0.5\\cdot AC{C}_{k}+0.5\\cdot AC{C}_{u}$$\\end{document}</tex-math><mml:math id=\"M86\" display=\"block\"><mml:mrow><mml:mi>A</mml:mi><mml:mi>C</mml:mi><mml:mi>C</mml:mi><mml:mo>=</mml:mo><mml:mn>0.5</mml:mn><mml:mo>·</mml:mo><mml:mi>A</mml:mi><mml:mi>C</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mn>0.5</mml:mn><mml:mo>·</mml:mo><mml:mi>A</mml:mi><mml:mi>C</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mi>u</mml:mi></mml:msub></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq38\"><alternatives><tex-math id=\"M87\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$AC{C}_{k}$$\\end{document}</tex-math><mml:math id=\"M88\"><mml:mrow><mml:mi>A</mml:mi><mml:mi>C</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ7\"><label>7</label><alternatives><tex-math id=\"M89\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$AC{C}_{k}=\\frac{1}{C}\\sum_{j = 0}^{C}\\frac{T{P}_{j} + T{N}_{j}}{T{P}_{j} + T{N}_{j} + F{N}_{j} + {FP}_{j}},C\\in \\; known \\; classes$$\\end{document}</tex-math><mml:math id=\"M90\" display=\"block\"><mml:mrow><mml:mi>A</mml:mi><mml:mi>C</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>C</mml:mi></mml:mfrac><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow><mml:mi>C</mml:mi></mml:munderover><mml:mfrac><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mi>P</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mi>T</mml:mi><mml:msub><mml:mi>N</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mi>P</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mi>T</mml:mi><mml:msub><mml:mi>N</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mi>F</mml:mi><mml:msub><mml:mi>N</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant=\"italic\">FP</mml:mi></mml:mrow><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>,</mml:mo><mml:mi>C</mml:mi><mml:mo>∈</mml:mo><mml:mspace width=\"0.277778em\"/><mml:mi>k</mml:mi><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mi>w</mml:mi><mml:mi>n</mml:mi><mml:mspace width=\"0.277778em\"/><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq39\"><alternatives><tex-math id=\"M91\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$AC{C}_{u}$$\\end{document}</tex-math><mml:math id=\"M92\"><mml:mrow><mml:mi>A</mml:mi><mml:mi>C</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mi>u</mml:mi></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ8\"><label>8</label><alternatives><tex-math id=\"M93\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$AC{C}_{u}=\\frac{1}{{C}^{\\prime}}\\sum_{j = 0}^{{C}^{\\prime}}\\frac{T{P}_{j} + T{N}_{j}}{T{P}_{j} + T{N}_{j} + F{N}_{j} + {FP}_{j}}{, C}^{\\prime}\\in \\; unknown \\;classes$$\\end{document}</tex-math><mml:math id=\"M94\" display=\"block\"><mml:mrow><mml:mi>A</mml:mi><mml:mi>C</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mi>u</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mo>′</mml:mo></mml:msup></mml:mfrac><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow><mml:msup><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mo>′</mml:mo></mml:msup></mml:munderover><mml:mfrac><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mi>P</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mi>T</mml:mi><mml:msub><mml:mi>N</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:msub><mml:mi>P</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mi>T</mml:mi><mml:msub><mml:mi>N</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mi>F</mml:mi><mml:msub><mml:mi>N</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant=\"italic\">FP</mml:mi></mml:mrow><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:msup><mml:mrow><mml:mo>,</mml:mo><mml:mi>C</mml:mi></mml:mrow><mml:mo>′</mml:mo></mml:msup><mml:mo>∈</mml:mo><mml:mspace width=\"0.277778em\"/><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>k</mml:mi><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mi>w</mml:mi><mml:mi>n</mml:mi><mml:mspace width=\"0.277778em\"/><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq40\"><alternatives><tex-math id=\"M95\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$y^{\\prime}$$\\end{document}</tex-math><mml:math id=\"M96\"><mml:msup><mml:mi>y</mml:mi><mml:mo>′</mml:mo></mml:msup></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq41\"><alternatives><tex-math id=\"M97\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\epsilon =$$\\end{document}</tex-math><mml:math id=\"M98\"><mml:mrow><mml:mi>ϵ</mml:mi><mml:mo>=</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq42\"><alternatives><tex-math id=\"M99\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\alpha $$\\end{document}</tex-math><mml:math id=\"M100\"><mml:mi>α</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq43\"><alternatives><tex-math id=\"M101\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta $$\\end{document}</tex-math><mml:math id=\"M102\"><mml:mi>β</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq44\"><alternatives><tex-math id=\"M103\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\gamma $$\\end{document}</tex-math><mml:math id=\"M104\"><mml:mi>γ</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq45\"><alternatives><tex-math id=\"M105\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\alpha $$\\end{document}</tex-math><mml:math id=\"M106\"><mml:mi>α</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq46\"><alternatives><tex-math id=\"M107\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta $$\\end{document}</tex-math><mml:math id=\"M108\"><mml:mi>β</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq47\"><alternatives><tex-math id=\"M109\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\gamma $$\\end{document}</tex-math><mml:math id=\"M110\"><mml:mi>γ</mml:mi></mml:math></alternatives></inline-formula>"],"string":"[\n \"<inline-formula id=\\\"IEq1\\\"><alternatives><tex-math id=\\\"M1\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${x}_{i} , {x}_{j}$$\\\\end{document}</tex-math><mml:math id=\\\"M2\\\"><mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq2\\\"><alternatives><tex-math id=\\\"M3\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${h}_{i} , {h}_{j}$$\\\\end{document}</tex-math><mml:math id=\\\"M4\\\"><mml:mrow><mml:msub><mml:mi>h</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>h</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq3\\\"><alternatives><tex-math id=\\\"M5\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${z}_{i} , {z}_{j}$$\\\\end{document}</tex-math><mml:math id=\\\"M6\\\"><mml:mrow><mml:msub><mml:mi>z</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>z</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq4\\\"><alternatives><tex-math id=\\\"M7\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${z}_{i} , {z}_{j}$$\\\\end{document}</tex-math><mml:math id=\\\"M8\\\"><mml:mrow><mml:msub><mml:mi>z</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>z</mml:mi><mml:mi>j</mml:mi></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ1\\\"><label>1</label><alternatives><tex-math id=\\\"M9\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${L}_{cont}^{self-sup}=-\\\\frac{1}{\\\\left|{Q}_{1}\\\\right|}\\\\sum_{{\\\\upsilon }^{+}\\\\subseteq {Q}_{1}}log\\\\frac{\\\\mathit{exp}\\\\left(sim\\\\left(\\\\upsilon ,{\\\\upsilon }^{+}\\\\right)/\\\\tau \\\\right)}{\\\\mathit{exp}\\\\left(sim\\\\left(\\\\upsilon ,{\\\\upsilon }^{+}\\\\right)/\\\\tau \\\\right)+\\\\sum_{{v}^{-}\\\\in {N}_{I} }exp\\\\left(sim\\\\left(\\\\upsilon ,{\\\\upsilon }^{-}\\\\right)/\\\\tau \\\\right)}$$\\\\end{document}</tex-math><mml:math id=\\\"M10\\\" display=\\\"block\\\"><mml:mrow><mml:msubsup><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">cont</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mi>f</mml:mi><mml:mo>-</mml:mo><mml:mi>s</mml:mi><mml:mi>u</mml:mi><mml:mi>p</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mfenced close=\\\"|\\\" open=\\\"|\\\"><mml:msub><mml:mi>Q</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mfenced></mml:mfrac><mml:munder><mml:mo>∑</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>υ</mml:mi></mml:mrow><mml:mo>+</mml:mo></mml:msup><mml:mo>⊆</mml:mo><mml:msub><mml:mi>Q</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:munder><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>g</mml:mi><mml:mfrac><mml:mrow><mml:mi mathvariant=\\\"italic\\\">exp</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>υ</mml:mi><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>υ</mml:mi></mml:mrow><mml:mo>+</mml:mo></mml:msup></mml:mfenced><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:mi>τ</mml:mi></mml:mfenced></mml:mrow><mml:mrow><mml:mi mathvariant=\\\"italic\\\">exp</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>υ</mml:mi><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>υ</mml:mi></mml:mrow><mml:mo>+</mml:mo></mml:msup></mml:mfenced><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:mi>τ</mml:mi></mml:mfenced><mml:mo>+</mml:mo><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>v</mml:mi></mml:mrow><mml:mo>-</mml:mo></mml:msup><mml:mo>∈</mml:mo><mml:msub><mml:mi>N</mml:mi><mml:mi>I</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>p</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>υ</mml:mi><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>υ</mml:mi></mml:mrow><mml:mo>-</mml:mo></mml:msup></mml:mfenced><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:mi>τ</mml:mi></mml:mfenced></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq5\\\"><alternatives><tex-math id=\\\"M11\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${Q}_{1}$$\\\\end{document}</tex-math><mml:math id=\\\"M12\\\"><mml:msub><mml:mi>Q</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq6\\\"><alternatives><tex-math id=\\\"M13\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\tau $$\\\\end{document}</tex-math><mml:math id=\\\"M14\\\"><mml:mi>τ</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq7\\\"><alternatives><tex-math id=\\\"M15\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\upsilon }^{+}$$\\\\end{document}</tex-math><mml:math id=\\\"M16\\\"><mml:msup><mml:mrow><mml:mi>υ</mml:mi></mml:mrow><mml:mo>+</mml:mo></mml:msup></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq8\\\"><alternatives><tex-math id=\\\"M17\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\upsilon }^{-}$$\\\\end{document}</tex-math><mml:math id=\\\"M18\\\"><mml:msup><mml:mrow><mml:mi>υ</mml:mi></mml:mrow><mml:mo>-</mml:mo></mml:msup></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq9\\\"><alternatives><tex-math id=\\\"M19\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\upsilon $$\\\\end{document}</tex-math><mml:math id=\\\"M20\\\"><mml:mi>υ</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq10\\\"><alternatives><tex-math id=\\\"M21\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${N}_{1}$$\\\\end{document}</tex-math><mml:math id=\\\"M22\\\"><mml:msub><mml:mi>N</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq11\\\"><alternatives><tex-math id=\\\"M23\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\upsilon }^{+}, {\\\\upsilon }^{-}$$\\\\end{document}</tex-math><mml:math id=\\\"M24\\\"><mml:mrow><mml:msup><mml:mrow><mml:mi>υ</mml:mi></mml:mrow><mml:mo>+</mml:mo></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>υ</mml:mi></mml:mrow><mml:mo>-</mml:mo></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq12\\\"><alternatives><tex-math id=\\\"M25\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${A}^{\\\\mathrm{^{\\\\prime}}}$$\\\\end{document}</tex-math><mml:math id=\\\"M26\\\"><mml:msup><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:msup></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq13\\\"><alternatives><tex-math id=\\\"M27\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${B}^{\\\\mathrm{^{\\\\prime}}}$$\\\\end{document}</tex-math><mml:math 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id=\\\"M31\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$({B}^{\\\\mathrm{^{\\\\prime}}}$$\\\\end{document}</tex-math><mml:math id=\\\"M32\\\"><mml:mrow><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:msup><mml:mrow><mml:mi>B</mml:mi></mml:mrow><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq16\\\"><alternatives><tex-math id=\\\"M33\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${B}^{\\\\mathrm{^{\\\\prime}}})$$\\\\end{document}</tex-math><mml:math id=\\\"M34\\\"><mml:mrow><mml:msup><mml:mrow><mml:mi>B</mml:mi></mml:mrow><mml:msup><mml:mrow/><mml:mo>′</mml:mo></mml:msup></mml:msup><mml:mrow><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq17\\\"><alternatives><tex-math id=\\\"M35\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$sim\\\\left(m, n\\\\right)={m}^{T}n / \\\\Vert m\\\\Vert \\\\Vert n\\\\Vert $$\\\\end{document}</tex-math><mml:math id=\\\"M36\\\"><mml:mrow><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mi>T</mml:mi></mml:msup><mml:mi>n</mml:mi><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:mrow><mml:mo stretchy=\\\"false\\\">‖</mml:mo><mml:mi>m</mml:mi><mml:mo stretchy=\\\"false\\\">‖</mml:mo><mml:mo stretchy=\\\"false\\\">‖</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\\\"false\\\">‖</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ2\\\"><label>2</label><alternatives><tex-math id=\\\"M37\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${L}_{seg} = {\\\\alpha L}_{ce} + \\\\beta {L}_{center} + \\\\gamma {L}_{cont}^{sup}$$\\\\end{document}</tex-math><mml:math id=\\\"M38\\\" display=\\\"block\\\"><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">seg</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>α</mml:mi><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\\\"italic\\\">ce</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mi>β</mml:mi><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">center</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mi>γ</mml:mi><mml:msubsup><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">cont</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\\\"italic\\\">sup</mml:mi></mml:mrow></mml:msubsup></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq18\\\"><alternatives><tex-math id=\\\"M39\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$({x}_{n}, {y}_{n})$$\\\\end{document}</tex-math><mml:math id=\\\"M40\\\"><mml:mrow><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>n</mml:mi></mml:msub><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ3\\\"><label>3</label><alternatives><tex-math id=\\\"M41\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${L}_{ce} =- \\\\frac{1}{N}\\\\sum_{n = 1}^{N}log\\\\left(\\\\frac{\\\\mathit{exp}\\\\left({f(x}_{n, {y}_{n}})\\\\right)}{{\\\\sum }_{k= 1}^{K}exp(f({x}_{n,k}))}\\\\right)$$\\\\end{document}</tex-math><mml:math id=\\\"M42\\\" display=\\\"block\\\"><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">ce</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>N</mml:mi></mml:mfrac><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>N</mml:mi></mml:munderover><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>g</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mfrac><mml:mrow><mml:mi mathvariant=\\\"italic\\\">exp</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:msub><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mrow></mml:msub><mml:mrow><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>K</mml:mi></mml:msubsup><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>p</mml:mi><mml:mrow><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:mi>f</mml:mi><mml:mrow><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow></mml:mrow></mml:mfrac></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq19\\\"><alternatives><tex-math id=\\\"M43\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$f({x}_{n,k})$$\\\\end{document}</tex-math><mml:math id=\\\"M44\\\"><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:msub><mml:mi>x</mml:mi><mml:mrow><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq20\\\"><alternatives><tex-math id=\\\"M45\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$k$$\\\\end{document}</tex-math><mml:math id=\\\"M46\\\"><mml:mi>k</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq21\\\"><alternatives><tex-math id=\\\"M47\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$x$$\\\\end{document}</tex-math><mml:math id=\\\"M48\\\"><mml:mi>x</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq22\\\"><alternatives><tex-math id=\\\"M49\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${c}_{i}$$\\\\end{document}</tex-math><mml:math id=\\\"M50\\\"><mml:msub><mml:mi>c</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq23\\\"><alternatives><tex-math id=\\\"M51\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${L}_{center}=\\\\frac{1}{2}\\\\sum_{n=1}^{N}{\\\\Vert {f(x}_{n})-{c}_{{y}_{n}}\\\\Vert }^{2}$$\\\\end{document}</tex-math><mml:math id=\\\"M52\\\"><mml:mrow><mml:msub><mml:mi>L</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">center</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mn>2</mml:mn></mml:mfrac><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>N</mml:mi></mml:msubsup><mml:msup><mml:mrow><mml:mrow><mml:mo stretchy=\\\"false\\\">‖</mml:mo></mml:mrow><mml:msub><mml:mrow><mml:mi>f</mml:mi><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:mi>x</mml:mi></mml:mrow><mml:mi>n</mml:mi></mml:msub><mml:mrow><mml:mo 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id=\\\"IEq25\\\"><alternatives><tex-math id=\\\"M55\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${y}_{n}$$\\\\end{document}</tex-math><mml:math id=\\\"M56\\\"><mml:msub><mml:mi>y</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq26\\\"><alternatives><tex-math id=\\\"M57\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${c}_{{y}_{n}}$$\\\\end{document}</tex-math><mml:math id=\\\"M58\\\"><mml:msub><mml:mi>c</mml:mi><mml:msub><mml:mi>y</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:msub></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ4\\\"><label>4</label><alternatives><tex-math id=\\\"M59\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${L}_{cont}^{sup}=-\\\\frac{1}{\\\\left|{P}_{1}\\\\right|}{\\\\sum }_{{x}^{1+}\\\\in 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close=\\\")\\\" open=\\\"(\\\"><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mn>1</mml:mn></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo></mml:mrow></mml:msup></mml:mfenced><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:mi>τ</mml:mi></mml:mfenced><mml:mo>+</mml:mo><mml:msub><mml:mo>∑</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>-</mml:mo></mml:mrow></mml:msup><mml:mo>∈</mml:mo><mml:msub><mml:mi>N</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:mi mathvariant=\\\"italic\\\">exp</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mn>1</mml:mn></mml:msup><mml:mo>,</mml:mo><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>-</mml:mo></mml:mrow></mml:msup></mml:mfenced><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:mi>τ</mml:mi></mml:mfenced></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq27\\\"><alternatives><tex-math id=\\\"M61\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$sim\\\\left(m,n\\\\right)\\\\triangleq \\\\frac{{m}^{t}n}{\\\\Vert m\\\\Vert \\\\Vert n\\\\Vert }$$\\\\end{document}</tex-math><mml:math id=\\\"M62\\\"><mml:mrow><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>n</mml:mi></mml:mfenced><mml:mo>≜</mml:mo><mml:mfrac><mml:mrow><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mi>t</mml:mi></mml:msup><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mo stretchy=\\\"false\\\">‖</mml:mo><mml:mi>m</mml:mi><mml:mo stretchy=\\\"false\\\">‖</mml:mo><mml:mo stretchy=\\\"false\\\">‖</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\\\"false\\\">‖</mml:mo></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq28\\\"><alternatives><tex-math id=\\\"M63\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${P}_{1}$$\\\\end{document}</tex-math><mml:math id=\\\"M64\\\"><mml:msub><mml:mi>P</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq29\\\"><alternatives><tex-math id=\\\"M65\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${N}_{1}$$\\\\end{document}</tex-math><mml:math id=\\\"M66\\\"><mml:msub><mml:mi>N</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq30\\\"><alternatives><tex-math id=\\\"M67\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${x}^{1}$$\\\\end{document}</tex-math><mml:math id=\\\"M68\\\"><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mn>1</mml:mn></mml:msup></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq31\\\"><alternatives><tex-math id=\\\"M69\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${x}^{1}$$\\\\end{document}</tex-math><mml:math id=\\\"M70\\\"><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mn>1</mml:mn></mml:msup></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ5\\\"><label>5</label><alternatives><tex-math id=\\\"M71\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\left\\\\{\\\\begin{array}{ll}class\\\\_label= unknown, & if\\\\; {{\\\\max}}_{k}\\\\left(p\\\\left(k|{x}_{n}\\\\right)\\\\right)<\\\\epsilon \\\\\\\\ class\\\\_label=argma{x}_{k}\\\\left(p\\\\left(k|{x}_{n}\\\\right)\\\\right),& otherwise\\\\end{array}\\\\right.$$\\\\end{document}</tex-math><mml:math id=\\\"M72\\\" display=\\\"block\\\"><mml:mfenced open=\\\"{\\\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\\\"left\\\"><mml:mrow><mml:mi>c</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mi>_</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>b</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mo>=</mml:mo><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>k</mml:mi><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mi>w</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\\\"left\\\"><mml:mrow><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width=\\\"0.277778em\\\"/><mml:msub><mml:mo movablelimits=\\\"true\\\">max</mml:mo><mml:mi>k</mml:mi></mml:msub><mml:mfenced close=\\\")\\\" 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stretchy=\\\"false\\\">|</mml:mo></mml:mrow><mml:msub><mml:mi>x</mml:mi><mml:mi>n</mml:mi></mml:msub></mml:mfenced></mml:mfenced><mml:mo>,</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\\\"left\\\"><mml:mrow><mml:mi mathvariant=\\\"italic\\\">otherwise</mml:mi></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mfenced></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq32\\\"><alternatives><tex-math id=\\\"M73\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$p\\\\left(k|{x}_{n}\\\\right)$$\\\\end{document}</tex-math><mml:math id=\\\"M74\\\"><mml:mrow><mml:mi>p</mml:mi><mml:mfenced close=\\\")\\\" 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O. \"], \"italic\": [\"Cognitive Foundations of Musical Pitch\"]}, {\"label\": [\"5.\"], \"surname\": [\"Xie\", \"Fu\", \"Feng\", \"Luo\"], \"given-names\": [\"L\", \"ZH\", \"W\", \"Y\"], \"article-title\": [\"Pitch-density-based features and an SVM binary tree approach for multi-class audio classification in broadcast news\"], \"source\": [\"Multimed. Syst.\"], \"year\": [\"2011\"], \"volume\": [\"17\"], \"fpage\": [\"101\"], \"lpage\": [\"112\"], \"pub-id\": [\"10.1007/s00530-010-0205-x\"]}, {\"label\": [\"6.\"], \"mixed-citation\": [\"Aljanaki, A. & Soleymani, M. A data-driven approach to mid-level perceptual musical feature modeling. arXiv preprint \"], \"ext-link\": [\"arXiv:1806.04903\"]}, {\"label\": [\"7.\"], \"surname\": [\"Ozer\", \"Ozer\", \"Findik\"], \"given-names\": [\"I\", \"Z\", \"O\"], \"article-title\": [\"Noise robust sound event classification with convolutional neural network\"], \"source\": [\"Neurocomputing\"], \"year\": [\"2018\"], \"volume\": [\"272\"], \"fpage\": [\"505\"], \"lpage\": [\"512\"], \"pub-id\": [\"10.1016/j.neucom.2017.07.021\"]}, {\"label\": [\"8.\"], \"surname\": [\"Dong\", \"Yin\", \"Cong\", \"Du\", \"Huang\"], \"given-names\": [\"X\", \"B\", \"Y\", \"Z\", \"X\"], \"article-title\": [\"Environment sound event classification with a two-stream convolutional neural network\"], \"source\": [\"IEEE Access\"], \"year\": [\"2020\"], \"volume\": [\"8\"], \"fpage\": [\"125714\"], \"lpage\": [\"125721\"], \"pub-id\": [\"10.1109/ACCESS.2020.3007906\"]}, {\"label\": [\"9.\"], \"mixed-citation\": [\"Chan, T. 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Segmental contrastive predictive coding for unsupervised word segmentation. arXiv preprint \"], \"ext-link\": [\"arXiv:2106.02170\"]}, {\"label\": [\"48.\"], \"mixed-citation\": [\"Cuervo, S. \"], \"italic\": [\"et al.\", \"ICASSP 2022\\u20132022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)\"]}, {\"label\": [\"49.\"], \"mixed-citation\": [\"Oord, A. V. D., Li, Y. & Vinyals, O. Representation learning with contrastive predictive coding. arXiv preprint \"], \"ext-link\": [\"arXiv:1807.03748\"]}, {\"label\": [\"50.\"], \"surname\": [\"Hsu\", \"Bolte\", \"Tsai\", \"Lakhotia\", \"Salakhutdinov\", \"Mohamed\"], \"given-names\": [\"WN\", \"B\", \"YHH\", \"K\", \"R\", \"A\"], \"article-title\": [\"Hubert: Self-supervised speech representation learning by masked prediction of hidden units\"], \"source\": [\"IEEE/ACM Trans. Audio Speech Lang. 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Multimed.\"], \"year\": [\"2015\"], \"volume\": [\"17\"], \"issue\": [\"11\"], \"fpage\": [\"2059\"], \"lpage\": [\"2071\"], \"pub-id\": [\"10.1109/TMM.2015.2478068\"]}, {\"label\": [\"64.\"], \"surname\": [\"Mesaros\", \"Heittola\", \"Virtanen\"], \"given-names\": [\"A\", \"T\", \"T\"], \"article-title\": [\"Metrics for polyphonic sound event detection\"], \"source\": [\"Appl. Sci.\"], \"year\": [\"2016\"], \"volume\": [\"6\"], \"issue\": [\"6\"], \"fpage\": [\"162\"], \"pub-id\": [\"10.3390/app6060162\"]}, {\"label\": [\"65.\"], \"mixed-citation\": [\"Kingma, D. P. & Ba, J. Adam: A method for stochastic optimization. arXiv preprint \"], \"ext-link\": [\"arXiv:1412.6980\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":65,"string":"65"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:56"},"citation":{"kind":"string","value":"Sci Rep. 2024 Jan 13; 14:1282"},"package_file":{"kind":"string","value":"oa_package/76/1f/PMC10787752.tar.gz"}}},{"rowIdx":83,"cells":{"accession_id":{"kind":"string","value":"PMC10787753"},"pmid":{"kind":"string","value":"38218989"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par2\">There is no need to dwell on the global obesity epidemic in 2023 and its many connections with environmental issues. It has been estimated that the economic impact of overweight and obesity will grow from 2.19 to 3.3% of gross domestic product in > 160 countries by 2060<sup>##REF##36130777##1##</sup>. While the development of obesity inherently involves a waste of natural resources, the health consequences of obesity also have severe repercussions, and bariatric surgical procedures, in their current configuration, undoubtedly have their share of responsibility. Indeed, obesity surgery itself generates a significant proportion of global greenhouse gas emissions (GHGe) to which operating rooms (OR) have been shown to contribute greatly<sup>##REF##29851650##2##–##UREF##0##4##</sup>. The time has now come for bariatric specialists to take an in-depth look at the relationship between these issues and how they should be addressed.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">There is no need to dwell on the global obesity epidemic in 2023 and its many connections with environmental issues. It has been estimated that the economic impact of overweight and obesity will grow from 2.19 to 3.3% of gross domestic product in > 160 countries by 2060<sup>##REF##36130777##1##</sup>. While the development of obesity inherently involves a waste of natural resources, the health consequences of obesity also have severe repercussions, and bariatric surgical procedures, in their current configuration, undoubtedly have their share of responsibility. Indeed, obesity surgery itself generates a significant proportion of global greenhouse gas emissions (GHGe) to which operating rooms (OR) have been shown to contribute greatly<sup>##REF##29851650##2##–##UREF##0##4##</sup>. The time has now come for bariatric specialists to take an in-depth look at the relationship between these issues and how they should be addressed.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Material and methods</title>","<p id=\"Par13\">Following the steps described in previous studies<sup>##REF##29851650##2##–##UREF##1##5##</sup> and taking into account local possibilities, we have identified several measures that could be implemented in our center and its bariatric component, with a combination of approaches: (1) original measures pertaining to recycling and maximizing instrument use; (2) improvements in waste management that could have been implemented earlier, as other centers have done; (3) relevant measures that should be implemented in the near future but have not been so far because of more or less temporary conditions at the local level or due to the current health regulations; (4) decreased use of anesthetic gas through new ways. The methods are listed in Table ##TAB##0##1##.</p>","<p id=\"Par14\">Some measurements have been suggested based on available data, i.e. the DD UK tables<sup>##UREF##2##7##</sup>. In this regard, it appeared that calculations concerning expensive and disposable instruments that are widely used in bariatrics (energy devices, staplers) were most meaningful, while others remained elusive.</p>"],"string":"[\n \"<title>Material and methods</title>\",\n \"<p id=\\\"Par13\\\">Following the steps described in previous studies<sup>##REF##29851650##2##–##UREF##1##5##</sup> and taking into account local possibilities, we have identified several measures that could be implemented in our center and its bariatric component, with a combination of approaches: (1) original measures pertaining to recycling and maximizing instrument use; (2) improvements in waste management that could have been implemented earlier, as other centers have done; (3) relevant measures that should be implemented in the near future but have not been so far because of more or less temporary conditions at the local level or due to the current health regulations; (4) decreased use of anesthetic gas through new ways. The methods are listed in Table ##TAB##0##1##.</p>\",\n \"<p id=\\\"Par14\\\">Some measurements have been suggested based on available data, i.e. the DD UK tables<sup>##UREF##2##7##</sup>. In this regard, it appeared that calculations concerning expensive and disposable instruments that are widely used in bariatrics (energy devices, staplers) were most meaningful, while others remained elusive.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<title>Difficulties encountered to implement the study</title>","<p id=\"Par25\">\n<list list-type=\"bullet\"><list-item><p>Sparing GHGe is a dynamic and challenging process that currently requires finesse and local negotiation. Not everything is possible in terms of packaging, sterilization process, or reprocessing.</p></list-item><list-item><p id=\"Par26\">- Recycling surgical instruments was our core initiative (Figs. ##FIG##0##1## and ##FIG##1##2##), since it has been pointed out that there was a high potential in using less surgical tools and/or reusing them<sup>##REF##29851650##2##,##REF##32516230##3##</sup>. Yet, recycling turned out to be tedious, and not cost-efficient: each instrument required 20–40 min to be dismantled, while only 30% of their two components (plastic and metal) could be recycled. As a market item, the recycled material has a highly volatile benefit that is poor for the time being, e.g. at best at the end of 2022 1t Inox = 800–150 € for transportation, other metals = 150€/t, plastic = 150–40 €/t for compacting, not including storage. Moreover, some items contain traces of rare-earth elements that are difficult to recycle. An economic equilibrium is not foreseeable in the next future in this respect, but the initial design of such instruments could be revised in order to facilitate their dismantling, for example in decreasing the glued parts.</p></list-item><list-item><p id=\"Par27\"> Some of our initiatives were indeed in line with current environmental recommendations, which had not been fully implemented in our hospital: packaging and other waste management, laundry, etc.</p></list-item><list-item><p id=\"Par28\">Recycling anesthetic gas canisters represents a recent initiative that has encountered issues of compatibility with current breathing machines. It also conflicted with another recent tool, the AGSS, which conveys anesthetic gases from the OR to the roof to be released in the outside atmosphere.</p></list-item></list>\n</p>","<title>Bias</title>","<p id=\"Par29\">\n<list list-type=\"bullet\"><list-item><p> Global GHGe attributed to the health sector ranges from 6 to 10%; these discrepancies arise from the parameters considered in calculations with on the one hand strictly on-site calculations and, on the other hand, those considering the whole scope of GHGe, e.g. the supply chains, which may account for 59% of these emissions<sup>##REF##29851650##2##</sup>, and the emissions due to staff/patient consumption and transportation, research and development for a given device, etc. Theoretically, only studies using the life cycle assessment (LCA) methodology for all items should be considered for analysis<sup>##REF##32516230##3##</sup>.</p></list-item><list-item><p id=\"Par30\"> Careful attention should be paid to the boundaries, because otherwise any study would remain speculative, as is the case when dealing with the scope of product inventory for a given functional unit, e.g. a surgical procedure.</p></list-item><list-item><p id=\"Par31\">Confusion may arise with regulatory measures that are imposed or advised ahead of common practice. A typical example concerns the anesthesia practice and the gases that are commonly used and/or locally excluded.</p></list-item><list-item><p id=\"Par32\"> Advocating reusable (e.g. cotton-made) gowns and surgical drapes is relevant, as is reusing/reprocessing instruments, but, depending on each location, it is possible that this measure is encouraged or, on the contrary, that it goes against recommendations regarding infection control.</p></list-item><list-item><p id=\"Par33\">The discussion about energy sources covers a variety of situations and energy mixes, for instance in the French Rhône-Alpes area, where this study has been conducted, > 70% of the electricity comes from nuclear energy, which has a very favorable GHGe profile, whereas parts of Germany still rely on coal-electricity supply.</p></list-item><list-item><p id=\"Par34\">The example of anesthetic gas emission is compelling: the AGSS has been introduced to protect the personnel from gas emissions inside the ORs, but it also consumes energy and releases GHGe in the atmosphere. The retrieving canister solves this problem, but at an extra-cost that had not been anticipated.</p></list-item><list-item><p id=\"Par35\"> Advances in surgical techniques can also be challenging, the typical example being robotic surgery. In a study conducted in the field of gynecology by Woods et al., the solid waste generated and energy consumed by robotic surgery represented 40.3 kg CO<sub>2</sub> eq./patient vs. 29.2 for laparoscopy (+ 38%) and 22.7 for laparotomy (+ 77%)<sup>##REF##25708320##10##</sup>. Returning to laparotomy is barely imaginable, and the robotic upgrade is far from insignificant as it represents an asset in many centers<sup>##REF##35726503##11##</sup>.</p></list-item><list-item><p id=\"Par36\"> In the study by Thiel<sup>##UREF##1##5##</sup> pertaining to laparoscopic hysterectomies, the difference from baseline in GHGe resulting from reprocessing was 9%. The difference resulting from minimizing the instruments was computed at 46%, most of them being seemingly disposable. In our model, we included the recycling of disposable components (staplers and energy devices), which represent the core of some operations (e.g. > 1500 € in a sleeve gastrectomy), and we took into account the emissions resulting from sterilization (autoclave).</p></list-item><list-item><p id=\"Par37\"> Other limitations: we did not take into account the whole scope of the surgical process, from pre-operative measures to post-operative care, including drug prescription; the computer and email activities have not been included; the water footprint has not been modified; an effort has been made regarding the surgical shirts and trousers for the staff that are no longer disposable, but not regarding the surgical drapes and gowns. Likewise, the duration of the procedures was not relevant, because these are standardized interventions with a limited set of variations (Table ##TAB##3##4##), the operator and team being the same.</p></list-item></list>\n</p>","<p id=\"Par38\">During the first study period, i.e. the control period, from October 1, 2021 to December 31, 2021, 59 operations were performed, while during the second study period, i.e. the action period, from January 1, 2022 to March 31, 2022, 56 operations were performed (Table ##TAB##3##4##).</p>","<p id=\"Par39\">There were neither complications in these two series of interventions, nor intra-operative adverse events that could be attributed to one or the either; the operative time was not different in the two series, regarding the all scope of procedures. None of the measures taken represented an actual “waste of time”.</p>","<p id=\"Par40\">For 1 laparoscopic sleeve gastrectomy = 12.3 kg CO<sub>2</sub> eq. saved, for 1 band removal = 5.9 kg CO<sub>2</sub> eq. saved, and approx. the equivalent for 1 Roux-en-Y gastric bypass, which amounts to 100 kg CO<sub>2</sub> eq. saved in 3 months, and by extrapolation, 2400 kg in one year (Table ##TAB##4##5##).</p>","<title>Estimation of our ECO-SCORE</title>","<p id=\"Par41\">Based on Table ##TAB##1##2##, we assessed a global SCORE at C+: waste 3+, recycling 3+, diminishing surgical instruments 4−, anesthetic 2, energy 4, other 1.</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>Difficulties encountered to implement the study</title>\",\n \"<p id=\\\"Par25\\\">\\n<list list-type=\\\"bullet\\\"><list-item><p>Sparing GHGe is a dynamic and challenging process that currently requires finesse and local negotiation. Not everything is possible in terms of packaging, sterilization process, or reprocessing.</p></list-item><list-item><p id=\\\"Par26\\\">- Recycling surgical instruments was our core initiative (Figs. ##FIG##0##1## and ##FIG##1##2##), since it has been pointed out that there was a high potential in using less surgical tools and/or reusing them<sup>##REF##29851650##2##,##REF##32516230##3##</sup>. Yet, recycling turned out to be tedious, and not cost-efficient: each instrument required 20–40 min to be dismantled, while only 30% of their two components (plastic and metal) could be recycled. As a market item, the recycled material has a highly volatile benefit that is poor for the time being, e.g. at best at the end of 2022 1t Inox = 800–150 € for transportation, other metals = 150€/t, plastic = 150–40 €/t for compacting, not including storage. Moreover, some items contain traces of rare-earth elements that are difficult to recycle. An economic equilibrium is not foreseeable in the next future in this respect, but the initial design of such instruments could be revised in order to facilitate their dismantling, for example in decreasing the glued parts.</p></list-item><list-item><p id=\\\"Par27\\\"> Some of our initiatives were indeed in line with current environmental recommendations, which had not been fully implemented in our hospital: packaging and other waste management, laundry, etc.</p></list-item><list-item><p id=\\\"Par28\\\">Recycling anesthetic gas canisters represents a recent initiative that has encountered issues of compatibility with current breathing machines. It also conflicted with another recent tool, the AGSS, which conveys anesthetic gases from the OR to the roof to be released in the outside atmosphere.</p></list-item></list>\\n</p>\",\n \"<title>Bias</title>\",\n \"<p id=\\\"Par29\\\">\\n<list list-type=\\\"bullet\\\"><list-item><p> Global GHGe attributed to the health sector ranges from 6 to 10%; these discrepancies arise from the parameters considered in calculations with on the one hand strictly on-site calculations and, on the other hand, those considering the whole scope of GHGe, e.g. the supply chains, which may account for 59% of these emissions<sup>##REF##29851650##2##</sup>, and the emissions due to staff/patient consumption and transportation, research and development for a given device, etc. Theoretically, only studies using the life cycle assessment (LCA) methodology for all items should be considered for analysis<sup>##REF##32516230##3##</sup>.</p></list-item><list-item><p id=\\\"Par30\\\"> Careful attention should be paid to the boundaries, because otherwise any study would remain speculative, as is the case when dealing with the scope of product inventory for a given functional unit, e.g. a surgical procedure.</p></list-item><list-item><p id=\\\"Par31\\\">Confusion may arise with regulatory measures that are imposed or advised ahead of common practice. A typical example concerns the anesthesia practice and the gases that are commonly used and/or locally excluded.</p></list-item><list-item><p id=\\\"Par32\\\"> Advocating reusable (e.g. cotton-made) gowns and surgical drapes is relevant, as is reusing/reprocessing instruments, but, depending on each location, it is possible that this measure is encouraged or, on the contrary, that it goes against recommendations regarding infection control.</p></list-item><list-item><p id=\\\"Par33\\\">The discussion about energy sources covers a variety of situations and energy mixes, for instance in the French Rhône-Alpes area, where this study has been conducted, > 70% of the electricity comes from nuclear energy, which has a very favorable GHGe profile, whereas parts of Germany still rely on coal-electricity supply.</p></list-item><list-item><p id=\\\"Par34\\\">The example of anesthetic gas emission is compelling: the AGSS has been introduced to protect the personnel from gas emissions inside the ORs, but it also consumes energy and releases GHGe in the atmosphere. The retrieving canister solves this problem, but at an extra-cost that had not been anticipated.</p></list-item><list-item><p id=\\\"Par35\\\"> Advances in surgical techniques can also be challenging, the typical example being robotic surgery. In a study conducted in the field of gynecology by Woods et al., the solid waste generated and energy consumed by robotic surgery represented 40.3 kg CO<sub>2</sub> eq./patient vs. 29.2 for laparoscopy (+ 38%) and 22.7 for laparotomy (+ 77%)<sup>##REF##25708320##10##</sup>. Returning to laparotomy is barely imaginable, and the robotic upgrade is far from insignificant as it represents an asset in many centers<sup>##REF##35726503##11##</sup>.</p></list-item><list-item><p id=\\\"Par36\\\"> In the study by Thiel<sup>##UREF##1##5##</sup> pertaining to laparoscopic hysterectomies, the difference from baseline in GHGe resulting from reprocessing was 9%. The difference resulting from minimizing the instruments was computed at 46%, most of them being seemingly disposable. In our model, we included the recycling of disposable components (staplers and energy devices), which represent the core of some operations (e.g. > 1500 € in a sleeve gastrectomy), and we took into account the emissions resulting from sterilization (autoclave).</p></list-item><list-item><p id=\\\"Par37\\\"> Other limitations: we did not take into account the whole scope of the surgical process, from pre-operative measures to post-operative care, including drug prescription; the computer and email activities have not been included; the water footprint has not been modified; an effort has been made regarding the surgical shirts and trousers for the staff that are no longer disposable, but not regarding the surgical drapes and gowns. Likewise, the duration of the procedures was not relevant, because these are standardized interventions with a limited set of variations (Table ##TAB##3##4##), the operator and team being the same.</p></list-item></list>\\n</p>\",\n \"<p id=\\\"Par38\\\">During the first study period, i.e. the control period, from October 1, 2021 to December 31, 2021, 59 operations were performed, while during the second study period, i.e. the action period, from January 1, 2022 to March 31, 2022, 56 operations were performed (Table ##TAB##3##4##).</p>\",\n \"<p id=\\\"Par39\\\">There were neither complications in these two series of interventions, nor intra-operative adverse events that could be attributed to one or the either; the operative time was not different in the two series, regarding the all scope of procedures. None of the measures taken represented an actual “waste of time”.</p>\",\n \"<p id=\\\"Par40\\\">For 1 laparoscopic sleeve gastrectomy = 12.3 kg CO<sub>2</sub> eq. saved, for 1 band removal = 5.9 kg CO<sub>2</sub> eq. saved, and approx. the equivalent for 1 Roux-en-Y gastric bypass, which amounts to 100 kg CO<sub>2</sub> eq. saved in 3 months, and by extrapolation, 2400 kg in one year (Table ##TAB##4##5##).</p>\",\n \"<title>Estimation of our ECO-SCORE</title>\",\n \"<p id=\\\"Par41\\\">Based on Table ##TAB##1##2##, we assessed a global SCORE at C+: waste 3+, recycling 3+, diminishing surgical instruments 4−, anesthetic 2, energy 4, other 1.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<title>Obesity, obesity surgery, and environment</title>","<p id=\"Par42\">Obesity per se is a major contributor to GHGe, in relation with the carbon footprint of food production and associated supply chain. The genesis of obesity thus significantly impacts GHGe worldwide. Regarding metabolic food waste (MFW), which is defined as the amount of food leading to excess body fat, Europe and North America were found to display the highest values for all three MFW footprints (i.e. carbon, water, and land footprints), being 14 times larger than in South Asia and South-East Asia<sup>##REF##31508421##12##</sup>. The modern food environment, i.e. food availability, also strongly contributes to this genesis in any country<sup>##UREF##5##13##</sup>.</p>","<p id=\"Par43\">One would assume that a more or less significant part of this waste may be compensated to a large extent by weight loss, notably achieved by bariatric procedures. While there is no substantial evidence for this, a large body of arguments point to reduced medical costs after surgical weight loss, with therefore a favorable impact on the environment<sup>##REF##34211116##14##</sup>.</p>","<p id=\"Par44\">Although bariatric procedures marginally contribute to lower resource consumption once patients have achieved sustained weight loss, an argument can be put forward in favor of such interventions, therefore justifying coverage by health insurances: the markedly increased well-being of the obese population<sup>##REF##25639648##15##</sup>. In other words, fighting the stigma that is often associated with obesity is beneficial to this population, since this stigma prevents obese patients from gaining easy access to treatments such as bariatric surgery<sup>##REF##32127716##16##</sup>.</p>","<p id=\"Par45\">Two current trends may adversely affect this reasoning: (1) the relatively aggressive approach towards Stage I obesity [body mass index (BMI) 30–35], with obesity surgery claiming success in those patients when affected by a comorbidity, typically type 2 diabetes<sup>##REF##24638958##17##</sup>, hence the assertion that metabolic surgery should be strongly promoted and offsets the costs of treating such conditions; (2) the relative extension of robotic surgery in this field also significantly impacts GHGe, as previously shown in gynecological surgery<sup>##REF##25708320##10##</sup> or other types of surgery<sup>##REF##35726503##11##</sup>.</p>","<title>Looking for opportunities and compromises</title>","<p id=\"Par46\">Other strategies pursuing common goals are currently being considered. On the one hand, “green surgical innovation”<sup>##REF##35781426##18##</sup> suggests that evaluating new surgical devices or new surgical options in general, such as robotics<sup>##REF##25708320##10##,##REF##35726503##11##</sup> or digitalized options, could benefit from the strict analysis of their carbon footprint, which thus determines whether a new strategy/device should be implemented or not. This is questionable, since innovations and environment may remain compatible. On the other hand, others rightly point out that surgical issues have boundaries that go beyond the strict perimeter of the OR<sup>##UREF##6##19##</sup>, or even suggest a much broader move that encompasses several items in order to build sustainable and resilient surgical systems, possibly at the level of a geographical area, e.g. the West Pacific region, including infrastructure, service delivery, finance, information systems, health workforce, and governance<sup>##UREF##7##20##</sup>. One may object that health systems are closely interconnected throughout the world, for example when it comes to the workforce, and that such definitions may be vague enough to hamper real and coordinated efforts.</p>","<p id=\"Par47\">As noted by Rizan et al.<sup>##REF##32516230##3##</sup>, the numbers are difficult to interpret because the various studies have different frameworks and use different references, with the authors placing emphasis on the various choices that can be debated, e.g. whether or not including anesthesiology, energy mix, life cycle analysis, etc. Hence, we suggest acting upon what is currently within reach at a given time and evaluating the progress that can be achieved at various levels. We propose to start from a given situation in a hospital and try to improve different scores, thereby contributing to a more global effort including recycling, re-processing, eliminating single-use items whenever possible, energy saving, and minimizing instrument use and anesthetic gas. It is common to feel that others should make environmental efforts before we do, or in other words, that other fields have a more detrimental impact on the environment than our own. In view of the substantial contribution of the Scope 2 (energy) to GHGe as compared to the others, one may claim to be powerless or favor green-washing options and focus on good intentions rather than real actions. Likewise, many choose to blend environmental issues with social issues when presenting results, which is politically relevant (at least regarding the so-called “social and environmental responsibility”), but probably scientifically irrelevant in the medical field.</p>","<p id=\"Par48\">Yet, it is interesting to look at other fields whose environmental strategy is nowadays being questioned, such as the automobile industry, fashion industry, construction, computer software and internet, etc. How fast are efforts being made and should we follow the same path? What kind of pressure is applied in each case? These are difficult questions, and it may be best that everyone acts on their own behalf, regardless of what others do.</p>","<p id=\"Par49\">For now, we overlooked the final aspect of GHGe in patients at the very end of the surgical process, i.e. considering the economical long-term benefits of weight loss, which offset the initial costs in many studies. For instance, the decrease in drug costs has been evaluated: In a meta-analysis performed by Lopes et al. in 2015, the mean reduction in total drug costs was estimated at 49.8% over a follow-up duration of 6–72 months after bariatric surgery<sup>##REF##26112137##21##</sup>. Yet, such studies are lacking for GHGe, and we need benchmarks. One study has been conducted in the field of esophageal reflux surgery, showing that the cost–benefit ratio was not favorable up to 9 years after surgery<sup>##REF##20932944##22##</sup>. Further studies are warranted to assess the benefits of GHGe reduction in the bariatric field.</p>","<p id=\"Par50\">Lastly, we addressed what could be called the “obesity debt”, i.e. the food waste associated with overweight and obesity, which amounts to 140 M tons/year according to Totti et al.<sup>##REF##31508421##12##</sup>. This does not impede the efforts towards weight loss, on the contrary, but it could be an incentive to favor less energy-consuming methods, e.g. endoscopic solutions rather than typical laparoscopic surgical options<sup>##REF##37156511##23##</sup>. While an ultimatum like the EU ban of combustion-engine cars by 2035 is barely conceivable, it makes sense to promote incentives to develop less impacting technologies such as endoscopic bariatric methods, perhaps associated with new drugs (GLP-1 receptor agonists).</p>","<p id=\"Par51\">There is no reason why bariatric surgery would be more or less environmentally friendly according to BMI range; yet adding more patients to the surgical workflow seems unfriendly to the same environment, particularly if alternative and sound options exist for those patients (drugs, endoscopy, etc.), which is the case for lower BMI patients, and even for selected higher BMI patients and/or unwilling to undergo surgery. To put it differently: such treatments, that are explicitly non-surgical, entail less GHGe, and are therefore more likely to concern this range of BMI (30–35). However, when surgery is performed in these patients, it does not mean more GHGe, but those could have been spared if surgery had not been the primary option.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<title>Obesity, obesity surgery, and environment</title>\",\n \"<p id=\\\"Par42\\\">Obesity per se is a major contributor to GHGe, in relation with the carbon footprint of food production and associated supply chain. The genesis of obesity thus significantly impacts GHGe worldwide. Regarding metabolic food waste (MFW), which is defined as the amount of food leading to excess body fat, Europe and North America were found to display the highest values for all three MFW footprints (i.e. carbon, water, and land footprints), being 14 times larger than in South Asia and South-East Asia<sup>##REF##31508421##12##</sup>. The modern food environment, i.e. food availability, also strongly contributes to this genesis in any country<sup>##UREF##5##13##</sup>.</p>\",\n \"<p id=\\\"Par43\\\">One would assume that a more or less significant part of this waste may be compensated to a large extent by weight loss, notably achieved by bariatric procedures. While there is no substantial evidence for this, a large body of arguments point to reduced medical costs after surgical weight loss, with therefore a favorable impact on the environment<sup>##REF##34211116##14##</sup>.</p>\",\n \"<p id=\\\"Par44\\\">Although bariatric procedures marginally contribute to lower resource consumption once patients have achieved sustained weight loss, an argument can be put forward in favor of such interventions, therefore justifying coverage by health insurances: the markedly increased well-being of the obese population<sup>##REF##25639648##15##</sup>. In other words, fighting the stigma that is often associated with obesity is beneficial to this population, since this stigma prevents obese patients from gaining easy access to treatments such as bariatric surgery<sup>##REF##32127716##16##</sup>.</p>\",\n \"<p id=\\\"Par45\\\">Two current trends may adversely affect this reasoning: (1) the relatively aggressive approach towards Stage I obesity [body mass index (BMI) 30–35], with obesity surgery claiming success in those patients when affected by a comorbidity, typically type 2 diabetes<sup>##REF##24638958##17##</sup>, hence the assertion that metabolic surgery should be strongly promoted and offsets the costs of treating such conditions; (2) the relative extension of robotic surgery in this field also significantly impacts GHGe, as previously shown in gynecological surgery<sup>##REF##25708320##10##</sup> or other types of surgery<sup>##REF##35726503##11##</sup>.</p>\",\n \"<title>Looking for opportunities and compromises</title>\",\n \"<p id=\\\"Par46\\\">Other strategies pursuing common goals are currently being considered. On the one hand, “green surgical innovation”<sup>##REF##35781426##18##</sup> suggests that evaluating new surgical devices or new surgical options in general, such as robotics<sup>##REF##25708320##10##,##REF##35726503##11##</sup> or digitalized options, could benefit from the strict analysis of their carbon footprint, which thus determines whether a new strategy/device should be implemented or not. This is questionable, since innovations and environment may remain compatible. On the other hand, others rightly point out that surgical issues have boundaries that go beyond the strict perimeter of the OR<sup>##UREF##6##19##</sup>, or even suggest a much broader move that encompasses several items in order to build sustainable and resilient surgical systems, possibly at the level of a geographical area, e.g. the West Pacific region, including infrastructure, service delivery, finance, information systems, health workforce, and governance<sup>##UREF##7##20##</sup>. One may object that health systems are closely interconnected throughout the world, for example when it comes to the workforce, and that such definitions may be vague enough to hamper real and coordinated efforts.</p>\",\n \"<p id=\\\"Par47\\\">As noted by Rizan et al.<sup>##REF##32516230##3##</sup>, the numbers are difficult to interpret because the various studies have different frameworks and use different references, with the authors placing emphasis on the various choices that can be debated, e.g. whether or not including anesthesiology, energy mix, life cycle analysis, etc. Hence, we suggest acting upon what is currently within reach at a given time and evaluating the progress that can be achieved at various levels. We propose to start from a given situation in a hospital and try to improve different scores, thereby contributing to a more global effort including recycling, re-processing, eliminating single-use items whenever possible, energy saving, and minimizing instrument use and anesthetic gas. It is common to feel that others should make environmental efforts before we do, or in other words, that other fields have a more detrimental impact on the environment than our own. In view of the substantial contribution of the Scope 2 (energy) to GHGe as compared to the others, one may claim to be powerless or favor green-washing options and focus on good intentions rather than real actions. Likewise, many choose to blend environmental issues with social issues when presenting results, which is politically relevant (at least regarding the so-called “social and environmental responsibility”), but probably scientifically irrelevant in the medical field.</p>\",\n \"<p id=\\\"Par48\\\">Yet, it is interesting to look at other fields whose environmental strategy is nowadays being questioned, such as the automobile industry, fashion industry, construction, computer software and internet, etc. How fast are efforts being made and should we follow the same path? What kind of pressure is applied in each case? These are difficult questions, and it may be best that everyone acts on their own behalf, regardless of what others do.</p>\",\n \"<p id=\\\"Par49\\\">For now, we overlooked the final aspect of GHGe in patients at the very end of the surgical process, i.e. considering the economical long-term benefits of weight loss, which offset the initial costs in many studies. For instance, the decrease in drug costs has been evaluated: In a meta-analysis performed by Lopes et al. in 2015, the mean reduction in total drug costs was estimated at 49.8% over a follow-up duration of 6–72 months after bariatric surgery<sup>##REF##26112137##21##</sup>. Yet, such studies are lacking for GHGe, and we need benchmarks. One study has been conducted in the field of esophageal reflux surgery, showing that the cost–benefit ratio was not favorable up to 9 years after surgery<sup>##REF##20932944##22##</sup>. Further studies are warranted to assess the benefits of GHGe reduction in the bariatric field.</p>\",\n \"<p id=\\\"Par50\\\">Lastly, we addressed what could be called the “obesity debt”, i.e. the food waste associated with overweight and obesity, which amounts to 140 M tons/year according to Totti et al.<sup>##REF##31508421##12##</sup>. This does not impede the efforts towards weight loss, on the contrary, but it could be an incentive to favor less energy-consuming methods, e.g. endoscopic solutions rather than typical laparoscopic surgical options<sup>##REF##37156511##23##</sup>. While an ultimatum like the EU ban of combustion-engine cars by 2035 is barely conceivable, it makes sense to promote incentives to develop less impacting technologies such as endoscopic bariatric methods, perhaps associated with new drugs (GLP-1 receptor agonists).</p>\",\n \"<p id=\\\"Par51\\\">There is no reason why bariatric surgery would be more or less environmentally friendly according to BMI range; yet adding more patients to the surgical workflow seems unfriendly to the same environment, particularly if alternative and sound options exist for those patients (drugs, endoscopy, etc.), which is the case for lower BMI patients, and even for selected higher BMI patients and/or unwilling to undergo surgery. To put it differently: such treatments, that are explicitly non-surgical, entail less GHGe, and are therefore more likely to concern this range of BMI (30–35). However, when surgery is performed in these patients, it does not mean more GHGe, but those could have been spared if surgery had not been the primary option.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":["<title>Conclusion</title>","<p id=\"Par52\">Is it cost-effective to try and diminish GHGe (and other items) in an operative setting? Does it affect surgical outcomes? Basically, the efforts we can make without further delay are mostly cheap or affordable, more strategic ones (e.g. shifting to less consuming operations) represent a different issue that would require funding/incentives and consensus. The efforts that we tried are allegedly quite doable even if facing reluctance; they did not and they should not impact the medical/surgical outcomes at all.</p>"],"string":"[\n \"<title>Conclusion</title>\",\n \"<p id=\\\"Par52\\\">Is it cost-effective to try and diminish GHGe (and other items) in an operative setting? Does it affect surgical outcomes? Basically, the efforts we can make without further delay are mostly cheap or affordable, more strategic ones (e.g. shifting to less consuming operations) represent a different issue that would require funding/incentives and consensus. The efforts that we tried are allegedly quite doable even if facing reluctance; they did not and they should not impact the medical/surgical outcomes at all.</p>\"\n]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Obesity is a growing issue worldwide, whose causes and consequences are linked to the environment and which therefore has a high carbon footprint. On the other hand, obesity surgery, along with other procedures in surgical suites, entails environmental consequences and responsibilities. We conducted a prospective comparative study on two groups of bariatric interventions (N = 59 and 56, respectively) during two consecutive periods of time (Oct 2021–March 2022), first without and then with specific measures aimed at reducing greenhouse gas emissions related to bariatric procedures by approximately 18%. These measures included recycling of disposable surgical equipment, minimizing its use, and curbing anesthetic gas emissions. Further and continuous efforts/incentives are warranted, including reframing the surgical strategies. Instead of comparing measurements, which is difficult at the present time, we suggest defining an ECO-SCORE in operating rooms, among other healthcare facilities.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Obesity is a growing issue worldwide, whose causes and consequences are linked to the environment and which therefore has a high carbon footprint. On the other hand, obesity surgery, along with other procedures in surgical suites, entails environmental consequences and responsibilities. We conducted a prospective comparative study on two groups of bariatric interventions (N = 59 and 56, respectively) during two consecutive periods of time (Oct 2021–March 2022), first without and then with specific measures aimed at reducing greenhouse gas emissions related to bariatric procedures by approximately 18%. These measures included recycling of disposable surgical equipment, minimizing its use, and curbing anesthetic gas emissions. Further and continuous efforts/incentives are warranted, including reframing the surgical strategies. Instead of comparing measurements, which is difficult at the present time, we suggest defining an ECO-SCORE in operating rooms, among other healthcare facilities.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Operating room and environment</title>","<p id=\"Par3\">Depending on calculations, the healthcare sector is responsible of up to 10% of total GHGe (USA, UK)<sup>##REF##29851650##2##,##REF##32516230##3##</sup>, which should raise awareness and call for urgent action<sup>##UREF##0##4##</sup>. It has been shown that ORs were a major source of GHGe worldwide, representing up to 60% of the emissions of a given hospital<sup>##REF##32516230##3##</sup>; their carbon footprint has been estimated at approximately 184 kg per intervention, which corresponds to the weekly consumption of a 4-person family in the western world<sup>##UREF##0##4##</sup>. Depending on the operations, locations, and calculation methods, this footprint varies from 6 to 814 kg<sup>##REF##32516230##3##</sup>. Among others, surgical operations account for 21–30% of hospital waste, and electricity alone represents more than 60% of the total<sup>##REF##29851650##2##,##REF##32516230##3##</sup>. There are three different scopes of GHGe in ORs (Scope 1: anesthetic gases; Scope 2: electricity use and heating; Scope 3: surgical supply chain and waste disposal), and it has been recommended to act separately on each of them by MacNeill et al.<sup>##REF##29851650##2##</sup>.</p>","<p id=\"Par4\">According to a meta-analysis by Rizan et al.<sup>##REF##32516230##3##</sup>, the carbon footprint of surgery can be reduced by improving the energy efficiency of ORs, using reusable or reprocessed surgical devices, and streamlining common procedures. While multiple approaches need to be combined, some limits have been encountered: commonly implemented means, such as recycling surgical waste, can result in a reduction in GHGe of less than 5%<sup>##UREF##1##5##</sup>.</p>","<p id=\"Par5\">Acting upon the elements that contribute to this situation proves difficult because of the great heterogeneity of potential measures that can be implemented in various locations<sup>##REF##29851650##2##</sup>. In a study by Thiel, the total carbon footprint generated by a laparoscopic hysterectomy was estimated at 562 kg and could be reduced to 285 kg and even to 125 kg if anesthesia was removed from the equation<sup>##UREF##1##5##</sup>. Anesthetic gas actually represents a very significant part of these emissions, as illustrated for example in 2020 by Ryan et al., who emphasized the global warming potential of sevoflurane<sup>##REF##20519425##6##</sup>.</p>","<p id=\"Par6\">Relevant leads have been suggested in order to decrease GHGe<sup>##UREF##1##5##</sup>: (1) minimizing the materials used in the OR, (2) maximizing instrument reuse and/or single-use instrument reprocessing, (3) moving away from some heat-trapping anesthetic gases, (4) reducing off-hour energy use in the OR. As a major user of those facilities and materials, bariatric surgery is a good example of the potential for savings in this regard.</p>","<p id=\"Par7\">Hence, we should answer the following questions:<list list-type=\"bullet\"><list-item><p id=\"Par8\">Can we clear a path towards less GHGe in a regular operating room, on a larger scale than currently and in a timely manner?</p></list-item><list-item><p id=\"Par9\">Can we elaborate a consensus regarding the best available options, despite the complexity of existing guide-lines, various focuses and regulations?</p></list-item><list-item><p id=\"Par10\">Do current habits take a reasonable path in terms of recycling, sparing resources, and finally looking for less energy-consuming procedures?</p></list-item><list-item><p id=\"Par11\">How do the specificities of bariatric/metabolic procedures impact this reasoning?</p></list-item><list-item><p id=\"Par12\">Ultimately, can we reduce the GHGe in the theatre by more than 10% for the time being?</p></list-item></list></p>","<title>Building a surgical ECO-SCORE</title>","<p id=\"Par15\">According to some reports for the industry in general, up to 60% of saved emissions may be obtained through inter-industrial cooperation, including 35% of recycling and of 5% energy consumption. These figures are actually difficult to extrapolate to the health sector, which displays great differences from one hospital or group of hospitals to another, as exemplified in Mac Neil’s paper<sup>##REF##29851650##2##</sup>.</p>","<p id=\"Par16\">Notwithstanding, hospitals could share mutual standards in many respects and despite their differences, from anesthetic gas to instrument reusing/reprocessing, with specific goals. Although some hospitals or groups of hospitals issued environmental or “responsibility” claims, it is difficult to assess whether or not a real green strategy has been implemented.</p>","<p id=\"Par17\">We suggest an ECO-SCORE including several indicators that would demonstrate a favorable trend starting from a given setting and would encompass the current variations among hospitals, areas, countries, etc. It seems reasonable to address several levels of objectives according to the different settings and to include progressive and planned measures in order to reach a valuable ECO-SCORE, starting from various baselines. We propose to draw inspiration from the Open Food project<sup>##UREF##4##9##</sup>, with grades ranging from A to F. Sequential strategies may be used. Balancing factors are often connected to local circumstances and leave room for evolutionary strategies: Table ##TAB##1##2##.</p>","<p id=\"Par18\">Several issues should be addressed in order to avoid misunderstandings:<list list-type=\"bullet\"><list-item><p id=\"Par19\"> Evaluating what can be implemented in a given location at a given time (i.e. ECO-SCORE in a context); examples: particular energy mix, recent initiatives taken at the local level or for instance at the level of a group of hospitals, such as waste policy.</p></list-item><list-item><p id=\"Par20\"> Addressing the strategic choices that have been or may be included, defined at a regional, national, or multinational level; examples: shift from bariatric surgery to bariatric endoscopy, promotion and/or prohibition of specific procedures. Such choices have an environmental background, but can face controversy at a given time, mostly on scientific grounds: for example, the shift to endoscopy could be implemented if and only if a longer duration of effect can be demonstrated.</p></list-item><list-item><p id=\"Par21\"> Pondering the potential arbitrations: robotic surgery, Enhanced Recovery After Surgery (ERAS), national or international guidelines, post-operative protocols etc.</p></list-item><list-item><p id=\"Par22\"> Estimating the factors that are overlooked because they fall into other categories of GHGe. These are typical and explain why figures may not match. A few examples: expenses for transportation, food, etc. for the staff and other employees in hospitals, research, education and training, choice of instruments (disposable or not).</p></list-item></list></p>","<p id=\"Par23\">We suggest the items of the ECO-SCORE may be accounted according to Table ##TAB##2##3##.</p>","<title>Statements</title>","<p id=\"Par24\">All procedures were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants for whom identifying information is included in this article. The study has been pre-approved by the Scientific Committee (Scientific Advisory Board) of the Private Hospitals Units from VIVALTO-France, GCS Merit Vivalto, as a licensing Committee. Figures ##FIG##0##1## and ##FIG##1##2## have been taken by the author (J Dargent), and therefore need no permission. They may be published under a CC BY open access license; permission is granted to publish them in all format i.e. print and digital.</p>"],"string":"[\n \"<title>Operating room and environment</title>\",\n \"<p id=\\\"Par3\\\">Depending on calculations, the healthcare sector is responsible of up to 10% of total GHGe (USA, UK)<sup>##REF##29851650##2##,##REF##32516230##3##</sup>, which should raise awareness and call for urgent action<sup>##UREF##0##4##</sup>. It has been shown that ORs were a major source of GHGe worldwide, representing up to 60% of the emissions of a given hospital<sup>##REF##32516230##3##</sup>; their carbon footprint has been estimated at approximately 184 kg per intervention, which corresponds to the weekly consumption of a 4-person family in the western world<sup>##UREF##0##4##</sup>. Depending on the operations, locations, and calculation methods, this footprint varies from 6 to 814 kg<sup>##REF##32516230##3##</sup>. Among others, surgical operations account for 21–30% of hospital waste, and electricity alone represents more than 60% of the total<sup>##REF##29851650##2##,##REF##32516230##3##</sup>. There are three different scopes of GHGe in ORs (Scope 1: anesthetic gases; Scope 2: electricity use and heating; Scope 3: surgical supply chain and waste disposal), and it has been recommended to act separately on each of them by MacNeill et al.<sup>##REF##29851650##2##</sup>.</p>\",\n \"<p id=\\\"Par4\\\">According to a meta-analysis by Rizan et al.<sup>##REF##32516230##3##</sup>, the carbon footprint of surgery can be reduced by improving the energy efficiency of ORs, using reusable or reprocessed surgical devices, and streamlining common procedures. While multiple approaches need to be combined, some limits have been encountered: commonly implemented means, such as recycling surgical waste, can result in a reduction in GHGe of less than 5%<sup>##UREF##1##5##</sup>.</p>\",\n \"<p id=\\\"Par5\\\">Acting upon the elements that contribute to this situation proves difficult because of the great heterogeneity of potential measures that can be implemented in various locations<sup>##REF##29851650##2##</sup>. In a study by Thiel, the total carbon footprint generated by a laparoscopic hysterectomy was estimated at 562 kg and could be reduced to 285 kg and even to 125 kg if anesthesia was removed from the equation<sup>##UREF##1##5##</sup>. Anesthetic gas actually represents a very significant part of these emissions, as illustrated for example in 2020 by Ryan et al., who emphasized the global warming potential of sevoflurane<sup>##REF##20519425##6##</sup>.</p>\",\n \"<p id=\\\"Par6\\\">Relevant leads have been suggested in order to decrease GHGe<sup>##UREF##1##5##</sup>: (1) minimizing the materials used in the OR, (2) maximizing instrument reuse and/or single-use instrument reprocessing, (3) moving away from some heat-trapping anesthetic gases, (4) reducing off-hour energy use in the OR. As a major user of those facilities and materials, bariatric surgery is a good example of the potential for savings in this regard.</p>\",\n \"<p id=\\\"Par7\\\">Hence, we should answer the following questions:<list list-type=\\\"bullet\\\"><list-item><p id=\\\"Par8\\\">Can we clear a path towards less GHGe in a regular operating room, on a larger scale than currently and in a timely manner?</p></list-item><list-item><p id=\\\"Par9\\\">Can we elaborate a consensus regarding the best available options, despite the complexity of existing guide-lines, various focuses and regulations?</p></list-item><list-item><p id=\\\"Par10\\\">Do current habits take a reasonable path in terms of recycling, sparing resources, and finally looking for less energy-consuming procedures?</p></list-item><list-item><p id=\\\"Par11\\\">How do the specificities of bariatric/metabolic procedures impact this reasoning?</p></list-item><list-item><p id=\\\"Par12\\\">Ultimately, can we reduce the GHGe in the theatre by more than 10% for the time being?</p></list-item></list></p>\",\n \"<title>Building a surgical ECO-SCORE</title>\",\n \"<p id=\\\"Par15\\\">According to some reports for the industry in general, up to 60% of saved emissions may be obtained through inter-industrial cooperation, including 35% of recycling and of 5% energy consumption. These figures are actually difficult to extrapolate to the health sector, which displays great differences from one hospital or group of hospitals to another, as exemplified in Mac Neil’s paper<sup>##REF##29851650##2##</sup>.</p>\",\n \"<p id=\\\"Par16\\\">Notwithstanding, hospitals could share mutual standards in many respects and despite their differences, from anesthetic gas to instrument reusing/reprocessing, with specific goals. Although some hospitals or groups of hospitals issued environmental or “responsibility” claims, it is difficult to assess whether or not a real green strategy has been implemented.</p>\",\n \"<p id=\\\"Par17\\\">We suggest an ECO-SCORE including several indicators that would demonstrate a favorable trend starting from a given setting and would encompass the current variations among hospitals, areas, countries, etc. It seems reasonable to address several levels of objectives according to the different settings and to include progressive and planned measures in order to reach a valuable ECO-SCORE, starting from various baselines. We propose to draw inspiration from the Open Food project<sup>##UREF##4##9##</sup>, with grades ranging from A to F. Sequential strategies may be used. Balancing factors are often connected to local circumstances and leave room for evolutionary strategies: Table ##TAB##1##2##.</p>\",\n \"<p id=\\\"Par18\\\">Several issues should be addressed in order to avoid misunderstandings:<list list-type=\\\"bullet\\\"><list-item><p id=\\\"Par19\\\"> Evaluating what can be implemented in a given location at a given time (i.e. ECO-SCORE in a context); examples: particular energy mix, recent initiatives taken at the local level or for instance at the level of a group of hospitals, such as waste policy.</p></list-item><list-item><p id=\\\"Par20\\\"> Addressing the strategic choices that have been or may be included, defined at a regional, national, or multinational level; examples: shift from bariatric surgery to bariatric endoscopy, promotion and/or prohibition of specific procedures. Such choices have an environmental background, but can face controversy at a given time, mostly on scientific grounds: for example, the shift to endoscopy could be implemented if and only if a longer duration of effect can be demonstrated.</p></list-item><list-item><p id=\\\"Par21\\\"> Pondering the potential arbitrations: robotic surgery, Enhanced Recovery After Surgery (ERAS), national or international guidelines, post-operative protocols etc.</p></list-item><list-item><p id=\\\"Par22\\\"> Estimating the factors that are overlooked because they fall into other categories of GHGe. These are typical and explain why figures may not match. A few examples: expenses for transportation, food, etc. for the staff and other employees in hospitals, research, education and training, choice of instruments (disposable or not).</p></list-item></list></p>\",\n \"<p id=\\\"Par23\\\">We suggest the items of the ECO-SCORE may be accounted according to Table ##TAB##2##3##.</p>\",\n \"<title>Statements</title>\",\n \"<p id=\\\"Par24\\\">All procedures were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants for whom identifying information is included in this article. The study has been pre-approved by the Scientific Committee (Scientific Advisory Board) of the Private Hospitals Units from VIVALTO-France, GCS Merit Vivalto, as a licensing Committee. Figures ##FIG##0##1## and ##FIG##1##2## have been taken by the author (J Dargent), and therefore need no permission. They may be published under a CC BY open access license; permission is granted to publish them in all format i.e. print and digital.</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Acknowledgements</title>","<p>Nisrine Nait, VEOLIA Lyon, France. ONYX Auvergne Rhône Alpes, 5 Rue des Frères Lumière, 69680 Chassieu.</p>","<title>Author contributions</title>","<p>J.D. wrote the manuscript text and reviewed it.</p>","<title>Data availability</title>","<p>Data will be made available on reasonable request; contact: Jerome Dargent, jerome.dargent@polyclinique-rillieux.fr.</p>","<title>Competing interests</title>","<p id=\"Par53\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Acknowledgements</title>\",\n \"<p>Nisrine Nait, VEOLIA Lyon, France. ONYX Auvergne Rhône Alpes, 5 Rue des Frères Lumière, 69680 Chassieu.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>J.D. wrote the manuscript text and reviewed it.</p>\",\n \"<title>Data availability</title>\",\n \"<p>Data will be made available on reasonable request; contact: Jerome Dargent, jerome.dargent@polyclinique-rillieux.fr.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par53\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Items to be recycled (disposable instruments), it may be published under a CC BY open access license.</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Separation of plastic parts and metallic parts, it may be published under a CC BY open access license.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>Items to be recycled (disposable instruments), it may be published under a CC BY open access license.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>Separation of plastic parts and metallic parts, it may be published under a CC BY open access license.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":["<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Calculations.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\" colspan=\"2\">Calculation methods: General<sup>##UREF##2##7##</sup></td></tr><tr><td align=\"left\" colspan=\"2\"> 1 kwH electricity = 0.0217 kg/ plastic production, 1 t = 3116 kg, metal production 1 t = 12,870 (3–12), paper and plaster 1 t = 872 kg</td></tr><tr><td align=\"left\" colspan=\"2\">Calculation methods: 1. Waste and recycling</td></tr><tr><td align=\"left\" colspan=\"2\"> Waste for a typical intervention, i.e. laparoscopic sleeve gastrectomy (yellow bin, contaminated): 1.45 kg (surgical) + 0.7 kg (anesthesia) + 1.2 kg (medical devices, container) = 3.3 kg = 0.3 kg CO<sub>2</sub> eq/0.75 kg (black bin) + 0.4 kg (anesthesia) = 1 kg = 0.02 kg CO<sub>2</sub> eq. Material provided for a sleeve gastrectomy is rather similar to gastric bypass (staplers and energy), whereas it is much less for a band removal (−60%). Given the important difference in GHGe between the two kinds of waste, we considered a more stringent way of separating them, according to recent guide-lines that were in favor of diminishing the contaminated part</td></tr><tr><td align=\"left\" colspan=\"2\">1 t waste = 21.35 kg CO<sub>2</sub> (10 kg = 0.2), yellow bin × 5</td></tr><tr><td align=\"left\" colspan=\"2\"> Medical device recycling: 1 t metal = 4302 kg CO<sub>2</sub> eq. (construction), 1 t plastic = 3116 kg CO<sub>2</sub> eq. 1 laparoscopic sleeve gastrectomy = 4.3 kg = 14 kg CO<sub>2</sub> eq</td></tr><tr><td align=\"left\" colspan=\"2\">Calculation methods: 2. Minimizing instrument use</td></tr><tr><td align=\"left\" colspan=\"2\"> Autoclave = a cycle lasts 80 min and consumes 20 kWh. The spared volume can be estimated at 6% of the load for one cycle. 20 kwH for 1 cycle. Energy spared if 1 small container is used instead of 1 mid-size container = 0.5/7.5 = 6% = 1.2 kwH</td></tr><tr><td align=\"left\" colspan=\"2\"> Method: the essential instruments for a given intervention were preserved, additional instruments were available in or out of the operating room upon request. No waste of time results from this strategy, except reassessing the instrument-boxes beforehand</td></tr><tr><td align=\"left\" colspan=\"2\">Calculation methods: 3. Anesthetic gas recycling</td></tr><tr><td align=\"left\" colspan=\"2\"> In most theatres, virtually all inhaled anesthetic agents are released in the atmosphere, because no mandatory capture device is inserted into the anesthetic respiratory machine. The anaesthetic gas scavenging system (AGSS) represents an improvement and has been conceived to protect the OR personnel, but still releases gas outside and causes energy expenses due its motorization</td></tr><tr><td align=\"left\" colspan=\"2\"> The CONTRAfluran™ anesthetic gas capture system (Baxter company) collects exhaled desflurane and sevoflurane in the surgical suite<sup>##UREF##3##8##</sup>. It is made of a canister that contains a porous material absorbing and retaining anesthetic gas, and is attached directly to the breathing machine. Once the canister is full (< 240 cc), it is collected and recycled for reuse. It can be used as an alternative or as an adjunct to the AGSS</td></tr><tr><td align=\"left\" colspan=\"2\"> It has been tested in a nearby hospital belonging to same group of private hospitals as ours, and for various administrative reasons, it could not be used in ours during the time of the study. Ad hoc measurements could be done, thereby simulating actual results in our facilities. 1 canister was retrieved for 2 reloads of sevoflurane, i.e. 44 kg CO<sub>2</sub> eq. 1 reload = approx. 10 h intervention. If the AGSS is stopped, + 22 kg CO<sub>2</sub> spared for the same use</td></tr><tr><td align=\"left\" colspan=\"2\">Calculation methods: 4. Others</td></tr><tr><td align=\"left\" colspan=\"2\"> Given the current situation of electricity supply and the important variations in the energy mix, calculations may be irrelevant within the framework of this study. Moreover, current recommendations regarding energy savings (e.g. limited night-time air-conditioning, off-hour heating, ventilation, etc.) were already in effect at the time of the study</td></tr><tr><td align=\"left\" colspan=\"2\"> Efforts concerning laundry and surgical gowns/drapes have been approximated at the time of their initiation</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>ECOSCORE in the bariatric setting.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Main items</th><th align=\"left\">Balancing factors</th></tr></thead><tbody><tr><td align=\"left\">Waste and packaging management</td><td align=\"left\">Local obligation in force</td></tr><tr><td align=\"left\">Recycling of disposable instruments</td><td align=\"left\">Local and inexpensive availability/partnerships</td></tr><tr><td align=\"left\">Diminishing/reusing surgical instruments</td><td align=\"left\">Voluntary and targeted action or large action, temporary or lasting (ex: anesthesiology)</td></tr><tr><td align=\"left\">Recycling anesthetic gas</td><td align=\"left\">Technological breakthrough, small or large (ex: endoscopy, robotics)</td></tr><tr><td align=\"left\">Energy mix</td><td align=\"left\">Social issues, sequential strategies</td></tr><tr><td align=\"left\">Other items: water supply, laundry, software and internet, etc</td><td align=\"left\">Other actions (ex: corporate green strategy)</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>ECO-SCORE marks.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Main items</th><th align=\"left\">Balancing factors</th></tr></thead><tbody><tr><td align=\"left\">Already effective = 4</td><td align=\"left\">Already in place = + 1</td></tr><tr><td align=\"left\">Implemented immediately = 3</td><td align=\"left\">Current impossibility from local issues = − 1</td></tr><tr><td align=\"left\">To be implemented soon (< 3 months) = 2</td><td align=\"left\">Not ready for use = 0</td></tr><tr><td align=\"left\">Impossible to implement for the time being = 1</td><td align=\"left\">–</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Operations performed during the two study periods.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Control group (N = 59)</th><th align=\"left\">Action group (N = 56)</th></tr></thead><tbody><tr><td align=\"left\">Sleeve gastrectomy</td><td align=\"left\">38</td><td align=\"left\">42</td></tr><tr><td align=\"left\">Gastric bypass</td><td align=\"left\">6</td><td align=\"left\">2</td></tr><tr><td align=\"left\">Band removal</td><td align=\"left\">14</td><td align=\"left\">11</td></tr><tr><td align=\"left\">Other</td><td align=\"left\">1</td><td align=\"left\">1</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>GHGe saved in a laparoscopic sleeve gastrectomy.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Items (sleeve gastrectomy)</th><th align=\"left\">GHGe saved per procedure (kg)</th><th align=\"left\">GHGe saved (%)</th></tr></thead><tbody><tr><td align=\"left\">Autoclave (minimizing instrument use)</td><td char=\".\" align=\"char\">0.8</td><td align=\"left\">6</td></tr><tr><td align=\"left\">Minimizing surgical waste</td><td char=\".\" align=\"char\">0.6</td><td align=\"left\">20</td></tr><tr><td align=\"left\">Sevoflurane reprocessing</td><td char=\".\" align=\"char\">4.4 (estimation)</td><td align=\"left\">30 (estimation)</td></tr><tr><td align=\"left\">Recycling of disposable instruments (staplers and energy devices)</td><td char=\".\" align=\"char\">6.4</td><td align=\"left\">30</td></tr><tr><td align=\"left\">Accessories: reprocessing/reusing, towels, etc</td><td char=\".\" align=\"char\">0.1</td><td align=\"left\">5</td></tr><tr><td align=\"left\">Total (energy not included)</td><td char=\".\" align=\"char\">12.3/80 (estimation)</td><td align=\"left\">18</td></tr></tbody></table></table-wrap>"],"string":"[\n \"<table-wrap id=\\\"Tab1\\\"><label>Table 1</label><caption><p>Calculations.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><tbody><tr><td align=\\\"left\\\" colspan=\\\"2\\\">Calculation methods: General<sup>##UREF##2##7##</sup></td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\"> 1 kwH electricity = 0.0217 kg/ plastic production, 1 t = 3116 kg, metal production 1 t = 12,870 (3–12), paper and plaster 1 t = 872 kg</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\">Calculation methods: 1. Waste and recycling</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\"> Waste for a typical intervention, i.e. laparoscopic sleeve gastrectomy (yellow bin, contaminated): 1.45 kg (surgical) + 0.7 kg (anesthesia) + 1.2 kg (medical devices, container) = 3.3 kg = 0.3 kg CO<sub>2</sub> eq/0.75 kg (black bin) + 0.4 kg (anesthesia) = 1 kg = 0.02 kg CO<sub>2</sub> eq. Material provided for a sleeve gastrectomy is rather similar to gastric bypass (staplers and energy), whereas it is much less for a band removal (−60%). Given the important difference in GHGe between the two kinds of waste, we considered a more stringent way of separating them, according to recent guide-lines that were in favor of diminishing the contaminated part</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\">1 t waste = 21.35 kg CO<sub>2</sub> (10 kg = 0.2), yellow bin × 5</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\"> Medical device recycling: 1 t metal = 4302 kg CO<sub>2</sub> eq. (construction), 1 t plastic = 3116 kg CO<sub>2</sub> eq. 1 laparoscopic sleeve gastrectomy = 4.3 kg = 14 kg CO<sub>2</sub> eq</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\">Calculation methods: 2. Minimizing instrument use</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\"> Autoclave = a cycle lasts 80 min and consumes 20 kWh. The spared volume can be estimated at 6% of the load for one cycle. 20 kwH for 1 cycle. Energy spared if 1 small container is used instead of 1 mid-size container = 0.5/7.5 = 6% = 1.2 kwH</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\"> Method: the essential instruments for a given intervention were preserved, additional instruments were available in or out of the operating room upon request. No waste of time results from this strategy, except reassessing the instrument-boxes beforehand</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\">Calculation methods: 3. Anesthetic gas recycling</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\"> In most theatres, virtually all inhaled anesthetic agents are released in the atmosphere, because no mandatory capture device is inserted into the anesthetic respiratory machine. The anaesthetic gas scavenging system (AGSS) represents an improvement and has been conceived to protect the OR personnel, but still releases gas outside and causes energy expenses due its motorization</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\"> The CONTRAfluran™ anesthetic gas capture system (Baxter company) collects exhaled desflurane and sevoflurane in the surgical suite<sup>##UREF##3##8##</sup>. It is made of a canister that contains a porous material absorbing and retaining anesthetic gas, and is attached directly to the breathing machine. Once the canister is full (< 240 cc), it is collected and recycled for reuse. It can be used as an alternative or as an adjunct to the AGSS</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\"> It has been tested in a nearby hospital belonging to same group of private hospitals as ours, and for various administrative reasons, it could not be used in ours during the time of the study. Ad hoc measurements could be done, thereby simulating actual results in our facilities. 1 canister was retrieved for 2 reloads of sevoflurane, i.e. 44 kg CO<sub>2</sub> eq. 1 reload = approx. 10 h intervention. If the AGSS is stopped, + 22 kg CO<sub>2</sub> spared for the same use</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\">Calculation methods: 4. Others</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\"> Given the current situation of electricity supply and the important variations in the energy mix, calculations may be irrelevant within the framework of this study. Moreover, current recommendations regarding energy savings (e.g. limited night-time air-conditioning, off-hour heating, ventilation, etc.) were already in effect at the time of the study</td></tr><tr><td align=\\\"left\\\" colspan=\\\"2\\\"> Efforts concerning laundry and surgical gowns/drapes have been approximated at the time of their initiation</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab2\\\"><label>Table 2</label><caption><p>ECOSCORE in the bariatric setting.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Main items</th><th align=\\\"left\\\">Balancing factors</th></tr></thead><tbody><tr><td align=\\\"left\\\">Waste and packaging management</td><td align=\\\"left\\\">Local obligation in force</td></tr><tr><td align=\\\"left\\\">Recycling of disposable instruments</td><td align=\\\"left\\\">Local and inexpensive availability/partnerships</td></tr><tr><td align=\\\"left\\\">Diminishing/reusing surgical instruments</td><td align=\\\"left\\\">Voluntary and targeted action or large action, temporary or lasting (ex: anesthesiology)</td></tr><tr><td align=\\\"left\\\">Recycling anesthetic gas</td><td align=\\\"left\\\">Technological breakthrough, small or large (ex: endoscopy, robotics)</td></tr><tr><td align=\\\"left\\\">Energy mix</td><td align=\\\"left\\\">Social issues, sequential strategies</td></tr><tr><td align=\\\"left\\\">Other items: water supply, laundry, software and internet, etc</td><td align=\\\"left\\\">Other actions (ex: corporate green strategy)</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab3\\\"><label>Table 3</label><caption><p>ECO-SCORE marks.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Main items</th><th align=\\\"left\\\">Balancing factors</th></tr></thead><tbody><tr><td align=\\\"left\\\">Already effective = 4</td><td align=\\\"left\\\">Already in place = + 1</td></tr><tr><td align=\\\"left\\\">Implemented immediately = 3</td><td align=\\\"left\\\">Current impossibility from local issues = − 1</td></tr><tr><td align=\\\"left\\\">To be implemented soon (< 3 months) = 2</td><td align=\\\"left\\\">Not ready for use = 0</td></tr><tr><td align=\\\"left\\\">Impossible to implement for the time being = 1</td><td align=\\\"left\\\">–</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab4\\\"><label>Table 4</label><caption><p>Operations performed during the two study periods.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\"/><th align=\\\"left\\\">Control group (N = 59)</th><th align=\\\"left\\\">Action group (N = 56)</th></tr></thead><tbody><tr><td align=\\\"left\\\">Sleeve gastrectomy</td><td align=\\\"left\\\">38</td><td align=\\\"left\\\">42</td></tr><tr><td align=\\\"left\\\">Gastric bypass</td><td align=\\\"left\\\">6</td><td align=\\\"left\\\">2</td></tr><tr><td align=\\\"left\\\">Band removal</td><td align=\\\"left\\\">14</td><td align=\\\"left\\\">11</td></tr><tr><td align=\\\"left\\\">Other</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">1</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab5\\\"><label>Table 5</label><caption><p>GHGe saved in a laparoscopic sleeve gastrectomy.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Items (sleeve gastrectomy)</th><th align=\\\"left\\\">GHGe saved per procedure (kg)</th><th align=\\\"left\\\">GHGe saved (%)</th></tr></thead><tbody><tr><td align=\\\"left\\\">Autoclave (minimizing instrument use)</td><td char=\\\".\\\" align=\\\"char\\\">0.8</td><td align=\\\"left\\\">6</td></tr><tr><td align=\\\"left\\\">Minimizing surgical waste</td><td char=\\\".\\\" align=\\\"char\\\">0.6</td><td align=\\\"left\\\">20</td></tr><tr><td align=\\\"left\\\">Sevoflurane reprocessing</td><td char=\\\".\\\" align=\\\"char\\\">4.4 (estimation)</td><td align=\\\"left\\\">30 (estimation)</td></tr><tr><td align=\\\"left\\\">Recycling of disposable instruments (staplers and energy devices)</td><td char=\\\".\\\" align=\\\"char\\\">6.4</td><td align=\\\"left\\\">30</td></tr><tr><td align=\\\"left\\\">Accessories: reprocessing/reusing, towels, etc</td><td char=\\\".\\\" align=\\\"char\\\">0.1</td><td align=\\\"left\\\">5</td></tr><tr><td align=\\\"left\\\">Total (energy not included)</td><td char=\\\".\\\" align=\\\"char\\\">12.3/80 (estimation)</td><td align=\\\"left\\\">18</td></tr></tbody></table></table-wrap>\"\n]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list 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Chir. Visc.\"], \"year\": [\"2020\"], \"volume\": [\"157\"], \"fpage\": [\"273\"], \"lpage\": [\"275\"]}, {\"label\": [\"5.\"], \"surname\": [\"Thiel\", \"Woods\", \"Bilec\"], \"given-names\": [\"CL\", \"NC\", \"MM\"], \"article-title\": [\"Strategies to reduce greenhouse gas emissions from laparoscopic surgery\"], \"source\": [\"AJPH Res.\"], \"year\": [\"2021\"], \"volume\": [\"108\"], \"issue\": [\"S2\"], \"fpage\": [\"158\"], \"lpage\": [\"164\"], \"pub-id\": [\"10.2105/AJPH.2018.304397\"]}, {\"label\": [\"7.\"], \"italic\": [\"UK Government GHG Factors for Company Reporting\"]}, {\"label\": [\"8.\"], \"collab\": [\"Data on file\"], \"source\": [\"Efficiency Test Canister\"], \"year\": [\"2020\"], \"publisher-name\": [\"ZeoSys Medical\"]}, {\"label\": [\"9.\"], \"ext-link\": [\"https://world.openfoodfacts.org/data\"]}, {\"label\": [\"13.\"], \"surname\": [\"Neve\", \"Isaacs\"], \"given-names\": [\"K\", \"A\"], \"article-title\": [\"How does the food environment influence people engaged in weight management? A systematic review and thematic synthesis of the qualitative literature\"], \"source\": [\"Ob Rev.\"], \"year\": [\"2020\"], \"volume\": [\"2\"], \"fpage\": [\"e13398\"]}, {\"label\": [\"19.\"], \"surname\": [\"Aldoori\", \"Hartley\", \"MacFie\"], \"given-names\": [\"J\", \"J\", \"J\"], \"article-title\": [\"Sustainable surgery in and out of the operative theatre\"], \"source\": [\"Br. J. Surg\"], \"year\": [\"2022\"], \"volume\": [\"108\"], \"issue\": [\"6\"], \"fpage\": [\"e219\"], \"lpage\": [\"e220\"], \"pub-id\": [\"10.1093/bjs/znab073\"]}, {\"label\": [\"20.\"], \"surname\": [\"Qin\", \"Velin\", \"Yates\", \"El Omrani\", \"Mc Leod\", \"Tudravu\"], \"given-names\": [\"RX\", \"L\", \"EF\", \"O\", \"E\", \"J\"], \"article-title\": [\"Building sustainable and resilient surgical systems: A narrative review of opportunities to integrate climate change into national surgical planning in the Western Pacific region\"], \"source\": [\"Lancet Region. Health-Western Pac.\"], \"year\": [\"2022\"], \"volume\": [\"22\"], \"fpage\": [\"100407\"], \"pub-id\": [\"10.1016/j.lanwpc.2022.100407\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":23,"string":"23"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:56"},"citation":{"kind":"string","value":"Sci Rep. 2024 Jan 13; 14:1252"},"package_file":{"kind":"string","value":"oa_package/c5/20/PMC10787753.tar.gz"}}},{"rowIdx":84,"cells":{"accession_id":{"kind":"string","value":"PMC10787754"},"pmid":{"kind":"string","value":"38218992"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par2\">Hot water balneotherapy in water rich in minerals and bicarbonate has traditionally been used around the world<sup>##UREF##0##1##</sup>, and various studies have been performed to investigate the effectiveness of balneotherapy in trauma<sup>##REF##32875343##2##</sup>, skin diseases<sup>##REF##37220472##3##</sup>, and musculoskeletal disorders<sup>##REF##36207541##4##–##UREF##1##7##</sup>. In recent years, studies have reported positive effects of balneotherapy on sleep and metal stress<sup>##REF##36207541##4##,##REF##28936572##8##–##REF##37145200##10##</sup>.</p>","<p id=\"Par3\">Mental stress was believed to affect biological homeostasis, such as emotional regulation and hormone secretion, and recently, it has become clear that it also causes sleep disorders, depression, and cardiovascular disease and increases susceptibility to infectious diseases and cancer<sup>##REF##29949726##11##–##REF##22473079##13##</sup>. In Japan, the morbidity rate due to mental stress has been continuously increasing since the period of high economic growth in the late 1950s, with over 60% of employees reported to be experiencing mental anxiety and stress<sup>##REF##24932413##14##</sup>.</p>","<p id=\"Par4\">The suicide rate in Japan is by far the highest among the Group of Seven industrialized nations, and the leading causes have been reported to include anxiety and depression related to mental stress in the workplace<sup>##REF##36962746##15##</sup>. Despite a modest decrease in the number of suicides after the enactment of the Basic Act on Suicide Prevention by the Japanese Government in 2006, the annual number of suicides remains high at 20,000<sup>##REF##21885102##16##</sup>. In 2015, the Japanese Ministry of Health, Labour and Welfare mandated that workplaces conduct occupational stress checks on their employees<sup>##REF##26607455##17##,##REF##33787084##18##</sup>. Nevertheless, the effectiveness of this measure is limited because of the small number of industrial physicians affiliated with workplaces and the lack of methods for successfully coping with mental stress.</p>","<p id=\"Par5\">The COVID-19 pandemic deeply affected the lives of people globally, not only because of the physical health risks of infection, but also because of the considerable mental stress related to the significant pandemic-related lifestyle changes, which in turn posed a threat to mental health and increased suicide rates<sup>##REF##33862016##19##</sup>. Against this background, the establishment of specific coping strategies to relieve stress is considered to be one of the most critical societal challenges<sup>##UREF##2##20##</sup>.</p>","<p id=\"Par6\">Previously, we performed a study on a neutral bicarbonate ionized water (NBIW) bath tablet. In contrast to hot springs, in which the water composition varies depending on area and weather conditions, the quality of our bathing tablet is stable, and people can enjoy balneotherapy at home without having to visit a hot spring. Our previous study showed that bathing in NBIW tends to increase blood bicarbonate ion concentrations and increase blood flow by increasing expression and phosphorylation levels of endothelial nitric oxide synthase and levels of nitric oxide (NO) in femoral vascular tissue, effects that are associated with enhanced blood flow<sup>##REF##33414495##21##</sup>. Moreover, in a preliminary randomized controlled trial conducted on the basis of those experiments, we found that the effects of higher body temperature upon waking and one hour after bathing tended to occur earlier in the intervention than in the control group. When a mood states profile was used to evaluate the effectiveness of bathing in NBIW on stress improvement, vigor/activity scores were improved<sup>##REF##33414495##21##</sup>. The earlier study did not evaluate effects on immunity, but the positive effects of balneotherapy on immunity have received much attention in recent years, and many studies have evaluated them<sup>##REF##34276372##6##,##UREF##1##7##,##REF##34035862##22##</sup>. Therefore, we conducted a randomized, open-label, crossover study to investigate the effect of NBIW on sleep, mental stress, and immune function. In particular, to more accurately evaluate the efficacy of NBIW in improving mental stress, we assessed sleep quality, which correlates with stress, not only subjectively with a questionnaire-based analysis, but also objectively with an activity meter.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">Hot water balneotherapy in water rich in minerals and bicarbonate has traditionally been used around the world<sup>##UREF##0##1##</sup>, and various studies have been performed to investigate the effectiveness of balneotherapy in trauma<sup>##REF##32875343##2##</sup>, skin diseases<sup>##REF##37220472##3##</sup>, and musculoskeletal disorders<sup>##REF##36207541##4##–##UREF##1##7##</sup>. In recent years, studies have reported positive effects of balneotherapy on sleep and metal stress<sup>##REF##36207541##4##,##REF##28936572##8##–##REF##37145200##10##</sup>.</p>\",\n \"<p id=\\\"Par3\\\">Mental stress was believed to affect biological homeostasis, such as emotional regulation and hormone secretion, and recently, it has become clear that it also causes sleep disorders, depression, and cardiovascular disease and increases susceptibility to infectious diseases and cancer<sup>##REF##29949726##11##–##REF##22473079##13##</sup>. In Japan, the morbidity rate due to mental stress has been continuously increasing since the period of high economic growth in the late 1950s, with over 60% of employees reported to be experiencing mental anxiety and stress<sup>##REF##24932413##14##</sup>.</p>\",\n \"<p id=\\\"Par4\\\">The suicide rate in Japan is by far the highest among the Group of Seven industrialized nations, and the leading causes have been reported to include anxiety and depression related to mental stress in the workplace<sup>##REF##36962746##15##</sup>. Despite a modest decrease in the number of suicides after the enactment of the Basic Act on Suicide Prevention by the Japanese Government in 2006, the annual number of suicides remains high at 20,000<sup>##REF##21885102##16##</sup>. In 2015, the Japanese Ministry of Health, Labour and Welfare mandated that workplaces conduct occupational stress checks on their employees<sup>##REF##26607455##17##,##REF##33787084##18##</sup>. Nevertheless, the effectiveness of this measure is limited because of the small number of industrial physicians affiliated with workplaces and the lack of methods for successfully coping with mental stress.</p>\",\n \"<p id=\\\"Par5\\\">The COVID-19 pandemic deeply affected the lives of people globally, not only because of the physical health risks of infection, but also because of the considerable mental stress related to the significant pandemic-related lifestyle changes, which in turn posed a threat to mental health and increased suicide rates<sup>##REF##33862016##19##</sup>. Against this background, the establishment of specific coping strategies to relieve stress is considered to be one of the most critical societal challenges<sup>##UREF##2##20##</sup>.</p>\",\n \"<p id=\\\"Par6\\\">Previously, we performed a study on a neutral bicarbonate ionized water (NBIW) bath tablet. In contrast to hot springs, in which the water composition varies depending on area and weather conditions, the quality of our bathing tablet is stable, and people can enjoy balneotherapy at home without having to visit a hot spring. Our previous study showed that bathing in NBIW tends to increase blood bicarbonate ion concentrations and increase blood flow by increasing expression and phosphorylation levels of endothelial nitric oxide synthase and levels of nitric oxide (NO) in femoral vascular tissue, effects that are associated with enhanced blood flow<sup>##REF##33414495##21##</sup>. Moreover, in a preliminary randomized controlled trial conducted on the basis of those experiments, we found that the effects of higher body temperature upon waking and one hour after bathing tended to occur earlier in the intervention than in the control group. When a mood states profile was used to evaluate the effectiveness of bathing in NBIW on stress improvement, vigor/activity scores were improved<sup>##REF##33414495##21##</sup>. The earlier study did not evaluate effects on immunity, but the positive effects of balneotherapy on immunity have received much attention in recent years, and many studies have evaluated them<sup>##REF##34276372##6##,##UREF##1##7##,##REF##34035862##22##</sup>. Therefore, we conducted a randomized, open-label, crossover study to investigate the effect of NBIW on sleep, mental stress, and immune function. In particular, to more accurately evaluate the efficacy of NBIW in improving mental stress, we assessed sleep quality, which correlates with stress, not only subjectively with a questionnaire-based analysis, but also objectively with an activity meter.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Materials and methods</title>","<title>Participant eligibility and recruitment</title>","<p id=\"Par26\">This randomized clinical trial was conducted and reported in compliance with the Declaration of Helsinki and the CONSORT Statement, respectively. It was approved by the Chiyoda Paramedical Care Clinic Ethics Review Board (IRB No.: 15000088; Approval No.: 22031805), and the protocol was registered with the UMIN-CTR (UMIN000047429) (07/04/2022). All participants provided written informed consent to participate in the study. The study was conducted between April 13, 2022, and August 11, 2022, and no major changes were made to the protocol during the study. Data were collected at Chiyoda Paramedical Care Clinic and other institutions, and statistical analyses were performed by the contract research organization, CPCC, Inc.</p>","<p id=\"Par27\">Participants were recruited by means of widespread dissemination of information at the study sites. Participation was voluntary. The inclusion criteria were as follows: (a) men and women aged 30 to 60 years at the time of informed consent, (b) individuals who reported experiencing daily stress, (c) individuals who were dissatisfied with their sleep, (d) women who were postmenopausal or had a regular menstrual cycle of 28 to 32 days, (e) individuals who were able to take a bath once a day during the study period, and (f) individuals who were able to receive a full explanation of the study, understand its contents, and give their written consent. The exclusion criteria included 14 items (see Supplementary Information ##SUPPL##0##2##) in consideration of their impact on the efficacy evaluation and the safety of the participants. The major exclusion criteria included individuals taking medicines or supplements that could potentially have an impact on the study; individuals with irregular lifestyle rhythms, such as shift workers; individuals who may experience loss of consciousness due to seizures; individuals who have experienced coarse oily skin due to bath salts; and individuals who had donated a certain amount of blood before the start of the study. Study investigators and others enrolled study participants who gave written informed consent, satisfied the inclusion criteria, did not meet the exclusion criteria, exhibited no clinical abnormalities based on the results of the screening test, and were deemed eligible for participation in the study, taking into account the results of the BJSQ<sup>##UREF##3##23##</sup> and PSQI-J<sup>##REF##2748771##24##</sup>. The allocation manager, who was independent of the study and analysis sites, used stratified randomization to allocate eligible participants to 2 groups based on age, sex, and BJSQ and PSQI-J scores at the time of the screening test. The parameters used for allocation were analyzed by an unpaired <italic>t</italic> test or a Wilcoxon rank sum test, and it was confirmed that there was no significant difference between the two groups.</p>","<p id=\"Par28\">Subsequently, an allocation list was prepared with group names and participant identification numbers.</p>","<title>Study design and intervention</title>","<p id=\"Par29\">This study was designed as a randomized, open-label, crossover trial with 2 intervention periods, each lasting 4 weeks, with a 1-week washout period between. The duration of the study product use was set to 4 weeks because another study showed an effect after 4 weeks of study product use, and the washout period was set to 1 week by referring to that study<sup>##REF##33414495##21##</sup>. The study findings at the start of the pre-observation period, 1 week before the start of the intervention, were used as the baseline data.</p>","<p id=\"Par30\">The study bath salts were NBIW tablets (HOTTAB Inc.) that were pH adjusted to release the maximum amount of bicarbonate ions when dissolved in warm tap water. The composition of the tablets is shown in Table ##TAB##7##8##. The tablets were white and weighed 15 g each; once participants had opened the package, they were asked to store the tablets in a cool, dark place with low humidity.</p>","<p id=\"Par31\">Participants were divided into 2 groups: after the first 1-week pre-observation period, one group bathed without NBIW tablets (control-NBIW group) and the other bathed with NBIW tablets (NBIW-control group). Then, after a 1-week washout period, each group participated in the other intervention. During the NBIW bathing period, participants were instructed to add 4 NBIW tablets to their bath (amount of water: 180 L) once a day and to enter the bath at least 15 min after adding the tablets. During the control period (including the pre-observation and washout periods), no NBIW tablets or other bath products were to be added to the once-daily baths. The bath water temperature was to be kept at 37 °C to 41 °C, and the bath was to be a full-body bath (i.e., shoulder depth) lasting a minimum of 20 min. Participants were permitted to drink room temperature water while bathing but were not permitted to drink cold water or read. The bath water was to be changed daily.</p>","<title>Outcome measures</title>","<title>Primary endpoints</title>","<p id=\"Par32\">Primary endpoints were mental stress assessed with the BJSQ (simplified version consisting of 23 questions<sup>##UREF##3##23##</sup>, subjective sleep quality assessed with the PSQ-J<sup>##REF##2748771##24##,##REF##11166088##32##</sup>, and sleep measurements assessed with an activity meter (actigraphy)<sup>##REF##32678211##57##,##UREF##4##58##</sup>. To obtain objective sleep variables, the small, lightweight waist-worn actigraphy device MTN-221 (Acos, Co., Ltd.) was used. Participants wore this device on their waist all times except when bathing. Sleep parameters were recorded for 3 weeks, from 5 days after the start of the intervention through to 2 days before its completion. Sleep and wakefulness were analyzed by SleepSign<sup>®</sup>-Act Ver. 2.0 (Kissei Comtec Co., Ltd, Matsumoto, Japan), which relies on an algorithm that uses the activity and posture data recorded by the actigraphy device in a series of linked calculations<sup>##REF##24383671##59##</sup>. This algorithm was used to evaluate bedtime, total sleep time, sleep latency, wake after sleep onset, sleep efficiency, time out of bed, and bed out latency.</p>","<title>Secondary endpoints</title>","<p id=\"Par33\">POMS2<sup>##UREF##5##60##,##UREF##6##61##</sup> was evaluated as the secondary endpoint of mental stress. Among immunological tests, lymphocyte subset, neutrophil phagocytosis, and NK cell activity were analyzed by flow cytometry (BD, FACS Caliber). Interleukin (IL)-6, IL-8, and IL-12 in blood were measured by enzyme-linked immunosorbent assay (LBIS Human IL-6 ELISA kit, LBIS Human IL-8 ELISA kit; FUJIFILM Wako Pure Chemical Corporation: Human IL-12 ELISA kit; abcam).</p>","<p id=\"Par34\">The BJSQ, PSQ-J, and POMS2 were assessed at the time of screening and at the end of each intervention period, and lymphocyte subset analyses were performed at the beginning of the pre-observation period and the end of each intervention period.</p>","<p id=\"Par35\">In addition, anthropometric measurements (height, weight, body mass index, abdominal circumference), blood pressure and pulse rate, general hematologic tests, eosinophil count, and white blood cell counts were performed. Furthermore, from the day after the start of the pre-observation period until the end of the second intervention period, participants completed a daily log on a dedicated website that included information on whether or not they had used the study product, whether or not they had taken baths, the duration of full-body bathing, the time they went to bed and woke up, their diet, and their intake of medicines and supplements.</p>","<title>Statistical analysis</title>","<p id=\"Par36\">Microsoft Excel (Microsoft Corporation), IBM® SPSS26 (IBM Japan), BellCurve for Excel (Social Survey Research Information Co. Ltd.), and G*Power 3.1<sup>##REF##19897823##62##</sup> were used for tabulation, graphing, and analysis.</p>","<p id=\"Par37\">Data were analyzed with appropriate methods depending on normality, distribution, or correspondence. A paired <italic>t</italic> test was used for parametric data, and Wilcoxon signed-rank test was used for non-parametric data. In the case of parametric methods (paired <italic>t</italic> test), the effect size was calculated as Cohen’s d, and in the case of non-parametric methods (Wilcoxon signed-rank test), it was calculated as r.</p>"],"string":"[\n \"<title>Materials and methods</title>\",\n \"<title>Participant eligibility and recruitment</title>\",\n \"<p id=\\\"Par26\\\">This randomized clinical trial was conducted and reported in compliance with the Declaration of Helsinki and the CONSORT Statement, respectively. It was approved by the Chiyoda Paramedical Care Clinic Ethics Review Board (IRB No.: 15000088; Approval No.: 22031805), and the protocol was registered with the UMIN-CTR (UMIN000047429) (07/04/2022). All participants provided written informed consent to participate in the study. The study was conducted between April 13, 2022, and August 11, 2022, and no major changes were made to the protocol during the study. Data were collected at Chiyoda Paramedical Care Clinic and other institutions, and statistical analyses were performed by the contract research organization, CPCC, Inc.</p>\",\n \"<p id=\\\"Par27\\\">Participants were recruited by means of widespread dissemination of information at the study sites. Participation was voluntary. The inclusion criteria were as follows: (a) men and women aged 30 to 60 years at the time of informed consent, (b) individuals who reported experiencing daily stress, (c) individuals who were dissatisfied with their sleep, (d) women who were postmenopausal or had a regular menstrual cycle of 28 to 32 days, (e) individuals who were able to take a bath once a day during the study period, and (f) individuals who were able to receive a full explanation of the study, understand its contents, and give their written consent. The exclusion criteria included 14 items (see Supplementary Information ##SUPPL##0##2##) in consideration of their impact on the efficacy evaluation and the safety of the participants. The major exclusion criteria included individuals taking medicines or supplements that could potentially have an impact on the study; individuals with irregular lifestyle rhythms, such as shift workers; individuals who may experience loss of consciousness due to seizures; individuals who have experienced coarse oily skin due to bath salts; and individuals who had donated a certain amount of blood before the start of the study. Study investigators and others enrolled study participants who gave written informed consent, satisfied the inclusion criteria, did not meet the exclusion criteria, exhibited no clinical abnormalities based on the results of the screening test, and were deemed eligible for participation in the study, taking into account the results of the BJSQ<sup>##UREF##3##23##</sup> and PSQI-J<sup>##REF##2748771##24##</sup>. The allocation manager, who was independent of the study and analysis sites, used stratified randomization to allocate eligible participants to 2 groups based on age, sex, and BJSQ and PSQI-J scores at the time of the screening test. The parameters used for allocation were analyzed by an unpaired <italic>t</italic> test or a Wilcoxon rank sum test, and it was confirmed that there was no significant difference between the two groups.</p>\",\n \"<p id=\\\"Par28\\\">Subsequently, an allocation list was prepared with group names and participant identification numbers.</p>\",\n \"<title>Study design and intervention</title>\",\n \"<p id=\\\"Par29\\\">This study was designed as a randomized, open-label, crossover trial with 2 intervention periods, each lasting 4 weeks, with a 1-week washout period between. The duration of the study product use was set to 4 weeks because another study showed an effect after 4 weeks of study product use, and the washout period was set to 1 week by referring to that study<sup>##REF##33414495##21##</sup>. The study findings at the start of the pre-observation period, 1 week before the start of the intervention, were used as the baseline data.</p>\",\n \"<p id=\\\"Par30\\\">The study bath salts were NBIW tablets (HOTTAB Inc.) that were pH adjusted to release the maximum amount of bicarbonate ions when dissolved in warm tap water. The composition of the tablets is shown in Table ##TAB##7##8##. The tablets were white and weighed 15 g each; once participants had opened the package, they were asked to store the tablets in a cool, dark place with low humidity.</p>\",\n \"<p id=\\\"Par31\\\">Participants were divided into 2 groups: after the first 1-week pre-observation period, one group bathed without NBIW tablets (control-NBIW group) and the other bathed with NBIW tablets (NBIW-control group). Then, after a 1-week washout period, each group participated in the other intervention. During the NBIW bathing period, participants were instructed to add 4 NBIW tablets to their bath (amount of water: 180 L) once a day and to enter the bath at least 15 min after adding the tablets. During the control period (including the pre-observation and washout periods), no NBIW tablets or other bath products were to be added to the once-daily baths. The bath water temperature was to be kept at 37 °C to 41 °C, and the bath was to be a full-body bath (i.e., shoulder depth) lasting a minimum of 20 min. Participants were permitted to drink room temperature water while bathing but were not permitted to drink cold water or read. The bath water was to be changed daily.</p>\",\n \"<title>Outcome measures</title>\",\n \"<title>Primary endpoints</title>\",\n \"<p id=\\\"Par32\\\">Primary endpoints were mental stress assessed with the BJSQ (simplified version consisting of 23 questions<sup>##UREF##3##23##</sup>, subjective sleep quality assessed with the PSQ-J<sup>##REF##2748771##24##,##REF##11166088##32##</sup>, and sleep measurements assessed with an activity meter (actigraphy)<sup>##REF##32678211##57##,##UREF##4##58##</sup>. To obtain objective sleep variables, the small, lightweight waist-worn actigraphy device MTN-221 (Acos, Co., Ltd.) was used. Participants wore this device on their waist all times except when bathing. Sleep parameters were recorded for 3 weeks, from 5 days after the start of the intervention through to 2 days before its completion. Sleep and wakefulness were analyzed by SleepSign<sup>®</sup>-Act Ver. 2.0 (Kissei Comtec Co., Ltd, Matsumoto, Japan), which relies on an algorithm that uses the activity and posture data recorded by the actigraphy device in a series of linked calculations<sup>##REF##24383671##59##</sup>. This algorithm was used to evaluate bedtime, total sleep time, sleep latency, wake after sleep onset, sleep efficiency, time out of bed, and bed out latency.</p>\",\n \"<title>Secondary endpoints</title>\",\n \"<p id=\\\"Par33\\\">POMS2<sup>##UREF##5##60##,##UREF##6##61##</sup> was evaluated as the secondary endpoint of mental stress. Among immunological tests, lymphocyte subset, neutrophil phagocytosis, and NK cell activity were analyzed by flow cytometry (BD, FACS Caliber). Interleukin (IL)-6, IL-8, and IL-12 in blood were measured by enzyme-linked immunosorbent assay (LBIS Human IL-6 ELISA kit, LBIS Human IL-8 ELISA kit; FUJIFILM Wako Pure Chemical Corporation: Human IL-12 ELISA kit; abcam).</p>\",\n \"<p id=\\\"Par34\\\">The BJSQ, PSQ-J, and POMS2 were assessed at the time of screening and at the end of each intervention period, and lymphocyte subset analyses were performed at the beginning of the pre-observation period and the end of each intervention period.</p>\",\n \"<p id=\\\"Par35\\\">In addition, anthropometric measurements (height, weight, body mass index, abdominal circumference), blood pressure and pulse rate, general hematologic tests, eosinophil count, and white blood cell counts were performed. Furthermore, from the day after the start of the pre-observation period until the end of the second intervention period, participants completed a daily log on a dedicated website that included information on whether or not they had used the study product, whether or not they had taken baths, the duration of full-body bathing, the time they went to bed and woke up, their diet, and their intake of medicines and supplements.</p>\",\n \"<title>Statistical analysis</title>\",\n \"<p id=\\\"Par36\\\">Microsoft Excel (Microsoft Corporation), IBM® SPSS26 (IBM Japan), BellCurve for Excel (Social Survey Research Information Co. Ltd.), and G*Power 3.1<sup>##REF##19897823##62##</sup> were used for tabulation, graphing, and analysis.</p>\",\n \"<p id=\\\"Par37\\\">Data were analyzed with appropriate methods depending on normality, distribution, or correspondence. A paired <italic>t</italic> test was used for parametric data, and Wilcoxon signed-rank test was used for non-parametric data. In the case of parametric methods (paired <italic>t</italic> test), the effect size was calculated as Cohen’s d, and in the case of non-parametric methods (Wilcoxon signed-rank test), it was calculated as r.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<title>Study population</title>","<p id=\"Par7\">A flow diagram of the study population is shown in Fig. ##FIG##0##1##. Of 41 potential participants who understood the content of the study and provided written informed consent, 39 completed the screening test, which comprised a simplified version of the Brief Job Stress Questionnaire (BJSQ) consisting of 23 questions<sup>##UREF##3##23##</sup>, the Pittsburgh Sleep Quality Index Japanese version (PSQI-J)<sup>##REF##2748771##24##</sup>, age, mean hours of sleep, height, weight, body mass index, blood pressure, and pulse rate. Of those 39 individuals, 25 met the inclusion criteria, did not meet the exclusion criteria, and exhibited no clinical abnormalities based on the results of the screening test, which included the results of the BJSQ and the PSQI-J. This study used a crossover design comprising NBIW bath tablet bathing (NBIW) and standard bathing (control). In one group, control bathing was conducted first (control-NBIW, n = 12), while in the other group, NBIW bathing was conducted first (NBIW-control, n = 13). All 25 individuals participated in and completed the study and were eligible for inclusion in the efficacy analysis. Outcomes of each parameter were measured before allocation and at completion of the intervention. After completion of the whole study period, a statistician independent from the study group performed the statistical analyses.</p>","<title>Background characteristics</title>","<p id=\"Par8\">Participant background characteristics are shown in Table ##TAB##0##1##. Analyses of background characteristics conducted when participants were allocated to groups after the screening test revealed no significant differences in age, mean sleep duration, physical measurements, physiological tests, PSQI-J, or BJSQ between the 2 crossover groups, i.e., control-NBIW and NBIW-control. The percentage of NBIW tablets used (% ± SE) in relation to the number of days of use was not significantly different between the groups (control-NBIW, 99.70% ± 1.03%; NBIW-control, 100.00% ± 0.00%). Bathing time during the intervention was calculated from the diary data recorded by each participant, and results confirmed that there was no significant difference in bathing time between NBIW bathing and control bathing.</p>","<title>Outcomes</title>","<title>Stress</title>","<p id=\"Par9\">The results of the BJSQ are shown in Fig. ##FIG##1##2##. Comparisons of percentage changes from baseline in 3 stressor categories (A, Job Stressors; B, Stress Reaction; and C, Social Support) revealed that the category C score decreased significantly more with NBIW bathing than with control bathing (<italic>p</italic> = 0.0193, d = − 0.52 [medium]). There was a significant difference in the BJSQ (category C score), one of the primary endpoints, between NBIW and control. The sample size was confirmed post hoc: The sample size estimated by power analysis with the BJSQ category C score was 25, and the power was 0.81. In addition, we confirmed that no significant carryover effect occurred (Fig. ##FIG##2##3##). Comparisons of the percentage change from baseline for each item in POMS2 are shown in Table ##TAB##1##2##. The item Confusion-Bewilderment decreased significantly more with NBIW bathing. A decrease in values or “Improvement in stress” were observed in both interventions in many items of the primary and secondary mental stress tests, but in both tests, the percentage changes were larger with the NBIW intervention than with the control intervention.</p>","<title>Sleep</title>","<p id=\"Par10\">The baseline PSQI-J scores (mean ± SD) were 9.3 ± 1.8 in the control-NBIW group and 9.4 ± 1.4 in the NBIW-control group, i.e., in both groups, the scores indicated poor sleep quality (Table ##TAB##0##1##); however, with the NBIW intervention, scores improved, i.e., decreased (to 5.8 ± 2.4; Supplementary Information ##SUPPL##0##1##), and the percentage change from baseline was significantly lower in the NBIW intervention than in the control intervention (<italic>p</italic> = 0.0238, d = − 0.42 [small]) (Fig. ##FIG##3##4##).</p>","<p id=\"Par11\">Sleep was also analyzed by actigraphy, which provided data on bedtime, total sleep time, sleep latency, wake after sleep onset, sleep efficiency, time out of bed, and bed out latency. We observed that over 4 weeks, NBIW bathing generally improved sleep compared with control bathing. The results showed increased total sleep time and sleep efficiency and decreased waking after sleep onset, sleep latency, and bed out latency, and paired <italic>t</italic> tests showed that the decreases in sleep latency and bed out latency were significantly larger with the NBIW intervention than with the control intervention (Table ##TAB##2##3##). A power analysis of the items that showed significant differences between the interventions determined that a sample size of 27 participants would be necessary, assuming an effect size of 0.5, a probability of 0.05, and a power of 0.8. The post hoc calculations revealed larger effect sizes for sleep latency (1.43) and bed out latency (1.59).</p>","<title>Immune functions</title>","<p id=\"Par12\">The results of the lymphocyte subset analyses are shown in Tables ##TAB##3##4## and ##TAB##4##5##. The proportion of CD4<sup>+</sup> cells, i.e., helper T cells, was significantly higher with the NBIW bathing intervention than with the control intervention (Table ##TAB##3##4##), whereas the concentration of CD8<sup>+</sup> cells, i.e., cytotoxic T cells, was significantly lower with the NBIW intervention than with the control intervention (Table ##TAB##4##5##). In comparison, in the control intervention, the proportion of CD4<sup>+</sup> cells was significantly lower and the proportion of CD8<sup>+</sup> and number of CD8<sup>+</sup> CD28<sup>+</sup> cells, which represent the progenitor cells of CD8<sup>+</sup> cells, were significantly higher (Tables ##TAB##3##4## and ##TAB##4##5##). Although there was no marked change in CD4<sup>+</sup> T cell counts in either intervention, CD8<sup>+</sup> T cell counts showed a significant increase with the control intervention (Table ##TAB##4##5##). These results indicate that the post-intervention CD4<sup>+</sup> to CD8<sup>+</sup> T cell abundance ratio (CD4<sup>+</sup>:CD8<sup>+</sup>) was higher in the NBIW intervention (although the difference compared with the control intervention was not significant) and showed a significant decrease with the control intervention (Table ##TAB##4##5##). CD4<sup>+</sup>CD45RA<sup>+</sup>, i.e., naive T cells, significantly decreased with the NBIW intervention (Table ##TAB##3##4##), resulting in a significant decrease in the ratio of the naive to memory T cells ratio (N:M ratio) by the NBIW intervention (Table ##TAB##4##5##). At Week 4, the N:M ratio was not significantly different between NBIW and control. However, when compared with the baseline, it was increased by the control intervention and significantly decreased by the NBIW intervention (Table ##TAB##4##5##). The proportion of CD16<sup>+</sup>CD56<sup>-</sup> cells, i.e., mature natural killer (NK) cells, decreased significantly in the NBIW intervention. At Week 4, CD16+CD56− (%) was not significantly different between NBIW and control. When compared with baseline, it decreased significantly in the NBIW intervention but showed no significant decrease in the control intervention (Table ##TAB##3##4##). The proportion of CD20<sup>+</sup> cells, i.e., B cells, and the number of B cells increased significantly after both the NBIW and control interventions (Tables ##TAB##3##4## and ##TAB##4##5##). The proportion of CD3<sup>+</sup> cells, i.e., mature T cells, in total lymphocyte counts did not differ significantly between the interventions. Neutrophil phagocytosis activity and NK cell activity increased significantly with both interventions. However, mean neutrophil phagocytosis activity at Week 4 was higher with the NBIW intervention than with the control intervention. As a result, effects on neutrophil phagocytosis activity were greater with the NBIW intervention than with the control intervention (Table ##TAB##5##6##).</p>","<title>Adverse events</title>","<p id=\"Par13\">A total of 10 adverse events were reported during the course of this study, 7 of which occurred during the control intervention. No significant differences in the occurrence of adverse events were observed between the 2 interventions. In addition, reports of abdominal pain, diarrhea (loose stools), weight loss, and elevated creatinine kinase during the use of NBIW were judged by the investigator to be not causally related to the study product. Fisher’s exact test did not find a significant difference in the incidence of adverse events between NBIW and control (P = 1.000, Effect size: φ = 0.0576). No severe or serious adverse events or adverse drug reactions were observed (Table ##TAB##6##7##).</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>Study population</title>\",\n \"<p id=\\\"Par7\\\">A flow diagram of the study population is shown in Fig. ##FIG##0##1##. Of 41 potential participants who understood the content of the study and provided written informed consent, 39 completed the screening test, which comprised a simplified version of the Brief Job Stress Questionnaire (BJSQ) consisting of 23 questions<sup>##UREF##3##23##</sup>, the Pittsburgh Sleep Quality Index Japanese version (PSQI-J)<sup>##REF##2748771##24##</sup>, age, mean hours of sleep, height, weight, body mass index, blood pressure, and pulse rate. Of those 39 individuals, 25 met the inclusion criteria, did not meet the exclusion criteria, and exhibited no clinical abnormalities based on the results of the screening test, which included the results of the BJSQ and the PSQI-J. This study used a crossover design comprising NBIW bath tablet bathing (NBIW) and standard bathing (control). In one group, control bathing was conducted first (control-NBIW, n = 12), while in the other group, NBIW bathing was conducted first (NBIW-control, n = 13). All 25 individuals participated in and completed the study and were eligible for inclusion in the efficacy analysis. Outcomes of each parameter were measured before allocation and at completion of the intervention. After completion of the whole study period, a statistician independent from the study group performed the statistical analyses.</p>\",\n \"<title>Background characteristics</title>\",\n \"<p id=\\\"Par8\\\">Participant background characteristics are shown in Table ##TAB##0##1##. Analyses of background characteristics conducted when participants were allocated to groups after the screening test revealed no significant differences in age, mean sleep duration, physical measurements, physiological tests, PSQI-J, or BJSQ between the 2 crossover groups, i.e., control-NBIW and NBIW-control. The percentage of NBIW tablets used (% ± SE) in relation to the number of days of use was not significantly different between the groups (control-NBIW, 99.70% ± 1.03%; NBIW-control, 100.00% ± 0.00%). Bathing time during the intervention was calculated from the diary data recorded by each participant, and results confirmed that there was no significant difference in bathing time between NBIW bathing and control bathing.</p>\",\n \"<title>Outcomes</title>\",\n \"<title>Stress</title>\",\n \"<p id=\\\"Par9\\\">The results of the BJSQ are shown in Fig. ##FIG##1##2##. Comparisons of percentage changes from baseline in 3 stressor categories (A, Job Stressors; B, Stress Reaction; and C, Social Support) revealed that the category C score decreased significantly more with NBIW bathing than with control bathing (<italic>p</italic> = 0.0193, d = − 0.52 [medium]). There was a significant difference in the BJSQ (category C score), one of the primary endpoints, between NBIW and control. The sample size was confirmed post hoc: The sample size estimated by power analysis with the BJSQ category C score was 25, and the power was 0.81. In addition, we confirmed that no significant carryover effect occurred (Fig. ##FIG##2##3##). Comparisons of the percentage change from baseline for each item in POMS2 are shown in Table ##TAB##1##2##. The item Confusion-Bewilderment decreased significantly more with NBIW bathing. A decrease in values or “Improvement in stress” were observed in both interventions in many items of the primary and secondary mental stress tests, but in both tests, the percentage changes were larger with the NBIW intervention than with the control intervention.</p>\",\n \"<title>Sleep</title>\",\n \"<p id=\\\"Par10\\\">The baseline PSQI-J scores (mean ± SD) were 9.3 ± 1.8 in the control-NBIW group and 9.4 ± 1.4 in the NBIW-control group, i.e., in both groups, the scores indicated poor sleep quality (Table ##TAB##0##1##); however, with the NBIW intervention, scores improved, i.e., decreased (to 5.8 ± 2.4; Supplementary Information ##SUPPL##0##1##), and the percentage change from baseline was significantly lower in the NBIW intervention than in the control intervention (<italic>p</italic> = 0.0238, d = − 0.42 [small]) (Fig. ##FIG##3##4##).</p>\",\n \"<p id=\\\"Par11\\\">Sleep was also analyzed by actigraphy, which provided data on bedtime, total sleep time, sleep latency, wake after sleep onset, sleep efficiency, time out of bed, and bed out latency. We observed that over 4 weeks, NBIW bathing generally improved sleep compared with control bathing. The results showed increased total sleep time and sleep efficiency and decreased waking after sleep onset, sleep latency, and bed out latency, and paired <italic>t</italic> tests showed that the decreases in sleep latency and bed out latency were significantly larger with the NBIW intervention than with the control intervention (Table ##TAB##2##3##). A power analysis of the items that showed significant differences between the interventions determined that a sample size of 27 participants would be necessary, assuming an effect size of 0.5, a probability of 0.05, and a power of 0.8. The post hoc calculations revealed larger effect sizes for sleep latency (1.43) and bed out latency (1.59).</p>\",\n \"<title>Immune functions</title>\",\n \"<p id=\\\"Par12\\\">The results of the lymphocyte subset analyses are shown in Tables ##TAB##3##4## and ##TAB##4##5##. The proportion of CD4<sup>+</sup> cells, i.e., helper T cells, was significantly higher with the NBIW bathing intervention than with the control intervention (Table ##TAB##3##4##), whereas the concentration of CD8<sup>+</sup> cells, i.e., cytotoxic T cells, was significantly lower with the NBIW intervention than with the control intervention (Table ##TAB##4##5##). In comparison, in the control intervention, the proportion of CD4<sup>+</sup> cells was significantly lower and the proportion of CD8<sup>+</sup> and number of CD8<sup>+</sup> CD28<sup>+</sup> cells, which represent the progenitor cells of CD8<sup>+</sup> cells, were significantly higher (Tables ##TAB##3##4## and ##TAB##4##5##). Although there was no marked change in CD4<sup>+</sup> T cell counts in either intervention, CD8<sup>+</sup> T cell counts showed a significant increase with the control intervention (Table ##TAB##4##5##). These results indicate that the post-intervention CD4<sup>+</sup> to CD8<sup>+</sup> T cell abundance ratio (CD4<sup>+</sup>:CD8<sup>+</sup>) was higher in the NBIW intervention (although the difference compared with the control intervention was not significant) and showed a significant decrease with the control intervention (Table ##TAB##4##5##). CD4<sup>+</sup>CD45RA<sup>+</sup>, i.e., naive T cells, significantly decreased with the NBIW intervention (Table ##TAB##3##4##), resulting in a significant decrease in the ratio of the naive to memory T cells ratio (N:M ratio) by the NBIW intervention (Table ##TAB##4##5##). At Week 4, the N:M ratio was not significantly different between NBIW and control. However, when compared with the baseline, it was increased by the control intervention and significantly decreased by the NBIW intervention (Table ##TAB##4##5##). The proportion of CD16<sup>+</sup>CD56<sup>-</sup> cells, i.e., mature natural killer (NK) cells, decreased significantly in the NBIW intervention. At Week 4, CD16+CD56− (%) was not significantly different between NBIW and control. When compared with baseline, it decreased significantly in the NBIW intervention but showed no significant decrease in the control intervention (Table ##TAB##3##4##). The proportion of CD20<sup>+</sup> cells, i.e., B cells, and the number of B cells increased significantly after both the NBIW and control interventions (Tables ##TAB##3##4## and ##TAB##4##5##). The proportion of CD3<sup>+</sup> cells, i.e., mature T cells, in total lymphocyte counts did not differ significantly between the interventions. Neutrophil phagocytosis activity and NK cell activity increased significantly with both interventions. However, mean neutrophil phagocytosis activity at Week 4 was higher with the NBIW intervention than with the control intervention. As a result, effects on neutrophil phagocytosis activity were greater with the NBIW intervention than with the control intervention (Table ##TAB##5##6##).</p>\",\n \"<title>Adverse events</title>\",\n \"<p id=\\\"Par13\\\">A total of 10 adverse events were reported during the course of this study, 7 of which occurred during the control intervention. No significant differences in the occurrence of adverse events were observed between the 2 interventions. In addition, reports of abdominal pain, diarrhea (loose stools), weight loss, and elevated creatinine kinase during the use of NBIW were judged by the investigator to be not causally related to the study product. Fisher’s exact test did not find a significant difference in the incidence of adverse events between NBIW and control (P = 1.000, Effect size: φ = 0.0576). No severe or serious adverse events or adverse drug reactions were observed (Table ##TAB##6##7##).</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par14\">Balneotherapy, i.e., the treatment of diseases by bathing, has been practiced since ancient times for therapeutic and medical purposes, and its therapeutic effects on cardiovascular and dermatological diseases have been documented<sup>##REF##30478742##25##–##REF##12919115##27##</sup>. Our previous double-blind, placebo-controlled study of NBIW and our in vitro and in vivo studies suggested that NBIW increases body temperature, promotes blood flow via nitric oxide production, and improves mental stress and sleep quality<sup>##REF##33414495##21##</sup>. However, the placebo control contained magnesium sulfate and sodium sulfate, both of which are known to promote blood circulation; hence, warm bathing in the placebo control also showed mild effects in some analysis items, making it difficult to analyze the effectiveness of NBIW. Therefore, we conducted the present randomized, open-label, crossover trial to further investigate the effects of bathing in NBIW. The study included assessments of immune function because of the close correlation between stress, sleep, and immune function.</p>","<p id=\"Par15\">A number of previous studies reported on the efficacy of balneotherapy in improving mental stress and sleep disorders, and a large-scale randomized controlled trial (n = 362) conducted in 2016 in Chongqing, China, showed that balneotherapy is effective in reducing mental stress and sleep disorders and alleviating general health concerns<sup>##REF##28936572##8##</sup>. Moreover, given that balneotherapy reduces levels of cortisol, a stress biomarker, some authors have suggested that it may be beneficial in controlling mental stress<sup>##REF##29455296##28##</sup>. In the stress assessment in the present study, BJSQ categories A (Job Stressors) and B (Stress Reaction) were significantly improved after both the NBIW and control interventions. On the other hand, category C (Social Support) was significantly improved by the NBIW intervention but showed no change with the control intervention. For Category A and B, bathing itself is seen to be effective in reducing stress. Because there was a significant improvement also in category C with NBIW bathing compared with standard bathing without any additions, NBIW bathing is considered to be more effective in reducing stress. Moreover, the questions related to BJSQ category C evaluate whether a study participant receives sufficient support from those around them. We suggest that NBIW bathing improved study participants’ mental condition, which resulted in a change in their interpersonal cognition.</p>","<p id=\"Par16\">Balneotherapy over a period of 2 to 3 weeks may have beneficial effects on sleep quality<sup>##REF##16765852##29##</sup>. It is thought to improve sleep by lowering systemic blood pressure and core body temperature by dilating peripheral blood vessels throughout the body, reducing pain by inhibiting inflammation and pain-related substances, and relaxing muscles<sup>##REF##36035468##30##</sup>. The decrease in blood pressure occurs when the parasympathetic nervous system is dominant. Dominance of the parasympathetic nervous system is also considered to improve sleep quality. In fact, percutaneous stimulation of the parasympathetic nerve was reported to improve sleep quality in retired veterans suffering from post-traumatic stress disorder<sup>##REF##37039398##31##</sup>. The PSQI-J, one of the primary sleep endpoints in this study, assesses subjective sleep quality, including insomnia; the total score ranges from 0 to 21, and a score of 6 or more indicates a potential sleep problem<sup>##REF##2748771##24##,##REF##11166088##32##</sup>. In the present study, the PSQI-J score confirmed that the NBIW intervention led to significantly greater improvements in subjective sleep quality than the control intervention. Furthermore, in the actigraphy sleep assessment, which was used as an objective assessment of sleep, the mean values of each item during the 3-week period showed a significant reduction in sleep latency and bed out latency and a trend towards improved sleep in many items with the NBIW intervention compared with the control intervention.</p>","<p id=\"Par17\">Mental stress and sleep quality are known to interact closely, and studies have shown that sleep quality is reduced in stressful environments<sup>##REF##28756825##33##,##REF##27860130##34##</sup> and that stress can be reduced by improving sleep quality<sup>##REF##26307483##35##</sup>. In this study, NBIW bathing was considered to improve the quality of sleep not only subjectively but also objectively. In our previous study, we demonstrated that the bicarbonate ions in NBIW affect endothelial cells, and through phosphorylation of endothelial nitric oxide synthase, promote synthesis of nitric oxide, which dilates blood vessels, leading to improved blood flow and temperature elevation. Moreover, 4-week NBIW bathing improved sleep quality according to PSQ-J score and reduced stress according to POMS2 scores<sup>##REF##33414495##21##</sup>. Together with the result of this study, these findings indicate that NBIW may improve sleep quality and decrease stress by promoting blood circulation via production of nitric oxide and increase of body temperature.</p>","<p id=\"Par18\">A large number of studies have shown that mental stress increases the risk of a wide range of diseases and may be a risk factor for cancer and autoimmune diseases<sup>##REF##31814865##36##,##REF##18190880##37##</sup>. Mental stress is also particularly strongly associated with cardiovascular disease<sup>##REF##22473079##38##</sup>. For example, several articles have reported that mental stress induces myocardial ischemia in patients with coronary artery disease<sup>##REF##34751723##39##,##REF##34751708##40##</sup> and that depression is correlated with cardiovascular disease<sup>##REF##27830572##41##</sup>. In addition, the Japan Collaborative Cohort Study for Evaluation of Cancer Risk (JACC study), which was sponsored by the Ministry of Education, Science, Sports and Culture of Japan, identified mental stress as being associated with increased coronary artery disease and increased stroke mortality in women<sup>##REF##12208798##42##</sup>, and in the large-scale international case–control INTERHEART study, psychosocial factors were found to more than double the risk of myocardial infarction<sup>##REF##15364186##43##</sup>. One of the primary mechanisms underlying the onset of cardiovascular disease due to mental stress may be inflammation-mediated vascular endothelial dysfunction resulting from the release of inflammatory cytokines as a result of suppression of the parasympathetic nervous system<sup>##REF##35512885##44##</sup>. A previous study showed that the bicarbonate ions in NBIW act directly on vascular endothelial cells to induce nitric oxide production through phosphorylation of endothelial nitric oxide synthase<sup>##REF##33414495##21##</sup>. As such, these findings suggest that continued NBIW bathing with warm water may also reduce the risk of cardiovascular disease by improving both mental stress and vascular endothelial function.</p>","<p id=\"Par19\">Immune function is also thought to be closely related to stress and sleep<sup>##REF##15250815##45##,##REF##30920354##46##</sup>. Several studies have shown that stress is a risk factor for cancer and autoimmune diseases, suggesting that stress affects immune tolerance and anticancer immunity<sup>##REF##18190880##37##,##REF##32983091##47##</sup>. Furthermore, sleep deprivation is known to alter the secretion of inflammatory markers such as interleukins, tumor necrosis factor-α, other cytokines, chemokines, and acute phase proteins<sup>##REF##16337444##48##,##REF##11150007##49##</sup>.</p>","<p id=\"Par20\">In this study, changes in several immune factors were observed with NBIW and control bathing. The CD4<sup>+</sup>:CD8<sup>+</sup> T cell ratio did not change significantly with the NBIW bathing intervention, but it decreased with the control bathing intervention because of an increase in CD8<sup>+</sup> T cells. CD8<sup>+</sup> T cells are known to fluctuate in number and function in response to stress<sup>##REF##15465465##50##–##REF##29145439##52##</sup>. These suggest that the CD8<sup>+</sup> T-cell count decreased because NBIW bathing resulted in a decrease in stress.</p>","<p id=\"Par21\">The proportion of naive T cells, as represented by CD4<sup>+</sup>CD45RA<sup>+</sup>, decreased slightly after NBIW bathing, while the number of memory T cells showed increase, resulting in a lower N:M ratio. Thus, given that tissue-resident and circulating memory T cells play an essential role in anti-tumor immunity, NBIW bathing may enhance anti-tumor immune function<sup>##REF##35803260##53##</sup>.</p>","<p id=\"Par22\">The proportion of CD16<sup>+</sup>CD56<sup>-</sup> cells, a marker of mature NK cells, decreased significantly in the NBIW bathing intervention. CD16<sup>+</sup>CD56<sup>-</sup> mature NK cells are increased by post-traumatic stress<sup>##REF##12208798##42##,##REF##27025668##54##</sup>, suggesting that NBIW bathing may reduce CD16<sup>+</sup>CD56<sup>−</sup> mature NK cells by alleviating stress.</p>","<p id=\"Par23\">The proportion of CD20<sup>+</sup> B cell markers among total lymphocytes, the number of B cells, and the level of neutrophil phagocytosis and NK cell activity increased significantly after both interventions, suggesting that warm bathing itself improves immune function. However, the mean value of neutrophil phagocytosis activity after 4 weeks was higher with NBIW bathing than with control bathing, and the change was greater with NBIW than with control.</p>","<p id=\"Par24\">This study has some limitations. First, most of the participants were middle aged. Second, people with self-perceived daily stress were recruited. Third, stress is subjective. These factors may limit the generalizability of the results.</p>","<p id=\"Par25\">Taken together, the results of this study suggest that NBIW bathing has a positive influence on stress, sleep, and immune function. Stress, sleep, and immune function interact with each other<sup>##REF##15250815##45##,##REF##30920354##46##</sup>, so the effect of NBIW on immune function may be mediated through the improvements in stress and sleep. In addition, given that bicarbonate ions stimulate nitric oxide production in macrophage cell lines stimulated by lipopolysaccharide and interferon-gamma, which in turn promotes inflammatory responses<sup>##REF##30063984##55##,##REF##31960927##56##</sup>, it is conceivable that increased bicarbonate ions in the blood due to NBIW bathing may have a direct effect on immune system cells. In the future, further studies of such mechanisms are warranted.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par14\\\">Balneotherapy, i.e., the treatment of diseases by bathing, has been practiced since ancient times for therapeutic and medical purposes, and its therapeutic effects on cardiovascular and dermatological diseases have been documented<sup>##REF##30478742##25##–##REF##12919115##27##</sup>. Our previous double-blind, placebo-controlled study of NBIW and our in vitro and in vivo studies suggested that NBIW increases body temperature, promotes blood flow via nitric oxide production, and improves mental stress and sleep quality<sup>##REF##33414495##21##</sup>. However, the placebo control contained magnesium sulfate and sodium sulfate, both of which are known to promote blood circulation; hence, warm bathing in the placebo control also showed mild effects in some analysis items, making it difficult to analyze the effectiveness of NBIW. Therefore, we conducted the present randomized, open-label, crossover trial to further investigate the effects of bathing in NBIW. The study included assessments of immune function because of the close correlation between stress, sleep, and immune function.</p>\",\n \"<p id=\\\"Par15\\\">A number of previous studies reported on the efficacy of balneotherapy in improving mental stress and sleep disorders, and a large-scale randomized controlled trial (n = 362) conducted in 2016 in Chongqing, China, showed that balneotherapy is effective in reducing mental stress and sleep disorders and alleviating general health concerns<sup>##REF##28936572##8##</sup>. Moreover, given that balneotherapy reduces levels of cortisol, a stress biomarker, some authors have suggested that it may be beneficial in controlling mental stress<sup>##REF##29455296##28##</sup>. In the stress assessment in the present study, BJSQ categories A (Job Stressors) and B (Stress Reaction) were significantly improved after both the NBIW and control interventions. On the other hand, category C (Social Support) was significantly improved by the NBIW intervention but showed no change with the control intervention. For Category A and B, bathing itself is seen to be effective in reducing stress. Because there was a significant improvement also in category C with NBIW bathing compared with standard bathing without any additions, NBIW bathing is considered to be more effective in reducing stress. Moreover, the questions related to BJSQ category C evaluate whether a study participant receives sufficient support from those around them. We suggest that NBIW bathing improved study participants’ mental condition, which resulted in a change in their interpersonal cognition.</p>\",\n \"<p id=\\\"Par16\\\">Balneotherapy over a period of 2 to 3 weeks may have beneficial effects on sleep quality<sup>##REF##16765852##29##</sup>. It is thought to improve sleep by lowering systemic blood pressure and core body temperature by dilating peripheral blood vessels throughout the body, reducing pain by inhibiting inflammation and pain-related substances, and relaxing muscles<sup>##REF##36035468##30##</sup>. The decrease in blood pressure occurs when the parasympathetic nervous system is dominant. Dominance of the parasympathetic nervous system is also considered to improve sleep quality. In fact, percutaneous stimulation of the parasympathetic nerve was reported to improve sleep quality in retired veterans suffering from post-traumatic stress disorder<sup>##REF##37039398##31##</sup>. The PSQI-J, one of the primary sleep endpoints in this study, assesses subjective sleep quality, including insomnia; the total score ranges from 0 to 21, and a score of 6 or more indicates a potential sleep problem<sup>##REF##2748771##24##,##REF##11166088##32##</sup>. In the present study, the PSQI-J score confirmed that the NBIW intervention led to significantly greater improvements in subjective sleep quality than the control intervention. Furthermore, in the actigraphy sleep assessment, which was used as an objective assessment of sleep, the mean values of each item during the 3-week period showed a significant reduction in sleep latency and bed out latency and a trend towards improved sleep in many items with the NBIW intervention compared with the control intervention.</p>\",\n \"<p id=\\\"Par17\\\">Mental stress and sleep quality are known to interact closely, and studies have shown that sleep quality is reduced in stressful environments<sup>##REF##28756825##33##,##REF##27860130##34##</sup> and that stress can be reduced by improving sleep quality<sup>##REF##26307483##35##</sup>. In this study, NBIW bathing was considered to improve the quality of sleep not only subjectively but also objectively. In our previous study, we demonstrated that the bicarbonate ions in NBIW affect endothelial cells, and through phosphorylation of endothelial nitric oxide synthase, promote synthesis of nitric oxide, which dilates blood vessels, leading to improved blood flow and temperature elevation. Moreover, 4-week NBIW bathing improved sleep quality according to PSQ-J score and reduced stress according to POMS2 scores<sup>##REF##33414495##21##</sup>. Together with the result of this study, these findings indicate that NBIW may improve sleep quality and decrease stress by promoting blood circulation via production of nitric oxide and increase of body temperature.</p>\",\n \"<p id=\\\"Par18\\\">A large number of studies have shown that mental stress increases the risk of a wide range of diseases and may be a risk factor for cancer and autoimmune diseases<sup>##REF##31814865##36##,##REF##18190880##37##</sup>. Mental stress is also particularly strongly associated with cardiovascular disease<sup>##REF##22473079##38##</sup>. For example, several articles have reported that mental stress induces myocardial ischemia in patients with coronary artery disease<sup>##REF##34751723##39##,##REF##34751708##40##</sup> and that depression is correlated with cardiovascular disease<sup>##REF##27830572##41##</sup>. In addition, the Japan Collaborative Cohort Study for Evaluation of Cancer Risk (JACC study), which was sponsored by the Ministry of Education, Science, Sports and Culture of Japan, identified mental stress as being associated with increased coronary artery disease and increased stroke mortality in women<sup>##REF##12208798##42##</sup>, and in the large-scale international case–control INTERHEART study, psychosocial factors were found to more than double the risk of myocardial infarction<sup>##REF##15364186##43##</sup>. One of the primary mechanisms underlying the onset of cardiovascular disease due to mental stress may be inflammation-mediated vascular endothelial dysfunction resulting from the release of inflammatory cytokines as a result of suppression of the parasympathetic nervous system<sup>##REF##35512885##44##</sup>. A previous study showed that the bicarbonate ions in NBIW act directly on vascular endothelial cells to induce nitric oxide production through phosphorylation of endothelial nitric oxide synthase<sup>##REF##33414495##21##</sup>. As such, these findings suggest that continued NBIW bathing with warm water may also reduce the risk of cardiovascular disease by improving both mental stress and vascular endothelial function.</p>\",\n \"<p id=\\\"Par19\\\">Immune function is also thought to be closely related to stress and sleep<sup>##REF##15250815##45##,##REF##30920354##46##</sup>. Several studies have shown that stress is a risk factor for cancer and autoimmune diseases, suggesting that stress affects immune tolerance and anticancer immunity<sup>##REF##18190880##37##,##REF##32983091##47##</sup>. Furthermore, sleep deprivation is known to alter the secretion of inflammatory markers such as interleukins, tumor necrosis factor-α, other cytokines, chemokines, and acute phase proteins<sup>##REF##16337444##48##,##REF##11150007##49##</sup>.</p>\",\n \"<p id=\\\"Par20\\\">In this study, changes in several immune factors were observed with NBIW and control bathing. The CD4<sup>+</sup>:CD8<sup>+</sup> T cell ratio did not change significantly with the NBIW bathing intervention, but it decreased with the control bathing intervention because of an increase in CD8<sup>+</sup> T cells. CD8<sup>+</sup> T cells are known to fluctuate in number and function in response to stress<sup>##REF##15465465##50##–##REF##29145439##52##</sup>. These suggest that the CD8<sup>+</sup> T-cell count decreased because NBIW bathing resulted in a decrease in stress.</p>\",\n \"<p id=\\\"Par21\\\">The proportion of naive T cells, as represented by CD4<sup>+</sup>CD45RA<sup>+</sup>, decreased slightly after NBIW bathing, while the number of memory T cells showed increase, resulting in a lower N:M ratio. Thus, given that tissue-resident and circulating memory T cells play an essential role in anti-tumor immunity, NBIW bathing may enhance anti-tumor immune function<sup>##REF##35803260##53##</sup>.</p>\",\n \"<p id=\\\"Par22\\\">The proportion of CD16<sup>+</sup>CD56<sup>-</sup> cells, a marker of mature NK cells, decreased significantly in the NBIW bathing intervention. CD16<sup>+</sup>CD56<sup>-</sup> mature NK cells are increased by post-traumatic stress<sup>##REF##12208798##42##,##REF##27025668##54##</sup>, suggesting that NBIW bathing may reduce CD16<sup>+</sup>CD56<sup>−</sup> mature NK cells by alleviating stress.</p>\",\n \"<p id=\\\"Par23\\\">The proportion of CD20<sup>+</sup> B cell markers among total lymphocytes, the number of B cells, and the level of neutrophil phagocytosis and NK cell activity increased significantly after both interventions, suggesting that warm bathing itself improves immune function. However, the mean value of neutrophil phagocytosis activity after 4 weeks was higher with NBIW bathing than with control bathing, and the change was greater with NBIW than with control.</p>\",\n \"<p id=\\\"Par24\\\">This study has some limitations. First, most of the participants were middle aged. Second, people with self-perceived daily stress were recruited. Third, stress is subjective. These factors may limit the generalizability of the results.</p>\",\n \"<p id=\\\"Par25\\\">Taken together, the results of this study suggest that NBIW bathing has a positive influence on stress, sleep, and immune function. Stress, sleep, and immune function interact with each other<sup>##REF##15250815##45##,##REF##30920354##46##</sup>, so the effect of NBIW on immune function may be mediated through the improvements in stress and sleep. In addition, given that bicarbonate ions stimulate nitric oxide production in macrophage cell lines stimulated by lipopolysaccharide and interferon-gamma, which in turn promotes inflammatory responses<sup>##REF##30063984##55##,##REF##31960927##56##</sup>, it is conceivable that increased bicarbonate ions in the blood due to NBIW bathing may have a direct effect on immune system cells. In the future, further studies of such mechanisms are warranted.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">We previously demonstrated that neutral bicarbonate ionized water (NBIW) bathing enhances blood flow by bicarbonate ions and described the underlying mechanism. However, additional clinical investigation was warranted to investigate the efficacy of NBIW bathing. Hence, we performed a randomized, open-label, crossover trial to examine the effects of NBIW bathing on mental stress, sleep, and immune function. Participants who regularly felt stressed were randomly assigned to NBIW or regular bathing for 4 weeks. Mental stress was assessed with the Brief Job Stress Questionnaire (BJSQ) and the Profile of Mood States Second Edition; sleep quality, with the Pittsburgh Sleep Quality Index Japanese version (PSQI-J) and actigraphy; and immune function, with laboratory tests. PSQI-J scores and actigraphy sleep latency and bed out latency improved significantly more with NBIW bathing than with regular bathing (<italic>p</italic> < 0.05). Furthermore, NBIW bathing reduced both stress-induced fluctuations in CD4<sup>+</sup> and CD8<sup>+</sup> T cell counts and fluctuations in the naive to memory T cell ratio and neutrophil phagocytosis, indicating improved immune function. These findings suggest that daily NBIW bathing could improve mental stress, sleep quality, and immune function and bring about positive health effects in those who experience stress in their daily lives.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">We previously demonstrated that neutral bicarbonate ionized water (NBIW) bathing enhances blood flow by bicarbonate ions and described the underlying mechanism. However, additional clinical investigation was warranted to investigate the efficacy of NBIW bathing. Hence, we performed a randomized, open-label, crossover trial to examine the effects of NBIW bathing on mental stress, sleep, and immune function. Participants who regularly felt stressed were randomly assigned to NBIW or regular bathing for 4 weeks. Mental stress was assessed with the Brief Job Stress Questionnaire (BJSQ) and the Profile of Mood States Second Edition; sleep quality, with the Pittsburgh Sleep Quality Index Japanese version (PSQI-J) and actigraphy; and immune function, with laboratory tests. PSQI-J scores and actigraphy sleep latency and bed out latency improved significantly more with NBIW bathing than with regular bathing (<italic>p</italic> < 0.05). Furthermore, NBIW bathing reduced both stress-induced fluctuations in CD4<sup>+</sup> and CD8<sup>+</sup> T cell counts and fluctuations in the naive to memory T cell ratio and neutrophil phagocytosis, indicating improved immune function. These findings suggest that daily NBIW bathing could improve mental stress, sleep quality, and immune function and bring about positive health effects in those who experience stress in their daily lives.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary Information</title>","<p>\n</p>"],"string":"[\n \"<title>Supplementary Information</title>\",\n \"<p>\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary Information</title>","<p>The online version contains supplementary material available at 10.1038/s41598-024-51851-9.</p>","<title>Acknowledgements</title>","<p>The study was funded by Cranescience Co., Ltd., Tokyo, Japan. Editing assistance was provided by Jacquie Klesing, Board-certified Editor in the Life Sciences (ELS), on behalf of Yamada Translation Bureau, Inc., Tokyo, Japan.</p>","<title>Author contributions</title>","<p>I.S. designed the study; T.Y., R.U.N., and S.N. contributed to the collection and analysis of study data; I.S., R.U.N., T.Y., D.O., N.M., H.I., C.N., and S.N. interpreted the data; and I.S., R.U.N., and S.N. wrote the article. All authors approved the final manuscript after critical revision of the manuscript and agree to accept responsibility for its scientific accuracy and consistency.</p>","<title>Data availability</title>","<p>The data used in this study are available from the corresponding author upon reasonable request.</p>","<title>Competing interests</title>","<p id=\"Par38\">IS a representative director of Cranescience Co., Ltd., Tokyo, Japan, and receives compensation and stock ownership. The other authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary Information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41598-024-51851-9.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>The study was funded by Cranescience Co., Ltd., Tokyo, Japan. Editing assistance was provided by Jacquie Klesing, Board-certified Editor in the Life Sciences (ELS), on behalf of Yamada Translation Bureau, Inc., Tokyo, Japan.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>I.S. designed the study; T.Y., R.U.N., and S.N. contributed to the collection and analysis of study data; I.S., R.U.N., T.Y., D.O., N.M., H.I., C.N., and S.N. interpreted the data; and I.S., R.U.N., and S.N. wrote the article. All authors approved the final manuscript after critical revision of the manuscript and agree to accept responsibility for its scientific accuracy and consistency.</p>\",\n \"<title>Data availability</title>\",\n \"<p>The data used in this study are available from the corresponding author upon reasonable request.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par38\\\">IS a representative director of Cranescience Co., Ltd., Tokyo, Japan, and receives compensation and stock ownership. The other authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Flow diagram of the randomized crossover-controlled trial. NBIW, neutral bicarbonate ionized water.</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Assessment of stress with the Brief Job Stress Questionnaire. The Brief Job Stress Questionnaire (BJSQ), simplified version for primary outcome of mental stress, consists of 23 questions and was scored separately for the 3 stress categories. The graph shows the rate of change in scores from baseline (minus SD) on the vertical axis. A: control, − 13.92 (− 14.13); NBIW, − 16.32 (− 11.92). B: control, − 18.96 (− 17.03); NBIW, − 24.08 (− 10.53). C: control, − 4.14 (− 11.25); NBIW − 11.11 (− 15.08). N = 50 (control, n = 25; NBIW, n = 25). *<italic>p</italic> < 0.05 tested by paired <italic>t</italic> test. #: Parameters with significant changes from baseline (<italic>p</italic> < 0.05; tested by Wilcoxon rank sum test). NBIW, neutral bicarbonate ionized water.</p></caption></fig>","<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Test for carryover effect. For the percentage change from baseline in the Brief Job Stress Questionnaire-Social Support (BJSQ-C), the intervention periods were grouped into NBIW and control and compared with a <italic>t</italic> test. The first and second periods of the control intervention showed no significant difference (<italic>p</italic> = 0.308), and the same was found for the first and second periods of the NBIW intervention (<italic>p</italic> = 0.335). Thus, no carryover effect was observed.</p></caption></fig>","<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Sleep quality assessment with the Japanese version of the Pittsburgh Sleep Quality Index. The primary outcome <italic>sleep quality</italic> was assessed by the Japanese version of the Pittsburgh Sleep Quality Index. The graph shows the percentage change in scores from baseline (minus SD) on the vertical axis. Control, − 25.67% (SD, − 27.67%); neutral bicarbonate ionized water, − 37.08% (SD, − 26.33%). *<italic>p</italic> < 0.05 tested by paired <italic>t</italic> test. NBIW, neutral bicarbonate ionized water.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>Flow diagram of the randomized crossover-controlled trial. NBIW, neutral bicarbonate ionized water.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>Assessment of stress with the Brief Job Stress Questionnaire. The Brief Job Stress Questionnaire (BJSQ), simplified version for primary outcome of mental stress, consists of 23 questions and was scored separately for the 3 stress categories. The graph shows the rate of change in scores from baseline (minus SD) on the vertical axis. A: control, − 13.92 (− 14.13); NBIW, − 16.32 (− 11.92). B: control, − 18.96 (− 17.03); NBIW, − 24.08 (− 10.53). C: control, − 4.14 (− 11.25); NBIW − 11.11 (− 15.08). N = 50 (control, n = 25; NBIW, n = 25). *<italic>p</italic> < 0.05 tested by paired <italic>t</italic> test. #: Parameters with significant changes from baseline (<italic>p</italic> < 0.05; tested by Wilcoxon rank sum test). NBIW, neutral bicarbonate ionized water.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Figure 3</label><caption><p>Test for carryover effect. For the percentage change from baseline in the Brief Job Stress Questionnaire-Social Support (BJSQ-C), the intervention periods were grouped into NBIW and control and compared with a <italic>t</italic> test. The first and second periods of the control intervention showed no significant difference (<italic>p</italic> = 0.308), and the same was found for the first and second periods of the NBIW intervention (<italic>p</italic> = 0.335). Thus, no carryover effect was observed.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Figure 4</label><caption><p>Sleep quality assessment with the Japanese version of the Pittsburgh Sleep Quality Index. The primary outcome <italic>sleep quality</italic> was assessed by the Japanese version of the Pittsburgh Sleep Quality Index. The graph shows the percentage change in scores from baseline (minus SD) on the vertical axis. Control, − 25.67% (SD, − 27.67%); neutral bicarbonate ionized water, − 37.08% (SD, − 26.33%). *<italic>p</italic> < 0.05 tested by paired <italic>t</italic> test. NBIW, neutral bicarbonate ionized water.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":["<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Background characteristics of the participants.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Items</th><th align=\"left\" colspan=\"2\">Mean (± SD)</th><th align=\"left\" rowspan=\"2\">Between-group comparison, <italic>p</italic> value</th></tr><tr><th align=\"left\">Group control-NBIW (n = 13)</th><th align=\"left\">Group NBIW-control (n = 12)</th></tr></thead><tbody><tr><td align=\"left\">Age (years)</td><td align=\"left\">49.3 ± 5.1</td><td align=\"left\">49.5 ± 7.6</td><td char=\".\" align=\"char\">0.6418</td></tr><tr><td align=\"left\" colspan=\"4\">Mean hours of sleep</td></tr><tr><td align=\"left\"> Weekdays</td><td align=\"left\">5.67 ± 0.62</td><td align=\"left\">5.83 ± 1.11</td><td char=\".\" align=\"char\">0.2905</td></tr><tr><td align=\"left\"> Weekends</td><td align=\"left\">6.38 ± 0.98</td><td align=\"left\">6.62 ± 1.02</td><td char=\".\" align=\"char\">0.5525</td></tr><tr><td align=\"left\">Height (cm)</td><td align=\"left\">164.97 ± 10.28</td><td align=\"left\">162.65 ± 5.76</td><td char=\".\" align=\"char\">0.4888</td></tr><tr><td align=\"left\">Weight (kg)</td><td align=\"left\">57.78 ± 11.39</td><td align=\"left\">63.63 ± 13.51</td><td char=\".\" align=\"char\">0.2554</td></tr><tr><td align=\"left\">Body mass index (kg/m<sup>2</sup>)</td><td align=\"left\">21.02 ± 2.00</td><td align=\"left\">23.92 ± 4.12</td><td char=\".\" align=\"char\">0.0502</td></tr><tr><td align=\"left\">Systolic blood pressure (mm Hg)</td><td align=\"left\">112.6 ± 19.1</td><td align=\"left\">120.0 ± 8.7</td><td char=\".\" align=\"char\">0.2360</td></tr><tr><td align=\"left\">Diastolic blood pressure (mm Hg)</td><td align=\"left\">72.4 ± 14.3</td><td align=\"left\">74.8 ± 8.7</td><td char=\".\" align=\"char\">0.6106</td></tr><tr><td align=\"left\">Pulse rate (bpm)</td><td align=\"left\">69.6 ± 9.9</td><td align=\"left\">72.0 ± 8.6</td><td char=\".\" align=\"char\">0.5214</td></tr><tr><td align=\"left\" colspan=\"4\">BJSQ score</td></tr><tr><td align=\"left\"> A: Job stressors</td><td align=\"left\">18.3 ± 2.5</td><td align=\"left\">18.8 ± 2.9</td><td char=\".\" align=\"char\">0.6344</td></tr><tr><td align=\"left\"> B: Stress reaction</td><td align=\"left\">25.0 ± 6.7</td><td align=\"left\">26.2 ± 5.2</td><td char=\".\" align=\"char\">0.6346</td></tr><tr><td align=\"left\"> C: Social support</td><td align=\"left\">16.8 ± 2.6</td><td align=\"left\">16.5 ± 3.2</td><td char=\".\" align=\"char\">0.8025</td></tr><tr><td align=\"left\">PSQI-J total score</td><td align=\"left\">9.3 ± 1.8</td><td align=\"left\">9.4 ± 1.4</td><td char=\".\" align=\"char\">0.8332</td></tr><tr><td align=\"left\" rowspan=\"2\">Bathing time during the intervention (min)</td><td align=\"left\">NBIW (n = 25)</td><td align=\"left\">Control (n = 25)</td><td char=\".\" align=\"char\" rowspan=\"2\">0.8269</td></tr><tr><td align=\"left\">22.8 ± 3.8</td><td align=\"left\">22.7 ± 3.5</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Assessment of sleep quality by the profile of mood states 2nd edition.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\" colspan=\"2\">Items</th><th align=\"left\" colspan=\"5\">Percentage change from baseline</th><th align=\"left\" colspan=\"2\">Within-group comparison</th></tr><tr><th align=\"left\" rowspan=\"2\">Control (n = 25), mean (± SD)</th><th align=\"left\" rowspan=\"2\">NBIW (n = 25), mean (± SD)</th><th align=\"left\" colspan=\"3\">Between-group comparison</th><th align=\"left\" colspan=\"2\"><italic>p</italic> value</th></tr><tr><th align=\"left\"><italic>p</italic> value</th><th align=\"left\"><italic>r</italic></th><th align=\"left\">Effect size</th><th align=\"left\">Control</th><th align=\"left\">NBIW</th></tr></thead><tbody><tr><td align=\"left\">AH (anger-hostility)</td><td align=\"left\">%</td><td align=\"left\">− 8.0 ± 14.7</td><td align=\"left\">− 9.6 ± 12.8</td><td char=\".\" align=\"char\">0.3341</td><td char=\".\" align=\"char\">− 0.14</td><td align=\"left\">Small</td><td char=\".\" align=\"char\">0.0083**</td><td char=\".\" align=\"char\">0.0014**</td></tr><tr><td align=\"left\">CB (confusion-bewilderment)</td><td align=\"left\">%</td><td align=\"left\">− 3.2 ± 11.9</td><td align=\"left\">− 10.0 ± 11.6</td><td char=\".\" align=\"char\">0.0478*</td><td char=\".\" align=\"char\">− 0.28</td><td align=\"left\">Small</td><td char=\".\" align=\"char\">0.1967</td><td char=\".\" align=\"char\">0.0005**</td></tr><tr><td align=\"left\">DD (depression-dejection)</td><td align=\"left\">%</td><td align=\"left\">− 6.1 ± 13.3</td><td align=\"left\">− 8.9 ± 12.1</td><td char=\".\" align=\"char\">0.2209</td><td char=\".\" align=\"char\">− 0.17</td><td align=\"left\">Small</td><td char=\".\" align=\"char\">0.0350*</td><td char=\".\" align=\"char\">0.0024**</td></tr><tr><td align=\"left\">FI (fatigue-inertia)</td><td align=\"left\">%</td><td align=\"left\">− 7.5 ± 13.4</td><td align=\"left\">− 13.0 ± 11.5</td><td char=\".\" align=\"char\">0.1064</td><td char=\".\" align=\"char\">− 0.23</td><td align=\"left\">Small</td><td char=\".\" align=\"char\">0.0090**</td><td char=\".\" align=\"char\">0.0001**</td></tr><tr><td align=\"left\">TA (tension-anxiety)</td><td align=\"left\">%</td><td align=\"left\">− 11.6 ± 13.5</td><td align=\"left\">− 14.2 ± 9.8</td><td char=\".\" align=\"char\">0.3651</td><td char=\".\" align=\"char\">− 0.13</td><td align=\"left\">Small</td><td char=\".\" align=\"char\">0.0007**</td><td char=\".\" align=\"char\">0.0000**</td></tr><tr><td align=\"left\">VA (vigor-activity)</td><td align=\"left\">%</td><td align=\"left\">15.1 ± 16.0</td><td align=\"left\">19.4 ± 21.4</td><td char=\".\" align=\"char\">0.3271</td><td char=\".\" align=\"char\">− 0.14</td><td align=\"left\">Small</td><td char=\".\" align=\"char\">0.0008**</td><td char=\".\" align=\"char\">0.0009**</td></tr><tr><td align=\"left\">F (friendliness)</td><td align=\"left\">%</td><td align=\"left\">1.4 ± 18.0</td><td align=\"left\">9.2 ± 17.8</td><td char=\".\" align=\"char\">0.0674</td><td char=\".\" align=\"char\">− 0.26</td><td align=\"left\">Small</td><td char=\".\" align=\"char\">0.5988**</td><td char=\".\" align=\"char\">0.0547</td></tr><tr><td align=\"left\">TMD (total mood disturbance)</td><td align=\"left\">%</td><td align=\"left\">− 10.4 ± 13.0</td><td align=\"left\">− 15.2 ± 9.9</td><td char=\".\" align=\"char\">0.0883</td><td char=\".\" align=\"char\">− 0.24</td><td align=\"left\">Small</td><td char=\".\" align=\"char\">0.0006**</td><td char=\".\" align=\"char\">0.0000**</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Evaluation of sleep quality by actigraphy.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Items</th><th align=\"left\">Control (n = 25), mean ± SD</th><th align=\"left\">NBIW (n = 25), mean (± SD)</th><th align=\"left\">Significant difference between groups</th></tr></thead><tbody><tr><td align=\"left\">Total sleep time</td><td char=\"±\" align=\"char\">318.44 ± 13.8</td><td char=\"±\" align=\"char\">320.96 ± 13.47</td><td align=\"left\"/></tr><tr><td align=\"left\">Sleep latency</td><td char=\"±\" align=\"char\">14.24 ± 1.68</td><td char=\"±\" align=\"char\">12.06 ± 1.28</td><td align=\"left\">*</td></tr><tr><td align=\"left\">Wake time after sleep onset</td><td char=\"±\" align=\"char\">72.24 ± 7.88</td><td char=\"±\" align=\"char\">68.12 ± 5.80</td><td align=\"left\"/></tr><tr><td align=\"left\">Sleep efficiency</td><td char=\"±\" align=\"char\">77.52 ± 1.84</td><td char=\"±\" align=\"char\">78.85 ± 1.57</td><td align=\"left\"/></tr><tr><td align=\"left\">Sleep period time</td><td char=\"±\" align=\"char\">390.68 ± 16.57</td><td char=\"±\" align=\"char\">389.08 ± 15.22</td><td align=\"left\"/></tr><tr><td align=\"left\">Time in bed</td><td char=\"±\" align=\"char\">412.74 ± 16.89</td><td char=\"±\" align=\"char\">407.57 ± 15.13</td><td align=\"left\"/></tr><tr><td align=\"left\">Bed out latency</td><td char=\"±\" align=\"char\">7.82 ± 0.98</td><td char=\"±\" align=\"char\">6.43 ± 0.71</td><td align=\"left\">*</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Effect of the intervention on lymphocyte subset percentages.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\" colspan=\"3\"/><th align=\"left\" rowspan=\"3\">Control (n = 25), mean (± SD)</th><th align=\"left\" rowspan=\"3\">NBIW (n = 25), mean (± SD)</th><th align=\"left\" colspan=\"3\">Between-group comparison (4 weeks)</th><th align=\"left\" colspan=\"2\">Within-group comparison</th></tr><tr><th align=\"left\" rowspan=\"2\"><italic>p</italic></th><th align=\"left\" rowspan=\"2\"><italic>d</italic> or <italic>r</italic></th><th align=\"left\" rowspan=\"2\">Effect size</th><th align=\"left\" colspan=\"2\"><italic>p</italic></th></tr><tr><th align=\"left\">Control</th><th align=\"left\">NBIW</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">CD3<sup>+</sup></td><td align=\"left\" rowspan=\"2\">%</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"2\">73.936 ± 6.476</td><td char=\".\" align=\"char\" rowspan=\"2\">0.3633</td><td align=\"left\" rowspan=\"2\">0.09</td><td align=\"left\" rowspan=\"2\">–</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0044**</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0834</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\">72.460 ± 6.052</td><td align=\"left\">72.998 ± 6.561</td></tr><tr><td align=\"left\" rowspan=\"2\">CD4 + </td><td align=\"left\" rowspan=\"2\">%</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"2\">51.861 ± 7.358</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0395*</td><td align=\"left\" rowspan=\"2\">0.22</td><td align=\"left\" rowspan=\"2\">Small</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0076**</td><td char=\".\" align=\"char\" rowspan=\"2\">0.6514</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\">50.025 ± 6.382</td><td align=\"left\">51.563 ± 7.453</td></tr><tr><td align=\"left\" rowspan=\"2\">CD4<sup>+</sup>CD45RA<sup>+</sup></td><td align=\"left\" rowspan=\"2\">%</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"2\">43.172 ± 11.520</td><td char=\".\" align=\"char\" rowspan=\"2\">0.1166</td><td align=\"left\" rowspan=\"2\">− 0.14</td><td align=\"left\" rowspan=\"2\">–</td><td char=\".\" align=\"char\" rowspan=\"2\">0.9746</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0062**</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\">43.142 ± 12.323</td><td align=\"left\">41.448 ± 11.545</td></tr><tr><td align=\"left\" rowspan=\"2\">CD8<sup>+</sup></td><td align=\"left\" rowspan=\"2\">%</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"2\">12.326 ± 5.408</td><td char=\".\" align=\"char\" rowspan=\"2\">0.1815</td><td align=\"left\" rowspan=\"2\">− 0.17</td><td align=\"left\" rowspan=\"2\">–</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0065**</td><td char=\".\" align=\"char\" rowspan=\"2\">0.8746</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\">13.382 ± 5.743</td><td align=\"left\">12.416 ± 5.927</td></tr><tr><td align=\"left\" rowspan=\"2\">CD8<sup>+</sup>CD28<sup>+</sup></td><td align=\"left\" rowspan=\"2\">%</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"2\">73.261 ± 12.081</td><td char=\".\" align=\"char\" rowspan=\"2\">0.7982</td><td align=\"left\" rowspan=\"2\">− 0.04</td><td align=\"left\" rowspan=\"2\">–</td><td char=\".\" align=\"char\" rowspan=\"2\">0.2641</td><td char=\".\" align=\"char\" rowspan=\"2\">0.4926</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\">72.256 ± 13.544</td><td align=\"left\">72.314 ± 12.797</td></tr><tr><td align=\"left\" rowspan=\"2\">CD16<sup>+</sup>CD56<sup>−</sup></td><td align=\"left\" rowspan=\"2\">%</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"2\">0.719 ± 0.431</td><td char=\".\" align=\"char\" rowspan=\"2\">0.1424</td><td align=\"left\" rowspan=\"2\">− 0.21</td><td align=\"left\" rowspan=\"2\">Small</td><td char=\".\" align=\"char\" rowspan=\"2\">0.1935</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0017**</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\">0.587 ± 0.405</td><td align=\"left\">0.500 ± 0.355</td></tr><tr><td align=\"left\" rowspan=\"2\">CD16<sup>−</sup>CD56<sup>+</sup></td><td align=\"left\" rowspan=\"2\">%</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"2\">4.456 ± 3.128</td><td char=\".\" align=\"char\" rowspan=\"2\">0.8823</td><td align=\"left\" rowspan=\"2\">− 0.02</td><td align=\"left\" rowspan=\"2\">–</td><td char=\".\" align=\"char\" rowspan=\"2\">0.7672</td><td char=\".\" align=\"char\" rowspan=\"2\">0.3817</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\">4.570 ± 3.418</td><td align=\"left\">4.477 ± 2.966</td></tr><tr><td align=\"left\" rowspan=\"2\">CD16<sup>+</sup>CD56<sup>+</sup></td><td align=\"left\" rowspan=\"2\">%</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"2\">7.868 ± 3.198</td><td char=\".\" align=\"char\" rowspan=\"2\">0.3958</td><td align=\"left\" rowspan=\"2\">− 0.16</td><td align=\"left\" rowspan=\"2\">–</td><td char=\".\" align=\"char\" rowspan=\"2\">0.2406</td><td char=\".\" align=\"char\" rowspan=\"2\">0.7995</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\">8.472 ± 2.885</td><td align=\"left\">8.008 ± 2.993</td></tr><tr><td align=\"left\" rowspan=\"2\">CD20<sup>+</sup></td><td align=\"left\" rowspan=\"2\">%</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"2\">15.623 ± 4.726</td><td char=\".\" align=\"char\" rowspan=\"2\">0.8625</td><td align=\"left\" rowspan=\"2\">− 0.01</td><td align=\"left\" rowspan=\"2\">–</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0366*</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0222*</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\">16.698 ± 5.421</td><td align=\"left\">16.630 ± 5.203</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Effect of the intervention on lymphocyte subset concentrations.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\" colspan=\"3\"/><th align=\"left\" rowspan=\"3\" colspan=\"3\">Control (n = 25), mean (± SD)</th><th align=\"left\" rowspan=\"3\" colspan=\"3\">NBIW (n = 25), mean (± SD)</th><th align=\"left\" colspan=\"3\">Between-group comparison (week 4)</th><th align=\"left\" colspan=\"2\">Within-group comparison</th></tr><tr><th align=\"left\" rowspan=\"2\"><italic>p</italic></th><th align=\"left\" rowspan=\"2\"><italic>d</italic> or <italic>r</italic></th><th align=\"left\" rowspan=\"2\">Effect size</th><th align=\"left\" colspan=\"2\"><italic>p</italic></th></tr><tr><th align=\"left\">Control</th><th align=\"left\">NBIW</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Number of T cells</td><td align=\"left\" rowspan=\"2\">/µL</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"6\">1154.2 ± 281.7</td><td align=\"left\" rowspan=\"2\">0.3065</td><td align=\"left\" rowspan=\"2\">− 0.14</td><td align=\"left\" rowspan=\"2\">Small</td><td char=\".\" align=\"char\" rowspan=\"2\">0.4926</td><td char=\".\" align=\"char\" rowspan=\"2\">0.9893</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\" colspan=\"3\">1203.0 ± 328.3</td><td align=\"left\" colspan=\"3\">1177.6 ± 352.9</td></tr><tr><td align=\"left\" rowspan=\"2\">Number of CD4<sup>+</sup> T cells</td><td align=\"left\" rowspan=\"2\">/µL</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"6\">805.4 ± 200.5</td><td align=\"left\" rowspan=\"2\">0.8191</td><td align=\"left\" rowspan=\"2\">− 0.03</td><td align=\"left\" rowspan=\"2\">–</td><td char=\".\" align=\"char\" rowspan=\"2\">0.7468</td><td char=\".\" align=\"char\" rowspan=\"2\">0.7064</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\" colspan=\"3\">819.0 ± 179.4</td><td align=\"left\" colspan=\"3\">830.6 ± 266.8</td></tr><tr><td align=\"left\" rowspan=\"2\">Number of CD8<sup>+</sup> T cells</td><td align=\"left\" rowspan=\"2\">/µL</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"6\">198.0 ± 111.6</td><td align=\"left\" rowspan=\"2\">0.0395*</td><td align=\"left\" rowspan=\"2\">− 0.29</td><td align=\"left\" rowspan=\"2\">Small</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0011**</td><td char=\".\" align=\"char\" rowspan=\"2\">0.7672</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\" colspan=\"3\">230.2 ± 131.3</td><td align=\"left\" colspan=\"3\">204.7 ± 123.3</td></tr><tr><td align=\"left\" rowspan=\"2\">CD4<sup>+</sup>:CD8<sup>+</sup> ratio</td><td align=\"left\" rowspan=\"2\">–</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"6\">5.209 ± 2.667</td><td align=\"left\" rowspan=\"2\">0.1829</td><td align=\"left\" rowspan=\"2\">− 0.19</td><td align=\"left\" rowspan=\"2\">Small</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0110*</td><td char=\".\" align=\"char\" rowspan=\"2\">0.9250</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\" colspan=\"3\">4.800 ± 2.985</td><td align=\"left\" colspan=\"3\">5.935 ± 4.399</td></tr><tr><td align=\"left\" rowspan=\"2\">Number of naive T cells</td><td align=\"left\" rowspan=\"2\">/µL</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"6\">349.4 ± 142.5</td><td align=\"left\" rowspan=\"2\">0.0614</td><td align=\"left\" rowspan=\"2\">− 0.26</td><td align=\"left\" rowspan=\"2\">Small</td><td char=\".\" align=\"char\" rowspan=\"2\">0.8401</td><td char=\".\" align=\"char\" rowspan=\"2\">0.1919</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\" colspan=\"3\">355.0 ± 139.2</td><td align=\"left\" colspan=\"3\">343.5 ± 166.7</td></tr><tr><td align=\"left\" rowspan=\"2\">Number of memory T cells</td><td align=\"left\" rowspan=\"2\">/µL</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"6\">456.0 ± 131.6</td><td align=\"left\" rowspan=\"2\">0.3529</td><td align=\"left\" rowspan=\"2\">0.15</td><td align=\"left\" rowspan=\"2\">–</td><td char=\".\" align=\"char\" rowspan=\"2\">0.5935</td><td char=\".\" align=\"char\" rowspan=\"2\">0.1065</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\" colspan=\"3\">464.0 ± 131.0</td><td align=\"left\" colspan=\"3\">487.2 ± 171.4</td></tr><tr><td align=\"left\" rowspan=\"2\">N:M ratio</td><td align=\"left\" rowspan=\"2\">–</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"6\">0.826 ± 0.354</td><td align=\"left\" rowspan=\"2\">0.1253</td><td align=\"left\" rowspan=\"2\">− 0.18</td><td align=\"left\" rowspan=\"2\">–</td><td char=\".\" align=\"char\" rowspan=\"2\">0.8578</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0070**</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\" colspan=\"3\">0.833 ± 0.375</td><td align=\"left\" colspan=\"3\">0.770 ± 0.334</td></tr><tr><td align=\"left\" rowspan=\"2\">Number of CD8<sup>+</sup>CD28<sup>+</sup> T cells</td><td align=\"left\" rowspan=\"2\">/µL</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"6\">145.8 ± 94.6</td><td align=\"left\" rowspan=\"2\">0.0561</td><td align=\"left\" rowspan=\"2\">− 0.27</td><td align=\"left\" rowspan=\"2\">Small</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0071**</td><td char=\".\" align=\"char\" rowspan=\"2\">0.7982</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\" colspan=\"3\">168.6 ± 114.2</td><td align=\"left\" colspan=\"3\">147.2 ± 99.7</td></tr><tr><td align=\"left\" rowspan=\"2\">Number of B cells</td><td align=\"left\" rowspan=\"2\">/µL</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"6\">251.3 ± 114.8</td><td align=\"left\" rowspan=\"2\">0.1918</td><td align=\"left\" rowspan=\"2\">− 0.18</td><td align=\"left\" rowspan=\"2\">Small</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0061**</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0333*</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\" colspan=\"3\">283.6 ± 133.8</td><td align=\"left\" colspan=\"3\">268.2 ± 107.8</td></tr><tr><td align=\"left\" rowspan=\"2\">Number of natural killer cells</td><td align=\"left\" rowspan=\"2\">/µL</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"6\">125.5 ± 69.6</td><td align=\"left\" rowspan=\"2\">0.2108</td><td align=\"left\" rowspan=\"2\">− 0.18</td><td align=\"left\" rowspan=\"2\">Small</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0979</td><td char=\".\" align=\"char\" rowspan=\"2\">0.3531</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\" colspan=\"3\">142.4 ± 69.2</td><td align=\"left\" colspan=\"3\">130.4 ± 74.3</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab6\"><label>Table 6</label><caption><p>Neutrophil phagocytosis activity and natural killer cell activity.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\" colspan=\"3\"/><th align=\"left\" rowspan=\"3\">Control (n = 25), mean (± SD)</th><th align=\"left\" rowspan=\"3\">NBIW (n = 25), mean (± SD)</th><th align=\"left\" colspan=\"3\">Between-group comparison (week 4)</th><th align=\"left\" colspan=\"2\">Within-group comparison</th></tr><tr><th align=\"left\" rowspan=\"2\"><italic>p</italic></th><th align=\"left\" rowspan=\"2\"><italic>r</italic></th><th align=\"left\" rowspan=\"2\">Effect size</th><th align=\"left\" colspan=\"2\"><italic>p</italic></th></tr><tr><th align=\"left\">Control</th><th align=\"left\">NBIW</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Neutrophil phagocytosis activity</td><td align=\"left\" rowspan=\"2\">%</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"2\">83.4 ± 2.4</td><td align=\"left\" rowspan=\"2\">0.1542</td><td align=\"left\" rowspan=\"2\">− 0.20</td><td align=\"left\" rowspan=\"2\">Small</td><td align=\"left\" rowspan=\"2\">0.0350*</td><td align=\"left\" rowspan=\"2\">0.0044**</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\">84.1 ± 2.0</td><td align=\"left\">84.6 ± 2.2</td></tr><tr><td align=\"left\" rowspan=\"2\">NK cell activity</td><td align=\"left\" rowspan=\"2\">%</td><td align=\"left\">Baseline</td><td align=\"left\" colspan=\"2\">24.9 ± 13.8</td><td align=\"left\" rowspan=\"2\">0.7378</td><td align=\"left\" rowspan=\"2\">− 0.05</td><td align=\"left\" rowspan=\"2\">–</td><td align=\"left\" rowspan=\"2\">0.0000**</td><td align=\"left\" rowspan=\"2\">0.0001**</td></tr><tr><td align=\"left\">Week 4</td><td align=\"left\">35.7 ± 11.8</td><td align=\"left\">34.9 ± 11.4</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab7\"><label>Table 7</label><caption><p>Incidence of adverse events and adverse drug reactions.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" colspan=\"3\">Item</th><th align=\"left\">Control</th><th align=\"left\">NBIW</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"14\">Adverse events</td><td align=\"left\" rowspan=\"9\">Symptoms</td><td align=\"left\">Nausea</td><td align=\"left\">1</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Malaise</td><td align=\"left\">2</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Headache</td><td align=\"left\">1</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Skin reddening</td><td align=\"left\">1</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Nasal discharge</td><td align=\"left\">1</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Abdominal pain</td><td align=\"left\">1</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Abdominal pain, diarrhea (loose stool)</td><td align=\"left\">0</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Weight loss</td><td align=\"left\">0</td><td align=\"left\">1</td></tr><tr><td align=\"left\">Increase in creatine kinase</td><td align=\"left\">0</td><td align=\"left\">1</td></tr><tr><td align=\"left\" rowspan=\"3\">Severity</td><td align=\"left\">Mild</td><td align=\"left\">4</td><td align=\"left\">3</td></tr><tr><td align=\"left\">Moderate</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Severe</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\" rowspan=\"2\">Incidence</td><td align=\"left\">Number of patients</td><td align=\"left\">4</td><td align=\"left\">3</td></tr><tr><td align=\"left\"><bold>Number of events</bold></td><td align=\"left\"><bold>7</bold></td><td align=\"left\"><bold>3</bold></td></tr><tr><td align=\"left\" rowspan=\"6\">Adverse drug reactions</td><td align=\"left\">Symptoms</td><td align=\"left\">–</td><td align=\"left\">–</td><td align=\"left\">–</td></tr><tr><td align=\"left\" rowspan=\"3\">Severity</td><td align=\"left\">Mild</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Moderate</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Severe</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\" rowspan=\"2\">Incidence</td><td align=\"left\">Number of patients</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Number of events</td><td align=\"left\">0</td><td align=\"left\">0</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab8\"><label>Table 8</label><caption><p>Ingredients of the neutral bicarbonate ionized water bath tablet.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Ingredients</th><th align=\"left\">%</th></tr></thead><tbody><tr><td align=\"left\">Sodium bicarbonate</td><td char=\".\" align=\"char\">75</td></tr><tr><td align=\"left\">Sodium carbonate</td><td char=\".\" align=\"char\">5</td></tr><tr><td align=\"left\">Citric acid</td><td char=\".\" align=\"char\">15</td></tr><tr><td align=\"left\">Other</td><td char=\".\" align=\"char\">5</td></tr></tbody></table></table-wrap>"],"string":"[\n \"<table-wrap id=\\\"Tab1\\\"><label>Table 1</label><caption><p>Background characteristics of the participants.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" rowspan=\\\"2\\\">Items</th><th align=\\\"left\\\" colspan=\\\"2\\\">Mean (± SD)</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Between-group comparison, <italic>p</italic> value</th></tr><tr><th align=\\\"left\\\">Group control-NBIW (n = 13)</th><th align=\\\"left\\\">Group NBIW-control (n = 12)</th></tr></thead><tbody><tr><td align=\\\"left\\\">Age (years)</td><td align=\\\"left\\\">49.3 ± 5.1</td><td align=\\\"left\\\">49.5 ± 7.6</td><td char=\\\".\\\" align=\\\"char\\\">0.6418</td></tr><tr><td align=\\\"left\\\" colspan=\\\"4\\\">Mean hours of sleep</td></tr><tr><td align=\\\"left\\\"> Weekdays</td><td align=\\\"left\\\">5.67 ± 0.62</td><td align=\\\"left\\\">5.83 ± 1.11</td><td char=\\\".\\\" align=\\\"char\\\">0.2905</td></tr><tr><td align=\\\"left\\\"> Weekends</td><td align=\\\"left\\\">6.38 ± 0.98</td><td align=\\\"left\\\">6.62 ± 1.02</td><td char=\\\".\\\" align=\\\"char\\\">0.5525</td></tr><tr><td align=\\\"left\\\">Height (cm)</td><td align=\\\"left\\\">164.97 ± 10.28</td><td align=\\\"left\\\">162.65 ± 5.76</td><td char=\\\".\\\" align=\\\"char\\\">0.4888</td></tr><tr><td align=\\\"left\\\">Weight (kg)</td><td align=\\\"left\\\">57.78 ± 11.39</td><td align=\\\"left\\\">63.63 ± 13.51</td><td char=\\\".\\\" align=\\\"char\\\">0.2554</td></tr><tr><td align=\\\"left\\\">Body mass index (kg/m<sup>2</sup>)</td><td align=\\\"left\\\">21.02 ± 2.00</td><td align=\\\"left\\\">23.92 ± 4.12</td><td char=\\\".\\\" align=\\\"char\\\">0.0502</td></tr><tr><td align=\\\"left\\\">Systolic blood pressure (mm Hg)</td><td align=\\\"left\\\">112.6 ± 19.1</td><td align=\\\"left\\\">120.0 ± 8.7</td><td char=\\\".\\\" align=\\\"char\\\">0.2360</td></tr><tr><td align=\\\"left\\\">Diastolic blood pressure (mm Hg)</td><td align=\\\"left\\\">72.4 ± 14.3</td><td align=\\\"left\\\">74.8 ± 8.7</td><td char=\\\".\\\" align=\\\"char\\\">0.6106</td></tr><tr><td align=\\\"left\\\">Pulse rate (bpm)</td><td align=\\\"left\\\">69.6 ± 9.9</td><td align=\\\"left\\\">72.0 ± 8.6</td><td char=\\\".\\\" align=\\\"char\\\">0.5214</td></tr><tr><td align=\\\"left\\\" colspan=\\\"4\\\">BJSQ score</td></tr><tr><td align=\\\"left\\\"> A: Job stressors</td><td align=\\\"left\\\">18.3 ± 2.5</td><td align=\\\"left\\\">18.8 ± 2.9</td><td char=\\\".\\\" align=\\\"char\\\">0.6344</td></tr><tr><td align=\\\"left\\\"> B: Stress reaction</td><td align=\\\"left\\\">25.0 ± 6.7</td><td align=\\\"left\\\">26.2 ± 5.2</td><td char=\\\".\\\" align=\\\"char\\\">0.6346</td></tr><tr><td align=\\\"left\\\"> C: Social support</td><td align=\\\"left\\\">16.8 ± 2.6</td><td align=\\\"left\\\">16.5 ± 3.2</td><td char=\\\".\\\" align=\\\"char\\\">0.8025</td></tr><tr><td align=\\\"left\\\">PSQI-J total score</td><td align=\\\"left\\\">9.3 ± 1.8</td><td align=\\\"left\\\">9.4 ± 1.4</td><td char=\\\".\\\" align=\\\"char\\\">0.8332</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Bathing time during the intervention (min)</td><td align=\\\"left\\\">NBIW (n = 25)</td><td align=\\\"left\\\">Control (n = 25)</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.8269</td></tr><tr><td align=\\\"left\\\">22.8 ± 3.8</td><td align=\\\"left\\\">22.7 ± 3.5</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab2\\\"><label>Table 2</label><caption><p>Assessment of sleep quality by the profile of mood states 2nd edition.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" rowspan=\\\"3\\\" colspan=\\\"2\\\">Items</th><th align=\\\"left\\\" colspan=\\\"5\\\">Percentage change from baseline</th><th align=\\\"left\\\" colspan=\\\"2\\\">Within-group comparison</th></tr><tr><th align=\\\"left\\\" rowspan=\\\"2\\\">Control (n = 25), mean (± SD)</th><th align=\\\"left\\\" rowspan=\\\"2\\\">NBIW (n = 25), mean (± SD)</th><th align=\\\"left\\\" colspan=\\\"3\\\">Between-group comparison</th><th align=\\\"left\\\" colspan=\\\"2\\\"><italic>p</italic> value</th></tr><tr><th align=\\\"left\\\"><italic>p</italic> value</th><th align=\\\"left\\\"><italic>r</italic></th><th align=\\\"left\\\">Effect size</th><th align=\\\"left\\\">Control</th><th align=\\\"left\\\">NBIW</th></tr></thead><tbody><tr><td align=\\\"left\\\">AH (anger-hostility)</td><td align=\\\"left\\\">%</td><td align=\\\"left\\\">− 8.0 ± 14.7</td><td align=\\\"left\\\">− 9.6 ± 12.8</td><td char=\\\".\\\" align=\\\"char\\\">0.3341</td><td char=\\\".\\\" align=\\\"char\\\">− 0.14</td><td align=\\\"left\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\">0.0083**</td><td char=\\\".\\\" align=\\\"char\\\">0.0014**</td></tr><tr><td align=\\\"left\\\">CB (confusion-bewilderment)</td><td align=\\\"left\\\">%</td><td align=\\\"left\\\">− 3.2 ± 11.9</td><td align=\\\"left\\\">− 10.0 ± 11.6</td><td char=\\\".\\\" align=\\\"char\\\">0.0478*</td><td char=\\\".\\\" align=\\\"char\\\">− 0.28</td><td align=\\\"left\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\">0.1967</td><td char=\\\".\\\" align=\\\"char\\\">0.0005**</td></tr><tr><td align=\\\"left\\\">DD (depression-dejection)</td><td align=\\\"left\\\">%</td><td align=\\\"left\\\">− 6.1 ± 13.3</td><td align=\\\"left\\\">− 8.9 ± 12.1</td><td char=\\\".\\\" align=\\\"char\\\">0.2209</td><td char=\\\".\\\" align=\\\"char\\\">− 0.17</td><td align=\\\"left\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\">0.0350*</td><td char=\\\".\\\" align=\\\"char\\\">0.0024**</td></tr><tr><td align=\\\"left\\\">FI (fatigue-inertia)</td><td align=\\\"left\\\">%</td><td align=\\\"left\\\">− 7.5 ± 13.4</td><td align=\\\"left\\\">− 13.0 ± 11.5</td><td char=\\\".\\\" align=\\\"char\\\">0.1064</td><td char=\\\".\\\" align=\\\"char\\\">− 0.23</td><td align=\\\"left\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\">0.0090**</td><td char=\\\".\\\" align=\\\"char\\\">0.0001**</td></tr><tr><td align=\\\"left\\\">TA (tension-anxiety)</td><td align=\\\"left\\\">%</td><td align=\\\"left\\\">− 11.6 ± 13.5</td><td align=\\\"left\\\">− 14.2 ± 9.8</td><td char=\\\".\\\" align=\\\"char\\\">0.3651</td><td char=\\\".\\\" align=\\\"char\\\">− 0.13</td><td align=\\\"left\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\">0.0007**</td><td char=\\\".\\\" align=\\\"char\\\">0.0000**</td></tr><tr><td align=\\\"left\\\">VA (vigor-activity)</td><td align=\\\"left\\\">%</td><td align=\\\"left\\\">15.1 ± 16.0</td><td align=\\\"left\\\">19.4 ± 21.4</td><td char=\\\".\\\" align=\\\"char\\\">0.3271</td><td char=\\\".\\\" align=\\\"char\\\">− 0.14</td><td align=\\\"left\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\">0.0008**</td><td char=\\\".\\\" align=\\\"char\\\">0.0009**</td></tr><tr><td align=\\\"left\\\">F (friendliness)</td><td align=\\\"left\\\">%</td><td align=\\\"left\\\">1.4 ± 18.0</td><td align=\\\"left\\\">9.2 ± 17.8</td><td char=\\\".\\\" align=\\\"char\\\">0.0674</td><td char=\\\".\\\" align=\\\"char\\\">− 0.26</td><td align=\\\"left\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\">0.5988**</td><td char=\\\".\\\" align=\\\"char\\\">0.0547</td></tr><tr><td align=\\\"left\\\">TMD (total mood disturbance)</td><td align=\\\"left\\\">%</td><td align=\\\"left\\\">− 10.4 ± 13.0</td><td align=\\\"left\\\">− 15.2 ± 9.9</td><td char=\\\".\\\" align=\\\"char\\\">0.0883</td><td char=\\\".\\\" align=\\\"char\\\">− 0.24</td><td align=\\\"left\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\">0.0006**</td><td char=\\\".\\\" align=\\\"char\\\">0.0000**</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab3\\\"><label>Table 3</label><caption><p>Evaluation of sleep quality by actigraphy.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Items</th><th align=\\\"left\\\">Control (n = 25), mean ± SD</th><th align=\\\"left\\\">NBIW (n = 25), mean (± SD)</th><th align=\\\"left\\\">Significant difference between groups</th></tr></thead><tbody><tr><td align=\\\"left\\\">Total sleep time</td><td char=\\\"±\\\" align=\\\"char\\\">318.44 ± 13.8</td><td char=\\\"±\\\" align=\\\"char\\\">320.96 ± 13.47</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">Sleep latency</td><td char=\\\"±\\\" align=\\\"char\\\">14.24 ± 1.68</td><td char=\\\"±\\\" align=\\\"char\\\">12.06 ± 1.28</td><td align=\\\"left\\\">*</td></tr><tr><td align=\\\"left\\\">Wake time after sleep onset</td><td char=\\\"±\\\" align=\\\"char\\\">72.24 ± 7.88</td><td char=\\\"±\\\" align=\\\"char\\\">68.12 ± 5.80</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">Sleep efficiency</td><td char=\\\"±\\\" align=\\\"char\\\">77.52 ± 1.84</td><td char=\\\"±\\\" align=\\\"char\\\">78.85 ± 1.57</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">Sleep period time</td><td char=\\\"±\\\" align=\\\"char\\\">390.68 ± 16.57</td><td char=\\\"±\\\" align=\\\"char\\\">389.08 ± 15.22</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">Time in bed</td><td char=\\\"±\\\" align=\\\"char\\\">412.74 ± 16.89</td><td char=\\\"±\\\" align=\\\"char\\\">407.57 ± 15.13</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">Bed out latency</td><td char=\\\"±\\\" align=\\\"char\\\">7.82 ± 0.98</td><td char=\\\"±\\\" align=\\\"char\\\">6.43 ± 0.71</td><td align=\\\"left\\\">*</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab4\\\"><label>Table 4</label><caption><p>Effect of the intervention on lymphocyte subset percentages.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" rowspan=\\\"3\\\" colspan=\\\"3\\\"/><th align=\\\"left\\\" rowspan=\\\"3\\\">Control (n = 25), mean (± SD)</th><th align=\\\"left\\\" rowspan=\\\"3\\\">NBIW (n = 25), mean (± SD)</th><th align=\\\"left\\\" colspan=\\\"3\\\">Between-group comparison (4 weeks)</th><th align=\\\"left\\\" colspan=\\\"2\\\">Within-group comparison</th></tr><tr><th align=\\\"left\\\" rowspan=\\\"2\\\"><italic>p</italic></th><th align=\\\"left\\\" rowspan=\\\"2\\\"><italic>d</italic> or <italic>r</italic></th><th align=\\\"left\\\" rowspan=\\\"2\\\">Effect size</th><th align=\\\"left\\\" colspan=\\\"2\\\"><italic>p</italic></th></tr><tr><th align=\\\"left\\\">Control</th><th align=\\\"left\\\">NBIW</th></tr></thead><tbody><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">CD3<sup>+</sup></td><td align=\\\"left\\\" rowspan=\\\"2\\\">%</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"2\\\">73.936 ± 6.476</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.3633</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.09</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0044**</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0834</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\">72.460 ± 6.052</td><td align=\\\"left\\\">72.998 ± 6.561</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">CD4 + </td><td align=\\\"left\\\" rowspan=\\\"2\\\">%</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"2\\\">51.861 ± 7.358</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0395*</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.22</td><td align=\\\"left\\\" rowspan=\\\"2\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0076**</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.6514</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\">50.025 ± 6.382</td><td align=\\\"left\\\">51.563 ± 7.453</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">CD4<sup>+</sup>CD45RA<sup>+</sup></td><td align=\\\"left\\\" rowspan=\\\"2\\\">%</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"2\\\">43.172 ± 11.520</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.1166</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.14</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.9746</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0062**</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\">43.142 ± 12.323</td><td align=\\\"left\\\">41.448 ± 11.545</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">CD8<sup>+</sup></td><td align=\\\"left\\\" rowspan=\\\"2\\\">%</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"2\\\">12.326 ± 5.408</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.1815</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.17</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0065**</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.8746</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\">13.382 ± 5.743</td><td align=\\\"left\\\">12.416 ± 5.927</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">CD8<sup>+</sup>CD28<sup>+</sup></td><td align=\\\"left\\\" rowspan=\\\"2\\\">%</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"2\\\">73.261 ± 12.081</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.7982</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.04</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.2641</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.4926</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\">72.256 ± 13.544</td><td align=\\\"left\\\">72.314 ± 12.797</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">CD16<sup>+</sup>CD56<sup>−</sup></td><td align=\\\"left\\\" rowspan=\\\"2\\\">%</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"2\\\">0.719 ± 0.431</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.1424</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.21</td><td align=\\\"left\\\" rowspan=\\\"2\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.1935</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0017**</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\">0.587 ± 0.405</td><td align=\\\"left\\\">0.500 ± 0.355</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">CD16<sup>−</sup>CD56<sup>+</sup></td><td align=\\\"left\\\" rowspan=\\\"2\\\">%</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"2\\\">4.456 ± 3.128</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.8823</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.02</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.7672</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.3817</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\">4.570 ± 3.418</td><td align=\\\"left\\\">4.477 ± 2.966</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">CD16<sup>+</sup>CD56<sup>+</sup></td><td align=\\\"left\\\" rowspan=\\\"2\\\">%</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"2\\\">7.868 ± 3.198</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.3958</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.16</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.2406</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.7995</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\">8.472 ± 2.885</td><td align=\\\"left\\\">8.008 ± 2.993</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">CD20<sup>+</sup></td><td align=\\\"left\\\" rowspan=\\\"2\\\">%</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"2\\\">15.623 ± 4.726</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.8625</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.01</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0366*</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0222*</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\">16.698 ± 5.421</td><td align=\\\"left\\\">16.630 ± 5.203</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab5\\\"><label>Table 5</label><caption><p>Effect of the intervention on lymphocyte subset concentrations.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" rowspan=\\\"3\\\" colspan=\\\"3\\\"/><th align=\\\"left\\\" rowspan=\\\"3\\\" colspan=\\\"3\\\">Control (n = 25), mean (± SD)</th><th align=\\\"left\\\" rowspan=\\\"3\\\" colspan=\\\"3\\\">NBIW (n = 25), mean (± SD)</th><th align=\\\"left\\\" colspan=\\\"3\\\">Between-group comparison (week 4)</th><th align=\\\"left\\\" colspan=\\\"2\\\">Within-group comparison</th></tr><tr><th align=\\\"left\\\" rowspan=\\\"2\\\"><italic>p</italic></th><th align=\\\"left\\\" rowspan=\\\"2\\\"><italic>d</italic> or <italic>r</italic></th><th align=\\\"left\\\" rowspan=\\\"2\\\">Effect size</th><th align=\\\"left\\\" colspan=\\\"2\\\"><italic>p</italic></th></tr><tr><th align=\\\"left\\\">Control</th><th align=\\\"left\\\">NBIW</th></tr></thead><tbody><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Number of T cells</td><td align=\\\"left\\\" rowspan=\\\"2\\\">/µL</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"6\\\">1154.2 ± 281.7</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.3065</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.14</td><td align=\\\"left\\\" rowspan=\\\"2\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.4926</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.9893</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\" colspan=\\\"3\\\">1203.0 ± 328.3</td><td align=\\\"left\\\" colspan=\\\"3\\\">1177.6 ± 352.9</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Number of CD4<sup>+</sup> T cells</td><td align=\\\"left\\\" rowspan=\\\"2\\\">/µL</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"6\\\">805.4 ± 200.5</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.8191</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.03</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.7468</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.7064</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\" colspan=\\\"3\\\">819.0 ± 179.4</td><td align=\\\"left\\\" colspan=\\\"3\\\">830.6 ± 266.8</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Number of CD8<sup>+</sup> T cells</td><td align=\\\"left\\\" rowspan=\\\"2\\\">/µL</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"6\\\">198.0 ± 111.6</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.0395*</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.29</td><td align=\\\"left\\\" rowspan=\\\"2\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0011**</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.7672</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\" colspan=\\\"3\\\">230.2 ± 131.3</td><td align=\\\"left\\\" colspan=\\\"3\\\">204.7 ± 123.3</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">CD4<sup>+</sup>:CD8<sup>+</sup> ratio</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"6\\\">5.209 ± 2.667</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.1829</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.19</td><td align=\\\"left\\\" rowspan=\\\"2\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0110*</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.9250</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\" colspan=\\\"3\\\">4.800 ± 2.985</td><td align=\\\"left\\\" colspan=\\\"3\\\">5.935 ± 4.399</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Number of naive T cells</td><td align=\\\"left\\\" rowspan=\\\"2\\\">/µL</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"6\\\">349.4 ± 142.5</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.0614</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.26</td><td align=\\\"left\\\" rowspan=\\\"2\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.8401</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.1919</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\" colspan=\\\"3\\\">355.0 ± 139.2</td><td align=\\\"left\\\" colspan=\\\"3\\\">343.5 ± 166.7</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Number of memory T cells</td><td align=\\\"left\\\" rowspan=\\\"2\\\">/µL</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"6\\\">456.0 ± 131.6</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.3529</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.15</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.5935</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.1065</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\" colspan=\\\"3\\\">464.0 ± 131.0</td><td align=\\\"left\\\" colspan=\\\"3\\\">487.2 ± 171.4</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">N:M ratio</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"6\\\">0.826 ± 0.354</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.1253</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.18</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.8578</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0070**</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\" colspan=\\\"3\\\">0.833 ± 0.375</td><td align=\\\"left\\\" colspan=\\\"3\\\">0.770 ± 0.334</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Number of CD8<sup>+</sup>CD28<sup>+</sup> T cells</td><td align=\\\"left\\\" rowspan=\\\"2\\\">/µL</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"6\\\">145.8 ± 94.6</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.0561</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.27</td><td align=\\\"left\\\" rowspan=\\\"2\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0071**</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.7982</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\" colspan=\\\"3\\\">168.6 ± 114.2</td><td align=\\\"left\\\" colspan=\\\"3\\\">147.2 ± 99.7</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Number of B cells</td><td align=\\\"left\\\" rowspan=\\\"2\\\">/µL</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"6\\\">251.3 ± 114.8</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.1918</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.18</td><td align=\\\"left\\\" rowspan=\\\"2\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0061**</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0333*</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\" colspan=\\\"3\\\">283.6 ± 133.8</td><td align=\\\"left\\\" colspan=\\\"3\\\">268.2 ± 107.8</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Number of natural killer cells</td><td align=\\\"left\\\" rowspan=\\\"2\\\">/µL</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"6\\\">125.5 ± 69.6</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.2108</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.18</td><td align=\\\"left\\\" rowspan=\\\"2\\\">Small</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.0979</td><td char=\\\".\\\" align=\\\"char\\\" rowspan=\\\"2\\\">0.3531</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\" colspan=\\\"3\\\">142.4 ± 69.2</td><td align=\\\"left\\\" colspan=\\\"3\\\">130.4 ± 74.3</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab6\\\"><label>Table 6</label><caption><p>Neutrophil phagocytosis activity and natural killer cell activity.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" rowspan=\\\"3\\\" colspan=\\\"3\\\"/><th align=\\\"left\\\" rowspan=\\\"3\\\">Control (n = 25), mean (± SD)</th><th align=\\\"left\\\" rowspan=\\\"3\\\">NBIW (n = 25), mean (± SD)</th><th align=\\\"left\\\" colspan=\\\"3\\\">Between-group comparison (week 4)</th><th align=\\\"left\\\" colspan=\\\"2\\\">Within-group comparison</th></tr><tr><th align=\\\"left\\\" rowspan=\\\"2\\\"><italic>p</italic></th><th align=\\\"left\\\" rowspan=\\\"2\\\"><italic>r</italic></th><th align=\\\"left\\\" rowspan=\\\"2\\\">Effect size</th><th align=\\\"left\\\" colspan=\\\"2\\\"><italic>p</italic></th></tr><tr><th align=\\\"left\\\">Control</th><th align=\\\"left\\\">NBIW</th></tr></thead><tbody><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Neutrophil phagocytosis activity</td><td align=\\\"left\\\" rowspan=\\\"2\\\">%</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"2\\\">83.4 ± 2.4</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.1542</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.20</td><td align=\\\"left\\\" rowspan=\\\"2\\\">Small</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.0350*</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.0044**</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\">84.1 ± 2.0</td><td align=\\\"left\\\">84.6 ± 2.2</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">NK cell activity</td><td align=\\\"left\\\" rowspan=\\\"2\\\">%</td><td align=\\\"left\\\">Baseline</td><td align=\\\"left\\\" colspan=\\\"2\\\">24.9 ± 13.8</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.7378</td><td align=\\\"left\\\" rowspan=\\\"2\\\">− 0.05</td><td align=\\\"left\\\" rowspan=\\\"2\\\">–</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.0000**</td><td align=\\\"left\\\" rowspan=\\\"2\\\">0.0001**</td></tr><tr><td align=\\\"left\\\">Week 4</td><td align=\\\"left\\\">35.7 ± 11.8</td><td align=\\\"left\\\">34.9 ± 11.4</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab7\\\"><label>Table 7</label><caption><p>Incidence of adverse events and adverse drug reactions.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" colspan=\\\"3\\\">Item</th><th align=\\\"left\\\">Control</th><th align=\\\"left\\\">NBIW</th></tr></thead><tbody><tr><td align=\\\"left\\\" rowspan=\\\"14\\\">Adverse events</td><td align=\\\"left\\\" rowspan=\\\"9\\\">Symptoms</td><td align=\\\"left\\\">Nausea</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">Malaise</td><td align=\\\"left\\\">2</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">Headache</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">Skin reddening</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">Nasal discharge</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">Abdominal pain</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">Abdominal pain, diarrhea (loose stool)</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">1</td></tr><tr><td align=\\\"left\\\">Weight loss</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">1</td></tr><tr><td align=\\\"left\\\">Increase in creatine kinase</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">1</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"3\\\">Severity</td><td align=\\\"left\\\">Mild</td><td align=\\\"left\\\">4</td><td align=\\\"left\\\">3</td></tr><tr><td align=\\\"left\\\">Moderate</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">Severe</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Incidence</td><td align=\\\"left\\\">Number of patients</td><td align=\\\"left\\\">4</td><td align=\\\"left\\\">3</td></tr><tr><td align=\\\"left\\\"><bold>Number of events</bold></td><td align=\\\"left\\\"><bold>7</bold></td><td align=\\\"left\\\"><bold>3</bold></td></tr><tr><td align=\\\"left\\\" rowspan=\\\"6\\\">Adverse drug reactions</td><td align=\\\"left\\\">Symptoms</td><td align=\\\"left\\\">–</td><td align=\\\"left\\\">–</td><td align=\\\"left\\\">–</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"3\\\">Severity</td><td align=\\\"left\\\">Mild</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">Moderate</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">Severe</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Incidence</td><td align=\\\"left\\\">Number of patients</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">Number of events</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab8\\\"><label>Table 8</label><caption><p>Ingredients of the neutral bicarbonate ionized water bath tablet.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Ingredients</th><th align=\\\"left\\\">%</th></tr></thead><tbody><tr><td align=\\\"left\\\">Sodium bicarbonate</td><td char=\\\".\\\" align=\\\"char\\\">75</td></tr><tr><td align=\\\"left\\\">Sodium carbonate</td><td char=\\\".\\\" align=\\\"char\\\">5</td></tr><tr><td align=\\\"left\\\">Citric acid</td><td char=\\\".\\\" align=\\\"char\\\">15</td></tr><tr><td align=\\\"left\\\">Other</td><td char=\\\".\\\" align=\\\"char\\\">5</td></tr></tbody></table></table-wrap>\"\n]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":["<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"],"string":"[\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM1\\\"></supplementary-material>\"\n]","isConversational":false},"footnote":{"kind":"list like","value":["<table-wrap-foot><p>Between-group comparisons of age, mean hours of sleep, body mass index, and bathing time during the intervention were tested by Wilcoxon rank sum test; height, weight, diastolic blood pressure, pulse rate, Brief Job Stress Questionnaire, and the Pittsburgh Sleep Quality Index Japanese version, by Student’s <italic>t</italic> test; and systolic blood pressure, by Welch’s <italic>t</italic> test.</p><p>BJSW, Brief Job Stress Questionnaire; NBIW, neutral bicarbonate ionized water; PSQI-J, Japanese version of the Pittsburgh Sleep Quality Index.</p></table-wrap-foot>","<table-wrap-foot><p>Between-group and within-group comparisons were tested by Wilcoxon signed-rank test.</p><p>*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.05.</p><p>Effect size was calculated as <italic>r</italic> family: small (0.1 ≤|<italic>r</italic>|< 0.3), medium (0.3 ≤|<italic>r</italic>|< 0.5), large (0.5 ≤|<italic>r</italic>|).</p><p>NBIW, neutral bicarbonate ionized water.</p></table-wrap-foot>","<table-wrap-foot><p>Between-group comparisons were tested by a paired <italic>t</italic> test (one-tailed).</p><p>NBIW, neutral bicarbonate ionized water.</p><p>*<italic>p</italic> < 0.05.</p></table-wrap-foot>","<table-wrap-foot><p>Distribution of CD3+, CD4+, CD4+CD45RA+, CD8+, CD16+CD56+, and CD20+ populations was parametric, and distribution of other populations was non-parametric.</p><p>Between-group and within-group comparisons were tested by a paired <italic>t</italic> test (for parametric data) and Wilcoxon signed-rank test (for non-parametric data).</p><p>*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01.</p><p>For parametric data, effect size was calculated as Cohen’s <italic>d</italic>: small (0.2 ≤|<italic>d</italic>|< 0.5), medium (0.5 ≤|<italic>d</italic>|< 0.8), large (0.8 ≤|<italic>d</italic>|); for non-parametric data, effect size was calculated as r family: small (0.1 ≤|<italic>r</italic>|< 0.3), medium (0.3 ≤|<italic>r</italic>|< 0.5), large (0.5 ≤|<italic>r</italic>|).</p><p>NBIW, neutral bicarbonate ionized water.</p></table-wrap-foot>","<table-wrap-foot><p>The distribution of memory T cell population and naive to memory T cells ratio (N:M ratio) were parametric; distribution of other populations was non-parametric.</p><p>Between-group and within-group comparisons were tested by paired <italic>t</italic> test (for parametric data) and Wilcoxon signed-rank test (for non-parametric data).</p><p>*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01.</p><p>Effect size was calculated for parametric data as Cohen’s <italic>d</italic>: small (0.2 ≤|<italic>d</italic>|< 0.5), medium (0.5 ≤|<italic>d</italic>|< 0.8), large (0.8 ≤|<italic>d</italic>|), for non-parametric as r family: small (0.1 ≤|<italic>r</italic>|< 0.3), medium (0.3 ≤|<italic>r</italic>|< 0.5), large (0.5 ≤|<italic>r</italic>|).</p><p>NBIW, neutral bicarbonate ionized water; N:M ratio, ratio of naive to memory T cells.</p></table-wrap-foot>","<table-wrap-foot><p>Between-group comparisons were tested by Wilcoxon signed-rank test.</p><p>*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01.</p><p>Effect size was calculated as <italic>r</italic> family: small (0.1 ≤|<italic>r</italic>|< 0.3), medium (0.3 ≤|<italic>r</italic>|< 0.5), large (0.5 ≤|<italic>r</italic>|).</p><p>NBIW, neutral bicarbonate ionized water; NK, natural killer.</p></table-wrap-foot>","<table-wrap-foot><p>Significant values are in bold.</p></table-wrap-foot>","<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"],"string":"[\n \"<table-wrap-foot><p>Between-group comparisons of age, mean hours of sleep, body mass index, and bathing time during the intervention were tested by Wilcoxon rank sum test; height, weight, diastolic blood pressure, pulse rate, Brief Job Stress Questionnaire, and the Pittsburgh Sleep Quality Index Japanese version, by Student’s <italic>t</italic> test; and systolic blood pressure, by Welch’s <italic>t</italic> test.</p><p>BJSW, Brief Job Stress Questionnaire; NBIW, neutral bicarbonate ionized water; PSQI-J, Japanese version of the Pittsburgh Sleep Quality Index.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>Between-group and within-group comparisons were tested by Wilcoxon signed-rank test.</p><p>*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.05.</p><p>Effect size was calculated as <italic>r</italic> family: small (0.1 ≤|<italic>r</italic>|< 0.3), medium (0.3 ≤|<italic>r</italic>|< 0.5), large (0.5 ≤|<italic>r</italic>|).</p><p>NBIW, neutral bicarbonate ionized water.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>Between-group comparisons were tested by a paired <italic>t</italic> test (one-tailed).</p><p>NBIW, neutral bicarbonate ionized water.</p><p>*<italic>p</italic> < 0.05.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>Distribution of CD3+, CD4+, CD4+CD45RA+, CD8+, CD16+CD56+, and CD20+ populations was parametric, and distribution of other populations was non-parametric.</p><p>Between-group and within-group comparisons were tested by a paired <italic>t</italic> test (for parametric data) and Wilcoxon signed-rank test (for non-parametric data).</p><p>*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01.</p><p>For parametric data, effect size was calculated as Cohen’s <italic>d</italic>: small (0.2 ≤|<italic>d</italic>|< 0.5), medium (0.5 ≤|<italic>d</italic>|< 0.8), large (0.8 ≤|<italic>d</italic>|); for non-parametric data, effect size was calculated as r family: small (0.1 ≤|<italic>r</italic>|< 0.3), medium (0.3 ≤|<italic>r</italic>|< 0.5), large (0.5 ≤|<italic>r</italic>|).</p><p>NBIW, neutral bicarbonate ionized water.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>The distribution of memory T cell population and naive to memory T cells ratio (N:M ratio) were parametric; distribution of other populations was non-parametric.</p><p>Between-group and within-group comparisons were tested by paired <italic>t</italic> test (for parametric data) and Wilcoxon signed-rank test (for non-parametric data).</p><p>*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01.</p><p>Effect size was calculated for parametric data as Cohen’s <italic>d</italic>: small (0.2 ≤|<italic>d</italic>|< 0.5), medium (0.5 ≤|<italic>d</italic>|< 0.8), large (0.8 ≤|<italic>d</italic>|), for non-parametric as r family: small (0.1 ≤|<italic>r</italic>|< 0.3), medium (0.3 ≤|<italic>r</italic>|< 0.5), large (0.5 ≤|<italic>r</italic>|).</p><p>NBIW, neutral bicarbonate ionized water; N:M ratio, ratio of naive to memory T cells.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>Between-group comparisons were tested by Wilcoxon signed-rank test.</p><p>*<italic>p</italic> < 0.05, **<italic>p</italic> < 0.01.</p><p>Effect size was calculated as <italic>r</italic> family: small (0.1 ≤|<italic>r</italic>|< 0.3), medium (0.3 ≤|<italic>r</italic>|< 0.5), large (0.5 ≤|<italic>r</italic>|).</p><p>NBIW, neutral bicarbonate ionized water; NK, natural killer.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>Significant values are in bold.</p></table-wrap-foot>\",\n \"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41598_2024_51851_Fig1_HTML\" id=\"MO1\"/>","<graphic xlink:href=\"41598_2024_51851_Fig2_HTML\" id=\"MO2\"/>","<graphic xlink:href=\"41598_2024_51851_Fig3_HTML\" id=\"MO3\"/>","<graphic xlink:href=\"41598_2024_51851_Fig4_HTML\" id=\"MO4\"/>"],"string":"[\n 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[\"2021\"], \"publisher-name\": [\"Springer\"]}, {\"label\": [\"7.\"], \"surname\": [\"Cheleschi\"], \"given-names\": [\"S\"], \"article-title\": [\"Balneotherapy year in review 2021: Focus on the mechanisms of action of balneotherapy in rheumatic diseases\"], \"source\": [\"Environ. Sci. Pollut. Res.\"], \"year\": [\"2022\"], \"volume\": [\"29\"], \"fpage\": [\"8054\"], \"lpage\": [\"8073\"], \"pub-id\": [\"10.1007/s11356-021-17780-0\"]}, {\"label\": [\"20.\"], \"surname\": [\"Bucci\", \"Schwannauer\", \"Berry\"], \"given-names\": [\"S\", \"M\", \"N\"], \"article-title\": [\"The digital revolution and its impact on mental health care\"], \"source\": [\"Psychol. Psychother. Theory Res. Pract.\"], \"year\": [\"2019\"], \"volume\": [\"92\"], \"fpage\": [\"277\"], \"lpage\": [\"297\"], \"pub-id\": [\"10.1111/papt.12222\"]}, {\"label\": [\"23.\"], \"mixed-citation\": [\"Shimomitsu, T. The final development of the Brief Job Stress Questionnaire mainly used for assessment of the individuals. \"], \"italic\": [\"Ministry of Labour sponsored grant for the prevention of work-related illness: The 1999 report\"]}, {\"label\": [\"58.\"], \"surname\": [\"Enomoto\"], \"given-names\": [\"M\"], \"article-title\": [\"Newly developed waist actigraphy and its sleep/wake scoring algorithm\"], \"source\": [\"Sleep Biol Rhythms\"], \"year\": [\"2009\"], \"volume\": [\"7\"], \"fpage\": [\"17\"], \"lpage\": [\"22\"], \"pub-id\": [\"10.1111/j.1479-8425.2008.00377.x\"]}, {\"label\": [\"60.\"], \"surname\": [\"Curran\", \"Andrykowski\", \"Studts\"], \"given-names\": [\"SL\", \"MA\", \"JL\"], \"article-title\": [\"Short form of the Profile of Mood States (POMS-SF): Psychometric information\"], \"source\": [\"Psychol. 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But in a globalized world, where various sectors of the economy can internationally interact without problems, the innovation and modification of healthcare systems specifically in Germany, have proven to be a difficult undertaking<sup>##UREF##0##1##</sup>.</p>","<p id=\"Par3\">The hurdles of digitization are ethically perfectly understandable and justifiable: physicians do not work with economic data, but more importantly with sensitive confidential information. The mishandling and possible privacy breach of secret patient data potentially poses serious real-world consequences for the patient<sup>##REF##26929900##2##</sup>. The protection of the privacy of patient data and their confidential information therefore is the logically overriding maxim in the implementation of digital ecosystems in hospitals - especially when aiming for the goal of establishing paperless hospitals<sup>##REF##29121240##3##</sup>. While innovation in the German healthcare system has picked up speed in recent years, there is room for improvement in a global comparison of the Electronic Medical Records Adoption Model (EMRAM) score<sup>##UREF##1##4##</sup>. The EMRAM Score is a tool to describe the level of digitization in hospitals on 7 levels with level 1 representing the lowest and level 7 the highest degree of digitization.</p>","<p id=\"Par4\">The need for digitized medicine does not only result from logistical, economic and ecological advantages, e.g. reduced storage space, less paper waste and savings in maintenance costs, but also leads to improved and more individual diagnostic and therapeutic concepts through the merger of local, regional and national hospitals into digital medical ecosystems. Furthermore, digitized medicine can enable more precise outcome analysis through improved and coherent longitudinal tracking of patient outcomes and provide feedback for therapeutic decisions. In the context of paper-based records, such data is either not captured at all or is lost over time<sup>##UREF##2##5##</sup>.</p>","<p id=\"Par5\">The creation of a digital medical ecosystem can thus establish better therapy algorithms and ensure significantly better treatment options on the premise of sharing medical data and the establishment of Big Data sets<sup>##UREF##3##6##</sup>. The sheer flood of highly scaled and information-dense data requires innovative technologies that further improve data sharing in medicine as it is almost fully accepted as standard and will develop further in the future<sup>##UREF##2##5##</sup>.</p>","<p id=\"Par6\">With the importance of the topic, it is understandable that digitization, digital ecosystem and cloud computing are buzzwords. In 2008, only 2 articles were published considering the above-mentioned topics, since then the number grown significantly with 820 articles considering cloud computing had been published in 2022 alone.</p>","<p id=\"Par7\">Several review articles address the theory of technical regulations and how to maintain adequate data integrity, confidentiality, anonymity and authenticity, only a few have given recommendations how to actually integrate a cloud system in a working clinic<sup>##REF##27155893##7##–##REF##35928494##9##</sup>. While in theory, a conversion to digital ecosystems is not really an issue, one has to face the reality that a complete conversion is a rather complex process<sup>##UREF##5##10##</sup>.</p>","<p id=\"Par8\">Therefore, we would like to present you the approach that we have successfully established at the Charité – University Hospital Berlin. Currently, there is no German hospital fully running on cloud computing. The approach described within this article characterizes a model approach to implementation of cloud computing within the framework of a running hospital information system (HIS). As this model approach is characterized by easy accessibility, HL7v2 – and FHIR interoperability as well as the possibility of using it without total integration into the current HIS, we aim to provide an example for other hospitals.</p>","<title>Cloud computing</title>","<p id=\"Par9\">Digital ecosystems are generally run on clouds. “Cloud computing” refers to the paradigm of delivering computational resources, including storage, processing power, and applications, as on-demand services over the internet. This model enables users to access and utilize these resources without the need for upfront infrastructure investments, allowing for flexible scalability and cost efficiency. Therefore, saving upfront investments for connection, individualized interfaces that cater to hospital’s needs and are able to adapt to new developments. Cloud computing is characterized by its service models, namely Infrastructure as a Sevice (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), which offer varying levels of control and management for users. This technology has gained significant traction due to its potential to revolutionize IT infrastructures and support various industries, such as healthcare, finance, and entertainment. Notably, cloud computing has been acknowledged for its role in facilitating resource sharing, improving accessibility and enabling collaboration among geographically dispersed users<sup>##UREF##6##11##,##UREF##7##12##</sup>.</p>","<p id=\"Par10\">Currently, cloud computing is being used successfully in various areas of medicine: In the provision and processization of telemedicine services<sup>##REF##24232290##13##</sup>, medical image analysis both for oncology services<sup>##UREF##8##14##</sup> and preoperative planning e.g. for hip arthroplasties<sup>##REF##22868590##15##</sup>, and in the context of citizen health applications to process lifestyle-related data and recommend lifestyle changes<sup>##REF##24346931##16##,##REF##23400205##17##</sup>. Cloud computing finds therapeutic use in supporting treatment decisions<sup>##REF##23545327##18##</sup>, early sepsis detection, and computation of complex procedures such as Montecarlo simulations for radiotherapy<sup>##REF##23188699##19##,##REF##23514937##20##</sup>. Furthermore, there are already some examples of the implementation of cloud computing as a clinical operating system: In China, for example, large regional hospitals exchange data about patients in a cloud with small grassroot hospitals. The usage of a cloud as SaaS leads to an investment reduction of around 90% while establishing sufficient and modern digital infrastructure<sup>##REF##25015761##21##</sup>.</p>","<p id=\"Par11\">The predecessor of avant-garde cloud computing are HIS are often described as legacy systems or legacy interfaces referring to computing software that has been outdated by recent technological advances. While legacy interfaces still meet the needs they were designed for (e.g., assessment of personal data, connection between different clinical specialties), they are costly to maintain, use up both computational and physical space and make innovation of their systems difficult. A simple switch to cloud computing could overburden legacy system manufacturers organizationally and financially, so a step-by-step migratory or integrative approach is preferable.</p>","<p id=\"Par12\">In comparison: cloud computing enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). These resources are accessible with minimal effort or extensive provider interaction<sup>##UREF##9##22##</sup>.</p>","<p id=\"Par13\">Currently, no complete cloud HIS software solution has been implemented in Germany. However, 98% of healthcare organizations are already running at least one of their applications in a cloud<sup>##UREF##10##23##</sup>. The global adoption of the FHIR standard is spearheaded in Germany through projects like the Medical Informatics Initiative, the Berlin Institute of Health (BIH) Health Data Platform or the AIQNET project, laying the foundation for the interoperability of medical data. The consensus along experts - not only from Germany - points out that the healthcare system is more than ready for the start of implementation of cloud-based medical data applications, which have the potential to create a decentralized, interoperable ecosystem for the legitimate use and exchange of medical data<sup>##UREF##11##24##</sup>. The use and improvement of Cloud Computing is mainly driven by the advantages of resilience, networking, and strict adherence to data protection<sup>##UREF##12##25##</sup>. The technical advantages of cloud computing cannot be denied. Apart from various technical challenges, which primarily occupy software developers, the implementation in everyday clinical practice is primarily dependent on the preservation of data protection and medical confidentiality in the processing, transfer, storage and retrieval of sensitive data<sup>##REF##32525944##26##–##UREF##14##28##</sup>.</p>","<title>Legal requirements for cloud computing in Germany and the EU according to GDPR</title>","<p id=\"Par14\">While healthcare remains to be a nationally regulated matter, the EU’s limited competences regarding the health care system do not apply to data protection competences<sup>##UREF##15##29##</sup>. EU countries and therefore also German hospitals are subject to the EU’s General Data Protection Regulation (GDPR)<sup>##UREF##15##29##</sup>. The GDPR regulates the processing of personal data by or on behalf of hospitals. According to GDPR, the processing and storage of personal data can either be a fully, partially or not at all automated process. This includes processes within clinical interfaces and the tasks they are supposed to fulfill<sup>##UREF##16##30##</sup>.</p>","<p id=\"Par15\">Based on Art.28 of the GDPR, the use of an external service provider (e.g., cloud computing providers) bound by instructions for the processing of personal data is possible under the general premise that data is not forwarded to third parties. From a legal point of view, this describes commissioned processing; the supervising client – meaning the person or company that has transferred the processing of the data to a cloud provider - remains responsible for the data and its security. According to Art. 4(7), 4(8) and 4(10) of the GDPR, the processor is not a third party, rather the processing is attributed to the controller<sup>##UREF##16##30##</sup>. The controller is defined as being a person, company or other organization responsible for determining how personal data is used. It is of upmost importance to clearly define the roles of every involved party.</p>","<p id=\"Par16\">Furthermore, the GDPR requires the precise treatment of the subject’s data and duration of the processing, the nature and purpose of the processing, the type of data, categories of data subjects, and the obligations and rights of the controller. According to Art. 28(3) of the GDPR, processing of personal data may only be carried out on instruction and by authorized persons. The third-party provider must consult the responsible person regarding technical infrastructure, compliance with obligations and the handling of any data protection breaches (Art. 33, 34 GDPR), apart from technical and organizational standards<sup>##UREF##17##31##</sup>. This means that both the clinic as well as the company supplying the cloud computing SaaS have to legally define the handling of the subject’s data and above-mentioned criteria of processing.</p>","<p id=\"Par17\">Medical confidentiality in Germany is determined by medical professional law (§ 9 MBO-Ä), the treatment contract (§§ 630a-630h BGB) and criminal law. Every violation of it is considered a criminal offense. Violations describe unauthorized disclosures of other people’s confidential information (from the personal sphere, business or trade secrets) entrusted to him as a member of a medical profession. Violations of confidentiality are punishable by fines or imprisonment<sup>##UREF##18##32##</sup>.</p>","<p id=\"Par18\">Considering potential collaborations with external service providers that operate cloud computing in third countries is accurately as well as restrictively regulated by the GDPR. In order for a European or German hospital to be able to cooperate with an external service provider with its headquarters in the USA, a sufficient level of data protection must be guaranteed in accordance with Art. 44 of the GDPR<sup>##UREF##19##33##</sup>. Such adequacy decisions have been described for the following countries: Andorra, Argentina, the Faroe Islands, Guernsey, Israel, the Isle of Man, Japan, Jersey, New Zealand, Switzerland, Uruguay and, to a limited extent, Canada. Since the 10<sup>th</sup> of July of 2023 the European Commission has adopted its adequacy decision for the EU-U.S. Data Privacy Framework. Therefore, data transferred between the countries is currently considered as protected as it is in the EU. However, the Austrian data privacy activist Max Schrems, who brought the previous two US-EU regulations to a fall, sees the current data privacy regulations nearly unchanged from the previous versions. It remains to be seen whether the current regulations will stand up to review by the EU court of justice, so there is still uncertainty regarding the use of data processors based in the USA.</p>","<title>Legal requirements for cloud computing in Germany according to German federal law, state law and hospital law</title>","<p id=\"Par19\">Since 2017, according to §203(3) para. 4 of the StGB (German Criminal Code), it is possible to involve external service providers for assistance activities for health care professions, which are described under §203 STGB<sup>##UREF##19##33##</sup>. After the reform, cloud computing providers have been regarded as aiding and abetting the persons responsible for the secrecy of judgments. This development should be rated as a sign from the federal government that the necessity of the development towards cloud computing must also be simplified in the legislative level. The reduction in the protection of secrets is compensated for by the inclusion of the external service provider in the criminal liability for violations. Furthermore, the Confidentiality Reform Act<sup>##UREF##20##34##</sup> describes that the economic advantage of storing data on an external information technology system (cloud) may be used if data protection is complied with.</p>","<p id=\"Par20\">The law also stipulates that the client and external service providers must ensure that the latest technical and organizational measures are in place to prevent the leak of personal data. Possible measures include anonymization, pseudonymization or data encryption using a key from the confidentiality provider<sup>##UREF##21##35##</sup>.</p>","<p id=\"Par21\">Furthermore, the federal division of Germany poses further difficulties for the implementation of digital medical ecosystem as the federal states own hospital laws can be restrictive to varying degrees: For example, in Berlin, the Federal Data Protection Act (BDSG)<sup>##UREF##22##36##</sup> applies to all hospitals in public or rural ownership. According to § 24(7) S3<sup>##UREF##23##37##,##REF##35852842##38##</sup>, the state hospital law (LKHG) permits the access to patient data by the contractor if it is ensured by technical protective measures that no personal reference can be established.</p>","<p id=\"Par22\">In their independence, the federal states are governed by their own hospital laws, which can be restrictive to varying degrees from state to state.</p>","<title>Aligning GDPR, BDSG and LKHG to implement cloud computing</title>","<p id=\"Par23\">How can the balancing act between the necessary digitization and the necessary protection of patient data be resolved? We would like to present the approach at a large German university hospital.</p>","<p id=\"Par24\">Two main methods were implemented to not only comply with German and EU data privacy regulations, but also international requirements: 1) Separation of personal health information (PHI) from medical data and 2) strong encryption of the data, with the storage and access of the encryption keys being restricted to the hospital as data owner or a trusted third party. Allowing a patient to consent or opt-out of the data collection within the platform is a third important aspect. Education and consent of the patients related to the data processing tasks is deferred to the individual hospitals according to their applicable legal constraints. In order to therefore solve the complex interplay of the GDPR with the national and local data sets, consent within the digital ecosystem was characterized as “broad consent” on the basis of § 6(1a) and § 9 (2b) of the GDPR. This grants the possibility for data analysis for various research purposes.</p>","<p id=\"Par25\">Privacy regulations vary not only internationally, but also within the EU and even within the federal states within Germany. Furthermore, the interpretation of privacy regulation fluctuates and is discussed controversial. Pseudonymized data was considered equivalent to anonymized data if the connection between data and identity cannot be made by a party other than the one possessing the pseudonym-to-identity table<sup>##UREF##24##39##</sup>. Over the past years, this view changed in a way that anonymized data is only data that cannot be linked to a person by any foreseeable means<sup>##UREF##25##40##</sup>. With not all medical data in clinical research, even if de-identified, are considered equivalent to anonymized data, patients typically need to be informed of the purposes and all involved parties for processing the data. This is very time-consuming and leads to lower acceptance rate by the patients. Research organizations attempt to generalize the purpose and data processing and exclude any commercial purposes through different types of a “broad consent” in which a patient may agree to data processing for clinical research purposes.</p>","<p id=\"Par26\">Another way followed by the AIQNET consortium is the clear definition of consented datasets, which are based on legal requirements, such as the collection of data related to the safety and performance of specific groups <strike>of</strike> and medical devices. The collection and analysis of such data is required by the Medical Device Regulation (MDR)<sup>##UREF##26##41##,##REF##35796837##42##</sup>. Thus, the MDR serves as legal basis for the collection and processing of clinical data related to the safety and performance of medical devices, according to Art. 6 (1c) of the GDPR<sup>##UREF##15##29##</sup>.</p>","<title>Integration of AIQNET at the Charité – University Medicine Berlin</title>","<p id=\"Par27\">AIQNET, with the Charité as one of its founding members alongside Raylytic GmbH (Leipzig, Sachsen, Deutschland) and BKK B. Braun Aesculap (Melsungen, Hessen, Deutschland) is a consortium consisting of 16 established organizations from various medical-relevant sectors that won the AI competition of the German government in 2019. Work on the infrastructure and applications based on it has been ongoing since January 2020 and is receiving funding from the Federal Ministry of Economics and Climate Protection (BMWi). Currently, it is possible to become a part of AIQNET as an associated partner after careful validation of the consortium. This federally funded project is the first model approach that implements cloud computing for medical AI applications, while establishing means for HIS connectivity and secondary use of medical data for research and compliance purposes as mandated by the MDR.</p>","<p id=\"Par28\">First and foremost, implementing such an ecosystem in a hospital that is currently still operating with a legacy system and has to maintain its functionality without any capacity for downtimes, is no simple undertaking. By analyzing legacy systems and creating connections in between the systems, AIQNET ensures a step-by-step integration. It processes unstructured and structured information from different medical systems and applications. The connection is established via an integration server that masters the protocols HL7v2, Fast Healthcare and Interoperability Resources (FHIR) and DICOM (Digital Imaging and Communcations in Medicine). This enables the extraction of medically relevant data from legacy systems. As a result, AIQNET supports clinics in automating internal processes.</p>","<p id=\"Par29\">A migration – or rather integration – of AIQNET involves 1) installation of a virtual machine within the hospital intranet, running the integration server 2) configuration of the transformations between the connected systems and the integration server and 3) configuration of the data collection task (surveys, follow-up time periods, data validation etc.)</p>","<p id=\"Par30\">AIQNET is operated on the UNITY Platform, which represents a granular software-as-a-service module in which various microservices are operated: a DICOM viewer with the option of AI analysis, automated data acquisition, case documentation and outcome recording with patient-reported outcome and experience measurements (PROMs, PREMs). The UNITY platform is developed by the company Raylytic GmBH (Fig. ##FIG##0##1##) and is the first and only digital solution to do automatic collection of clinical data and AI-powered medical image analysis. The Unity platform is already compliant with GDPR, Google Cloud Platform (GCP) and the Health Insurance Portability and Accountability Act (HIPAA). Furthermore, it is certified according to Information Security Management (ISO) 27001 and ISO 13485. Currently, the UNITY Platform is integrated into the Charité infrastructure for testing purposes. The Spine department of the Charité’s Center for Musculoskeletal Surgery uses it to collect both PROM and PREM data. Also, it has been implemented and prepared for AI analysis of Big Data sets such as pre – and postoperative images of the lumbar spine and whole spine, respectively. The reliability and validity of the AI-analysis software has been proven in prior studies<sup>##REF##31453374##44##</sup>.</p>","<p id=\"Par31\">The software is characterized by a clear-structured, fine-granular management, which allows only authorized and trained persons access to data with different degrees of access to information regarding patient identity, based on the role assigned. To facilitate this functionality, medical data must be analyzed, de-identified, and linked to a patient via a pseudonym prior to submitting data to the platform. The submission itself occurs through encrypted message protocols, and the storage at rest on the cloud system is encrypted.</p>","<p id=\"Par32\">The above procedure is exemplified by the upload in the AI-enabled DICOM viewer of RAYLYTIC GmbH. Only authorized staff can upload images via the “RayView DICOM Viewer” after sufficient de-identification. Anonymization and removal of metadata is ensured before upload and storage. An image “fingerprint” reduces replicative uploads of data. Each of these data is further subject to internal quality control to ensure completion of the above requirements and suitability for subsequent analysis processes.</p>","<p id=\"Par33\">The storage of the data takes place in accordance with the GDPR and is securely stored on the local Charité servers. Strict adherence to minimize the storage of personal data is ensured. The design of AIQNET (Fig. ##FIG##1##2##) foresees 1) PHI never leaving the hospital and 2) only the medical data needed will be collected from the patients or pulled from the Electronic Health Records (HER) systems. Data in transit and at rest is always encrypted. Software architecture, implementation, personnel competence and physical access needed to be independently assessed and a certification according to ISO 27001 was obtained.</p>","<p id=\"Par34\">In order to ensure that internal quality controls are up-to-date and free of errors, the UNITY Platform and utilized AI algorithms are undergoing regular internal controls regarding the validity of the algorithms and the resulting data integrity.</p>","<p id=\"Par35\">In view of the legislative requirements, consideration and fulfillment of all these requirements may seem overwhelming in parts. However, within the framework of a funded pilot project, we would like to prove one efficient possibility of implementation and the resulting benefits.</p>","<p id=\"Par36\">The consortium is focused on establishing interoperability, structuring data with the help of AI and creating a legally secure framework for data-based patient care. In the future, for example, the performance and safety of medical devices can be demonstrated objectively and largely in the safe framework of AIQNET. By contrast, the current legacy implementations of electronic healthcare record systems (EHR) largely prevent the aggregation of data for purposes such as benchmarking or answering highly relevant questions, such as patient outcome associated with a particular type of treatment, device or pathology, and as such, preventing evidence-based precision medicine. Cloud computing, based on an open, standardized data model could help in the transition of hospitals from minimal-interoperable systems to more specialized, interacting services that advance medicine. However, interoperability should not be limited to the local network, but should enable regional, national and, if necessary, international interoperability by means of a coding system commonly used in the consortium – like the Logical Oberservation Identifiers Names and Codes (LOINC) system. With this it is possible to use the UNITY platform of AIQNET, for example, to pool results of Patient-reported outcome measurements (PROMs) by connecting several hospitals within the ecosystem. The UNITY Platform can be used to facilitate Big Data studies, regional and national projects, and prospective, multicenter studies. Data exchange will be further enhanced by the integrated translation of medical and clinical data into a universally exchangeable FHIR format<sup>##REF##36707190##43##,##REF##31453374##44##</sup>.</p>","<p id=\"Par37\">Cloud computing interfaces that exhibit functionality via an Application Programming Interface (API) empower third parties to access their data programmatically through self-developed or 3<sup>rd</sup> party applications. A third party is defined as a natural or legal person, public authority, agency or body other than the data subject, controller, processor and persosn who, under the direct authority of the controller or processor, are authorized to process personal data. Examples are data analytics or process automation applications. The AIQNET consortium has streamlined the development of such applications by standardizing on FHIR as data model and “SMART on FHIR” as healthcare IT-systems API definition.</p>","<p id=\"Par38\">Access of third parties to the data is currently possible via exports of raw data in neutral formats, such as CSV. The exports can be scheduled and transferred via sFTP. In the future, we plan to allow access to the FHIR data through the Smart on FHIR API.</p>","<p id=\"Par39\">Based on this, AIQNET members are able to develop applications to perform administrative tasks in health care, e.g., to follow-up with a patient, to submit data to a medical registry, to automate the generation of case documentation, or to provide analysis algorithms to aid in treatment decision making. Due to the close cooperation between industry, research and healthcare institutions and the consecutive access to technical and scientific data, the ecosystem’s partners will increasingly benefit for the growing number of applications and medical insight provided through a legally and technically secure, validated framework.</p>","<p id=\"Par40\">The management of the cloud infrastructure not only ensures a high level of security against the continuously evolving cyber-security risks, but also means that the in-house IT staff can concentrate on user support and infrastructure needs over solving specialist issues related to the maintenance and compliance of legacy systems. Ultimately, by basing the AIQNET ecosystem on open standards and a data model that is receiving a high adoption rate by innovative EHR providers and fairly new players in the healthcare IT space, such as Apple, Amazon Web Services (AWS), Google and Microsoft, the AIQNET participants benefit from long-term investment security of their own development efforts and a growing selection of software applications and human talent.</p>","<p id=\"Par41\">The close cross-hospital collaboration between pharmaceutical and medical device companies improves the control and monitoring of new products: AIQNET creates an ecosystem for the broad use of health data for research and evidence-based medicine, while complying with legal requirements (compliance). Pharmaceutical and medical device companies also benefit from AIQNET, as they are required by regulatory requirements such as the Medical Device Regulation of the EU (MDR) to continuously monitor their products as part of post-market surveillance (PMS). With AIQNET, hospital data of routine care is generated in a data protection-compliant manner for the testing of the safety and performance of medical devices by notified bodies.</p>","<p id=\"Par42\">The step towards integration of cloud computing shows the need of cloud computing to keep pace with the rapid development of medicine, the dramatic increase of medical data storage and need for regional, national and international interoperability. Transparent explanations of systematic integration of cloud computing in well-running hospitals are rare and therefore this model approach can be used as a guide. Careful consideration has to be applied when considering privacy regulations within the different member states of the EU, making every case an individual one.</p>","<title>Reporting summary</title>","<p id=\"Par43\">Further information on research design is available in the ##SUPPL##0##Nature Research Reporting Summary## linked to this article.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">The need for digitization in the German healthcare system is undisputed. But in a globalized world, where various sectors of the economy can internationally interact without problems, the innovation and modification of healthcare systems specifically in Germany, have proven to be a difficult undertaking<sup>##UREF##0##1##</sup>.</p>\",\n \"<p id=\\\"Par3\\\">The hurdles of digitization are ethically perfectly understandable and justifiable: physicians do not work with economic data, but more importantly with sensitive confidential information. The mishandling and possible privacy breach of secret patient data potentially poses serious real-world consequences for the patient<sup>##REF##26929900##2##</sup>. The protection of the privacy of patient data and their confidential information therefore is the logically overriding maxim in the implementation of digital ecosystems in hospitals - especially when aiming for the goal of establishing paperless hospitals<sup>##REF##29121240##3##</sup>. While innovation in the German healthcare system has picked up speed in recent years, there is room for improvement in a global comparison of the Electronic Medical Records Adoption Model (EMRAM) score<sup>##UREF##1##4##</sup>. The EMRAM Score is a tool to describe the level of digitization in hospitals on 7 levels with level 1 representing the lowest and level 7 the highest degree of digitization.</p>\",\n \"<p id=\\\"Par4\\\">The need for digitized medicine does not only result from logistical, economic and ecological advantages, e.g. reduced storage space, less paper waste and savings in maintenance costs, but also leads to improved and more individual diagnostic and therapeutic concepts through the merger of local, regional and national hospitals into digital medical ecosystems. Furthermore, digitized medicine can enable more precise outcome analysis through improved and coherent longitudinal tracking of patient outcomes and provide feedback for therapeutic decisions. In the context of paper-based records, such data is either not captured at all or is lost over time<sup>##UREF##2##5##</sup>.</p>\",\n \"<p id=\\\"Par5\\\">The creation of a digital medical ecosystem can thus establish better therapy algorithms and ensure significantly better treatment options on the premise of sharing medical data and the establishment of Big Data sets<sup>##UREF##3##6##</sup>. The sheer flood of highly scaled and information-dense data requires innovative technologies that further improve data sharing in medicine as it is almost fully accepted as standard and will develop further in the future<sup>##UREF##2##5##</sup>.</p>\",\n \"<p id=\\\"Par6\\\">With the importance of the topic, it is understandable that digitization, digital ecosystem and cloud computing are buzzwords. In 2008, only 2 articles were published considering the above-mentioned topics, since then the number grown significantly with 820 articles considering cloud computing had been published in 2022 alone.</p>\",\n \"<p id=\\\"Par7\\\">Several review articles address the theory of technical regulations and how to maintain adequate data integrity, confidentiality, anonymity and authenticity, only a few have given recommendations how to actually integrate a cloud system in a working clinic<sup>##REF##27155893##7##–##REF##35928494##9##</sup>. While in theory, a conversion to digital ecosystems is not really an issue, one has to face the reality that a complete conversion is a rather complex process<sup>##UREF##5##10##</sup>.</p>\",\n \"<p id=\\\"Par8\\\">Therefore, we would like to present you the approach that we have successfully established at the Charité – University Hospital Berlin. Currently, there is no German hospital fully running on cloud computing. The approach described within this article characterizes a model approach to implementation of cloud computing within the framework of a running hospital information system (HIS). As this model approach is characterized by easy accessibility, HL7v2 – and FHIR interoperability as well as the possibility of using it without total integration into the current HIS, we aim to provide an example for other hospitals.</p>\",\n \"<title>Cloud computing</title>\",\n \"<p id=\\\"Par9\\\">Digital ecosystems are generally run on clouds. “Cloud computing” refers to the paradigm of delivering computational resources, including storage, processing power, and applications, as on-demand services over the internet. This model enables users to access and utilize these resources without the need for upfront infrastructure investments, allowing for flexible scalability and cost efficiency. Therefore, saving upfront investments for connection, individualized interfaces that cater to hospital’s needs and are able to adapt to new developments. Cloud computing is characterized by its service models, namely Infrastructure as a Sevice (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), which offer varying levels of control and management for users. This technology has gained significant traction due to its potential to revolutionize IT infrastructures and support various industries, such as healthcare, finance, and entertainment. Notably, cloud computing has been acknowledged for its role in facilitating resource sharing, improving accessibility and enabling collaboration among geographically dispersed users<sup>##UREF##6##11##,##UREF##7##12##</sup>.</p>\",\n \"<p id=\\\"Par10\\\">Currently, cloud computing is being used successfully in various areas of medicine: In the provision and processization of telemedicine services<sup>##REF##24232290##13##</sup>, medical image analysis both for oncology services<sup>##UREF##8##14##</sup> and preoperative planning e.g. for hip arthroplasties<sup>##REF##22868590##15##</sup>, and in the context of citizen health applications to process lifestyle-related data and recommend lifestyle changes<sup>##REF##24346931##16##,##REF##23400205##17##</sup>. Cloud computing finds therapeutic use in supporting treatment decisions<sup>##REF##23545327##18##</sup>, early sepsis detection, and computation of complex procedures such as Montecarlo simulations for radiotherapy<sup>##REF##23188699##19##,##REF##23514937##20##</sup>. Furthermore, there are already some examples of the implementation of cloud computing as a clinical operating system: In China, for example, large regional hospitals exchange data about patients in a cloud with small grassroot hospitals. The usage of a cloud as SaaS leads to an investment reduction of around 90% while establishing sufficient and modern digital infrastructure<sup>##REF##25015761##21##</sup>.</p>\",\n \"<p id=\\\"Par11\\\">The predecessor of avant-garde cloud computing are HIS are often described as legacy systems or legacy interfaces referring to computing software that has been outdated by recent technological advances. While legacy interfaces still meet the needs they were designed for (e.g., assessment of personal data, connection between different clinical specialties), they are costly to maintain, use up both computational and physical space and make innovation of their systems difficult. A simple switch to cloud computing could overburden legacy system manufacturers organizationally and financially, so a step-by-step migratory or integrative approach is preferable.</p>\",\n \"<p id=\\\"Par12\\\">In comparison: cloud computing enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services). These resources are accessible with minimal effort or extensive provider interaction<sup>##UREF##9##22##</sup>.</p>\",\n \"<p id=\\\"Par13\\\">Currently, no complete cloud HIS software solution has been implemented in Germany. However, 98% of healthcare organizations are already running at least one of their applications in a cloud<sup>##UREF##10##23##</sup>. The global adoption of the FHIR standard is spearheaded in Germany through projects like the Medical Informatics Initiative, the Berlin Institute of Health (BIH) Health Data Platform or the AIQNET project, laying the foundation for the interoperability of medical data. The consensus along experts - not only from Germany - points out that the healthcare system is more than ready for the start of implementation of cloud-based medical data applications, which have the potential to create a decentralized, interoperable ecosystem for the legitimate use and exchange of medical data<sup>##UREF##11##24##</sup>. The use and improvement of Cloud Computing is mainly driven by the advantages of resilience, networking, and strict adherence to data protection<sup>##UREF##12##25##</sup>. The technical advantages of cloud computing cannot be denied. Apart from various technical challenges, which primarily occupy software developers, the implementation in everyday clinical practice is primarily dependent on the preservation of data protection and medical confidentiality in the processing, transfer, storage and retrieval of sensitive data<sup>##REF##32525944##26##–##UREF##14##28##</sup>.</p>\",\n \"<title>Legal requirements for cloud computing in Germany and the EU according to GDPR</title>\",\n \"<p id=\\\"Par14\\\">While healthcare remains to be a nationally regulated matter, the EU’s limited competences regarding the health care system do not apply to data protection competences<sup>##UREF##15##29##</sup>. EU countries and therefore also German hospitals are subject to the EU’s General Data Protection Regulation (GDPR)<sup>##UREF##15##29##</sup>. The GDPR regulates the processing of personal data by or on behalf of hospitals. According to GDPR, the processing and storage of personal data can either be a fully, partially or not at all automated process. This includes processes within clinical interfaces and the tasks they are supposed to fulfill<sup>##UREF##16##30##</sup>.</p>\",\n \"<p id=\\\"Par15\\\">Based on Art.28 of the GDPR, the use of an external service provider (e.g., cloud computing providers) bound by instructions for the processing of personal data is possible under the general premise that data is not forwarded to third parties. From a legal point of view, this describes commissioned processing; the supervising client – meaning the person or company that has transferred the processing of the data to a cloud provider - remains responsible for the data and its security. According to Art. 4(7), 4(8) and 4(10) of the GDPR, the processor is not a third party, rather the processing is attributed to the controller<sup>##UREF##16##30##</sup>. The controller is defined as being a person, company or other organization responsible for determining how personal data is used. It is of upmost importance to clearly define the roles of every involved party.</p>\",\n \"<p id=\\\"Par16\\\">Furthermore, the GDPR requires the precise treatment of the subject’s data and duration of the processing, the nature and purpose of the processing, the type of data, categories of data subjects, and the obligations and rights of the controller. According to Art. 28(3) of the GDPR, processing of personal data may only be carried out on instruction and by authorized persons. The third-party provider must consult the responsible person regarding technical infrastructure, compliance with obligations and the handling of any data protection breaches (Art. 33, 34 GDPR), apart from technical and organizational standards<sup>##UREF##17##31##</sup>. This means that both the clinic as well as the company supplying the cloud computing SaaS have to legally define the handling of the subject’s data and above-mentioned criteria of processing.</p>\",\n \"<p id=\\\"Par17\\\">Medical confidentiality in Germany is determined by medical professional law (§ 9 MBO-Ä), the treatment contract (§§ 630a-630h BGB) and criminal law. Every violation of it is considered a criminal offense. Violations describe unauthorized disclosures of other people’s confidential information (from the personal sphere, business or trade secrets) entrusted to him as a member of a medical profession. Violations of confidentiality are punishable by fines or imprisonment<sup>##UREF##18##32##</sup>.</p>\",\n \"<p id=\\\"Par18\\\">Considering potential collaborations with external service providers that operate cloud computing in third countries is accurately as well as restrictively regulated by the GDPR. In order for a European or German hospital to be able to cooperate with an external service provider with its headquarters in the USA, a sufficient level of data protection must be guaranteed in accordance with Art. 44 of the GDPR<sup>##UREF##19##33##</sup>. Such adequacy decisions have been described for the following countries: Andorra, Argentina, the Faroe Islands, Guernsey, Israel, the Isle of Man, Japan, Jersey, New Zealand, Switzerland, Uruguay and, to a limited extent, Canada. Since the 10<sup>th</sup> of July of 2023 the European Commission has adopted its adequacy decision for the EU-U.S. Data Privacy Framework. Therefore, data transferred between the countries is currently considered as protected as it is in the EU. However, the Austrian data privacy activist Max Schrems, who brought the previous two US-EU regulations to a fall, sees the current data privacy regulations nearly unchanged from the previous versions. It remains to be seen whether the current regulations will stand up to review by the EU court of justice, so there is still uncertainty regarding the use of data processors based in the USA.</p>\",\n \"<title>Legal requirements for cloud computing in Germany according to German federal law, state law and hospital law</title>\",\n \"<p id=\\\"Par19\\\">Since 2017, according to §203(3) para. 4 of the StGB (German Criminal Code), it is possible to involve external service providers for assistance activities for health care professions, which are described under §203 STGB<sup>##UREF##19##33##</sup>. After the reform, cloud computing providers have been regarded as aiding and abetting the persons responsible for the secrecy of judgments. This development should be rated as a sign from the federal government that the necessity of the development towards cloud computing must also be simplified in the legislative level. The reduction in the protection of secrets is compensated for by the inclusion of the external service provider in the criminal liability for violations. Furthermore, the Confidentiality Reform Act<sup>##UREF##20##34##</sup> describes that the economic advantage of storing data on an external information technology system (cloud) may be used if data protection is complied with.</p>\",\n \"<p id=\\\"Par20\\\">The law also stipulates that the client and external service providers must ensure that the latest technical and organizational measures are in place to prevent the leak of personal data. Possible measures include anonymization, pseudonymization or data encryption using a key from the confidentiality provider<sup>##UREF##21##35##</sup>.</p>\",\n \"<p id=\\\"Par21\\\">Furthermore, the federal division of Germany poses further difficulties for the implementation of digital medical ecosystem as the federal states own hospital laws can be restrictive to varying degrees: For example, in Berlin, the Federal Data Protection Act (BDSG)<sup>##UREF##22##36##</sup> applies to all hospitals in public or rural ownership. According to § 24(7) S3<sup>##UREF##23##37##,##REF##35852842##38##</sup>, the state hospital law (LKHG) permits the access to patient data by the contractor if it is ensured by technical protective measures that no personal reference can be established.</p>\",\n \"<p id=\\\"Par22\\\">In their independence, the federal states are governed by their own hospital laws, which can be restrictive to varying degrees from state to state.</p>\",\n \"<title>Aligning GDPR, BDSG and LKHG to implement cloud computing</title>\",\n \"<p id=\\\"Par23\\\">How can the balancing act between the necessary digitization and the necessary protection of patient data be resolved? We would like to present the approach at a large German university hospital.</p>\",\n \"<p id=\\\"Par24\\\">Two main methods were implemented to not only comply with German and EU data privacy regulations, but also international requirements: 1) Separation of personal health information (PHI) from medical data and 2) strong encryption of the data, with the storage and access of the encryption keys being restricted to the hospital as data owner or a trusted third party. Allowing a patient to consent or opt-out of the data collection within the platform is a third important aspect. Education and consent of the patients related to the data processing tasks is deferred to the individual hospitals according to their applicable legal constraints. In order to therefore solve the complex interplay of the GDPR with the national and local data sets, consent within the digital ecosystem was characterized as “broad consent” on the basis of § 6(1a) and § 9 (2b) of the GDPR. This grants the possibility for data analysis for various research purposes.</p>\",\n \"<p id=\\\"Par25\\\">Privacy regulations vary not only internationally, but also within the EU and even within the federal states within Germany. Furthermore, the interpretation of privacy regulation fluctuates and is discussed controversial. Pseudonymized data was considered equivalent to anonymized data if the connection between data and identity cannot be made by a party other than the one possessing the pseudonym-to-identity table<sup>##UREF##24##39##</sup>. Over the past years, this view changed in a way that anonymized data is only data that cannot be linked to a person by any foreseeable means<sup>##UREF##25##40##</sup>. With not all medical data in clinical research, even if de-identified, are considered equivalent to anonymized data, patients typically need to be informed of the purposes and all involved parties for processing the data. This is very time-consuming and leads to lower acceptance rate by the patients. Research organizations attempt to generalize the purpose and data processing and exclude any commercial purposes through different types of a “broad consent” in which a patient may agree to data processing for clinical research purposes.</p>\",\n \"<p id=\\\"Par26\\\">Another way followed by the AIQNET consortium is the clear definition of consented datasets, which are based on legal requirements, such as the collection of data related to the safety and performance of specific groups <strike>of</strike> and medical devices. The collection and analysis of such data is required by the Medical Device Regulation (MDR)<sup>##UREF##26##41##,##REF##35796837##42##</sup>. Thus, the MDR serves as legal basis for the collection and processing of clinical data related to the safety and performance of medical devices, according to Art. 6 (1c) of the GDPR<sup>##UREF##15##29##</sup>.</p>\",\n \"<title>Integration of AIQNET at the Charité – University Medicine Berlin</title>\",\n \"<p id=\\\"Par27\\\">AIQNET, with the Charité as one of its founding members alongside Raylytic GmbH (Leipzig, Sachsen, Deutschland) and BKK B. Braun Aesculap (Melsungen, Hessen, Deutschland) is a consortium consisting of 16 established organizations from various medical-relevant sectors that won the AI competition of the German government in 2019. Work on the infrastructure and applications based on it has been ongoing since January 2020 and is receiving funding from the Federal Ministry of Economics and Climate Protection (BMWi). Currently, it is possible to become a part of AIQNET as an associated partner after careful validation of the consortium. This federally funded project is the first model approach that implements cloud computing for medical AI applications, while establishing means for HIS connectivity and secondary use of medical data for research and compliance purposes as mandated by the MDR.</p>\",\n \"<p id=\\\"Par28\\\">First and foremost, implementing such an ecosystem in a hospital that is currently still operating with a legacy system and has to maintain its functionality without any capacity for downtimes, is no simple undertaking. By analyzing legacy systems and creating connections in between the systems, AIQNET ensures a step-by-step integration. It processes unstructured and structured information from different medical systems and applications. The connection is established via an integration server that masters the protocols HL7v2, Fast Healthcare and Interoperability Resources (FHIR) and DICOM (Digital Imaging and Communcations in Medicine). This enables the extraction of medically relevant data from legacy systems. As a result, AIQNET supports clinics in automating internal processes.</p>\",\n \"<p id=\\\"Par29\\\">A migration – or rather integration – of AIQNET involves 1) installation of a virtual machine within the hospital intranet, running the integration server 2) configuration of the transformations between the connected systems and the integration server and 3) configuration of the data collection task (surveys, follow-up time periods, data validation etc.)</p>\",\n \"<p id=\\\"Par30\\\">AIQNET is operated on the UNITY Platform, which represents a granular software-as-a-service module in which various microservices are operated: a DICOM viewer with the option of AI analysis, automated data acquisition, case documentation and outcome recording with patient-reported outcome and experience measurements (PROMs, PREMs). The UNITY platform is developed by the company Raylytic GmBH (Fig. ##FIG##0##1##) and is the first and only digital solution to do automatic collection of clinical data and AI-powered medical image analysis. The Unity platform is already compliant with GDPR, Google Cloud Platform (GCP) and the Health Insurance Portability and Accountability Act (HIPAA). Furthermore, it is certified according to Information Security Management (ISO) 27001 and ISO 13485. Currently, the UNITY Platform is integrated into the Charité infrastructure for testing purposes. The Spine department of the Charité’s Center for Musculoskeletal Surgery uses it to collect both PROM and PREM data. Also, it has been implemented and prepared for AI analysis of Big Data sets such as pre – and postoperative images of the lumbar spine and whole spine, respectively. The reliability and validity of the AI-analysis software has been proven in prior studies<sup>##REF##31453374##44##</sup>.</p>\",\n \"<p id=\\\"Par31\\\">The software is characterized by a clear-structured, fine-granular management, which allows only authorized and trained persons access to data with different degrees of access to information regarding patient identity, based on the role assigned. To facilitate this functionality, medical data must be analyzed, de-identified, and linked to a patient via a pseudonym prior to submitting data to the platform. The submission itself occurs through encrypted message protocols, and the storage at rest on the cloud system is encrypted.</p>\",\n \"<p id=\\\"Par32\\\">The above procedure is exemplified by the upload in the AI-enabled DICOM viewer of RAYLYTIC GmbH. Only authorized staff can upload images via the “RayView DICOM Viewer” after sufficient de-identification. Anonymization and removal of metadata is ensured before upload and storage. An image “fingerprint” reduces replicative uploads of data. Each of these data is further subject to internal quality control to ensure completion of the above requirements and suitability for subsequent analysis processes.</p>\",\n \"<p id=\\\"Par33\\\">The storage of the data takes place in accordance with the GDPR and is securely stored on the local Charité servers. Strict adherence to minimize the storage of personal data is ensured. The design of AIQNET (Fig. ##FIG##1##2##) foresees 1) PHI never leaving the hospital and 2) only the medical data needed will be collected from the patients or pulled from the Electronic Health Records (HER) systems. Data in transit and at rest is always encrypted. Software architecture, implementation, personnel competence and physical access needed to be independently assessed and a certification according to ISO 27001 was obtained.</p>\",\n \"<p id=\\\"Par34\\\">In order to ensure that internal quality controls are up-to-date and free of errors, the UNITY Platform and utilized AI algorithms are undergoing regular internal controls regarding the validity of the algorithms and the resulting data integrity.</p>\",\n \"<p id=\\\"Par35\\\">In view of the legislative requirements, consideration and fulfillment of all these requirements may seem overwhelming in parts. However, within the framework of a funded pilot project, we would like to prove one efficient possibility of implementation and the resulting benefits.</p>\",\n \"<p id=\\\"Par36\\\">The consortium is focused on establishing interoperability, structuring data with the help of AI and creating a legally secure framework for data-based patient care. In the future, for example, the performance and safety of medical devices can be demonstrated objectively and largely in the safe framework of AIQNET. By contrast, the current legacy implementations of electronic healthcare record systems (EHR) largely prevent the aggregation of data for purposes such as benchmarking or answering highly relevant questions, such as patient outcome associated with a particular type of treatment, device or pathology, and as such, preventing evidence-based precision medicine. Cloud computing, based on an open, standardized data model could help in the transition of hospitals from minimal-interoperable systems to more specialized, interacting services that advance medicine. However, interoperability should not be limited to the local network, but should enable regional, national and, if necessary, international interoperability by means of a coding system commonly used in the consortium – like the Logical Oberservation Identifiers Names and Codes (LOINC) system. With this it is possible to use the UNITY platform of AIQNET, for example, to pool results of Patient-reported outcome measurements (PROMs) by connecting several hospitals within the ecosystem. The UNITY Platform can be used to facilitate Big Data studies, regional and national projects, and prospective, multicenter studies. Data exchange will be further enhanced by the integrated translation of medical and clinical data into a universally exchangeable FHIR format<sup>##REF##36707190##43##,##REF##31453374##44##</sup>.</p>\",\n \"<p id=\\\"Par37\\\">Cloud computing interfaces that exhibit functionality via an Application Programming Interface (API) empower third parties to access their data programmatically through self-developed or 3<sup>rd</sup> party applications. A third party is defined as a natural or legal person, public authority, agency or body other than the data subject, controller, processor and persosn who, under the direct authority of the controller or processor, are authorized to process personal data. Examples are data analytics or process automation applications. The AIQNET consortium has streamlined the development of such applications by standardizing on FHIR as data model and “SMART on FHIR” as healthcare IT-systems API definition.</p>\",\n \"<p id=\\\"Par38\\\">Access of third parties to the data is currently possible via exports of raw data in neutral formats, such as CSV. The exports can be scheduled and transferred via sFTP. In the future, we plan to allow access to the FHIR data through the Smart on FHIR API.</p>\",\n \"<p id=\\\"Par39\\\">Based on this, AIQNET members are able to develop applications to perform administrative tasks in health care, e.g., to follow-up with a patient, to submit data to a medical registry, to automate the generation of case documentation, or to provide analysis algorithms to aid in treatment decision making. Due to the close cooperation between industry, research and healthcare institutions and the consecutive access to technical and scientific data, the ecosystem’s partners will increasingly benefit for the growing number of applications and medical insight provided through a legally and technically secure, validated framework.</p>\",\n \"<p id=\\\"Par40\\\">The management of the cloud infrastructure not only ensures a high level of security against the continuously evolving cyber-security risks, but also means that the in-house IT staff can concentrate on user support and infrastructure needs over solving specialist issues related to the maintenance and compliance of legacy systems. Ultimately, by basing the AIQNET ecosystem on open standards and a data model that is receiving a high adoption rate by innovative EHR providers and fairly new players in the healthcare IT space, such as Apple, Amazon Web Services (AWS), Google and Microsoft, the AIQNET participants benefit from long-term investment security of their own development efforts and a growing selection of software applications and human talent.</p>\",\n \"<p id=\\\"Par41\\\">The close cross-hospital collaboration between pharmaceutical and medical device companies improves the control and monitoring of new products: AIQNET creates an ecosystem for the broad use of health data for research and evidence-based medicine, while complying with legal requirements (compliance). Pharmaceutical and medical device companies also benefit from AIQNET, as they are required by regulatory requirements such as the Medical Device Regulation of the EU (MDR) to continuously monitor their products as part of post-market surveillance (PMS). With AIQNET, hospital data of routine care is generated in a data protection-compliant manner for the testing of the safety and performance of medical devices by notified bodies.</p>\",\n \"<p id=\\\"Par42\\\">The step towards integration of cloud computing shows the need of cloud computing to keep pace with the rapid development of medicine, the dramatic increase of medical data storage and need for regional, national and international interoperability. Transparent explanations of systematic integration of cloud computing in well-running hospitals are rare and therefore this model approach can be used as a guide. Careful consideration has to be applied when considering privacy regulations within the different member states of the EU, making every case an individual one.</p>\",\n \"<title>Reporting summary</title>\",\n \"<p id=\\\"Par43\\\">Further information on research design is available in the ##SUPPL##0##Nature Research Reporting Summary## linked to this article.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":[],"string":"[]","isConversational":false},"results":{"kind":"list like","value":[],"string":"[]","isConversational":false},"discussion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">With the advent of artificial intelligence and Big Data - projects, the necessity for a transition from analog medicine to modern-day solutions such as cloud computing becomes unavoidable. Even though this need is now common knowledge, the process is not always easy to start. Legislative changes, for example at the level of the European Union, are helping the respective healthcare systems to take the necessary steps. This article provides an overview of how a German university hospital is dealing with European data protection laws on the integration of cloud computing into everyday clinical practice. By describing our model approach, we aim to identify opportunities and possible pitfalls to sustainably influence digitization in Germany.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">With the advent of artificial intelligence and Big Data - projects, the necessity for a transition from analog medicine to modern-day solutions such as cloud computing becomes unavoidable. Even though this need is now common knowledge, the process is not always easy to start. Legislative changes, for example at the level of the European Union, are helping the respective healthcare systems to take the necessary steps. This article provides an overview of how a German university hospital is dealing with European data protection laws on the integration of cloud computing into everyday clinical practice. By describing our model approach, we aim to identify opportunities and possible pitfalls to sustainably influence digitization in Germany.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary information</title>","<p>\n\n</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>\\n\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary information</title>","<p>The online version contains supplementary material available at 10.1038/s41746-024-01000-3.</p>","<title>Author contributions</title>","<p>Conceptualization, M.P.; N.T.; methodology M.P., T.K., S.T., M.D.; F.T., N.T.; resources, M.P.; writing—original draft M.P., N.T.; writing—review and editing, T.K., S.T., M.D., F.T.; supervision, M.P., N.T., project administration, M.P. All authors have read and agreed to the published version of the manuscript.</p>","<title>Funding</title>","<p>Open Access funding enabled and organized by Projekt DEAL.</p>","<title>Data availability</title>","<p>All data relevant to the study are included in the article or uploaded as supplementary information. Data are available on reasonable request.</p>","<title>Competing interests</title>","<p id=\"Par44\">The authors declare no competing interests</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41746-024-01000-3.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>Conceptualization, M.P.; N.T.; methodology M.P., T.K., S.T., M.D.; F.T., N.T.; resources, M.P.; writing—original draft M.P., N.T.; writing—review and editing, T.K., S.T., M.D., F.T.; supervision, M.P., N.T., project administration, M.P. All authors have read and agreed to the published version of the manuscript.</p>\",\n \"<title>Funding</title>\",\n \"<p>Open Access funding enabled and organized by Projekt DEAL.</p>\",\n \"<title>Data availability</title>\",\n \"<p>All data relevant to the study are included in the article or uploaded as supplementary information. Data are available on reasonable request.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par44\\\">The authors declare no competing interests</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Example of UNITY platform and RayView DICOM viewer.</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Dataflow and systematic overview of the AIQNET architecture.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><p>Example of UNITY platform and RayView DICOM viewer.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><p>Dataflow and systematic overview of the AIQNET architecture.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":["<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"],"string":"[\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM1\\\"></supplementary-material>\"\n]","isConversational":false},"footnote":{"kind":"list like","value":["<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"],"string":"[\n \"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41746_2024_1000_Fig1_HTML\" id=\"d32e448\"/>","<graphic xlink:href=\"41746_2024_1000_Fig2_HTML\" id=\"d32e464\"/>"],"string":"[\n \"<graphic xlink:href=\\\"41746_2024_1000_Fig1_HTML\\\" id=\\\"d32e448\\\"/>\",\n \"<graphic xlink:href=\\\"41746_2024_1000_Fig2_HTML\\\" id=\\\"d32e464\\\"/>\"\n]","isConversational":false},"media":{"kind":"list like","value":["<media xlink:href=\"41746_2024_1000_MOESM1_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>"],"string":"[\n \"<media xlink:href=\\\"41746_2024_1000_MOESM1_ESM.pdf\\\"><caption><p>Reporting Summary</p></caption></media>\"\n]","isConversational":false},"unknown_pub":{"kind":"string","value":"[{\"label\": [\"1.\"], \"surname\": [\"Caumanns\"], \"given-names\": [\"J\"], \"article-title\": [\"Zur Diskussion: Stand der Digitalisierung im deutschen Gesundheitswesen [Fordiscussion: The state of digitization of the German healthcaresystem]\"], \"source\": [\"Z. EvidFortbild Qual. Gesundhwes.\"], \"year\": [\"2019\"], \"volume\": [\"143\"], \"fpage\": [\"22\"], \"lpage\": [\"29\"], \"pub-id\": [\"10.1016/j.zefq.2019.04.002\"]}, {\"label\": [\"4.\"], \"mixed-citation\": [\"Klauber J., Geraedts M., Friedrich J. & Wasem J. Krankenhaus-Report 2019 \\u2013 das digitale Krankenhaus. Springer Verlag. (2019).\"]}, {\"label\": [\"5.\"], \"surname\": [\"Wirth\", \"Meurers\", \"Johns\", \"Prasser\"], \"given-names\": [\"FN\", \"T\", \"M\", \"F\"], \"article-title\": [\"Privacy-preserving data sharing infrastructures for medical research: systematization and comparison\"], \"source\": [\"BMC Med Inf. DecisMak.\"], \"year\": [\"2021\"], \"volume\": [\"21\"], \"fpage\": [\"242\"], \"pub-id\": [\"10.1186/s12911-021-01602-x\"]}, {\"label\": [\"6.\"], \"surname\": [\"Koppad\", \"Annappa\", \"Gkoutos\", \"Acharjee\"], \"given-names\": [\"S\", \"B\", \"GV\", \"A\"], \"article-title\": [\"Cloud Computing Enabled Big Multi-Omics Data Analytics\"], \"source\": [\"BioinformBiolInsights\"], \"year\": [\"2021\"], \"volume\": [\"15\"], \"fpage\": [\"11779322211035921\"]}, {\"label\": [\"8.\"], \"surname\": [\"Griebel\"], \"given-names\": [\"L\"], \"article-title\": [\"A scoping review of cloud computing in healthcare\"], \"source\": [\"BMC Med. Inf. Decis. Mak.\"], \"year\": [\"2015\"], \"volume\": [\"15\"], \"fpage\": [\"17\"], \"pub-id\": [\"10.1186/s12911-015-0145-7\"]}, {\"label\": [\"10.\"], \"ext-link\": [\"https://www.infoq.com/articles/cloud-native-panel/\"]}, {\"label\": [\"11.\"], \"mixed-citation\": [\"Mell/Grance: The NIST Definition of Cloud Computing. S. 2. (2011).\"]}, {\"label\": [\"12.\"], \"surname\": [\"Armbrust\"], \"given-names\": [\"M\"], \"article-title\": [\"A View of Cloud Computing\"], \"source\": [\"Commun. ACM\"], \"year\": [\"2010\"], \"volume\": [\"53\"], \"fpage\": [\"50\"], \"lpage\": [\"58\"], \"pub-id\": [\"10.1145/1721654.1721672\"]}, {\"label\": [\"14.\"], \"mixed-citation\": [\"Avila-Garcia, M. S., Trefethen, A. E., Brady, M., Gleeson, F. & Goodman, D. Lowering the Barriers to Cancer Imaging,\\u201d 2008 IEEE Fourth International Conference on eScience, Indianapolis, IN, USA, pp. 63\\u201370, 10.1109/eScience.2008.33 (2008).\"]}, {\"label\": [\"22.\"], \"mixed-citation\": [\"Schwanke-Seer/Helling: Cloud-Networking im Gesundheitswesen; 24.11.202.\"]}, {\"label\": [\"23.\"], \"mixed-citation\": [\"Extreme Networks: Welcome to 2020! Survey says: Healthcare is ready for Cloud Networking.\"]}, {\"label\": [\"24.\"], \"mixed-citation\": [\"IDG Research Services: Studie Cloud Native (2020).\"]}, {\"label\": [\"25.\"], \"surname\": [\"Hassan\"], \"given-names\": [\"J\"], \"article-title\": [\"The Rise of Cloud Computing: Data Protection, Privacy, and Open Research Challenges-A Systematic Literature Review (SLR)\"], \"source\": [\"ComputIntellNeurosci\"], \"year\": [\"2022\"], \"volume\": [\"2022\"], \"fpage\": [\"8303504\"]}, {\"label\": [\"27.\"], \"surname\": [\"Ogiela\", \"Ogiela\", \"Ko\"], \"given-names\": [\"L\", \"MR\", \"H\"], \"article-title\": [\"Intelligent Data Management and Security in Cloud Computing\"], \"source\": [\"Sens. (Basel).\"], \"year\": [\"2020\"], \"volume\": [\"20\"], \"fpage\": [\"3458\"], \"pub-id\": [\"10.3390/s20123458\"]}, {\"label\": [\"28.\"], \"mixed-citation\": [\"Schneider, Einrichtungs\\u00fcbergreifende elektronische Patientenakten, S. 415 ff. m.w.N.).\"]}, {\"label\": [\"29.\"], \"mixed-citation\": [\"Verordnung (EU) 2016/679 des Europ\\u00e4ischen Parlaments und des Rates vom 27. April 2016 zum Schutz nat\\u00fcrlicher Personen bei der Verarbeitung personenbezogener Daten, zum freien Datenverkehr und zur Aufhebung der Richtlinie 95/46/EG, ABl. EU L 119 v. 04.05.2016, S. 1-88, mit Berichtigungen der deutschen Sprachfassung v. 22.11.2016 (ABl. L 314, S. 72) und v. 23.05.2018 (ABl. EU L 127, S.2.\"]}, {\"label\": [\"30.\"], \"mixed-citation\": [\"Vgl. Kurzpapier Nr. 13 der Datenschutzkonferenz (DSK) vom 17.12.2018.\"]}, {\"label\": [\"31.\"], \"ext-link\": [\"https://dsgvo-gesetz.de/art-28-dsgvo/\"]}, {\"label\": [\"32.\"], \"mixed-citation\": [\"(Muster-)Berufsordnung f\\u00fcr die in Deutschland t\\u00e4tigen \\u00c4rztinnen und \\u00c4rzte.\"]}, {\"label\": [\"33.\"], \"mixed-citation\": [\"Artikel 1 Nr. 2 Buchst. c des Gesetzes zur Neuregelung des Schutzes von Geheimnissen bei der Mitwirkung Dritter an der Berufsaus\\u00fcbung schweigepflichtiger Personen vom 30. Oktober, BGBl (2017).\"]}, {\"label\": [\"34.\"], \"mixed-citation\": [\"Cornelius NJW, 3571, 3572 (2017).\"]}, {\"label\": [\"35.\"], \"ext-link\": [\"https://dsgvo-gesetz.de/blndsg/\"]}, {\"label\": [\"36.\"], \"ext-link\": [\"https://gesetze.berlin.de/bsbe/document/jlr-KHGBE2011V6P24\"]}, {\"label\": [\"37.\"], \"ext-link\": [\"https://www.privacy-regulation.eu/en/article-44-general-principle-for-transfers-GDPR.htm\"]}, {\"label\": [\"39.\"], \"ext-link\": [\"https://hapifhir.io/hapi-fhir/\"]}, {\"label\": [\"40.\"], \"ext-link\": [\"https://www.datenschutzzentrum.de/uploads/projekte/its-apt/ITS.APT_D2.3_DatenschutzrechtlicheBetrachtung.pdf\"]}, {\"label\": [\"41.\"], \"ext-link\": [\"https://gesundheitsdatenschutz.org/download/datenschutz_klin-studien.pdf\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":44,"string":"44"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:56"},"citation":{"kind":"string","value":"NPJ Digit Med. 2024 Jan 13; 7:12"},"package_file":{"kind":"string","value":"oa_package/36/19/PMC10787755.tar.gz"}}},{"rowIdx":86,"cells":{"accession_id":{"kind":"string","value":"PMC10787756"},"pmid":{"kind":"string","value":"38218993"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par2\">Groundwater is a crucial source of drinking water, and its availability is essential for economic growth in urban and rural areas worldwide<sup>##UREF##0##1##,##REF##37775015##2##</sup>. Groundwater is less vulnerable to contamination and pollution than surface water and is widely used for domestic purposes<sup>##UREF##1##3##,##UREF##2##4##</sup>. Due to its high percentage, reduced sensitivity to pollution, and large storage capacity, groundwater is more important than surface water at a socioeconomic level worldwide. Groundwater undergoes a natural filtration process that removes bacteria and odors, making it suitable for drinking<sup>##UREF##3##5##</sup>. Groundwater has many advantages, including meeting water supply needs for industrial, agricultural, and other sectors. In many parts of the world, groundwater is the primary source of fresh water with 50% of portable water demands being met by groundwater, 40% of which is used for industry, and the remaining portion used for irrigation<sup>##UREF##4##6##</sup>. As the world's population grows, its dependence on groundwater also increases, with 33% of people depending on it to meet their daily needs<sup>##UREF##5##7##,##UREF##6##8##</sup>. Unfortunately, more groundwater is being consumed than replenished or recharged, which stresses on the availability of this precious natural resource. As a result, groundwater overuse has led to declining water tables, declining water quality, and ongoing frequent land subsidence activities<sup>##UREF##7##9##</sup>.</p>","<p id=\"Par3\">Despite all the advantages of groundwater, many nations, especially developing countries like India and Bangladesh, are quickly experiencing a crisis of diminishing groundwater quality due to misuse and contamination<sup>##UREF##8##10##</sup>. Groundwater contamination can result from both natural and human causes. However, the quantity and groundwater quality are highly vulnerable today. Anthropogenic activities have accelerated the rate at which the quality of groundwater is declining. Unplanned land-use activities owing to industrialization and subsequent urbanization have led to rising groundwater contamination in recent decades<sup>##UREF##9##11##</sup>. Urbanization increases impervious surfaces, worsens ephemeral runoff, increases flood risk, and reduces subsequent groundwater recharge. In addition, saltwater intrusion exacerbates the situation in coastal areas, severely threatening the city's water supply and lowering the living standards of residential homes<sup>##REF##34715108##12##</sup>. Human activities such as irrigation and climate change are currently impacting groundwater quality and increasing its sensitivity to contamination on a broader scale<sup>##UREF##10##13##</sup>. Chemical fertilizers are exacerbating the critical issue of nitrate poisoning of aquifers<sup>##UREF##11##14##</sup>.</p>","<p id=\"Par4\">One of the largest natural groundwater catastrophes for humanity has been reported to be arsenic (As) pollution in the groundwater. A study revealed that only five Asian nations, namely “Taiwan, China, India, Bangladesh, and Thailand”, were acknowledged as having groundwater contamination due to As in the late twentieth century<sup>##REF##34343549##15##</sup>. At least 100,000 individuals in these impacted nations are exposed to As poisoning through their drinking water. In India, several places in the Brahmaputra and Ganges River’s floodplain have been affected by groundwater contamination with arsenic at levels higher than the permitted limit of 10 µg/L<sup>##UREF##12##16##</sup>. Currently, Bangladesh and India have the highest number of As contaminated areas and associated health issues<sup>##REF##35104519##17##</sup>. Specifically, the Ganges delta of Indo-Bangladesh region is highly affected by groundwater contamination due to As<sup>##UREF##11##14##,##REF##36150620##18##,##UREF##13##19##</sup>. These floodplains are made of recent alluvial aquifers from the Holocene period that originated in the Himalayan region<sup>##UREF##14##20##</sup>. Consequently, people in these afflicted areas have been regularly exposed to drinking water from hand tube wells contaminated with arsenic. In Bangladesh, using contaminated surface water resources, such as, ponds, rivers, and shallow dug-wells has led to water-borne illnesses like cholera, diarrhea, and dysentery<sup>##REF##14643629##21##</sup>. Recent estimates suggest that 5 million people in West Bengal's North 24 Paraganas district consume water with an arsenic concentration of more than 50 µg/l and As rich causing approximately 50,000 people to develop skin sores in West Bengal. The population impacted in the geographic area of the issue are alarmingly growing each year<sup>##UREF##15##22##</sup>. Moreover, As has also entered the food chain through rice (paddy) production in the Indo-Gangetic plains via irrigation water carrying As<sup>##UREF##16##23##</sup>.</p>","<p id=\"Par5\">From a methodological viewpoint, statistical, machine learning (ML), and artificial intelligence have been utilized to evaluate groundwater vulnerability globally. ML algorithms offer several advantages over statistical methods, as they can efficiently analyze large datasets<sup>##UREF##17##24##,##UREF##18##25##</sup>. Additionally, geospatial approaches provide quick, efficient spatial, temporal, and spectral analysis of data over a wide area<sup>##UREF##19##26##</sup>. As a result, numerous researchers have combined geospatial technology with ML algorithms, such as the deep learning network used by Elzain et al.<sup>##UREF##20##27##</sup> in south Korea, the BRT model used by<sup>##UREF##21##28##</sup> in Iran, RF used by Pal et al.<sup>##UREF##11##14##</sup> in coastal areas of West Bengal, and the Bayesian model averaging (BMA) used by Gharekhani et al.<sup>##REF##34894494##29##</sup> in West Azerbaijan, Iran, to assess groundwater vulnerability.</p>","<p id=\"Par6\">Considering the ongoing phenomena related to groundwater resources worldwide, particularly in the Ganges delta, very few studies have been undertaken that couple hydrochemical factors with ML algorithms<sup>##UREF##22##30##–##UREF##23##32##</sup>. Literature review on groundwater vulnerability highlights that the Ganga–Brahmaputra delta in the Indo-Bangladesh region stands out as a significant area globally affected by arsenic contamination<sup>##REF##35104519##17##,##UREF##24##33##</sup>. The widespread use of tube wells for water supply in the Ganges Delta is a critical concern, leading to severe arsenic poisoning. In addition to arsenic, some regions in India also face challenges with elevated fluoride levels in groundwater. The Ganges Delta, marked by high population density and robust agricultural activity, necessitates sustainable water resource management to ensure optimal utilization. Hence, our study focuses on assessing groundwater vulnerability in the Ganges delta, emphasizing the urgency of effective water resource management in this crucial region. Therefore, researching groundwater vulnerability in this area is crucial for managing groundwater effectively and making it safe for consumption. In this regard, the presents study uses geospatial techniques and ML algorithms, including the LR, RF, and ANN models, to evaluate groundwater vulnerability in the study region. The distinctive aspect of this research resides in integrating statistical, ML, and neural network algorithms with hydrochemical factors. This fusion aims to comprehend the fluctuations in modeling outcomes and their corresponding spatial distribution in such a vast region. Furthermore, quality assessment for irrigation water in this study region has been assessed using USSL and Wilcox’s diagram. This study has distinctive contribution in its novel outcomes and insights, which contribute optimal perspectives to the current body of existing literatures. Furthermore, the research insight into regional disparities, illuminating differences or distinctions within a specific geographical area. The outcomes of this study will be helpful to environmentalists and policy-makers in planning for the local people regarding the safe consumption of water resources.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">Groundwater is a crucial source of drinking water, and its availability is essential for economic growth in urban and rural areas worldwide<sup>##UREF##0##1##,##REF##37775015##2##</sup>. Groundwater is less vulnerable to contamination and pollution than surface water and is widely used for domestic purposes<sup>##UREF##1##3##,##UREF##2##4##</sup>. Due to its high percentage, reduced sensitivity to pollution, and large storage capacity, groundwater is more important than surface water at a socioeconomic level worldwide. Groundwater undergoes a natural filtration process that removes bacteria and odors, making it suitable for drinking<sup>##UREF##3##5##</sup>. Groundwater has many advantages, including meeting water supply needs for industrial, agricultural, and other sectors. In many parts of the world, groundwater is the primary source of fresh water with 50% of portable water demands being met by groundwater, 40% of which is used for industry, and the remaining portion used for irrigation<sup>##UREF##4##6##</sup>. As the world's population grows, its dependence on groundwater also increases, with 33% of people depending on it to meet their daily needs<sup>##UREF##5##7##,##UREF##6##8##</sup>. Unfortunately, more groundwater is being consumed than replenished or recharged, which stresses on the availability of this precious natural resource. As a result, groundwater overuse has led to declining water tables, declining water quality, and ongoing frequent land subsidence activities<sup>##UREF##7##9##</sup>.</p>\",\n \"<p id=\\\"Par3\\\">Despite all the advantages of groundwater, many nations, especially developing countries like India and Bangladesh, are quickly experiencing a crisis of diminishing groundwater quality due to misuse and contamination<sup>##UREF##8##10##</sup>. Groundwater contamination can result from both natural and human causes. However, the quantity and groundwater quality are highly vulnerable today. Anthropogenic activities have accelerated the rate at which the quality of groundwater is declining. Unplanned land-use activities owing to industrialization and subsequent urbanization have led to rising groundwater contamination in recent decades<sup>##UREF##9##11##</sup>. Urbanization increases impervious surfaces, worsens ephemeral runoff, increases flood risk, and reduces subsequent groundwater recharge. In addition, saltwater intrusion exacerbates the situation in coastal areas, severely threatening the city's water supply and lowering the living standards of residential homes<sup>##REF##34715108##12##</sup>. Human activities such as irrigation and climate change are currently impacting groundwater quality and increasing its sensitivity to contamination on a broader scale<sup>##UREF##10##13##</sup>. Chemical fertilizers are exacerbating the critical issue of nitrate poisoning of aquifers<sup>##UREF##11##14##</sup>.</p>\",\n \"<p id=\\\"Par4\\\">One of the largest natural groundwater catastrophes for humanity has been reported to be arsenic (As) pollution in the groundwater. A study revealed that only five Asian nations, namely “Taiwan, China, India, Bangladesh, and Thailand”, were acknowledged as having groundwater contamination due to As in the late twentieth century<sup>##REF##34343549##15##</sup>. At least 100,000 individuals in these impacted nations are exposed to As poisoning through their drinking water. In India, several places in the Brahmaputra and Ganges River’s floodplain have been affected by groundwater contamination with arsenic at levels higher than the permitted limit of 10 µg/L<sup>##UREF##12##16##</sup>. Currently, Bangladesh and India have the highest number of As contaminated areas and associated health issues<sup>##REF##35104519##17##</sup>. Specifically, the Ganges delta of Indo-Bangladesh region is highly affected by groundwater contamination due to As<sup>##UREF##11##14##,##REF##36150620##18##,##UREF##13##19##</sup>. These floodplains are made of recent alluvial aquifers from the Holocene period that originated in the Himalayan region<sup>##UREF##14##20##</sup>. Consequently, people in these afflicted areas have been regularly exposed to drinking water from hand tube wells contaminated with arsenic. In Bangladesh, using contaminated surface water resources, such as, ponds, rivers, and shallow dug-wells has led to water-borne illnesses like cholera, diarrhea, and dysentery<sup>##REF##14643629##21##</sup>. Recent estimates suggest that 5 million people in West Bengal's North 24 Paraganas district consume water with an arsenic concentration of more than 50 µg/l and As rich causing approximately 50,000 people to develop skin sores in West Bengal. The population impacted in the geographic area of the issue are alarmingly growing each year<sup>##UREF##15##22##</sup>. Moreover, As has also entered the food chain through rice (paddy) production in the Indo-Gangetic plains via irrigation water carrying As<sup>##UREF##16##23##</sup>.</p>\",\n \"<p id=\\\"Par5\\\">From a methodological viewpoint, statistical, machine learning (ML), and artificial intelligence have been utilized to evaluate groundwater vulnerability globally. ML algorithms offer several advantages over statistical methods, as they can efficiently analyze large datasets<sup>##UREF##17##24##,##UREF##18##25##</sup>. Additionally, geospatial approaches provide quick, efficient spatial, temporal, and spectral analysis of data over a wide area<sup>##UREF##19##26##</sup>. As a result, numerous researchers have combined geospatial technology with ML algorithms, such as the deep learning network used by Elzain et al.<sup>##UREF##20##27##</sup> in south Korea, the BRT model used by<sup>##UREF##21##28##</sup> in Iran, RF used by Pal et al.<sup>##UREF##11##14##</sup> in coastal areas of West Bengal, and the Bayesian model averaging (BMA) used by Gharekhani et al.<sup>##REF##34894494##29##</sup> in West Azerbaijan, Iran, to assess groundwater vulnerability.</p>\",\n \"<p id=\\\"Par6\\\">Considering the ongoing phenomena related to groundwater resources worldwide, particularly in the Ganges delta, very few studies have been undertaken that couple hydrochemical factors with ML algorithms<sup>##UREF##22##30##–##UREF##23##32##</sup>. Literature review on groundwater vulnerability highlights that the Ganga–Brahmaputra delta in the Indo-Bangladesh region stands out as a significant area globally affected by arsenic contamination<sup>##REF##35104519##17##,##UREF##24##33##</sup>. The widespread use of tube wells for water supply in the Ganges Delta is a critical concern, leading to severe arsenic poisoning. In addition to arsenic, some regions in India also face challenges with elevated fluoride levels in groundwater. The Ganges Delta, marked by high population density and robust agricultural activity, necessitates sustainable water resource management to ensure optimal utilization. Hence, our study focuses on assessing groundwater vulnerability in the Ganges delta, emphasizing the urgency of effective water resource management in this crucial region. Therefore, researching groundwater vulnerability in this area is crucial for managing groundwater effectively and making it safe for consumption. In this regard, the presents study uses geospatial techniques and ML algorithms, including the LR, RF, and ANN models, to evaluate groundwater vulnerability in the study region. The distinctive aspect of this research resides in integrating statistical, ML, and neural network algorithms with hydrochemical factors. This fusion aims to comprehend the fluctuations in modeling outcomes and their corresponding spatial distribution in such a vast region. Furthermore, quality assessment for irrigation water in this study region has been assessed using USSL and Wilcox’s diagram. This study has distinctive contribution in its novel outcomes and insights, which contribute optimal perspectives to the current body of existing literatures. Furthermore, the research insight into regional disparities, illuminating differences or distinctions within a specific geographical area. The outcomes of this study will be helpful to environmentalists and policy-makers in planning for the local people regarding the safe consumption of water resources.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Materials and methods</title>","<title>Methodology</title>","<p id=\"Par8\">In this study, the following methodological steps have been followed to fulfill the current research objectives:<list list-type=\"bullet\"><list-item><p id=\"Par9\">In the initial stage, 352 water samples were collected from the existing tube-wells in the field to assess different hydro-geochemical properties. Additionally, 352 non-sample points were created for modelling purposes. Furthermore, it is necessary to divide the dataset into train and test to assess how well machine learning models are performed. Where, train dataset is used to fit the model and the test dataset is used for validation of the respective model. The entire dataset was split into two categories in a “70:30 ratio for training and validation” of the respective models.</p></list-item><list-item><p id=\"Par10\">A total of fifteen hydro-geochemical parameters were identified for modelling groundwater vulnerability. These parameters are “Depth (m), pH, EC (μS/cm), Salinity (ppt), Ca<sup>2+</sup> (mg/l), Mg<sup>2+</sup> (mg/l), Na<sup>+</sup> (mg/l), K<sup>+</sup> (mg/l), Cl<sup>−</sup> (mg/l), HCO<sub>3</sub><sup>−</sup> (mg/l), NO<sub>3</sub><sup>−</sup> (mg/l), SO<sub>4</sub><sup>2−</sup> (mg/l), PO<sub>4</sub><sup>2−</sup> (mg/l), F<sup>−</sup> (mg/l), As (μg/l)”.</p></list-item><list-item><p id=\"Par11\">Statistical analysis, including “Pearson’s correlation coefficient, principal component analysis (PCA) and multicollinearity (MC)” test, was conducted to understand the nature of data.</p></list-item><list-item><p id=\"Par12\">Statistical, ML and neural network algorithms i.e., “logistic regression (LR), random forest (RF) and artificial neural network (ANN)” were used for groundwater vulnerability assessment.</p></list-item><list-item><p id=\"Par13\">Statistical evaluation metrices, such as “sensitivity, specificity, AUC-ROC, F score, Kappa coefficient, and graphical measures such as the Taylor diagram” were used to optimize the assessment of modelling output.</p></list-item><list-item><p id=\"Par14\">“USSL and Wilcox’s diagram” used to assess groundwater quality.</p></list-item></list></p>","<p id=\"Par15\">The following sub-section described in details regarding the methods used in this study.</p>","<title>Sampling and inventory dataset</title>","<p id=\"Par16\">Field-based water sample collection was the primary task to prepare several hydro-chemical parameters for assessing groundwater vulnerability. In this regard, a “random stratified” sampling method was used to collect water sample across the study region. A total of 352 water samples were collected to prepare the inventory dataset (Fig. ##FIG##0##1##). Standard procedures were followed during the collection of water samples. Sampling was done by running wells for 5 min as it removes the stagnant water from bore wells as well as hand pumps. The sample tube well was kept pumping until the pH and EC achieved stable conditions. Two independent (dry and clean) sample kits were used each with its own collection methods and safety measures, to keep the water samples that were taken. In order to transport each water sample from the field to the lab and keep it at 4 °C, we stored it in a water sample kit during sample collection. Measurements were made to analyze the groundwater samples obtained both on-site and off-site. The analyzed samples were split into two categories on the ArcGIS 10.4.1 platform based on a ratio of 70:30. The sample was used for training (70%) while the other was used for validation (30%).</p>","<p id=\"Par17\">In our current research, we opted for dry season (March–early June) data to model and map groundwater vulnerability in this susceptible region, excluding wet season data. Existing literature indicates a prevalent use of dry season data in studies related to arsenic-induced vulnerability studies<sup>##REF##33445121##40##</sup>, as it is deemed more suitable for assessing vulnerability to arsenic-related risks. In the wet season, groundwater contamination occurs through the percolation and infiltration of surface water, facilitated by ample rainfall. This leads to the transfer of various particles, metals, and ions from surface water bodies to groundwater, resulting in temporary water contamination, which is not ideal for assessing water-related health hazards. In contrast, during the dry season, water levels remain normal, and there is no risk of water contamination through surface metals or other substances. Therefore, based on these considerations, we have exclusively utilized dry season data in our study.</p>","<title>MC test</title>","<p id=\"Par18\">To ensure the accuracy of the model’s output, it is crucial to select appropriate parameters for any vulnerability assessment. To achieve this, MC analysis is one of the most important techniques. Correlation analysis has shown that a link between two or more input variables can create deviations. “Tolerance (TOL) and Variance Inflation Factor (VIF)” are two statistical measures often used to test multi-collinearity among distinct components. The predictor variables have a high degree of multicollinearity when the “TOL value is < 0.10 and the VIF value is > 5”. If the MC result exceeds this limit, the highly correlated factors are not suitable for modelling purposes and should be removed from the dataset; otherwise, the output result will not be optimal. The equations for TOL and VIF are presented below:where is the R-squared value of regression using the j on all other variables regression model.</p>","<title>Adopted methods for groundwater vulnerability modelling</title>","<title>LR</title>","<p id=\"Par19\">One can create a multivariate regression relationship between a dependent variable and several independent factors using LR. LR is a multivariate analysis model that can be used to forecast the existence or absence of a characteristic or result based on the values of many response variables. Many studies used LR as a standard or conventional way to verify the effectiveness of a new algorithm in vulnerability studies. The benefit of LR is that, unlike traditional linear regression, where the variables must all have normal distributions, it can use any combination of continuous and discrete variables as well as appropriate link functions<sup>##UREF##31##41##</sup>. The challenge in conducting vulnerability analysis using a LR model is choosing the appropriate sample size for the dependent and independent variables<sup>##UREF##32##42##</sup>. The components in multi-regression analysis must be numerical, and the variables in discriminant analysis, a related statistical model, should have a normal distribution. After converting the dependent variable into a logit variable, the LR procedure uses maximum likelihood estimation<sup>##UREF##33##43##</sup>. This is how LR calculates the likelihood of a specific event occurring<sup>##UREF##34##44##</sup>. The fundamental idea behind LR is investigating a problem in which a result assessed using dichotomous variables i.e., true or false (0 and 1) is determined based on a single or a series of independent factors<sup>##UREF##35##45##</sup>. The LR can expressed by the following equation:</p>","<p id=\"Par20\">where indicates a linear combination of a constant and the independent variables’ product, and their corresponding coefficients. The value of z varies from − ∞ to ∞, subsequently f(z) ranges from 0 to 1”:where indicates constant; represent the coefficients and are the independent variables”.</p>","<title>RF</title>","<p id=\"Par21\">The RF model is a reliable AI method for classifying various natural hazards, including groundwater vulnerability. Breiman<sup>##UREF##36##46##</sup> proposed a potent ensemble-learning method called random forest, which is one of the most widely used classifier ensemble techniques for feature selection, regression, and classification applications. RF is a tree-based ensemble learning technique that builds several decision trees while constructing models. Each tree structure in the ensemble model uses the original input data to train a bootstrapped sample<sup>##UREF##37##47##</sup>. Decision trees use a collection of binary rules to select a target variable. The data used to train the model comprises the target variable being predicted and a set of predictor variables. Using the predictor variables, the decision tree divides the data into homogenous datasets based on the target variable. The programme then assesses each predictor variable's ability to categorize the predicted value into the two groups. The splitting process continues until there are no more splits to be made<sup>##UREF##38##48##</sup>. RF prediction is viewed as the unweighted majority of class votes when solving classification issues. The bagging approach is used to select random samples of variables as part of the training dataset for model calibration<sup>##UREF##39##49##</sup>. The algorithm for RF is expressed as follows:where represents flood occurrence conditioning factors; 1, 2,…n are input vector <italic>x</italic>.</p>","<p id=\"Par22\">In a RF the general errors can be defined as follows:</p>","<p id=\"Par23\">where <italic>x</italic> and <italic>y</italic> indicate the different flood occurrence conditioning factors, and <italic>mg</italic> represents the margin function. Again, margin function” can be described as follows</p>","<title>ANN</title>","<p id=\"Par24\">The ANN is a computational method that can obtain, display, and compute mapping from one multivariate data space to another. The objective of the ANN model is to provide a technique for forecasting results from inputs that have not been used in the modelling process<sup>##UREF##40##50##</sup>. An artificial neural network is trained using a series of examples of related input and output values. The goal of an artificial neural network is to create a model of the data-generation process in order to generalize and predict outcomes from inputs that it has never seen before. Back-propagation learning is the neural network approach that is most often utilized in the ANN model<sup>##UREF##41##51##</sup>. This neural network learning technique has three levels: an input layer, hidden layers, and an output layer. The network is trained using the back-propagation technique until a predetermined minimal error between the network's desired and actual output values is reached. When training is complete, the network is utilized as a feed-forward structure to provide a classification for the entire database (Paola and Schowengerdt<sup>##UREF##42##52##</sup>). The ANN assigns each input element a specific weight, multiplies the results, adds them up, and then uses a nonlinear transfer function to construct the outcomes. The back propagation of the ANN model is expressed by the following equations:</p>","<p id=\"Par25\">The net input of jth neuron of layer l and I iteration</p>","<p id=\"Par26\"> Factor for neuron jth in the output layer ith factor for neuron jth in the hidden layer ithwhere is the momentum rate and is the learning rate within this model.</p>","<title>Selected evaluation measures</title>","<p id=\"Par27\">Evaluating a model's performance, which establishes whether it is relevant or not, is one of the key goals of model comparison. In the geoscientific discipline, assessment metrics for applied models are crucial to estimating their best-case performance in making predictions, especially for modelling approaches based on machine learning. Henceforth, several evaluation measures have been used by many researchers in different fields of study to optimally assess the modelling output<sup>##UREF##11##14##,##UREF##17##24##,##UREF##43##53##,##UREF##44##54##</sup>. After a rigorous literature survey, five prevalent evaluations metrics i.e., “sensitivity, specificity, PPV, NPV, ROC-AUC, Kappa-coefficient and F-score”, were selected for this study. Alongside, the Taylor diagram is also applied in this study, which is a graphical representation of evaluation measures expressing the relationship. A useful tool for displaying and assessing classifiers is the “receiver operating characteristics (ROC) curve” the common name for a performance indicator for classification problems at different threshold levels is the AUC-ROC curve. The ROC curve, which is a graph based on the true positive rate (sensitivity) and the false positive rate (1-specificity), may be thought of as a statistic that measures how well the model performed overall<sup>##UREF##45##55##</sup>. The AUC-ROC value ranged from 0 to 1 and indicates a poor and good performance accordingly<sup>##REF##7063747##56##</sup>. The following formulas were used to create the performance evaluation criteria for this study:</p>","<p id=\"Par28\">Here, “TP is true positive, TN is true negative, FN is false negative, FP is false positive, and kappa coefficient is represented by k, observed samples by and predicted result by ”.</p>"],"string":"[\n \"<title>Materials and methods</title>\",\n \"<title>Methodology</title>\",\n \"<p id=\\\"Par8\\\">In this study, the following methodological steps have been followed to fulfill the current research objectives:<list list-type=\\\"bullet\\\"><list-item><p id=\\\"Par9\\\">In the initial stage, 352 water samples were collected from the existing tube-wells in the field to assess different hydro-geochemical properties. Additionally, 352 non-sample points were created for modelling purposes. Furthermore, it is necessary to divide the dataset into train and test to assess how well machine learning models are performed. Where, train dataset is used to fit the model and the test dataset is used for validation of the respective model. The entire dataset was split into two categories in a “70:30 ratio for training and validation” of the respective models.</p></list-item><list-item><p id=\\\"Par10\\\">A total of fifteen hydro-geochemical parameters were identified for modelling groundwater vulnerability. These parameters are “Depth (m), pH, EC (μS/cm), Salinity (ppt), Ca<sup>2+</sup> (mg/l), Mg<sup>2+</sup> (mg/l), Na<sup>+</sup> (mg/l), K<sup>+</sup> (mg/l), Cl<sup>−</sup> (mg/l), HCO<sub>3</sub><sup>−</sup> (mg/l), NO<sub>3</sub><sup>−</sup> (mg/l), SO<sub>4</sub><sup>2−</sup> (mg/l), PO<sub>4</sub><sup>2−</sup> (mg/l), F<sup>−</sup> (mg/l), As (μg/l)”.</p></list-item><list-item><p id=\\\"Par11\\\">Statistical analysis, including “Pearson’s correlation coefficient, principal component analysis (PCA) and multicollinearity (MC)” test, was conducted to understand the nature of data.</p></list-item><list-item><p id=\\\"Par12\\\">Statistical, ML and neural network algorithms i.e., “logistic regression (LR), random forest (RF) and artificial neural network (ANN)” were used for groundwater vulnerability assessment.</p></list-item><list-item><p id=\\\"Par13\\\">Statistical evaluation metrices, such as “sensitivity, specificity, AUC-ROC, F score, Kappa coefficient, and graphical measures such as the Taylor diagram” were used to optimize the assessment of modelling output.</p></list-item><list-item><p id=\\\"Par14\\\">“USSL and Wilcox’s diagram” used to assess groundwater quality.</p></list-item></list></p>\",\n \"<p id=\\\"Par15\\\">The following sub-section described in details regarding the methods used in this study.</p>\",\n \"<title>Sampling and inventory dataset</title>\",\n \"<p id=\\\"Par16\\\">Field-based water sample collection was the primary task to prepare several hydro-chemical parameters for assessing groundwater vulnerability. In this regard, a “random stratified” sampling method was used to collect water sample across the study region. A total of 352 water samples were collected to prepare the inventory dataset (Fig. ##FIG##0##1##). Standard procedures were followed during the collection of water samples. Sampling was done by running wells for 5 min as it removes the stagnant water from bore wells as well as hand pumps. The sample tube well was kept pumping until the pH and EC achieved stable conditions. Two independent (dry and clean) sample kits were used each with its own collection methods and safety measures, to keep the water samples that were taken. In order to transport each water sample from the field to the lab and keep it at 4 °C, we stored it in a water sample kit during sample collection. Measurements were made to analyze the groundwater samples obtained both on-site and off-site. The analyzed samples were split into two categories on the ArcGIS 10.4.1 platform based on a ratio of 70:30. The sample was used for training (70%) while the other was used for validation (30%).</p>\",\n \"<p id=\\\"Par17\\\">In our current research, we opted for dry season (March–early June) data to model and map groundwater vulnerability in this susceptible region, excluding wet season data. Existing literature indicates a prevalent use of dry season data in studies related to arsenic-induced vulnerability studies<sup>##REF##33445121##40##</sup>, as it is deemed more suitable for assessing vulnerability to arsenic-related risks. In the wet season, groundwater contamination occurs through the percolation and infiltration of surface water, facilitated by ample rainfall. This leads to the transfer of various particles, metals, and ions from surface water bodies to groundwater, resulting in temporary water contamination, which is not ideal for assessing water-related health hazards. In contrast, during the dry season, water levels remain normal, and there is no risk of water contamination through surface metals or other substances. Therefore, based on these considerations, we have exclusively utilized dry season data in our study.</p>\",\n \"<title>MC test</title>\",\n \"<p id=\\\"Par18\\\">To ensure the accuracy of the model’s output, it is crucial to select appropriate parameters for any vulnerability assessment. To achieve this, MC analysis is one of the most important techniques. Correlation analysis has shown that a link between two or more input variables can create deviations. “Tolerance (TOL) and Variance Inflation Factor (VIF)” are two statistical measures often used to test multi-collinearity among distinct components. The predictor variables have a high degree of multicollinearity when the “TOL value is < 0.10 and the VIF value is > 5”. If the MC result exceeds this limit, the highly correlated factors are not suitable for modelling purposes and should be removed from the dataset; otherwise, the output result will not be optimal. The equations for TOL and VIF are presented below:where is the R-squared value of regression using the j on all other variables regression model.</p>\",\n \"<title>Adopted methods for groundwater vulnerability modelling</title>\",\n \"<title>LR</title>\",\n \"<p id=\\\"Par19\\\">One can create a multivariate regression relationship between a dependent variable and several independent factors using LR. LR is a multivariate analysis model that can be used to forecast the existence or absence of a characteristic or result based on the values of many response variables. Many studies used LR as a standard or conventional way to verify the effectiveness of a new algorithm in vulnerability studies. The benefit of LR is that, unlike traditional linear regression, where the variables must all have normal distributions, it can use any combination of continuous and discrete variables as well as appropriate link functions<sup>##UREF##31##41##</sup>. The challenge in conducting vulnerability analysis using a LR model is choosing the appropriate sample size for the dependent and independent variables<sup>##UREF##32##42##</sup>. The components in multi-regression analysis must be numerical, and the variables in discriminant analysis, a related statistical model, should have a normal distribution. After converting the dependent variable into a logit variable, the LR procedure uses maximum likelihood estimation<sup>##UREF##33##43##</sup>. This is how LR calculates the likelihood of a specific event occurring<sup>##UREF##34##44##</sup>. The fundamental idea behind LR is investigating a problem in which a result assessed using dichotomous variables i.e., true or false (0 and 1) is determined based on a single or a series of independent factors<sup>##UREF##35##45##</sup>. The LR can expressed by the following equation:</p>\",\n \"<p id=\\\"Par20\\\">where indicates a linear combination of a constant and the independent variables’ product, and their corresponding coefficients. The value of z varies from − ∞ to ∞, subsequently f(z) ranges from 0 to 1”:where indicates constant; represent the coefficients and are the independent variables”.</p>\",\n \"<title>RF</title>\",\n \"<p id=\\\"Par21\\\">The RF model is a reliable AI method for classifying various natural hazards, including groundwater vulnerability. Breiman<sup>##UREF##36##46##</sup> proposed a potent ensemble-learning method called random forest, which is one of the most widely used classifier ensemble techniques for feature selection, regression, and classification applications. RF is a tree-based ensemble learning technique that builds several decision trees while constructing models. Each tree structure in the ensemble model uses the original input data to train a bootstrapped sample<sup>##UREF##37##47##</sup>. Decision trees use a collection of binary rules to select a target variable. The data used to train the model comprises the target variable being predicted and a set of predictor variables. Using the predictor variables, the decision tree divides the data into homogenous datasets based on the target variable. The programme then assesses each predictor variable's ability to categorize the predicted value into the two groups. The splitting process continues until there are no more splits to be made<sup>##UREF##38##48##</sup>. RF prediction is viewed as the unweighted majority of class votes when solving classification issues. The bagging approach is used to select random samples of variables as part of the training dataset for model calibration<sup>##UREF##39##49##</sup>. The algorithm for RF is expressed as follows:where represents flood occurrence conditioning factors; 1, 2,…n are input vector <italic>x</italic>.</p>\",\n \"<p id=\\\"Par22\\\">In a RF the general errors can be defined as follows:</p>\",\n \"<p id=\\\"Par23\\\">where <italic>x</italic> and <italic>y</italic> indicate the different flood occurrence conditioning factors, and <italic>mg</italic> represents the margin function. Again, margin function” can be described as follows</p>\",\n \"<title>ANN</title>\",\n \"<p id=\\\"Par24\\\">The ANN is a computational method that can obtain, display, and compute mapping from one multivariate data space to another. The objective of the ANN model is to provide a technique for forecasting results from inputs that have not been used in the modelling process<sup>##UREF##40##50##</sup>. An artificial neural network is trained using a series of examples of related input and output values. The goal of an artificial neural network is to create a model of the data-generation process in order to generalize and predict outcomes from inputs that it has never seen before. Back-propagation learning is the neural network approach that is most often utilized in the ANN model<sup>##UREF##41##51##</sup>. This neural network learning technique has three levels: an input layer, hidden layers, and an output layer. The network is trained using the back-propagation technique until a predetermined minimal error between the network's desired and actual output values is reached. When training is complete, the network is utilized as a feed-forward structure to provide a classification for the entire database (Paola and Schowengerdt<sup>##UREF##42##52##</sup>). The ANN assigns each input element a specific weight, multiplies the results, adds them up, and then uses a nonlinear transfer function to construct the outcomes. The back propagation of the ANN model is expressed by the following equations:</p>\",\n \"<p id=\\\"Par25\\\">The net input of jth neuron of layer l and I iteration</p>\",\n \"<p id=\\\"Par26\\\"> Factor for neuron jth in the output layer ith factor for neuron jth in the hidden layer ithwhere is the momentum rate and is the learning rate within this model.</p>\",\n \"<title>Selected evaluation measures</title>\",\n \"<p id=\\\"Par27\\\">Evaluating a model's performance, which establishes whether it is relevant or not, is one of the key goals of model comparison. In the geoscientific discipline, assessment metrics for applied models are crucial to estimating their best-case performance in making predictions, especially for modelling approaches based on machine learning. Henceforth, several evaluation measures have been used by many researchers in different fields of study to optimally assess the modelling output<sup>##UREF##11##14##,##UREF##17##24##,##UREF##43##53##,##UREF##44##54##</sup>. After a rigorous literature survey, five prevalent evaluations metrics i.e., “sensitivity, specificity, PPV, NPV, ROC-AUC, Kappa-coefficient and F-score”, were selected for this study. Alongside, the Taylor diagram is also applied in this study, which is a graphical representation of evaluation measures expressing the relationship. A useful tool for displaying and assessing classifiers is the “receiver operating characteristics (ROC) curve” the common name for a performance indicator for classification problems at different threshold levels is the AUC-ROC curve. The ROC curve, which is a graph based on the true positive rate (sensitivity) and the false positive rate (1-specificity), may be thought of as a statistic that measures how well the model performed overall<sup>##UREF##45##55##</sup>. The AUC-ROC value ranged from 0 to 1 and indicates a poor and good performance accordingly<sup>##REF##7063747##56##</sup>. The following formulas were used to create the performance evaluation criteria for this study:</p>\",\n \"<p id=\\\"Par28\\\">Here, “TP is true positive, TN is true negative, FN is false negative, FP is false positive, and kappa coefficient is represented by k, observed samples by and predicted result by ”.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Result</title>","<title>Statistical measures of selected hydrochemical parameters</title>","<p id=\"Par29\">In this study, three statistical tests were conducted on the selected hydrochemical dataset: MC, correlation coefficient, and PCA. The MC test (Table ##TAB##0##1##) showed that all factors were within the threshold value of MC, and therefore suitable for modelling purposes., The depth factor had the highest TOL and lowest VIF (0.66 and 1.515 respectively), while the Ca<sup>2+</sup> factor had the lowest TOL and highest VIF (0.38 and 0.632 respectively). Pearson’s correlation coefficient was used to understand the nature of the substantial association between physical and chemical properties. The correlation coefficient (r) ranges from − 1 to + 1, with values of 0.5, 0.5–0.8 and 0.8 indicating weak, moderate, and strongly correlation, respectively. The highest correlation values were found between pH and K<sup>+</sup> (0.952) and EC and CI (0.973), while moderate relationships were found between pH and salinity (0.546), pH and Mg<sup>2+</sup> (0.506), EC and Na<sup>+</sup> (0.644), Mg<sup>2+</sup> and K<sup>+</sup> (0.593), Na<sup>+</sup> and CI (0.613), and the lowest values were found between Ca<sup>2+</sup> and Mg<sup>2+</sup> (0.422), EC and Ca<sup>2+</sup> (0.365), depth and HCO<sub>3</sub><sup>−</sup> (0.359), etc. Details about the correlation coefficient map and table are presented in Fig. ##FIG##1##2## and Table ##TAB##1##2##. PCA analysis showed that PC 1 consisted of 43.21% eigenvalue, followed by PC 2 and PC 3, which had 31.02% and 17.08% eigenvalue, respectively. In PC 1, the dominant factors were EC (0.933), salinity (0.927), Mg<sup>2+</sup> (0.874) and CI (0.924), while important factors in PC 2 important factors were F<sup>−</sup> (0.765), As (0.599) and HCO<sub>3-</sub> (0.582) and in PC 3 dominant factors were PO<sub>4</sub><sup>2−</sup> (0.620), NO<sub>3</sub> (0.582) and K<sup>+</sup> (0.339). The biplot map of PC 1, PC 2 and PC 3 is presented in Fig. ##FIG##2##3##.</p>","<title>Assessment of groundwater vulnerability</title>","<p id=\"Par30\">Groundwater vulnerability in the aquifers of Ganges delta was assessed using LR, RF and ANN models, and the results are presented in Fig. ##FIG##3##4##. Statistical, ML, and neural network algorithms were used to understand the spatial distribution of groundwater vulnerability in the vulnerable mega-delta region. We used ArcGIS 10.5 software to map the final spatial distribution of vulnerability using the respective modelling outcomes. Each map was classified into five vulnerability zones namely “very low, low, moderate, high and very high” using “Jenk’s natural break method”. The final vulnerability maps show that very high groundwater vulnerability zones are found in the eastern and some isolated south-eastern and central middle portions. Conversely, very low groundwater vulnerability zones are found in the north-western, eastern, and south-western parts. The moderate vulnerability zone is found in the central part and isolated patches of the south-eastern and southern parts of the study area. Due to the high concentration of As and other contaminated factors in the groundwater, the eastern part of the Ganges delta, i.e., the region of Bangladesh, is very vulnerable to groundwater compared to the western part of the delta region, i.e., the state of West Bengal in India. Although two isolated patches are found to be in the very high vulnerable zone in the western region of the Ganges delta i.e., part of India in RF and ANN models (Fig. ##FIG##3##4##).</p>","<title>Importance hydrochemical parameters for groundwater vulnerability</title>","<p id=\"Par31\">It is a fact that all selected hydrochemical parameters have not equal responsibility for groundwater vulnerability assessment in this study. Therefore, it is fundamental to determine the dominant factors in each applied learning model for groundwater vulnerability. The most dominant factors were identified for the three applied models, i.e., LR, RF, and ANN. The results of the dominant factors for groundwater vulnerability are presented in Table ##TAB##2##3## for the three applied models. Factors such as F<sup>−</sup> (0.74), Na<sup>+</sup> (0.77), As (0.69), Mg<sup>2+</sup> (0.58) and HCO<sub>3</sub> (0.54) are more dominant, while SO<sub>4</sub><sup>2</sup> (0.2), pH (0.21), EC (0.31) and PO<sub>4</sub><sup>2</sup> (0.32) are less dominant in the LR model. The “mean decrease accuracy (MDA) method of RF algorithm” revealed that Na<sup>+</sup> (0.84), F<sup>−</sup> (0.77) and As (0.72) are the most influential factors on groundwater resources followed by HCO<sub>3</sub> (0.55), and Mg<sup>2+</sup> (0.54). In ANN, the dominant factors are Na<sup>+</sup> (0.88), F<sup>−</sup> (0.81), As (0.78) and HCO<sub>3</sub> (0.67), and the less dominant factors are SO42 (0.19), pH (0.24), salinity (0.31), and EC (0.33).</p>","<title>Evaluation assessment</title>","<p id=\"Par32\">All three models were evaluated using various metrics such as “sensitivity, specificity, NPV, PPV, ROC-AUC, Kappa-coefficient, and F-score”. Among the three models, the ANN model is the most suitable for modelling groundwater vulnerability, with a ROC value of 0.912 and 0.902, for training and validation, respectively. This is followed by the RF model with 0.817 and 0.792 for training and validation, and then the LR model with 0.749 and 0.712 for training and validation. The PPV and NPV are also high in the ANN model, with values of 0.883 and 0.885 in the validation stage. The sensitivity analysis showed that the ANN model had the highest result at 0.889, followed by RF and LR, with 0.782 and 0.721, respectively, in the validation stage. The Kappa and F-score also indicate that the ANN model is the best fit with values of 0.643 and 0.882 in the validation stage, followed by RF and LR (Table ##TAB##3##4##). The Taylor diagram in Fig. ##FIG##4##5## also shows that the ANN is optimal based on standard deviation and correlation.</p>","<title>Quality assessment of groundwater</title>","<p id=\"Par33\">Piper, USSL, and Wilcox diagrams were used to assess the hydrochemical properties and quality of groundwater in the Ganges delta region. The Piper diagram (Fig. ##FIG##5##6##a) showed that alkaline earth (Ca<sup>2+</sup>+Mg<sup>2+</sup>) dominates over alkalies (Na+K) and that CI and NO<sub>3</sub> dominate over HCO<sub>3</sub>. The Wilcox diagram (Fig. ##FIG##5##6##b) showed that two samples were unsuitable, while the others fell into the doubtful to unsuitable, permissible to doubtful, good to permissible and excellent categories. The USSL diagram (Fig. ##FIG##5##6##c) revealed a high salinity, low sodium and alkali hazard dominance. The collected datasets were grouped and analyzed using a hierarchical clustering method, which showed that the second cluster, and to a lesser extent, the first cluster, significantly influenced the state and the groundwater quality. The dendrogram (Fig. ##FIG##5##6##d) showed that the first cluster covered approximately 32% of the datasets, the second cluster covered the maximum dataset (47%) and the third cluster covered the lowest dataset (21%).</p>"],"string":"[\n \"<title>Result</title>\",\n \"<title>Statistical measures of selected hydrochemical parameters</title>\",\n \"<p id=\\\"Par29\\\">In this study, three statistical tests were conducted on the selected hydrochemical dataset: MC, correlation coefficient, and PCA. The MC test (Table ##TAB##0##1##) showed that all factors were within the threshold value of MC, and therefore suitable for modelling purposes., The depth factor had the highest TOL and lowest VIF (0.66 and 1.515 respectively), while the Ca<sup>2+</sup> factor had the lowest TOL and highest VIF (0.38 and 0.632 respectively). Pearson’s correlation coefficient was used to understand the nature of the substantial association between physical and chemical properties. The correlation coefficient (r) ranges from − 1 to + 1, with values of 0.5, 0.5–0.8 and 0.8 indicating weak, moderate, and strongly correlation, respectively. The highest correlation values were found between pH and K<sup>+</sup> (0.952) and EC and CI (0.973), while moderate relationships were found between pH and salinity (0.546), pH and Mg<sup>2+</sup> (0.506), EC and Na<sup>+</sup> (0.644), Mg<sup>2+</sup> and K<sup>+</sup> (0.593), Na<sup>+</sup> and CI (0.613), and the lowest values were found between Ca<sup>2+</sup> and Mg<sup>2+</sup> (0.422), EC and Ca<sup>2+</sup> (0.365), depth and HCO<sub>3</sub><sup>−</sup> (0.359), etc. Details about the correlation coefficient map and table are presented in Fig. ##FIG##1##2## and Table ##TAB##1##2##. PCA analysis showed that PC 1 consisted of 43.21% eigenvalue, followed by PC 2 and PC 3, which had 31.02% and 17.08% eigenvalue, respectively. In PC 1, the dominant factors were EC (0.933), salinity (0.927), Mg<sup>2+</sup> (0.874) and CI (0.924), while important factors in PC 2 important factors were F<sup>−</sup> (0.765), As (0.599) and HCO<sub>3-</sub> (0.582) and in PC 3 dominant factors were PO<sub>4</sub><sup>2−</sup> (0.620), NO<sub>3</sub> (0.582) and K<sup>+</sup> (0.339). The biplot map of PC 1, PC 2 and PC 3 is presented in Fig. ##FIG##2##3##.</p>\",\n \"<title>Assessment of groundwater vulnerability</title>\",\n \"<p id=\\\"Par30\\\">Groundwater vulnerability in the aquifers of Ganges delta was assessed using LR, RF and ANN models, and the results are presented in Fig. ##FIG##3##4##. Statistical, ML, and neural network algorithms were used to understand the spatial distribution of groundwater vulnerability in the vulnerable mega-delta region. We used ArcGIS 10.5 software to map the final spatial distribution of vulnerability using the respective modelling outcomes. Each map was classified into five vulnerability zones namely “very low, low, moderate, high and very high” using “Jenk’s natural break method”. The final vulnerability maps show that very high groundwater vulnerability zones are found in the eastern and some isolated south-eastern and central middle portions. Conversely, very low groundwater vulnerability zones are found in the north-western, eastern, and south-western parts. The moderate vulnerability zone is found in the central part and isolated patches of the south-eastern and southern parts of the study area. Due to the high concentration of As and other contaminated factors in the groundwater, the eastern part of the Ganges delta, i.e., the region of Bangladesh, is very vulnerable to groundwater compared to the western part of the delta region, i.e., the state of West Bengal in India. Although two isolated patches are found to be in the very high vulnerable zone in the western region of the Ganges delta i.e., part of India in RF and ANN models (Fig. ##FIG##3##4##).</p>\",\n \"<title>Importance hydrochemical parameters for groundwater vulnerability</title>\",\n \"<p id=\\\"Par31\\\">It is a fact that all selected hydrochemical parameters have not equal responsibility for groundwater vulnerability assessment in this study. Therefore, it is fundamental to determine the dominant factors in each applied learning model for groundwater vulnerability. The most dominant factors were identified for the three applied models, i.e., LR, RF, and ANN. The results of the dominant factors for groundwater vulnerability are presented in Table ##TAB##2##3## for the three applied models. Factors such as F<sup>−</sup> (0.74), Na<sup>+</sup> (0.77), As (0.69), Mg<sup>2+</sup> (0.58) and HCO<sub>3</sub> (0.54) are more dominant, while SO<sub>4</sub><sup>2</sup> (0.2), pH (0.21), EC (0.31) and PO<sub>4</sub><sup>2</sup> (0.32) are less dominant in the LR model. The “mean decrease accuracy (MDA) method of RF algorithm” revealed that Na<sup>+</sup> (0.84), F<sup>−</sup> (0.77) and As (0.72) are the most influential factors on groundwater resources followed by HCO<sub>3</sub> (0.55), and Mg<sup>2+</sup> (0.54). In ANN, the dominant factors are Na<sup>+</sup> (0.88), F<sup>−</sup> (0.81), As (0.78) and HCO<sub>3</sub> (0.67), and the less dominant factors are SO42 (0.19), pH (0.24), salinity (0.31), and EC (0.33).</p>\",\n \"<title>Evaluation assessment</title>\",\n \"<p id=\\\"Par32\\\">All three models were evaluated using various metrics such as “sensitivity, specificity, NPV, PPV, ROC-AUC, Kappa-coefficient, and F-score”. Among the three models, the ANN model is the most suitable for modelling groundwater vulnerability, with a ROC value of 0.912 and 0.902, for training and validation, respectively. This is followed by the RF model with 0.817 and 0.792 for training and validation, and then the LR model with 0.749 and 0.712 for training and validation. The PPV and NPV are also high in the ANN model, with values of 0.883 and 0.885 in the validation stage. The sensitivity analysis showed that the ANN model had the highest result at 0.889, followed by RF and LR, with 0.782 and 0.721, respectively, in the validation stage. The Kappa and F-score also indicate that the ANN model is the best fit with values of 0.643 and 0.882 in the validation stage, followed by RF and LR (Table ##TAB##3##4##). The Taylor diagram in Fig. ##FIG##4##5## also shows that the ANN is optimal based on standard deviation and correlation.</p>\",\n \"<title>Quality assessment of groundwater</title>\",\n \"<p id=\\\"Par33\\\">Piper, USSL, and Wilcox diagrams were used to assess the hydrochemical properties and quality of groundwater in the Ganges delta region. The Piper diagram (Fig. ##FIG##5##6##a) showed that alkaline earth (Ca<sup>2+</sup>+Mg<sup>2+</sup>) dominates over alkalies (Na+K) and that CI and NO<sub>3</sub> dominate over HCO<sub>3</sub>. The Wilcox diagram (Fig. ##FIG##5##6##b) showed that two samples were unsuitable, while the others fell into the doubtful to unsuitable, permissible to doubtful, good to permissible and excellent categories. The USSL diagram (Fig. ##FIG##5##6##c) revealed a high salinity, low sodium and alkali hazard dominance. The collected datasets were grouped and analyzed using a hierarchical clustering method, which showed that the second cluster, and to a lesser extent, the first cluster, significantly influenced the state and the groundwater quality. The dendrogram (Fig. ##FIG##5##6##d) showed that the first cluster covered approximately 32% of the datasets, the second cluster covered the maximum dataset (47%) and the third cluster covered the lowest dataset (21%).</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par34\">The fundamental reasons for spatio-temporal fluctuations in the groundwater supply are increasing water demand across all sectors and changing climatic conditions<sup>##UREF##46##57##</sup>. These factors present a significant challenge to water resource planners. This study has demonstrated that considerable concentrations of elevated arsenic and nitrate in groundwater, as well as salinization, are among the groundwater quality issues in the coastal areas of the multi-aquifers of the Ganges delta. The quality of groundwater along the coast primarily depends on geological conditions, hydrogeological processes, and chemical activities<sup>##REF##34653454##58##</sup>. Therefore, a trustworthy assessment of groundwater vulnerability is a crucial first step in choosing the best design or framework for future water resource development.</p>","<p id=\"Par35\">It can be challenging to select a preferred model for assessing inherent vulnerability that can effectively match the research topic's features and the study area's geo-environmental characteristics. The literature reveals that many academics have compared two or more vulnerability indices to create a meticulously tailored intrinsic vulnerability model for their research, aiming to achieve optimal output<sup>##UREF##47##59##,##UREF##48##60##</sup>. In the current scenario, statistical and machine learning (ML) algorithms are widely employed in groundwater-related studies worldwide. For example, Yu et al. (2022)Vu<sup>##UREF##49##61##</sup> applied an integrated Variable Weight Model (VWM) and DRASTIC model to assess groundwater vulnerability in China and found that the VWM-DRASTIC combination provided optimal predictive analysis. Vu et al.<sup>##UREF##50##62##</sup> used a numerical model and the index-overlay method in conjunction with climate scenarios (RCPs) to evaluate groundwater vulnerability and associated sustainability in Taiwan, and they recommended optimal predictive analysis. Furthermore, several machine learning models have been utilized in various groundwater-related studies, including groundwater vulnerability<sup>##UREF##2##4##,##UREF##11##14##,##REF##36150620##18##,##UREF##51##63##</sup>, nitrate concentration in groundwater<sup>##REF##32927161##64##–##REF##33414495##66##</sup>, and more. The random forest (RF) model is well-known for its numerous advantages and has been employed in various geoscientific fields, including groundwater vulnerability studies. Lahjouj et al.<sup>##UREF##52##67##</sup> utilized the RF algorithm in a survey of groundwater vulnerability to nitrate concentration in Morocco and achieved an accuracy assessment of 0.822 in terms of AUC-ROC. Similarly, Saha et al.<sup>##REF##36150620##18##</sup> used RF to assess hydrochemical-based groundwater vulnerability in parts of the Ganges delta and achieved optimal accuracy rates of 0.849 and 0.812 in the training and validation data of the ROC. Various statistical techniques are available, ranging from straightforward descriptive statistics of concentrations of specific contaminants to more complex regression analyses that consider the impacts of multiple predictor variables<sup>##UREF##53##68##</sup>. Binary logistic regression, sometimes known as logistic regression (LR), is a frequently used statistical technique for estimating groundwater vulnerability. LR models relate the potential influencing factors to the likelihood that a pollutant concentration will exceed a threshold value. Mohammaddost et al.<sup>##UREF##54##69##</sup> employed DRASTIC, EBF, and LR models in the Kabul basin of Afghanistan to assess groundwater vulnerability, and they found that LR provided 66% accuracy in AUC-ROC prediction analysis. Adiat et al.<sup>##REF##32778965##70##</sup> applied LR for the same assessment in the Ilesa gold mining area of Nigeria, achieving an 85.7% accuracy in model prediction. Recently, with the significant advantages of neural network algorithms, several neural network models have also been used in groundwater studies. For instance, Elzain et al.<sup>##UREF##20##27##</sup> used the DLNN model in aquifer vulnerability studies in South Korea, while Elzain et al.<sup>##REF##34902776##71##</sup> employed the RBNN model to assess groundwater vulnerability to nitrate contamination in the southern part of Korea.</p>","<p id=\"Par36\">Based on the discussion above and considering the significant advantages of statistical, machine learning (ML), and neural network algorithms, three popular learning algorithms, namely logistic regression (LR), random forest (RF), and artificial neural network (ANN), were selected for the optimal assessment of groundwater vulnerability in the mega delta of the Ganges delta, taking into account field-based hydrochemical parameters. The findings of this study demonstrate that among the applied models, ANN yields the most optimal results, with AUC-ROC scores of 0.912 and 0.902 in training and validation, respectively, for groundwater vulnerability studies. RF follows with scores of 0.817 and 0.792 in training and validation, and LR with scores of 0.749 and 0.712 in training and validation. The high performance of the ANN model can be attributed to its capacity for parallel processing, enabling it to handle multiple tasks simultaneously. The statistical analysis of all selected hydrochemical parameters reveals that pH and K<sup>+</sup> (0.952) and EC and Cl<sup>−</sup> (0.973) are highly correlated, while pH and salinity (0.546), pH and Mg<sup>2+</sup> (0.506), EC and Na<sup>+</sup> (0.644), Mg<sup>2+</sup> and K<sup>+</sup> (0.593), and Na<sup>+</sup> and Cl<sup>−</sup> (0.613) show moderate correlations. It is also found that pH, NO<sub>3</sub><sup>−</sup>, As, and K<sup>+</sup> are the most influential factors for groundwater vulnerability in this study region.</p>","<p id=\"Par37\">Henceforth, studies on groundwater vulnerability serve as crucial measurements for the sustainable management of water resources, environmental preservation, and the guarantee of a secure and uncontaminated drinking water supply for both present and future generations.</p>","<p id=\"Par38\">Nonetheless, it is a fact that employing combined techniques and methodologies can aid in resolving ambiguities related to GIS-based vulnerability assessment frameworks in geoscientific fields. The approaches presented in this research can be tested in various hydrogeological and geo-environmental contexts to understand the spatial distribution of vulnerability. Evaluating groundwater vulnerability studies requires careful consideration of the data and tools used for validation. Furthermore, the limitations of this study are not considered various important factors, such as the hydrogeological process of groundwater, land use land cover, and aquifer and soil characteristics, as all of these factors affect groundwater quality. In the future, other neural networks and deep learning algorithms can be beneficial for the optimal assessment of groundwater vulnerability in the mega-delta, considering changing climate and land use land cover. Therefore, the results of this study will be valuable to land use planners and provide fundamental information for the optimal assessment and management of groundwater risk zones accordingly.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par34\\\">The fundamental reasons for spatio-temporal fluctuations in the groundwater supply are increasing water demand across all sectors and changing climatic conditions<sup>##UREF##46##57##</sup>. These factors present a significant challenge to water resource planners. This study has demonstrated that considerable concentrations of elevated arsenic and nitrate in groundwater, as well as salinization, are among the groundwater quality issues in the coastal areas of the multi-aquifers of the Ganges delta. The quality of groundwater along the coast primarily depends on geological conditions, hydrogeological processes, and chemical activities<sup>##REF##34653454##58##</sup>. Therefore, a trustworthy assessment of groundwater vulnerability is a crucial first step in choosing the best design or framework for future water resource development.</p>\",\n \"<p id=\\\"Par35\\\">It can be challenging to select a preferred model for assessing inherent vulnerability that can effectively match the research topic's features and the study area's geo-environmental characteristics. The literature reveals that many academics have compared two or more vulnerability indices to create a meticulously tailored intrinsic vulnerability model for their research, aiming to achieve optimal output<sup>##UREF##47##59##,##UREF##48##60##</sup>. In the current scenario, statistical and machine learning (ML) algorithms are widely employed in groundwater-related studies worldwide. For example, Yu et al. (2022)Vu<sup>##UREF##49##61##</sup> applied an integrated Variable Weight Model (VWM) and DRASTIC model to assess groundwater vulnerability in China and found that the VWM-DRASTIC combination provided optimal predictive analysis. Vu et al.<sup>##UREF##50##62##</sup> used a numerical model and the index-overlay method in conjunction with climate scenarios (RCPs) to evaluate groundwater vulnerability and associated sustainability in Taiwan, and they recommended optimal predictive analysis. Furthermore, several machine learning models have been utilized in various groundwater-related studies, including groundwater vulnerability<sup>##UREF##2##4##,##UREF##11##14##,##REF##36150620##18##,##UREF##51##63##</sup>, nitrate concentration in groundwater<sup>##REF##32927161##64##–##REF##33414495##66##</sup>, and more. The random forest (RF) model is well-known for its numerous advantages and has been employed in various geoscientific fields, including groundwater vulnerability studies. Lahjouj et al.<sup>##UREF##52##67##</sup> utilized the RF algorithm in a survey of groundwater vulnerability to nitrate concentration in Morocco and achieved an accuracy assessment of 0.822 in terms of AUC-ROC. Similarly, Saha et al.<sup>##REF##36150620##18##</sup> used RF to assess hydrochemical-based groundwater vulnerability in parts of the Ganges delta and achieved optimal accuracy rates of 0.849 and 0.812 in the training and validation data of the ROC. Various statistical techniques are available, ranging from straightforward descriptive statistics of concentrations of specific contaminants to more complex regression analyses that consider the impacts of multiple predictor variables<sup>##UREF##53##68##</sup>. Binary logistic regression, sometimes known as logistic regression (LR), is a frequently used statistical technique for estimating groundwater vulnerability. LR models relate the potential influencing factors to the likelihood that a pollutant concentration will exceed a threshold value. Mohammaddost et al.<sup>##UREF##54##69##</sup> employed DRASTIC, EBF, and LR models in the Kabul basin of Afghanistan to assess groundwater vulnerability, and they found that LR provided 66% accuracy in AUC-ROC prediction analysis. Adiat et al.<sup>##REF##32778965##70##</sup> applied LR for the same assessment in the Ilesa gold mining area of Nigeria, achieving an 85.7% accuracy in model prediction. Recently, with the significant advantages of neural network algorithms, several neural network models have also been used in groundwater studies. For instance, Elzain et al.<sup>##UREF##20##27##</sup> used the DLNN model in aquifer vulnerability studies in South Korea, while Elzain et al.<sup>##REF##34902776##71##</sup> employed the RBNN model to assess groundwater vulnerability to nitrate contamination in the southern part of Korea.</p>\",\n \"<p id=\\\"Par36\\\">Based on the discussion above and considering the significant advantages of statistical, machine learning (ML), and neural network algorithms, three popular learning algorithms, namely logistic regression (LR), random forest (RF), and artificial neural network (ANN), were selected for the optimal assessment of groundwater vulnerability in the mega delta of the Ganges delta, taking into account field-based hydrochemical parameters. The findings of this study demonstrate that among the applied models, ANN yields the most optimal results, with AUC-ROC scores of 0.912 and 0.902 in training and validation, respectively, for groundwater vulnerability studies. RF follows with scores of 0.817 and 0.792 in training and validation, and LR with scores of 0.749 and 0.712 in training and validation. The high performance of the ANN model can be attributed to its capacity for parallel processing, enabling it to handle multiple tasks simultaneously. The statistical analysis of all selected hydrochemical parameters reveals that pH and K<sup>+</sup> (0.952) and EC and Cl<sup>−</sup> (0.973) are highly correlated, while pH and salinity (0.546), pH and Mg<sup>2+</sup> (0.506), EC and Na<sup>+</sup> (0.644), Mg<sup>2+</sup> and K<sup>+</sup> (0.593), and Na<sup>+</sup> and Cl<sup>−</sup> (0.613) show moderate correlations. It is also found that pH, NO<sub>3</sub><sup>−</sup>, As, and K<sup>+</sup> are the most influential factors for groundwater vulnerability in this study region.</p>\",\n \"<p id=\\\"Par37\\\">Henceforth, studies on groundwater vulnerability serve as crucial measurements for the sustainable management of water resources, environmental preservation, and the guarantee of a secure and uncontaminated drinking water supply for both present and future generations.</p>\",\n \"<p id=\\\"Par38\\\">Nonetheless, it is a fact that employing combined techniques and methodologies can aid in resolving ambiguities related to GIS-based vulnerability assessment frameworks in geoscientific fields. The approaches presented in this research can be tested in various hydrogeological and geo-environmental contexts to understand the spatial distribution of vulnerability. Evaluating groundwater vulnerability studies requires careful consideration of the data and tools used for validation. Furthermore, the limitations of this study are not considered various important factors, such as the hydrogeological process of groundwater, land use land cover, and aquifer and soil characteristics, as all of these factors affect groundwater quality. In the future, other neural networks and deep learning algorithms can be beneficial for the optimal assessment of groundwater vulnerability in the mega-delta, considering changing climate and land use land cover. Therefore, the results of this study will be valuable to land use planners and provide fundamental information for the optimal assessment and management of groundwater risk zones accordingly.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":["<title>Conclusion</title>","<p id=\"Par39\">Globally, assessing susceptibility to groundwater contamination is crucial for proactive management aimed at safeguarding groundwater resources for various uses. Creating more effective sustainable development policies regarding potential groundwater pollution by utilizing more precise vulnerability maps. In the Ganges deltaic region, the high concentrations of contaminants, such as arsenic (As), are primarily responsible for groundwater vulnerability, and the associated human health hazards are a significant concern for global researchers. In the present research, there is a focus on creating an effective vulnerability map for a mega-delta, specifically the Ganges delta. This involves the application of LR, RF, and ANN models in the modelling and mapping process. Sensitivity analysis indicates that the ANN output is the most optimal, followed by RF and LR. The study reveals that the neural network algorithm is the best suited for assessing groundwater vulnerability related to contamination in the study region, surpassing traditional statistical analysis. Hydrochemical parameters such as pH, NO<sub>3</sub><sup>−</sup>, As, and K<sup>+</sup> dominate this deltaic aquifer, contributing to vulnerability. Overall, all vulnerability maps indicate that the study area’s western, central, south, and eastern parts are highly vulnerable. Due to elevated levels of As and various ion contaminations, most groundwater samples from the Ganges delta are unsuitable for drinking and irrigation. Consequently, the improper implementation of government policies, a lack of awareness, and inadequate management are the primary concerns leading to groundwater deterioration in this region. Therefore, immediate action is necessary to sustain and conserve groundwater resources in the world's largest and most densely populated deltaic region. Henceforth, in future application of deep learning and both the dataset i.e., dry and wet season for sampling procedure will be helpful for better understanding of groundwater vulnerability in this vulnerable region.</p>"],"string":"[\n \"<title>Conclusion</title>\",\n \"<p id=\\\"Par39\\\">Globally, assessing susceptibility to groundwater contamination is crucial for proactive management aimed at safeguarding groundwater resources for various uses. Creating more effective sustainable development policies regarding potential groundwater pollution by utilizing more precise vulnerability maps. In the Ganges deltaic region, the high concentrations of contaminants, such as arsenic (As), are primarily responsible for groundwater vulnerability, and the associated human health hazards are a significant concern for global researchers. In the present research, there is a focus on creating an effective vulnerability map for a mega-delta, specifically the Ganges delta. This involves the application of LR, RF, and ANN models in the modelling and mapping process. Sensitivity analysis indicates that the ANN output is the most optimal, followed by RF and LR. The study reveals that the neural network algorithm is the best suited for assessing groundwater vulnerability related to contamination in the study region, surpassing traditional statistical analysis. Hydrochemical parameters such as pH, NO<sub>3</sub><sup>−</sup>, As, and K<sup>+</sup> dominate this deltaic aquifer, contributing to vulnerability. Overall, all vulnerability maps indicate that the study area’s western, central, south, and eastern parts are highly vulnerable. Due to elevated levels of As and various ion contaminations, most groundwater samples from the Ganges delta are unsuitable for drinking and irrigation. Consequently, the improper implementation of government policies, a lack of awareness, and inadequate management are the primary concerns leading to groundwater deterioration in this region. Therefore, immediate action is necessary to sustain and conserve groundwater resources in the world's largest and most densely populated deltaic region. Henceforth, in future application of deep learning and both the dataset i.e., dry and wet season for sampling procedure will be helpful for better understanding of groundwater vulnerability in this vulnerable region.</p>\"\n]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Determining the degree of high groundwater arsenic (As) and fluoride (F<sup>−</sup>) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F<sup>−</sup> concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F<sup>−</sup> contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Determining the degree of high groundwater arsenic (As) and fluoride (F<sup>−</sup>) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F<sup>−</sup> concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F<sup>−</sup> contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Study area</title>","<p id=\"Par7\">The Ganges and Brahmaputra delta, known as the Ganges delta, is one of the mega-deltas in the world, covering an area of approximately 105,000 km<sup>2</sup>. It consists of Bangladesh and parts of India’s state of West Bengal, formed by sedimentation of the Ganga, Meghna and Brahmaputra rivers at the Bay of Bengal during the late Holocene to recent times<sup>##UREF##14##20##</sup>. The delta stretches from 21° 10′ 42″ to 24° 50′ 39″ N latitude and 87° 30′ 21″ to 91° 26′ 46″ E longitude (Fig. ##FIG##0##1##) and has a shoreline of nearly 350 km along the Bay of Bengal. The Ganges delta has been divided into three parts from a geological perspective i.e., “Moribund delta, Active delta and Mature delta”<sup>##UREF##25##34##</sup>. The delta's stratigraphic section shows alternating sand-dominated and fine-grained phases with intricate interfingerings between them<sup>##UREF##24##33##</sup>. This delta enclosed by “Precambrian crystalline rocks” to the north and west and the “Assam-Arakan Neogene fold belt” to the east, signifies a comprehensive sedimentation history during the late Quaternary period<sup>##UREF##26##35##</sup>. Literatures indicates that numerous elevated terraces from the Pleistocene era are present both within and along the periphery of its alluvial plain<sup>##UREF##27##36##</sup>. Present evidence considering remote sensing supporting neotectonics activities in the Gangetic plain<sup>##UREF##28##37##</sup>. Salinity has impacted aquifers in the coastal regions of Bangladesh, reaching depths of up to 350 m, furthermore the salinity levels in the upper aquifers of the coastal region, reaching depths of 200–250 m, demonstrate notable fluctuations and experience abrupt changes over short distances<sup>##UREF##29##38##</sup>. The monsoon season (June–October) accounts for more than 80% of the annual rainfall, which ranges from 1500 to 2000 mm<sup>##UREF##30##39##</sup>. During the monsoon months, high rainfall and frequent tropical cyclones cause catastrophic flooding and saltwater intrusion in the land areas. The minimum seasonal temperature of the region varies from 12 to 24 °C, and the maximum ranges from 25 to 35 °C. The area has the largest population density compared to other deltaic regions due to the high soil fertility<sup>##UREF##22##30##</sup>. The Sundarbans, the world's largest mangrove forest, covers the southernmost part of this deltaic region also known as the Sunderban delta. Borehole data indicates that sediment primarily consists of sand and clay types.</p>"],"string":"[\n \"<title>Study area</title>\",\n \"<p id=\\\"Par7\\\">The Ganges and Brahmaputra delta, known as the Ganges delta, is one of the mega-deltas in the world, covering an area of approximately 105,000 km<sup>2</sup>. It consists of Bangladesh and parts of India’s state of West Bengal, formed by sedimentation of the Ganga, Meghna and Brahmaputra rivers at the Bay of Bengal during the late Holocene to recent times<sup>##UREF##14##20##</sup>. The delta stretches from 21° 10′ 42″ to 24° 50′ 39″ N latitude and 87° 30′ 21″ to 91° 26′ 46″ E longitude (Fig. ##FIG##0##1##) and has a shoreline of nearly 350 km along the Bay of Bengal. The Ganges delta has been divided into three parts from a geological perspective i.e., “Moribund delta, Active delta and Mature delta”<sup>##UREF##25##34##</sup>. The delta's stratigraphic section shows alternating sand-dominated and fine-grained phases with intricate interfingerings between them<sup>##UREF##24##33##</sup>. This delta enclosed by “Precambrian crystalline rocks” to the north and west and the “Assam-Arakan Neogene fold belt” to the east, signifies a comprehensive sedimentation history during the late Quaternary period<sup>##UREF##26##35##</sup>. Literatures indicates that numerous elevated terraces from the Pleistocene era are present both within and along the periphery of its alluvial plain<sup>##UREF##27##36##</sup>. Present evidence considering remote sensing supporting neotectonics activities in the Gangetic plain<sup>##UREF##28##37##</sup>. Salinity has impacted aquifers in the coastal regions of Bangladesh, reaching depths of up to 350 m, furthermore the salinity levels in the upper aquifers of the coastal region, reaching depths of 200–250 m, demonstrate notable fluctuations and experience abrupt changes over short distances<sup>##UREF##29##38##</sup>. The monsoon season (June–October) accounts for more than 80% of the annual rainfall, which ranges from 1500 to 2000 mm<sup>##UREF##30##39##</sup>. During the monsoon months, high rainfall and frequent tropical cyclones cause catastrophic flooding and saltwater intrusion in the land areas. The minimum seasonal temperature of the region varies from 12 to 24 °C, and the maximum ranges from 25 to 35 °C. The area has the largest population density compared to other deltaic regions due to the high soil fertility<sup>##UREF##22##30##</sup>. The Sundarbans, the world's largest mangrove forest, covers the southernmost part of this deltaic region also known as the Sunderban delta. Borehole data indicates that sediment primarily consists of sand and clay types.</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Acknowledgements</title>","<p>This publication was supported by the Deanship of Scientific Research at the King Faisal University, Saudi Arabia (Grant: 3422).</p>","<title>Author contributions</title>","<p>A.S.: conceptualization, methodology, investigation, formal analysis, visualization, writing-original draft, writing—review and editing; S.C.P.: supervision, conceptualization, methodology, investigation, formal analysis, visualization, writing-original draft, writing—review and editing; A.R.M.T.I.: investigation, formal analysis, visualization, writing-original draft, writing—review and editing; A.I.: investigation, formal analysis, visualization, writing-original draft, writing—review and editing; E.A.: investigation, formal analysis, visualization, writing-original draft, writing—review and editing; M.K.I.: investigation, formal analysis, visualization, writing-original draft, writing—review and editing.</p>","<title>Data availability</title>","<p>The datasets used and/or analyzed during the current study are available from the reasonable request.</p>","<title>Competing interests</title>","<p id=\"Par40\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Acknowledgements</title>\",\n \"<p>This publication was supported by the Deanship of Scientific Research at the King Faisal University, Saudi Arabia (Grant: 3422).</p>\",\n \"<title>Author contributions</title>\",\n \"<p>A.S.: conceptualization, methodology, investigation, formal analysis, visualization, writing-original draft, writing—review and editing; S.C.P.: supervision, conceptualization, methodology, investigation, formal analysis, visualization, writing-original draft, writing—review and editing; A.R.M.T.I.: investigation, formal analysis, visualization, writing-original draft, writing—review and editing; A.I.: investigation, formal analysis, visualization, writing-original draft, writing—review and editing; E.A.: investigation, formal analysis, visualization, writing-original draft, writing—review and editing; M.K.I.: investigation, formal analysis, visualization, writing-original draft, writing—review and editing.</p>\",\n \"<title>Data availability</title>\",\n \"<p>The datasets used and/or analyzed during the current study are available from the reasonable request.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par40\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Details about the study area: (<bold>a</bold>) Ganges delta in a transnational boundary of Indo-Bangladesh region, (<bold>b</bold>) Ganges delta and its morphological types and (<bold>c</bold>) litholog profile of some selected points (this map was generated using ArcGIS, version: 10.3.1, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.esri.com/arcgis\">www.esri.com/arcgis</ext-link>).</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Pearson’s correlationship among the factors.</p></caption></fig>","<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Bi-plot among the applied hydro-chemical factors.</p></caption></fig>","<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Groundwater vulnerability maps of Ganges delta: (<bold>a</bold>) LR, (<bold>b</bold>) RF and (<bold>c</bold>) ANN.</p></caption></fig>","<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Graphical evaluation measure of applied models using Taylor diagram.</p></caption></fig>","<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Quality assessment of groundwater: (<bold>a</bold>) Piper diagram, (<bold>b</bold>) Wilcox’s diagram, (<bold>c</bold>) USSL diagram, and (<bold>d</bold>) cluster analysis using dendrogram.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>Details about the study area: (<bold>a</bold>) Ganges delta in a transnational boundary of Indo-Bangladesh region, (<bold>b</bold>) Ganges delta and its morphological types and (<bold>c</bold>) litholog profile of some selected points (this map was generated using ArcGIS, version: 10.3.1, <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"http://www.esri.com/arcgis\\\">www.esri.com/arcgis</ext-link>).</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>Pearson’s correlationship among the factors.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Figure 3</label><caption><p>Bi-plot among the applied hydro-chemical factors.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Figure 4</label><caption><p>Groundwater vulnerability maps of Ganges delta: (<bold>a</bold>) LR, (<bold>b</bold>) RF and (<bold>c</bold>) ANN.</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Figure 5</label><caption><p>Graphical evaluation measure of applied models using Taylor diagram.</p></caption></fig>\",\n \"<fig id=\\\"Fig6\\\"><label>Figure 6</label><caption><p>Quality assessment of groundwater: (<bold>a</bold>) Piper diagram, (<bold>b</bold>) Wilcox’s diagram, (<bold>c</bold>) USSL diagram, and (<bold>d</bold>) cluster analysis using dendrogram.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":["<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Multi-collinearity analysis of selected factors.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Factors</th><th align=\"left\">VIF</th><th align=\"left\">TOL</th></tr></thead><tbody><tr><td align=\"left\">Depth (m)</td><td char=\".\" align=\"char\">1.515</td><td char=\".\" align=\"char\">0.66</td></tr><tr><td align=\"left\">pH</td><td char=\".\" align=\"char\">2.564</td><td char=\".\" align=\"char\">0.39</td></tr><tr><td align=\"left\">EC (μS/cm)</td><td char=\".\" align=\"char\">2.041</td><td char=\".\" align=\"char\">0.49</td></tr><tr><td align=\"left\">Salinity (ppt)</td><td char=\".\" align=\"char\">1.852</td><td char=\".\" align=\"char\">0.54</td></tr><tr><td align=\"left\">Ca<sup>2+</sup> (mg/l)</td><td char=\".\" align=\"char\">2.632</td><td char=\".\" align=\"char\">0.38</td></tr><tr><td align=\"left\">Mg<sup>2+</sup> (mg/l)</td><td char=\".\" align=\"char\">1.818</td><td char=\".\" align=\"char\">0.55</td></tr><tr><td align=\"left\">Na<sup>+</sup> (mg/l)</td><td char=\".\" align=\"char\">2.128</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">K<sup>+</sup> (mg/l)</td><td char=\".\" align=\"char\">2.564</td><td char=\".\" align=\"char\">0.39</td></tr><tr><td align=\"left\">Cl<sup>−</sup> (mg/l)</td><td char=\".\" align=\"char\">1.852</td><td char=\".\" align=\"char\">0.54</td></tr><tr><td align=\"left\">HCO<sub>3</sub><sup>−</sup> (mg/l)</td><td char=\".\" align=\"char\">2.381</td><td char=\".\" align=\"char\">0.42</td></tr><tr><td align=\"left\">NO<sub>3</sub><sup>−</sup> (mg/l)</td><td char=\".\" align=\"char\">2.778</td><td char=\".\" align=\"char\">0.36</td></tr><tr><td align=\"left\">SO<sub>4</sub><sup>2−</sup> (mg/l)</td><td char=\".\" align=\"char\">1.852</td><td char=\".\" align=\"char\">0.54</td></tr><tr><td align=\"left\">PO<sub>4</sub><sup>2−</sup> (mg/l)</td><td char=\".\" align=\"char\">2.083</td><td char=\".\" align=\"char\">0.48</td></tr><tr><td align=\"left\">F<sup>−</sup> (mg/l)</td><td char=\".\" align=\"char\">1.724</td><td char=\".\" align=\"char\">0.58</td></tr><tr><td align=\"left\">As (μg/l)</td><td char=\".\" align=\"char\">2.326</td><td char=\".\" align=\"char\">0.43</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Correlationship test among the selected factors.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" colspan=\"17\">Correlations</th></tr><tr><th align=\"left\">Factors</th><th align=\"left\">Depth (m)</th><th align=\"left\">Temperature (°C)</th><th align=\"left\">pH</th><th align=\"left\">EC (μS/cm)</th><th align=\"left\">Salinity (ppt)</th><th align=\"left\">Ca<sup>2+</sup> (mg/l)</th><th align=\"left\">Mg<sup>2+</sup> (mg/l)</th><th align=\"left\">Na<sup>+</sup> (mg/l)</th><th align=\"left\">K<sup>+</sup> (mg/l)</th><th align=\"left\">Cl<sup>−</sup> (mg/l)</th><th align=\"left\">HCO<sub>3</sub><sup>−</sup> (mg/l)</th><th align=\"left\">NO<sub>3</sub><sup>−</sup> (mg/l)</th><th align=\"left\">SO<sub>4</sub><sup>2−</sup> (mg/l)</th><th align=\"left\">PO<sub>4</sub><sup>2−</sup> (mg/l)</th><th align=\"left\">F<sup>−</sup> (mg/l)</th><th align=\"left\">As (μg/l)</th></tr></thead><tbody><tr><td align=\"left\">Depth</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.192**</td><td align=\"left\">− 0.048</td><td align=\"left\">0.044</td><td align=\"left\">− 0.014</td><td align=\"left\">− 0.009</td><td align=\"left\">− 0.011</td><td align=\"left\">0.040</td><td align=\"left\">0.025</td><td align=\"left\">0.044</td><td align=\"left\">0.033</td><td align=\"left\">− 0.063</td><td align=\"left\">− 0.009</td><td align=\"left\">0.015</td><td align=\"left\">0.414**</td><td align=\"left\">0.027</td></tr><tr><td align=\"left\">pH</td><td align=\"left\">− 0.048</td><td char=\".\" align=\"char\">− 0.102*</td><td align=\"left\">1</td><td align=\"left\">− 0.264**</td><td align=\"left\">− 0.299**</td><td align=\"left\">− 0.213**</td><td align=\"left\">− 0.271**</td><td align=\"left\">− 0.329**</td><td align=\"left\">− 0.108*</td><td align=\"left\">− 0.300**</td><td align=\"left\">0.172**</td><td align=\"left\">− 0.068</td><td align=\"left\">− 0.054</td><td align=\"left\">− 0.012</td><td align=\"left\">0.046</td><td align=\"left\">− 0.164**</td></tr><tr><td align=\"left\">EC</td><td align=\"left\">0.044</td><td char=\".\" align=\"char\">− 0.157**</td><td align=\"left\">− 0.264**</td><td align=\"left\">1</td><td align=\"left\">0.835**</td><td align=\"left\">0.251**</td><td align=\"left\">0.551**</td><td align=\"left\">0.541**</td><td align=\"left\">0.384**</td><td align=\"left\">0.842**</td><td align=\"left\">0.016</td><td align=\"left\">0.250**</td><td align=\"left\">0.143**</td><td align=\"left\">0.087*</td><td align=\"left\">0.239**</td><td align=\"left\">0.176**</td></tr><tr><td align=\"left\">Salinity</td><td align=\"left\">− 0.014</td><td char=\".\" align=\"char\">0.079</td><td align=\"left\">− 0.299**</td><td align=\"left\">0.835**</td><td align=\"left\">1</td><td align=\"left\">0.303**</td><td align=\"left\">0.584**</td><td align=\"left\">0.615**</td><td align=\"left\">0.237**</td><td align=\"left\">0.861**</td><td align=\"left\">− 0.014</td><td align=\"left\">0.294**</td><td align=\"left\">0.101*</td><td align=\"left\">0.092*</td><td align=\"left\">0.187**</td><td align=\"left\">0.188**</td></tr><tr><td align=\"left\">Ca<sup>2+</sup> </td><td align=\"left\">− 0.009</td><td char=\".\" align=\"char\">0.071</td><td align=\"left\">− 0.213**</td><td align=\"left\">0.251**</td><td align=\"left\">0.303**</td><td align=\"left\">1</td><td align=\"left\">0.366**</td><td align=\"left\">0.302**</td><td align=\"left\">0.142**</td><td align=\"left\">0.313**</td><td align=\"left\">− 0.273**</td><td align=\"left\">0.055</td><td align=\"left\">0.169**</td><td align=\"left\">− 0.088*</td><td align=\"left\">− 0.040</td><td align=\"left\">0.075</td></tr><tr><td align=\"left\">Mg<sup>2+</sup> </td><td align=\"left\">− 0.011</td><td char=\".\" align=\"char\">0.164**</td><td align=\"left\">− 0.271**</td><td align=\"left\">0.551**</td><td align=\"left\">0.584**</td><td align=\"left\">0.366**</td><td align=\"left\">1</td><td align=\"left\">0.587**</td><td align=\"left\">0.264**</td><td align=\"left\">0.675**</td><td align=\"left\">− 0.106*</td><td align=\"left\">0.112**</td><td align=\"left\">0.146**</td><td align=\"left\">0.095*</td><td align=\"left\">− 0.061</td><td align=\"left\">0.213**</td></tr><tr><td align=\"left\">Na<sup>+</sup> </td><td align=\"left\">0.040</td><td char=\".\" align=\"char\">0.129**</td><td align=\"left\">− 0.329**</td><td align=\"left\">0.541**</td><td align=\"left\">0.615**</td><td align=\"left\">0.302**</td><td align=\"left\">0.587**</td><td align=\"left\">1</td><td align=\"left\">0.267**</td><td align=\"left\">0.633**</td><td align=\"left\">0.011</td><td align=\"left\">0.239**</td><td align=\"left\">0.127**</td><td align=\"left\">0.130**</td><td align=\"left\">− 0.001</td><td align=\"left\">0.289**</td></tr><tr><td align=\"left\">K<sup>+</sup> </td><td align=\"left\">0.025</td><td char=\".\" align=\"char\">0.132**</td><td align=\"left\">− 0.108*</td><td align=\"left\">0.384**</td><td align=\"left\">0.237**</td><td align=\"left\">0.142**</td><td align=\"left\">0.264**</td><td align=\"left\">0.267**</td><td align=\"left\">1</td><td align=\"left\">0.469**</td><td align=\"left\">− 0.125**</td><td align=\"left\">0.180**</td><td align=\"left\">− 0.040</td><td align=\"left\">− 0.041</td><td align=\"left\">0.059</td><td align=\"left\">0.027</td></tr><tr><td align=\"left\">Cl<sup>−</sup></td><td align=\"left\">0.044</td><td char=\".\" align=\"char\">0.168**</td><td align=\"left\">− 0.300**</td><td align=\"left\">0.842**</td><td align=\"left\">0.861**</td><td align=\"left\">0.313**</td><td align=\"left\">0.675**</td><td align=\"left\">0.633**</td><td align=\"left\">0.469**</td><td align=\"left\">1</td><td align=\"left\">− 0.088*</td><td align=\"left\">0.280**</td><td align=\"left\">0.178**</td><td align=\"left\">0.083</td><td align=\"left\">0.192**</td><td align=\"left\">0.204**</td></tr><tr><td align=\"left\">HCO<sub>3</sub><sup>−</sup></td><td align=\"left\">0.033</td><td char=\".\" align=\"char\">− 0.022</td><td align=\"left\">0.172**</td><td align=\"left\">0.016</td><td align=\"left\">− 0.014</td><td align=\"left\">− 0.273**</td><td align=\"left\">− 0.106*</td><td align=\"left\">0.011</td><td align=\"left\">− 0.125**</td><td align=\"left\">− 0.088*</td><td align=\"left\">1</td><td align=\"left\">0.001</td><td align=\"left\">0.014</td><td align=\"left\">0.179**</td><td align=\"left\">0.197**</td><td align=\"left\">− 0.061</td></tr><tr><td align=\"left\">NO<sub>3</sub><sup>−</sup></td><td align=\"left\">− 0.063</td><td char=\".\" align=\"char\">− 0.029</td><td align=\"left\">− 0.068</td><td align=\"left\">0.250**</td><td align=\"left\">0.294**</td><td align=\"left\">0.055</td><td align=\"left\">0.112**</td><td align=\"left\">0.239**</td><td align=\"left\">0.180**</td><td align=\"left\">0.280**</td><td align=\"left\">0.001</td><td align=\"left\">1</td><td align=\"left\">− 0.029</td><td align=\"left\">0.049</td><td align=\"left\">− 0.035</td><td align=\"left\">0.010</td></tr><tr><td align=\"left\">SO<sub>4</sub><sup>2−</sup></td><td align=\"left\">− 0.009</td><td char=\".\" align=\"char\">0.010</td><td align=\"left\">− 0.054</td><td align=\"left\">0.143**</td><td align=\"left\">0.101*</td><td align=\"left\">0.169**</td><td align=\"left\">0.146**</td><td align=\"left\">0.127**</td><td align=\"left\">− 0.040</td><td align=\"left\">0.178**</td><td align=\"left\">0.014</td><td align=\"left\">− 0.029</td><td align=\"left\">1</td><td align=\"left\">− 0.031</td><td align=\"left\">0.130**</td><td align=\"left\">− 0.006</td></tr><tr><td align=\"left\">PO<sub>4</sub><sup>2−</sup></td><td align=\"left\">0.015</td><td char=\".\" align=\"char\">− 0.005</td><td align=\"left\">− 0.012</td><td align=\"left\">0.087*</td><td align=\"left\">0.092*</td><td align=\"left\">− 0.088*</td><td align=\"left\">0.095*</td><td align=\"left\">0.130**</td><td align=\"left\">− 0.041</td><td align=\"left\">0.083</td><td align=\"left\">0.179**</td><td align=\"left\">0.049</td><td align=\"left\">− 0.031</td><td align=\"left\">1</td><td align=\"left\">0.005</td><td align=\"left\">0.192**</td></tr><tr><td align=\"left\">F<sup>−</sup></td><td align=\"left\">0.414**</td><td char=\".\" align=\"char\">0.125**</td><td align=\"left\">0.046</td><td align=\"left\">0.239**</td><td align=\"left\">0.187**</td><td align=\"left\">− 0.040</td><td align=\"left\">− 0.061</td><td align=\"left\">− 0.001</td><td align=\"left\">0.059</td><td align=\"left\">0.192**</td><td align=\"left\">0.197**</td><td align=\"left\">− 0.035</td><td align=\"left\">0.130**</td><td align=\"left\">0.005</td><td align=\"left\">1</td><td align=\"left\">− 0.104*</td></tr><tr><td align=\"left\">As</td><td align=\"left\">0.027</td><td char=\".\" align=\"char\">0.019</td><td align=\"left\">− 0.164**</td><td align=\"left\">0.176**</td><td align=\"left\">0.188**</td><td align=\"left\">0.075</td><td align=\"left\">0.213**</td><td align=\"left\">0.289**</td><td align=\"left\">0.027</td><td align=\"left\">0.204**</td><td align=\"left\">− 0.061</td><td align=\"left\">0.010</td><td align=\"left\">− 0.006</td><td align=\"left\">0.192**</td><td align=\"left\">− 0.104*</td><td align=\"left\">1</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Variables importance of selected factors through applied three models.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Factors</th><th align=\"left\">LR</th><th align=\"left\">RF</th><th align=\"left\">ANN</th></tr></thead><tbody><tr><td align=\"left\">NO<sub>3</sub></td><td char=\".\" align=\"char\">0.44</td><td char=\".\" align=\"char\">0.35</td><td char=\".\" align=\"char\">0.41</td></tr><tr><td align=\"left\">AS</td><td char=\".\" align=\"char\">0.69</td><td char=\".\" align=\"char\">0.72</td><td char=\".\" align=\"char\">0.78</td></tr><tr><td align=\"left\">PO<sub>4</sub><sup>2−</sup></td><td char=\".\" align=\"char\">0.32</td><td char=\".\" align=\"char\">0.21</td><td char=\".\" align=\"char\">0.38</td></tr><tr><td align=\"left\">SO<sub>4</sub><sup>2−</sup></td><td char=\".\" align=\"char\">0.2</td><td char=\".\" align=\"char\">0.22</td><td char=\".\" align=\"char\">0.19</td></tr><tr><td align=\"left\">Salinity</td><td char=\".\" align=\"char\">0.32</td><td char=\".\" align=\"char\">0.24</td><td char=\".\" align=\"char\">0.31</td></tr><tr><td align=\"left\">HCO<sub>3</sub></td><td char=\".\" align=\"char\">0.54</td><td char=\".\" align=\"char\">0.55</td><td char=\".\" align=\"char\">0.67</td></tr><tr><td align=\"left\">CI</td><td char=\".\" align=\"char\">0.41</td><td char=\".\" align=\"char\">0.38</td><td char=\".\" align=\"char\">0.47</td></tr><tr><td align=\"left\">K+ </td><td char=\".\" align=\"char\">0.24</td><td char=\".\" align=\"char\">0.25</td><td char=\".\" align=\"char\">0.28</td></tr><tr><td align=\"left\">Na<sup>+</sup> </td><td char=\".\" align=\"char\">0.77</td><td char=\".\" align=\"char\">0.84</td><td char=\".\" align=\"char\">0.88</td></tr><tr><td align=\"left\">Mg<sup>2+</sup></td><td char=\".\" align=\"char\">0.58</td><td char=\".\" align=\"char\">0.54</td><td char=\".\" align=\"char\">0.51</td></tr><tr><td align=\"left\">F<sup>−</sup></td><td char=\".\" align=\"char\">0.74</td><td char=\".\" align=\"char\">0.77</td><td char=\".\" align=\"char\">0.81</td></tr><tr><td align=\"left\">pH</td><td char=\".\" align=\"char\">0.21</td><td char=\".\" align=\"char\">0.28</td><td char=\".\" align=\"char\">0.24</td></tr><tr><td align=\"left\">EC</td><td char=\".\" align=\"char\">0.31</td><td char=\".\" align=\"char\">0.39</td><td char=\".\" align=\"char\">0.33</td></tr><tr><td align=\"left\">Depth</td><td char=\".\" align=\"char\">0.4</td><td char=\".\" align=\"char\">0.41</td><td char=\".\" align=\"char\">0.41</td></tr><tr><td align=\"left\">Ca<sup>2+</sup></td><td char=\".\" align=\"char\">0.33</td><td char=\".\" align=\"char\">0.31</td><td char=\".\" align=\"char\">0.35</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Evaluation metrices of applied models.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Models</th><th align=\"left\" rowspan=\"2\">Stage</th><th align=\"left\" colspan=\"5\">Parameters</th><th align=\"left\" rowspan=\"2\">Kappa</th><th align=\"left\" rowspan=\"2\">F-Score</th></tr><tr><th align=\"left\">Sensitivity</th><th align=\"left\">Specificity</th><th align=\"left\">PPV</th><th align=\"left\">NPV</th><th align=\"left\">AUC</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">LR</td><td align=\"left\">Train</td><td char=\".\" align=\"char\">0.741</td><td char=\".\" align=\"char\">0.749</td><td char=\".\" align=\"char\">0.752</td><td char=\".\" align=\"char\">0.721</td><td char=\".\" align=\"char\">0.749</td><td char=\".\" align=\"char\">0.532</td><td char=\".\" align=\"char\">0.811</td></tr><tr><td align=\"left\">Validation</td><td char=\".\" align=\"char\">0.721</td><td char=\".\" align=\"char\">0.722</td><td char=\".\" align=\"char\">0.711</td><td char=\".\" align=\"char\">0.701</td><td char=\".\" align=\"char\">0.712</td><td char=\".\" align=\"char\">0.513</td><td char=\".\" align=\"char\">0.792</td></tr><tr><td align=\"left\" rowspan=\"2\">RF</td><td align=\"left\">Train</td><td char=\".\" align=\"char\">0.821</td><td char=\".\" align=\"char\">0.812</td><td char=\".\" align=\"char\">0.813</td><td char=\".\" align=\"char\">0.825</td><td char=\".\" align=\"char\">0.817</td><td char=\".\" align=\"char\">0.63</td><td char=\".\" align=\"char\">0.863</td></tr><tr><td align=\"left\">Validation</td><td char=\".\" align=\"char\">0.782</td><td char=\".\" align=\"char\">0.751</td><td char=\".\" align=\"char\">0.794</td><td char=\".\" align=\"char\">0.744</td><td char=\".\" align=\"char\">0.792</td><td char=\".\" align=\"char\">0.601</td><td char=\".\" align=\"char\">0.841</td></tr><tr><td align=\"left\" rowspan=\"2\">ANN</td><td align=\"left\">Train</td><td char=\".\" align=\"char\">0.907</td><td char=\".\" align=\"char\">0.903</td><td char=\".\" align=\"char\">0.901</td><td char=\".\" align=\"char\">0.901</td><td char=\".\" align=\"char\">0.912</td><td char=\".\" align=\"char\">0.651</td><td char=\".\" align=\"char\">0.912</td></tr><tr><td align=\"left\">Validation</td><td char=\".\" align=\"char\">0.889</td><td char=\".\" align=\"char\">0.891</td><td char=\".\" align=\"char\">0.883</td><td char=\".\" align=\"char\">0.885</td><td char=\".\" align=\"char\">0.902</td><td char=\".\" align=\"char\">0.643</td><td char=\".\" align=\"char\">0.882</td></tr></tbody></table></table-wrap>"],"string":"[\n \"<table-wrap id=\\\"Tab1\\\"><label>Table 1</label><caption><p>Multi-collinearity analysis of selected factors.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Factors</th><th align=\\\"left\\\">VIF</th><th align=\\\"left\\\">TOL</th></tr></thead><tbody><tr><td align=\\\"left\\\">Depth (m)</td><td char=\\\".\\\" align=\\\"char\\\">1.515</td><td char=\\\".\\\" align=\\\"char\\\">0.66</td></tr><tr><td align=\\\"left\\\">pH</td><td char=\\\".\\\" align=\\\"char\\\">2.564</td><td char=\\\".\\\" align=\\\"char\\\">0.39</td></tr><tr><td align=\\\"left\\\">EC (μS/cm)</td><td char=\\\".\\\" align=\\\"char\\\">2.041</td><td char=\\\".\\\" align=\\\"char\\\">0.49</td></tr><tr><td align=\\\"left\\\">Salinity (ppt)</td><td char=\\\".\\\" align=\\\"char\\\">1.852</td><td char=\\\".\\\" align=\\\"char\\\">0.54</td></tr><tr><td align=\\\"left\\\">Ca<sup>2+</sup> (mg/l)</td><td char=\\\".\\\" align=\\\"char\\\">2.632</td><td char=\\\".\\\" align=\\\"char\\\">0.38</td></tr><tr><td align=\\\"left\\\">Mg<sup>2+</sup> (mg/l)</td><td char=\\\".\\\" align=\\\"char\\\">1.818</td><td char=\\\".\\\" align=\\\"char\\\">0.55</td></tr><tr><td align=\\\"left\\\">Na<sup>+</sup> (mg/l)</td><td char=\\\".\\\" align=\\\"char\\\">2.128</td><td char=\\\".\\\" align=\\\"char\\\">0.47</td></tr><tr><td align=\\\"left\\\">K<sup>+</sup> (mg/l)</td><td char=\\\".\\\" align=\\\"char\\\">2.564</td><td char=\\\".\\\" align=\\\"char\\\">0.39</td></tr><tr><td align=\\\"left\\\">Cl<sup>−</sup> (mg/l)</td><td char=\\\".\\\" align=\\\"char\\\">1.852</td><td char=\\\".\\\" align=\\\"char\\\">0.54</td></tr><tr><td align=\\\"left\\\">HCO<sub>3</sub><sup>−</sup> (mg/l)</td><td char=\\\".\\\" align=\\\"char\\\">2.381</td><td char=\\\".\\\" align=\\\"char\\\">0.42</td></tr><tr><td align=\\\"left\\\">NO<sub>3</sub><sup>−</sup> (mg/l)</td><td char=\\\".\\\" align=\\\"char\\\">2.778</td><td char=\\\".\\\" align=\\\"char\\\">0.36</td></tr><tr><td align=\\\"left\\\">SO<sub>4</sub><sup>2−</sup> (mg/l)</td><td char=\\\".\\\" align=\\\"char\\\">1.852</td><td char=\\\".\\\" align=\\\"char\\\">0.54</td></tr><tr><td align=\\\"left\\\">PO<sub>4</sub><sup>2−</sup> (mg/l)</td><td char=\\\".\\\" align=\\\"char\\\">2.083</td><td char=\\\".\\\" align=\\\"char\\\">0.48</td></tr><tr><td align=\\\"left\\\">F<sup>−</sup> (mg/l)</td><td char=\\\".\\\" align=\\\"char\\\">1.724</td><td char=\\\".\\\" align=\\\"char\\\">0.58</td></tr><tr><td align=\\\"left\\\">As (μg/l)</td><td char=\\\".\\\" align=\\\"char\\\">2.326</td><td char=\\\".\\\" align=\\\"char\\\">0.43</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab2\\\"><label>Table 2</label><caption><p>Correlationship test among the selected factors.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" colspan=\\\"17\\\">Correlations</th></tr><tr><th align=\\\"left\\\">Factors</th><th align=\\\"left\\\">Depth (m)</th><th align=\\\"left\\\">Temperature (°C)</th><th align=\\\"left\\\">pH</th><th align=\\\"left\\\">EC (μS/cm)</th><th align=\\\"left\\\">Salinity (ppt)</th><th align=\\\"left\\\">Ca<sup>2+</sup> (mg/l)</th><th align=\\\"left\\\">Mg<sup>2+</sup> (mg/l)</th><th align=\\\"left\\\">Na<sup>+</sup> (mg/l)</th><th align=\\\"left\\\">K<sup>+</sup> (mg/l)</th><th align=\\\"left\\\">Cl<sup>−</sup> (mg/l)</th><th align=\\\"left\\\">HCO<sub>3</sub><sup>−</sup> (mg/l)</th><th align=\\\"left\\\">NO<sub>3</sub><sup>−</sup> (mg/l)</th><th align=\\\"left\\\">SO<sub>4</sub><sup>2−</sup> (mg/l)</th><th align=\\\"left\\\">PO<sub>4</sub><sup>2−</sup> (mg/l)</th><th align=\\\"left\\\">F<sup>−</sup> (mg/l)</th><th align=\\\"left\\\">As (μg/l)</th></tr></thead><tbody><tr><td align=\\\"left\\\">Depth</td><td align=\\\"left\\\">1</td><td char=\\\".\\\" align=\\\"char\\\">0.192**</td><td align=\\\"left\\\">− 0.048</td><td align=\\\"left\\\">0.044</td><td align=\\\"left\\\">− 0.014</td><td align=\\\"left\\\">− 0.009</td><td align=\\\"left\\\">− 0.011</td><td align=\\\"left\\\">0.040</td><td align=\\\"left\\\">0.025</td><td align=\\\"left\\\">0.044</td><td align=\\\"left\\\">0.033</td><td align=\\\"left\\\">− 0.063</td><td align=\\\"left\\\">− 0.009</td><td align=\\\"left\\\">0.015</td><td align=\\\"left\\\">0.414**</td><td align=\\\"left\\\">0.027</td></tr><tr><td align=\\\"left\\\">pH</td><td align=\\\"left\\\">− 0.048</td><td char=\\\".\\\" align=\\\"char\\\">− 0.102*</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">− 0.264**</td><td align=\\\"left\\\">− 0.299**</td><td align=\\\"left\\\">− 0.213**</td><td align=\\\"left\\\">− 0.271**</td><td align=\\\"left\\\">− 0.329**</td><td align=\\\"left\\\">− 0.108*</td><td align=\\\"left\\\">− 0.300**</td><td align=\\\"left\\\">0.172**</td><td align=\\\"left\\\">− 0.068</td><td align=\\\"left\\\">− 0.054</td><td align=\\\"left\\\">− 0.012</td><td align=\\\"left\\\">0.046</td><td align=\\\"left\\\">− 0.164**</td></tr><tr><td align=\\\"left\\\">EC</td><td align=\\\"left\\\">0.044</td><td char=\\\".\\\" align=\\\"char\\\">− 0.157**</td><td align=\\\"left\\\">− 0.264**</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0.835**</td><td align=\\\"left\\\">0.251**</td><td align=\\\"left\\\">0.551**</td><td align=\\\"left\\\">0.541**</td><td align=\\\"left\\\">0.384**</td><td align=\\\"left\\\">0.842**</td><td align=\\\"left\\\">0.016</td><td align=\\\"left\\\">0.250**</td><td align=\\\"left\\\">0.143**</td><td align=\\\"left\\\">0.087*</td><td align=\\\"left\\\">0.239**</td><td align=\\\"left\\\">0.176**</td></tr><tr><td align=\\\"left\\\">Salinity</td><td align=\\\"left\\\">− 0.014</td><td char=\\\".\\\" align=\\\"char\\\">0.079</td><td align=\\\"left\\\">− 0.299**</td><td align=\\\"left\\\">0.835**</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0.303**</td><td align=\\\"left\\\">0.584**</td><td align=\\\"left\\\">0.615**</td><td align=\\\"left\\\">0.237**</td><td align=\\\"left\\\">0.861**</td><td align=\\\"left\\\">− 0.014</td><td align=\\\"left\\\">0.294**</td><td align=\\\"left\\\">0.101*</td><td align=\\\"left\\\">0.092*</td><td align=\\\"left\\\">0.187**</td><td align=\\\"left\\\">0.188**</td></tr><tr><td align=\\\"left\\\">Ca<sup>2+</sup> </td><td align=\\\"left\\\">− 0.009</td><td char=\\\".\\\" align=\\\"char\\\">0.071</td><td align=\\\"left\\\">− 0.213**</td><td align=\\\"left\\\">0.251**</td><td align=\\\"left\\\">0.303**</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0.366**</td><td align=\\\"left\\\">0.302**</td><td align=\\\"left\\\">0.142**</td><td align=\\\"left\\\">0.313**</td><td align=\\\"left\\\">− 0.273**</td><td align=\\\"left\\\">0.055</td><td align=\\\"left\\\">0.169**</td><td align=\\\"left\\\">− 0.088*</td><td align=\\\"left\\\">− 0.040</td><td align=\\\"left\\\">0.075</td></tr><tr><td align=\\\"left\\\">Mg<sup>2+</sup> </td><td align=\\\"left\\\">− 0.011</td><td char=\\\".\\\" align=\\\"char\\\">0.164**</td><td align=\\\"left\\\">− 0.271**</td><td align=\\\"left\\\">0.551**</td><td align=\\\"left\\\">0.584**</td><td align=\\\"left\\\">0.366**</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0.587**</td><td align=\\\"left\\\">0.264**</td><td align=\\\"left\\\">0.675**</td><td align=\\\"left\\\">− 0.106*</td><td align=\\\"left\\\">0.112**</td><td align=\\\"left\\\">0.146**</td><td align=\\\"left\\\">0.095*</td><td align=\\\"left\\\">− 0.061</td><td align=\\\"left\\\">0.213**</td></tr><tr><td align=\\\"left\\\">Na<sup>+</sup> </td><td align=\\\"left\\\">0.040</td><td char=\\\".\\\" align=\\\"char\\\">0.129**</td><td align=\\\"left\\\">− 0.329**</td><td align=\\\"left\\\">0.541**</td><td align=\\\"left\\\">0.615**</td><td align=\\\"left\\\">0.302**</td><td align=\\\"left\\\">0.587**</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0.267**</td><td align=\\\"left\\\">0.633**</td><td align=\\\"left\\\">0.011</td><td align=\\\"left\\\">0.239**</td><td align=\\\"left\\\">0.127**</td><td align=\\\"left\\\">0.130**</td><td align=\\\"left\\\">− 0.001</td><td align=\\\"left\\\">0.289**</td></tr><tr><td align=\\\"left\\\">K<sup>+</sup> </td><td align=\\\"left\\\">0.025</td><td char=\\\".\\\" align=\\\"char\\\">0.132**</td><td align=\\\"left\\\">− 0.108*</td><td align=\\\"left\\\">0.384**</td><td align=\\\"left\\\">0.237**</td><td align=\\\"left\\\">0.142**</td><td align=\\\"left\\\">0.264**</td><td align=\\\"left\\\">0.267**</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0.469**</td><td align=\\\"left\\\">− 0.125**</td><td align=\\\"left\\\">0.180**</td><td align=\\\"left\\\">− 0.040</td><td align=\\\"left\\\">− 0.041</td><td align=\\\"left\\\">0.059</td><td align=\\\"left\\\">0.027</td></tr><tr><td align=\\\"left\\\">Cl<sup>−</sup></td><td align=\\\"left\\\">0.044</td><td char=\\\".\\\" align=\\\"char\\\">0.168**</td><td align=\\\"left\\\">− 0.300**</td><td align=\\\"left\\\">0.842**</td><td align=\\\"left\\\">0.861**</td><td align=\\\"left\\\">0.313**</td><td align=\\\"left\\\">0.675**</td><td align=\\\"left\\\">0.633**</td><td align=\\\"left\\\">0.469**</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">− 0.088*</td><td align=\\\"left\\\">0.280**</td><td align=\\\"left\\\">0.178**</td><td align=\\\"left\\\">0.083</td><td align=\\\"left\\\">0.192**</td><td align=\\\"left\\\">0.204**</td></tr><tr><td align=\\\"left\\\">HCO<sub>3</sub><sup>−</sup></td><td align=\\\"left\\\">0.033</td><td char=\\\".\\\" align=\\\"char\\\">− 0.022</td><td align=\\\"left\\\">0.172**</td><td align=\\\"left\\\">0.016</td><td align=\\\"left\\\">− 0.014</td><td align=\\\"left\\\">− 0.273**</td><td align=\\\"left\\\">− 0.106*</td><td align=\\\"left\\\">0.011</td><td align=\\\"left\\\">− 0.125**</td><td align=\\\"left\\\">− 0.088*</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0.001</td><td align=\\\"left\\\">0.014</td><td align=\\\"left\\\">0.179**</td><td align=\\\"left\\\">0.197**</td><td align=\\\"left\\\">− 0.061</td></tr><tr><td align=\\\"left\\\">NO<sub>3</sub><sup>−</sup></td><td align=\\\"left\\\">− 0.063</td><td char=\\\".\\\" align=\\\"char\\\">− 0.029</td><td align=\\\"left\\\">− 0.068</td><td align=\\\"left\\\">0.250**</td><td align=\\\"left\\\">0.294**</td><td align=\\\"left\\\">0.055</td><td align=\\\"left\\\">0.112**</td><td align=\\\"left\\\">0.239**</td><td align=\\\"left\\\">0.180**</td><td align=\\\"left\\\">0.280**</td><td align=\\\"left\\\">0.001</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">− 0.029</td><td align=\\\"left\\\">0.049</td><td align=\\\"left\\\">− 0.035</td><td align=\\\"left\\\">0.010</td></tr><tr><td align=\\\"left\\\">SO<sub>4</sub><sup>2−</sup></td><td align=\\\"left\\\">− 0.009</td><td char=\\\".\\\" align=\\\"char\\\">0.010</td><td align=\\\"left\\\">− 0.054</td><td align=\\\"left\\\">0.143**</td><td align=\\\"left\\\">0.101*</td><td align=\\\"left\\\">0.169**</td><td align=\\\"left\\\">0.146**</td><td align=\\\"left\\\">0.127**</td><td align=\\\"left\\\">− 0.040</td><td align=\\\"left\\\">0.178**</td><td align=\\\"left\\\">0.014</td><td align=\\\"left\\\">− 0.029</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">− 0.031</td><td align=\\\"left\\\">0.130**</td><td align=\\\"left\\\">− 0.006</td></tr><tr><td align=\\\"left\\\">PO<sub>4</sub><sup>2−</sup></td><td align=\\\"left\\\">0.015</td><td char=\\\".\\\" align=\\\"char\\\">− 0.005</td><td align=\\\"left\\\">− 0.012</td><td align=\\\"left\\\">0.087*</td><td align=\\\"left\\\">0.092*</td><td align=\\\"left\\\">− 0.088*</td><td align=\\\"left\\\">0.095*</td><td align=\\\"left\\\">0.130**</td><td align=\\\"left\\\">− 0.041</td><td align=\\\"left\\\">0.083</td><td align=\\\"left\\\">0.179**</td><td align=\\\"left\\\">0.049</td><td align=\\\"left\\\">− 0.031</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0.005</td><td align=\\\"left\\\">0.192**</td></tr><tr><td align=\\\"left\\\">F<sup>−</sup></td><td align=\\\"left\\\">0.414**</td><td char=\\\".\\\" align=\\\"char\\\">0.125**</td><td align=\\\"left\\\">0.046</td><td align=\\\"left\\\">0.239**</td><td align=\\\"left\\\">0.187**</td><td align=\\\"left\\\">− 0.040</td><td align=\\\"left\\\">− 0.061</td><td align=\\\"left\\\">− 0.001</td><td align=\\\"left\\\">0.059</td><td align=\\\"left\\\">0.192**</td><td align=\\\"left\\\">0.197**</td><td align=\\\"left\\\">− 0.035</td><td align=\\\"left\\\">0.130**</td><td align=\\\"left\\\">0.005</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">− 0.104*</td></tr><tr><td align=\\\"left\\\">As</td><td align=\\\"left\\\">0.027</td><td char=\\\".\\\" align=\\\"char\\\">0.019</td><td align=\\\"left\\\">− 0.164**</td><td align=\\\"left\\\">0.176**</td><td align=\\\"left\\\">0.188**</td><td align=\\\"left\\\">0.075</td><td align=\\\"left\\\">0.213**</td><td align=\\\"left\\\">0.289**</td><td align=\\\"left\\\">0.027</td><td align=\\\"left\\\">0.204**</td><td align=\\\"left\\\">− 0.061</td><td align=\\\"left\\\">0.010</td><td align=\\\"left\\\">− 0.006</td><td align=\\\"left\\\">0.192**</td><td align=\\\"left\\\">− 0.104*</td><td align=\\\"left\\\">1</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab3\\\"><label>Table 3</label><caption><p>Variables importance of selected factors through applied three models.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Factors</th><th align=\\\"left\\\">LR</th><th align=\\\"left\\\">RF</th><th align=\\\"left\\\">ANN</th></tr></thead><tbody><tr><td align=\\\"left\\\">NO<sub>3</sub></td><td char=\\\".\\\" align=\\\"char\\\">0.44</td><td char=\\\".\\\" align=\\\"char\\\">0.35</td><td char=\\\".\\\" align=\\\"char\\\">0.41</td></tr><tr><td align=\\\"left\\\">AS</td><td char=\\\".\\\" align=\\\"char\\\">0.69</td><td char=\\\".\\\" align=\\\"char\\\">0.72</td><td char=\\\".\\\" align=\\\"char\\\">0.78</td></tr><tr><td align=\\\"left\\\">PO<sub>4</sub><sup>2−</sup></td><td char=\\\".\\\" align=\\\"char\\\">0.32</td><td char=\\\".\\\" align=\\\"char\\\">0.21</td><td char=\\\".\\\" align=\\\"char\\\">0.38</td></tr><tr><td align=\\\"left\\\">SO<sub>4</sub><sup>2−</sup></td><td char=\\\".\\\" align=\\\"char\\\">0.2</td><td char=\\\".\\\" align=\\\"char\\\">0.22</td><td char=\\\".\\\" align=\\\"char\\\">0.19</td></tr><tr><td align=\\\"left\\\">Salinity</td><td char=\\\".\\\" align=\\\"char\\\">0.32</td><td char=\\\".\\\" align=\\\"char\\\">0.24</td><td char=\\\".\\\" align=\\\"char\\\">0.31</td></tr><tr><td align=\\\"left\\\">HCO<sub>3</sub></td><td char=\\\".\\\" align=\\\"char\\\">0.54</td><td char=\\\".\\\" align=\\\"char\\\">0.55</td><td char=\\\".\\\" align=\\\"char\\\">0.67</td></tr><tr><td align=\\\"left\\\">CI</td><td char=\\\".\\\" align=\\\"char\\\">0.41</td><td char=\\\".\\\" align=\\\"char\\\">0.38</td><td char=\\\".\\\" align=\\\"char\\\">0.47</td></tr><tr><td align=\\\"left\\\">K+ </td><td char=\\\".\\\" align=\\\"char\\\">0.24</td><td char=\\\".\\\" align=\\\"char\\\">0.25</td><td char=\\\".\\\" align=\\\"char\\\">0.28</td></tr><tr><td align=\\\"left\\\">Na<sup>+</sup> </td><td char=\\\".\\\" align=\\\"char\\\">0.77</td><td char=\\\".\\\" align=\\\"char\\\">0.84</td><td char=\\\".\\\" align=\\\"char\\\">0.88</td></tr><tr><td align=\\\"left\\\">Mg<sup>2+</sup></td><td char=\\\".\\\" align=\\\"char\\\">0.58</td><td char=\\\".\\\" align=\\\"char\\\">0.54</td><td char=\\\".\\\" align=\\\"char\\\">0.51</td></tr><tr><td align=\\\"left\\\">F<sup>−</sup></td><td char=\\\".\\\" align=\\\"char\\\">0.74</td><td char=\\\".\\\" align=\\\"char\\\">0.77</td><td char=\\\".\\\" align=\\\"char\\\">0.81</td></tr><tr><td align=\\\"left\\\">pH</td><td char=\\\".\\\" align=\\\"char\\\">0.21</td><td char=\\\".\\\" align=\\\"char\\\">0.28</td><td char=\\\".\\\" align=\\\"char\\\">0.24</td></tr><tr><td align=\\\"left\\\">EC</td><td char=\\\".\\\" align=\\\"char\\\">0.31</td><td char=\\\".\\\" align=\\\"char\\\">0.39</td><td char=\\\".\\\" align=\\\"char\\\">0.33</td></tr><tr><td align=\\\"left\\\">Depth</td><td char=\\\".\\\" align=\\\"char\\\">0.4</td><td char=\\\".\\\" align=\\\"char\\\">0.41</td><td char=\\\".\\\" align=\\\"char\\\">0.41</td></tr><tr><td align=\\\"left\\\">Ca<sup>2+</sup></td><td char=\\\".\\\" align=\\\"char\\\">0.33</td><td char=\\\".\\\" align=\\\"char\\\">0.31</td><td char=\\\".\\\" align=\\\"char\\\">0.35</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab4\\\"><label>Table 4</label><caption><p>Evaluation metrices of applied models.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" rowspan=\\\"2\\\">Models</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Stage</th><th align=\\\"left\\\" colspan=\\\"5\\\">Parameters</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Kappa</th><th align=\\\"left\\\" rowspan=\\\"2\\\">F-Score</th></tr><tr><th align=\\\"left\\\">Sensitivity</th><th align=\\\"left\\\">Specificity</th><th align=\\\"left\\\">PPV</th><th align=\\\"left\\\">NPV</th><th align=\\\"left\\\">AUC</th></tr></thead><tbody><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">LR</td><td align=\\\"left\\\">Train</td><td char=\\\".\\\" align=\\\"char\\\">0.741</td><td char=\\\".\\\" align=\\\"char\\\">0.749</td><td char=\\\".\\\" align=\\\"char\\\">0.752</td><td char=\\\".\\\" align=\\\"char\\\">0.721</td><td char=\\\".\\\" align=\\\"char\\\">0.749</td><td char=\\\".\\\" align=\\\"char\\\">0.532</td><td char=\\\".\\\" align=\\\"char\\\">0.811</td></tr><tr><td align=\\\"left\\\">Validation</td><td char=\\\".\\\" align=\\\"char\\\">0.721</td><td char=\\\".\\\" align=\\\"char\\\">0.722</td><td char=\\\".\\\" align=\\\"char\\\">0.711</td><td char=\\\".\\\" align=\\\"char\\\">0.701</td><td char=\\\".\\\" align=\\\"char\\\">0.712</td><td char=\\\".\\\" align=\\\"char\\\">0.513</td><td char=\\\".\\\" align=\\\"char\\\">0.792</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">RF</td><td align=\\\"left\\\">Train</td><td char=\\\".\\\" align=\\\"char\\\">0.821</td><td char=\\\".\\\" align=\\\"char\\\">0.812</td><td char=\\\".\\\" align=\\\"char\\\">0.813</td><td char=\\\".\\\" align=\\\"char\\\">0.825</td><td char=\\\".\\\" align=\\\"char\\\">0.817</td><td char=\\\".\\\" align=\\\"char\\\">0.63</td><td char=\\\".\\\" align=\\\"char\\\">0.863</td></tr><tr><td align=\\\"left\\\">Validation</td><td char=\\\".\\\" align=\\\"char\\\">0.782</td><td char=\\\".\\\" align=\\\"char\\\">0.751</td><td char=\\\".\\\" align=\\\"char\\\">0.794</td><td char=\\\".\\\" align=\\\"char\\\">0.744</td><td char=\\\".\\\" align=\\\"char\\\">0.792</td><td char=\\\".\\\" align=\\\"char\\\">0.601</td><td char=\\\".\\\" align=\\\"char\\\">0.841</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">ANN</td><td align=\\\"left\\\">Train</td><td char=\\\".\\\" align=\\\"char\\\">0.907</td><td char=\\\".\\\" align=\\\"char\\\">0.903</td><td char=\\\".\\\" align=\\\"char\\\">0.901</td><td char=\\\".\\\" align=\\\"char\\\">0.901</td><td char=\\\".\\\" align=\\\"char\\\">0.912</td><td char=\\\".\\\" align=\\\"char\\\">0.651</td><td char=\\\".\\\" align=\\\"char\\\">0.912</td></tr><tr><td align=\\\"left\\\">Validation</td><td char=\\\".\\\" align=\\\"char\\\">0.889</td><td char=\\\".\\\" align=\\\"char\\\">0.891</td><td char=\\\".\\\" align=\\\"char\\\">0.883</td><td char=\\\".\\\" align=\\\"char\\\">0.885</td><td char=\\\".\\\" align=\\\"char\\\">0.902</td><td char=\\\".\\\" align=\\\"char\\\">0.643</td><td char=\\\".\\\" align=\\\"char\\\">0.882</td></tr></tbody></table></table-wrap>\"\n]","isConversational":false},"formula":{"kind":"list like","value":["<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{TOL}}=1-{{\\text{R}}}_{{\\text{j}}}^{2}$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:mtext>TOL</mml:mtext><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msubsup><mml:mtext>R</mml:mtext><mml:mrow><mml:mtext>j</mml:mtext></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{VIF}}=\\frac{1}{{\\text{TOL}}}$$\\end{document}</tex-math><mml:math id=\"M4\" display=\"block\"><mml:mrow><mml:mtext>VIF</mml:mtext><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mtext>TOL</mml:mtext></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\text{R}}}_{{\\text{j}}}^{2}$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:msubsup><mml:mtext>R</mml:mtext><mml:mrow><mml:mtext>j</mml:mtext></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f(z)=\\frac{1}{1+{e}^{-z}}$$\\end{document}</tex-math><mml:math id=\"M8\" display=\"block\"><mml:mrow><mml:mi>f</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>z</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mi>z</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$z$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:mi>z</mml:mi></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{z}}=\\mathrm{\\alpha }+{\\upbeta }_{1}{{\\text{X}}}_{1}+{\\upbeta }_{2}{{\\text{X}}}_{2}+\\dots \\mathrm{\\beta nXn}$$\\end{document}</tex-math><mml:math id=\"M12\" display=\"block\"><mml:mrow><mml:mtext>z</mml:mtext><mml:mo>=</mml:mo><mml:mi>α</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi mathvariant=\"normal\">β</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:msub><mml:mtext>X</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi mathvariant=\"normal\">β</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mtext>X</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mo>⋯</mml:mo><mml:mrow><mml:mi>β</mml:mi><mml:mi mathvariant=\"normal\">nXn</mml:mi></mml:mrow></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{\\alpha }$$\\end{document}</tex-math><mml:math id=\"M14\"><mml:mi>α</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\upbeta }_{1}, {\\upbeta }_{2},\\dots \\mathrm{ \\beta n}$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:mrow><mml:msub><mml:mi mathvariant=\"normal\">β</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi mathvariant=\"normal\">β</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mo>⋯</mml:mo><mml:mrow><mml:mi>β</mml:mi><mml:mi mathvariant=\"normal\">n</mml:mi></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\text{X}}}_{1}, {{\\text{X}}}_{2}, \\dots {\\text{Xn}}$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:mrow><mml:msub><mml:mtext>X</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mtext>X</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mo>⋯</mml:mo><mml:mtext>Xn</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ5\"><label>5</label><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$h\\left(x,{i}_{k}\\right),k=\\mathrm{1,2},\\dots n$$\\end{document}</tex-math><mml:math id=\"M20\" display=\"block\"><mml:mrow><mml:mi>h</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>i</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:mfenced><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow><mml:mo>,</mml:mo><mml:mo>⋯</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${i}_{k}$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:msub><mml:mi>i</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ6\"><label>6</label><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$GE={P}_{x,y}\\left(mg\\left(x,y\\right)<0\\right)$$\\end{document}</tex-math><mml:math id=\"M24\" display=\"block\"><mml:mrow><mml:mi>G</mml:mi><mml:mi>E</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>P</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>m</mml:mi><mml:mi>g</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mfenced><mml:mo><</mml:mo><mml:mn>0</mml:mn></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ7\"><label>7</label><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$mg\\left(x,y\\right)={av}_{k}I\\left({h}_{k}\\left(x\\right)=y\\right)-{max}_{j\\ne i}{av}_{k}I\\left({h}_{k}\\left(x\\right)=j\\right)$$\\end{document}</tex-math><mml:math id=\"M26\" display=\"block\"><mml:mrow><mml:mi>m</mml:mi><mml:mi>g</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant=\"italic\">av</mml:mi></mml:mrow><mml:mi>k</mml:mi></mml:msub><mml:mi>I</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:msub><mml:mi>h</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>x</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mi>y</mml:mi></mml:mfenced><mml:mo>-</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant=\"italic\">max</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mo>≠</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant=\"italic\">av</mml:mi></mml:mrow><mml:mi>k</mml:mi></mml:msub><mml:mi>I</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:msub><mml:mi>h</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>x</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mi>j</mml:mi></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ8\"><label>8</label><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${net}_{j}^{l}\\left(t\\right)=\\sum_{i=o}^{p}({y}_{i}^{i-1}\\left(t\\right){w}_{ji}^{l}(t))$$\\end{document}</tex-math><mml:math id=\"M28\" display=\"block\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant=\"italic\">net</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mi>o</mml:mi></mml:mrow><mml:mi>p</mml:mi></mml:munderover><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msubsup><mml:mi>y</mml:mi><mml:mrow><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ji</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ9\"><label>9</label><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${y}_{j}^{l}\\left(t\\right)=f({net}_{j}^{\\left(l\\right)}\\left(t\\right)$$\\end{document}</tex-math><mml:math id=\"M30\" display=\"block\"><mml:mrow><mml:msubsup><mml:mi>y</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mrow><mml:mo>=</mml:mo><mml:mi>f</mml:mi><mml:mo stretchy=\"false\">(</mml:mo></mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant=\"italic\">net</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mi>l</mml:mi></mml:mfenced></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ10\"><label>10</label><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$f\\left(net\\right)=\\frac{1}{1+{e}^{(-net)}}$$\\end{document}</tex-math><mml:math id=\"M32\" display=\"block\"><mml:mrow><mml:mi>f</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>-</mml:mo><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>t</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ11\"><label>11</label><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${e}_{j}\\left(t\\right)={c}_{j}\\left(t\\right)-{a}_{j}\\left(t\\right)$$\\end{document}</tex-math><mml:math id=\"M34\" display=\"block\"><mml:mrow><mml:msub><mml:mi>e</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:msub><mml:mi>c</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>-</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ12\"><label>12</label><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\delta }_{j}^{l}\\left(t\\right)={e}_{j}^{l}\\left(t\\right){a}_{j}\\left(t\\right)\\left[1-{a}_{j}x\\left(t\\right)\\right]$$\\end{document}</tex-math><mml:math id=\"M36\" display=\"block\"><mml:mrow><mml:msubsup><mml:mi>δ</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:msubsup><mml:mi>e</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:msub><mml:mi>a</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mfenced close=\"]\" open=\"[\"><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mi>x</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\delta$$\\end{document}</tex-math><mml:math id=\"M38\"><mml:mi>δ</mml:mi></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ13\"><label>13</label><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\delta }_{j}^{l}\\left(t\\right)={y}_{j}^{l}\\left(t\\right)\\left[1-{y}_{j}\\left(t\\right)\\right]\\sum {\\delta }_{j}^{l}\\left(t\\right){w}_{kj}^{\\left(l+1\\right)}\\left(t\\right)$$\\end{document}</tex-math><mml:math id=\"M40\" display=\"block\"><mml:mrow><mml:msubsup><mml:mi>δ</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:msubsup><mml:mi>y</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mfenced close=\"]\" open=\"[\"><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced></mml:mfenced><mml:mo>∑</mml:mo><mml:msubsup><mml:mi>δ</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">kj</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mi>l</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mfenced></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\delta$$\\end{document}</tex-math><mml:math id=\"M42\"><mml:mi>δ</mml:mi></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ14\"><label>14</label><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${w}_{ji}^{l}\\left(t+1\\right)={w}_{ji}^{l}\\left(t\\right)+\\alpha \\left[{w}_{ji}^{l}\\left(t\\right)-{w}_{ji}^{l}\\left(t-1\\right)\\right]+n{\\delta }_{j}^{\\left(l\\right)}\\left(t\\right){y}_{j}^{\\left(l-1\\right)}\\left(t\\right)$$\\end{document}</tex-math><mml:math id=\"M44\" display=\"block\"><mml:mrow><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ji</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mfenced><mml:mo>=</mml:mo><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ji</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>+</mml:mo><mml:mi>α</mml:mi><mml:mfenced close=\"]\" open=\"[\"><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ji</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>-</mml:mo><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ji</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mfenced></mml:mfenced><mml:mo>+</mml:mo><mml:mi>n</mml:mi><mml:msubsup><mml:mi>δ</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mi>l</mml:mi></mml:mfenced></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced><mml:msubsup><mml:mi>y</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mi>l</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mfenced></mml:msubsup><mml:mfenced close=\")\" open=\"(\"><mml:mi>t</mml:mi></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M45\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\alpha$$\\end{document}</tex-math><mml:math id=\"M46\"><mml:mi>α</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M47\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$n$$\\end{document}</tex-math><mml:math id=\"M48\"><mml:mi>n</mml:mi></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ15\"><label>15</label><alternatives><tex-math id=\"M49\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Sensitivity= {\\text{TP}}/({\\text{TP}}+{\\text{FN}})$$\\end{document}</tex-math><mml:math id=\"M50\" display=\"block\"><mml:mrow><mml:mi>S</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>v</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>y</mml:mi><mml:mo>=</mml:mo><mml:mtext>TP</mml:mtext><mml:mo stretchy=\"false\">/</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mtext>TP</mml:mtext><mml:mo>+</mml:mo><mml:mtext>FN</mml:mtext><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ16\"><label>16</label><alternatives><tex-math id=\"M51\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Specificity= {\\text{TN}}/({\\text{FP}}+{\\text{TN}})$$\\end{document}</tex-math><mml:math id=\"M52\" display=\"block\"><mml:mrow><mml:mi>S</mml:mi><mml:mi>p</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>y</mml:mi><mml:mo>=</mml:mo><mml:mtext>TN</mml:mtext><mml:mo stretchy=\"false\">/</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mtext>FP</mml:mtext><mml:mo>+</mml:mo><mml:mtext>TN</mml:mtext><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ17\"><label>17</label><alternatives><tex-math id=\"M53\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$PPV=\\frac{TP}{FP+TP}$$\\end{document}</tex-math><mml:math id=\"M54\" display=\"block\"><mml:mrow><mml:mi>P</mml:mi><mml:mi>P</mml:mi><mml:mi>V</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi mathvariant=\"italic\">TP</mml:mi></mml:mrow><mml:mrow><mml:mi>F</mml:mi><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mi>T</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ18\"><label>18</label><alternatives><tex-math id=\"M55\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$NPV=\\frac{TN}{TN+FN}$$\\end{document}</tex-math><mml:math id=\"M56\" display=\"block\"><mml:mrow><mml:mi>N</mml:mi><mml:mi>P</mml:mi><mml:mi>V</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi mathvariant=\"italic\">TN</mml:mi></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:mi>N</mml:mi><mml:mo>+</mml:mo><mml:mi>F</mml:mi><mml:mi>N</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ19\"><label>19</label><alternatives><tex-math id=\"M57\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$AUC=\\frac{(\\sum TP+\\sum TN)}{(P+N)}$$\\end{document}</tex-math><mml:math id=\"M58\" display=\"block\"><mml:mrow><mml:mi>A</mml:mi><mml:mi>U</mml:mi><mml:mi>C</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>∑</mml:mo><mml:mi>T</mml:mi><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mo>∑</mml:mo><mml:mi>T</mml:mi><mml:mi>N</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mi>N</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ20\"><label>20</label><alternatives><tex-math id=\"M59\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Precession=\\frac{{\\text{TP}}}{{\\text{TP}}+{\\text{FP}}}$$\\end{document}</tex-math><mml:math id=\"M60\" display=\"block\"><mml:mrow><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mtext>TP</mml:mtext><mml:mrow><mml:mtext>TP</mml:mtext><mml:mo>+</mml:mo><mml:mtext>FP</mml:mtext></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ21\"><label>21</label><alternatives><tex-math id=\"M61\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Recall=\\frac{{\\text{TP}}}{{\\text{TP}}+{\\text{FN}}}$$\\end{document}</tex-math><mml:math id=\"M62\" display=\"block\"><mml:mrow><mml:mi>R</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>l</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mtext>TP</mml:mtext><mml:mrow><mml:mtext>TP</mml:mtext><mml:mo>+</mml:mo><mml:mtext>FN</mml:mtext></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ22\"><label>22</label><alternatives><tex-math id=\"M63\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$F score=2* \\frac{Precession*Recall}{Precession+Recall}$$\\end{document}</tex-math><mml:math id=\"M64\" display=\"block\"><mml:mrow><mml:mi>F</mml:mi><mml:mi>s</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn><mml:mrow/><mml:mo>∗</mml:mo><mml:mfrac><mml:mrow><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>R</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>+</mml:mo><mml:mi>R</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ23\"><label>23</label><alternatives><tex-math id=\"M65\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$k= \\frac{{P}_{o}-{P}_{e}}{1-{P}_{e}}$$\\end{document}</tex-math><mml:math id=\"M66\" display=\"block\"><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi>o</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>P</mml:mi><mml:mi>e</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msub><mml:mi>P</mml:mi><mml:mi>e</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M67\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${P}_{o}$$\\end{document}</tex-math><mml:math id=\"M68\"><mml:msub><mml:mi>P</mml:mi><mml:mi>o</mml:mi></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M69\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${P}_{e}$$\\end{document}</tex-math><mml:math id=\"M70\"><mml:msub><mml:mi>P</mml:mi><mml:mi>e</mml:mi></mml:msub></mml:math></alternatives></inline-formula>"],"string":"[\n \"<disp-formula id=\\\"Equ1\\\"><label>1</label><alternatives><tex-math id=\\\"M1\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\text{TOL}}=1-{{\\\\text{R}}}_{{\\\\text{j}}}^{2}$$\\\\end{document}</tex-math><mml:math id=\\\"M2\\\" display=\\\"block\\\"><mml:mrow><mml:mtext>TOL</mml:mtext><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msubsup><mml:mtext>R</mml:mtext><mml:mrow><mml:mtext>j</mml:mtext></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ2\\\"><label>2</label><alternatives><tex-math id=\\\"M3\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\text{VIF}}=\\\\frac{1}{{\\\\text{TOL}}}$$\\\\end{document}</tex-math><mml:math id=\\\"M4\\\" display=\\\"block\\\"><mml:mrow><mml:mtext>VIF</mml:mtext><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mtext>TOL</mml:mtext></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq1\\\"><alternatives><tex-math id=\\\"M5\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${{\\\\text{R}}}_{{\\\\text{j}}}^{2}$$\\\\end{document}</tex-math><mml:math id=\\\"M6\\\"><mml:msubsup><mml:mtext>R</mml:mtext><mml:mrow><mml:mtext>j</mml:mtext></mml:mrow><mml:mn>2</mml:mn></mml:msubsup></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ3\\\"><label>3</label><alternatives><tex-math id=\\\"M7\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$f(z)=\\\\frac{1}{1+{e}^{-z}}$$\\\\end{document}</tex-math><mml:math id=\\\"M8\\\" display=\\\"block\\\"><mml:mrow><mml:mi>f</mml:mi><mml:mrow><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:mi>z</mml:mi><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mo>-</mml:mo><mml:mi>z</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq2\\\"><alternatives><tex-math id=\\\"M9\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$z$$\\\\end{document}</tex-math><mml:math id=\\\"M10\\\"><mml:mi>z</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ4\\\"><label>4</label><alternatives><tex-math id=\\\"M11\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\text{z}}=\\\\mathrm{\\\\alpha }+{\\\\upbeta }_{1}{{\\\\text{X}}}_{1}+{\\\\upbeta }_{2}{{\\\\text{X}}}_{2}+\\\\dots \\\\mathrm{\\\\beta nXn}$$\\\\end{document}</tex-math><mml:math id=\\\"M12\\\" display=\\\"block\\\"><mml:mrow><mml:mtext>z</mml:mtext><mml:mo>=</mml:mo><mml:mi>α</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mi mathvariant=\\\"normal\\\">β</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:msub><mml:mtext>X</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi mathvariant=\\\"normal\\\">β</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mtext>X</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mo>⋯</mml:mo><mml:mrow><mml:mi>β</mml:mi><mml:mi mathvariant=\\\"normal\\\">nXn</mml:mi></mml:mrow></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq3\\\"><alternatives><tex-math id=\\\"M13\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\mathrm{\\\\alpha }$$\\\\end{document}</tex-math><mml:math id=\\\"M14\\\"><mml:mi>α</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq4\\\"><alternatives><tex-math id=\\\"M15\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\upbeta }_{1}, {\\\\upbeta }_{2},\\\\dots \\\\mathrm{ \\\\beta n}$$\\\\end{document}</tex-math><mml:math id=\\\"M16\\\"><mml:mrow><mml:msub><mml:mi mathvariant=\\\"normal\\\">β</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi mathvariant=\\\"normal\\\">β</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mo>⋯</mml:mo><mml:mrow><mml:mi>β</mml:mi><mml:mi mathvariant=\\\"normal\\\">n</mml:mi></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq5\\\"><alternatives><tex-math id=\\\"M17\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${{\\\\text{X}}}_{1}, {{\\\\text{X}}}_{2}, \\\\dots {\\\\text{Xn}}$$\\\\end{document}</tex-math><mml:math id=\\\"M18\\\"><mml:mrow><mml:msub><mml:mtext>X</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mtext>X</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:mo>⋯</mml:mo><mml:mtext>Xn</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ5\\\"><label>5</label><alternatives><tex-math id=\\\"M19\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$h\\\\left(x,{i}_{k}\\\\right),k=\\\\mathrm{1,2},\\\\dots n$$\\\\end{document}</tex-math><mml:math id=\\\"M20\\\" display=\\\"block\\\"><mml:mrow><mml:mi>h</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>i</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:mfenced><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow><mml:mo>,</mml:mo><mml:mo>⋯</mml:mo><mml:mi>n</mml:mi></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq6\\\"><alternatives><tex-math id=\\\"M21\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${i}_{k}$$\\\\end{document}</tex-math><mml:math id=\\\"M22\\\"><mml:msub><mml:mi>i</mml:mi><mml:mi>k</mml:mi></mml:msub></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ6\\\"><label>6</label><alternatives><tex-math id=\\\"M23\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$GE={P}_{x,y}\\\\left(mg\\\\left(x,y\\\\right)<0\\\\right)$$\\\\end{document}</tex-math><mml:math id=\\\"M24\\\" display=\\\"block\\\"><mml:mrow><mml:mi>G</mml:mi><mml:mi>E</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>P</mml:mi><mml:mrow><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>m</mml:mi><mml:mi>g</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mfenced><mml:mo><</mml:mo><mml:mn>0</mml:mn></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ7\\\"><label>7</label><alternatives><tex-math id=\\\"M25\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$mg\\\\left(x,y\\\\right)={av}_{k}I\\\\left({h}_{k}\\\\left(x\\\\right)=y\\\\right)-{max}_{j\\\\ne i}{av}_{k}I\\\\left({h}_{k}\\\\left(x\\\\right)=j\\\\right)$$\\\\end{document}</tex-math><mml:math id=\\\"M26\\\" display=\\\"block\\\"><mml:mrow><mml:mi>m</mml:mi><mml:mi>g</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant=\\\"italic\\\">av</mml:mi></mml:mrow><mml:mi>k</mml:mi></mml:msub><mml:mi>I</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:msub><mml:mi>h</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>x</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mi>y</mml:mi></mml:mfenced><mml:mo>-</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant=\\\"italic\\\">max</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mo>≠</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant=\\\"italic\\\">av</mml:mi></mml:mrow><mml:mi>k</mml:mi></mml:msub><mml:mi>I</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:msub><mml:mi>h</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>x</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mi>j</mml:mi></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ8\\\"><label>8</label><alternatives><tex-math id=\\\"M27\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${net}_{j}^{l}\\\\left(t\\\\right)=\\\\sum_{i=o}^{p}({y}_{i}^{i-1}\\\\left(t\\\\right){w}_{ji}^{l}(t))$$\\\\end{document}</tex-math><mml:math id=\\\"M28\\\" display=\\\"block\\\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant=\\\"italic\\\">net</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mi>o</mml:mi></mml:mrow><mml:mi>p</mml:mi></mml:munderover><mml:mrow><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:msubsup><mml:mi>y</mml:mi><mml:mrow><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">ji</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mrow><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ9\\\"><label>9</label><alternatives><tex-math id=\\\"M29\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${y}_{j}^{l}\\\\left(t\\\\right)=f({net}_{j}^{\\\\left(l\\\\right)}\\\\left(t\\\\right)$$\\\\end{document}</tex-math><mml:math id=\\\"M30\\\" display=\\\"block\\\"><mml:mrow><mml:msubsup><mml:mi>y</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:mrow><mml:mo>=</mml:mo><mml:mi>f</mml:mi><mml:mo stretchy=\\\"false\\\">(</mml:mo></mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant=\\\"italic\\\">net</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>l</mml:mi></mml:mfenced></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ10\\\"><label>10</label><alternatives><tex-math id=\\\"M31\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$f\\\\left(net\\\\right)=\\\\frac{1}{1+{e}^{(-net)}}$$\\\\end{document}</tex-math><mml:math id=\\\"M32\\\" display=\\\"block\\\"><mml:mrow><mml:mi>f</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:mo>-</mml:mo><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>t</mml:mi><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ11\\\"><label>11</label><alternatives><tex-math id=\\\"M33\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${e}_{j}\\\\left(t\\\\right)={c}_{j}\\\\left(t\\\\right)-{a}_{j}\\\\left(t\\\\right)$$\\\\end{document}</tex-math><mml:math id=\\\"M34\\\" display=\\\"block\\\"><mml:mrow><mml:msub><mml:mi>e</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:msub><mml:mi>c</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>-</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ12\\\"><label>12</label><alternatives><tex-math id=\\\"M35\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\delta }_{j}^{l}\\\\left(t\\\\right)={e}_{j}^{l}\\\\left(t\\\\right){a}_{j}\\\\left(t\\\\right)\\\\left[1-{a}_{j}x\\\\left(t\\\\right)\\\\right]$$\\\\end{document}</tex-math><mml:math id=\\\"M36\\\" display=\\\"block\\\"><mml:mrow><mml:msubsup><mml:mi>δ</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:msubsup><mml:mi>e</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:msub><mml:mi>a</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:mfenced close=\\\"]\\\" open=\\\"[\\\"><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mi>x</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq7\\\"><alternatives><tex-math id=\\\"M37\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\delta$$\\\\end{document}</tex-math><mml:math id=\\\"M38\\\"><mml:mi>δ</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ13\\\"><label>13</label><alternatives><tex-math id=\\\"M39\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\delta }_{j}^{l}\\\\left(t\\\\right)={y}_{j}^{l}\\\\left(t\\\\right)\\\\left[1-{y}_{j}\\\\left(t\\\\right)\\\\right]\\\\sum {\\\\delta }_{j}^{l}\\\\left(t\\\\right){w}_{kj}^{\\\\left(l+1\\\\right)}\\\\left(t\\\\right)$$\\\\end{document}</tex-math><mml:math id=\\\"M40\\\" display=\\\"block\\\"><mml:mrow><mml:msubsup><mml:mi>δ</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>=</mml:mo><mml:msubsup><mml:mi>y</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:mfenced close=\\\"]\\\" open=\\\"[\\\"><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:msub><mml:mi>y</mml:mi><mml:mi>j</mml:mi></mml:msub><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced></mml:mfenced><mml:mo>∑</mml:mo><mml:msubsup><mml:mi>δ</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">kj</mml:mi></mml:mrow><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>l</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mfenced></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq8\\\"><alternatives><tex-math id=\\\"M41\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\delta$$\\\\end{document}</tex-math><mml:math id=\\\"M42\\\"><mml:mi>δ</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ14\\\"><label>14</label><alternatives><tex-math id=\\\"M43\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${w}_{ji}^{l}\\\\left(t+1\\\\right)={w}_{ji}^{l}\\\\left(t\\\\right)+\\\\alpha \\\\left[{w}_{ji}^{l}\\\\left(t\\\\right)-{w}_{ji}^{l}\\\\left(t-1\\\\right)\\\\right]+n{\\\\delta }_{j}^{\\\\left(l\\\\right)}\\\\left(t\\\\right){y}_{j}^{\\\\left(l-1\\\\right)}\\\\left(t\\\\right)$$\\\\end{document}</tex-math><mml:math id=\\\"M44\\\" display=\\\"block\\\"><mml:mrow><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">ji</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mfenced><mml:mo>=</mml:mo><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">ji</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>+</mml:mo><mml:mi>α</mml:mi><mml:mfenced close=\\\"]\\\" open=\\\"[\\\"><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">ji</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:mo>-</mml:mo><mml:msubsup><mml:mi>w</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">ji</mml:mi></mml:mrow><mml:mi>l</mml:mi></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mfenced></mml:mfenced><mml:mo>+</mml:mo><mml:mi>n</mml:mi><mml:msubsup><mml:mi>δ</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>l</mml:mi></mml:mfenced></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced><mml:msubsup><mml:mi>y</mml:mi><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>l</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn></mml:mfenced></mml:msubsup><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mi>t</mml:mi></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq9\\\"><alternatives><tex-math id=\\\"M45\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\alpha$$\\\\end{document}</tex-math><mml:math id=\\\"M46\\\"><mml:mi>α</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq10\\\"><alternatives><tex-math id=\\\"M47\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$n$$\\\\end{document}</tex-math><mml:math id=\\\"M48\\\"><mml:mi>n</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ15\\\"><label>15</label><alternatives><tex-math id=\\\"M49\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$Sensitivity= {\\\\text{TP}}/({\\\\text{TP}}+{\\\\text{FN}})$$\\\\end{document}</tex-math><mml:math id=\\\"M50\\\" display=\\\"block\\\"><mml:mrow><mml:mi>S</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>v</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>y</mml:mi><mml:mo>=</mml:mo><mml:mtext>TP</mml:mtext><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:mtext>TP</mml:mtext><mml:mo>+</mml:mo><mml:mtext>FN</mml:mtext><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ16\\\"><label>16</label><alternatives><tex-math id=\\\"M51\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$Specificity= {\\\\text{TN}}/({\\\\text{FP}}+{\\\\text{TN}})$$\\\\end{document}</tex-math><mml:math id=\\\"M52\\\" display=\\\"block\\\"><mml:mrow><mml:mi>S</mml:mi><mml:mi>p</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mi>i</mml:mi><mml:mi>c</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>y</mml:mi><mml:mo>=</mml:mo><mml:mtext>TN</mml:mtext><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:mtext>FP</mml:mtext><mml:mo>+</mml:mo><mml:mtext>TN</mml:mtext><mml:mo 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mathvariant=\\\"italic\\\">TP</mml:mi></mml:mrow><mml:mrow><mml:mi>F</mml:mi><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mi>T</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ18\\\"><label>18</label><alternatives><tex-math id=\\\"M55\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$NPV=\\\\frac{TN}{TN+FN}$$\\\\end{document}</tex-math><mml:math id=\\\"M56\\\" display=\\\"block\\\"><mml:mrow><mml:mi>N</mml:mi><mml:mi>P</mml:mi><mml:mi>V</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi 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J. Geosci.\"], \"year\": [\"2022\"], \"volume\": [\"15\"], \"fpage\": [\"1\"], \"lpage\": [\"13\"], \"pub-id\": [\"10.1007/s12517-021-08548-4\"]}, {\"label\": [\"3.\"], \"surname\": [\"Jaydhar\", \"ChandraPal\", \"Saha\", \"Islam\", \"Ruidas\"], \"given-names\": [\"AK\", \"S\", \"A\", \"ARMdT\", \"D\"], \"article-title\": [\"Hydrogeochemical evaluation and corresponding health risk from elevated arsenic and fluoride contamination in recurrent coastal multi-aquifers of eastern India\"], \"source\": [\"J. Clean. Prod.\"], \"year\": [\"2022\"], \"volume\": [\"369\"], \"fpage\": [\"133150\"], \"pub-id\": [\"10.1016/j.jclepro.2022.133150\"]}, {\"label\": [\"4.\"], \"surname\": [\"Ruidas\", \"Pal\", \"Md. Towfiqul Islam\", \"Saha\"], \"given-names\": [\"D\", \"SC\", \"AR\", \"A\"], \"article-title\": [\"Hydrogeochemical evaluation of groundwater aquifers and associated health hazard risk mapping using ensemble data driven model in a water Scares Plateau region of Eastern India\"], \"source\": [\"Expo Health\"], \"year\": [\"2022\"], \"pub-id\": [\"10.1007/s12403-022-00480-6\"]}, {\"label\": [\"5.\"], \"surname\": [\"Jayaprakash\", \"Giridharan\", \"Venugopal\", \"KrishnaKumar\", \"Periakali\"], \"given-names\": [\"M\", \"L\", \"T\", \"SP\", \"P\"], \"article-title\": [\"Characterization and evaluation of the factors affecting the geochemistry of groundwater in Neyveli, Tamil Nadu, India\"], \"source\": [\"Environ. Geol.\"], \"year\": [\"2008\"], \"volume\": [\"54\"], \"fpage\": [\"855\"], \"lpage\": [\"867\"], \"pub-id\": [\"10.1007/s00254-007-0868-6\"]}, {\"label\": [\"6.\"], \"surname\": [\"Khan\", \"Liaqat\", \"Mohamed\"], \"given-names\": [\"Q\", \"MU\", \"MM\"], \"article-title\": [\"A comparative assessment of modeling groundwater vulnerability using DRASTIC method from GIS and a novel classification method using machine learning classifiers\"], \"source\": [\"Geocarto Int.\"], \"year\": [\"2021\"], \"volume\": [\"20\"], \"fpage\": [\"1\"], \"lpage\": [\"19\"]}, {\"label\": [\"7.\"], \"surname\": [\"Kirlas\", \"Karpouzos\", \"Georgiou\", \"Katsifarakis\"], \"given-names\": [\"MC\", \"DK\", \"PE\", \"KL\"], \"article-title\": [\"A comparative study of groundwater vulnerability methods in a porous aquifer in Greece\"], \"source\": [\"Appl. 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Dev.\"], \"year\": [\"2022\"], \"volume\": [\"19\"], \"fpage\": [\"100810\"], \"pub-id\": [\"10.1016/j.gsd.2022.100810\"]}, {\"label\": [\"13.\"], \"surname\": [\"Mohamed\", \"Parimalarenganayaki\", \"Khan\", \"Murad\"], \"given-names\": [\"MM\", \"S\", \"Q\", \"A\"], \"article-title\": [\"Review on the use of environmental isotopes for groundwater recharge and evaporation studies in the GCC countries\"], \"source\": [\"Groundw. Sustain. Dev.\"], \"year\": [\"2021\"], \"volume\": [\"12\"], \"fpage\": [\"100546\"], \"pub-id\": [\"10.1016/j.gsd.2021.100546\"]}, {\"label\": [\"14.\"], \"surname\": [\"Pal\", \"Ruidas\", \"Saha\", \"Islam\", \"Chowdhuri\"], \"given-names\": [\"S\", \"D\", \"A\", \"ARMdT\", \"I\"], \"article-title\": [\"Application of novel data-mining technique-based nitrate concentration susceptibility prediction approach for coastal aquifers in India\"], \"source\": [\"J. Clean. 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Environ.\"], \"year\": [\"2020\"], \"volume\": [\"6\"], \"fpage\": [\"1451\"], \"lpage\": [\"1466\"], \"pub-id\": [\"10.1007/s40808-020-00761-6\"]}, {\"label\": [\"68.\"], \"surname\": [\"Worrall\", \"Kolpin\"], \"given-names\": [\"F\", \"DW\"], \"article-title\": [\"Direct assessment of groundwater vulnerability from single observations of multiple contaminants\"], \"source\": [\"Water Resour. Res.\"], \"year\": [\"2003\"], \"volume\": [\"39\"], \"fpage\": [\"25\"], \"pub-id\": [\"10.1029/2002WR001212\"]}, {\"label\": [\"69.\"], \"surname\": [\"Mohammaddost\", \"Mohammadi\", \"Rezaei\", \"Pourghasemi\", \"Farahmand\"], \"given-names\": [\"A\", \"Z\", \"M\", \"HR\", \"A\"], \"article-title\": [\"Assessment of groundwater vulnerability in an urban area: A comparative study based on DRASTIC, EBF, and LR models\"], \"source\": [\"Environ. Sci. Pollut. Res.\"], \"year\": [\"2022\"], \"volume\": [\"29\"], \"fpage\": [\"72908\"], \"lpage\": [\"72928\"], \"pub-id\": [\"10.1007/s11356-022-20767-0\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":71,"string":"71"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:56"},"citation":{"kind":"string","value":"Sci Rep. 2024 Jan 13; 14:1265"},"package_file":{"kind":"string","value":"oa_package/30/f4/PMC10787756.tar.gz"}}},{"rowIdx":87,"cells":{"accession_id":{"kind":"string","value":"PMC10787757"},"pmid":{"kind":"string","value":"38218904"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par2\">Burning biomass, including wood, leaves, grass, and other materials, is likely to be a contributor to air pollution in many parts of the world<sup>##REF##27908624##1##</sup>. The transition from a natural to an agricultural ecosystem resulted in the loss of 20–80 tons of carbon (C) per hectare, which had negative effects on the quality of water and soil as well as the productivity of biomass<sup>##REF##15192216##2##</sup>. To maintain sustainable agriculture and environment, actions must be taken to reduce C losses from soil to atmosphere. Thermal carbonization is one of the suitable options for mitigation of climate change and minimize C losses. Pyrolysis and hydrothermal carbonization usually use high-temperature processes for transforming biomass into solid materials such as biochar (BC) and hydrochar (HC)<sup>##UREF##0##3##–##REF##29091840##5##</sup>. The BC and HC have garnered global interest recently because of their considerable potential applications in various sectors, such as soil remediation, wastewater treatment, climate change mitigation, CO<sub>2</sub> capture, and energy storage<sup>##REF##26477952##6##–##UREF##3##10##</sup>.The C storage potential of BC and HC is a result of their high recalcitrance, which reduces the amount at which the photosynthetic processes of biomass fix C and release it into the atmosphere<sup>##UREF##4##11##,##UREF##5##12##</sup>. Also, aromatic ring structures in BC and HC make them more stable and stubborn material against thermal and microbial degradation<sup>##UREF##6##13##,##UREF##7##14##</sup>. Uncontrolled open burning of biomass releases harmful gases, enhancing environmental pollution. Therefore, converting biomass to BC can sequester C for 1000 years <sup>##UREF##8##15##</sup>. On the other hand, HC can sequester C lower than that of BC<sup>##REF##22751047##16##</sup>. It has been estimated that BC can sequester around 50% of the initial C<sup>##UREF##8##15##</sup>. Likewise, HC is also considered to be more stable than biomass<sup>##UREF##9##17##</sup>. The stability and recalcitrance of BC and HC are the most decisive factors that determine their C sequestration potential. Therefore, these properties of produced BC and HC depend on the type of feedstock, pyrolysis temperature, residence time, and pretreatment conditions<sup>##UREF##10##18##</sup>. The literature showed that the pyrolysis temperature, residence time, and heating rate are significantly correlated to pH, yield, surface area, fixed C, volatile matter, and ash content of biochar<sup>##UREF##10##18##</sup>. For instance, pyrolysis temperature is positively correlated with pH, surface area, microporous, and ash content, while having a negative relationship with yield, functional groups, and volatile matter <sup>##UREF##10##18##</sup>. Additionally, BC and HC could improve the water holding capacity, fertility of soil, soil nutrients, and productivity of agriculture, while reducing the greenhouse gases emissions. Current researches have indicated that BC and HC can be employed as inexpensive alternative sorbents to eliminate a range of different organic and inorganic contaminants from environments.</p>","<p id=\"Par3\">Typically, BC is produced from organic waste materials such as agricultural waste, municipal sewage sludge, and manure, etc. under limited oxygen supply during pyrolytic processes<sup>##UREF##11##19##–##UREF##13##22##</sup>, while Pauline and Joseph<sup>##REF##31455552##23##</sup> mentioned that HC is produced by controlling the carbonization of biomass through thermochemical decomposition under pressure in hot compressed water at 180–300 °C for many hours. In terms of their physical and chemical characteristics, BC and HC (both chars) differ from one another, which makes them highly adaptable tools in a wide range of industries and environments. Despite these advantages, some disadvantages are associated with such materials. For instance, degradation of cellulose and lignin may generate phenolic chemicals such as PAHs and dioxins during the thermochemical decomposition of biomass, which could make the resultant material toxic and potentially pose a risk to the soil biota if applied as soil amendment<sup>##UREF##14##24##,##UREF##15##25##</sup>. Hence, to minimize the negative impacts of HC and BC on soil biota, it is crucial to examine the compositions of ecotoxicology of BC and HC before their application as soil amendments. Washing char materials with deionized water is a common practice to minimize toxicity potential<sup>##REF##27567477##26##</sup>. Moreover, due to their surface characteristics and heterogeneous nature, BC and HC are not always successful<sup>##UREF##16##27##</sup>. Therefore, to enhance the physical and chemical characteristics of the charred materials, modification of the charred materials with other substances, such as acids, oxides, polymers etc. has been developed. Combining BC or HC with foreign materials may combine both materials' benefits, subsequently resulting in improved performance. However, in majority of cases, these materials could be either expensive, have a specific use<sup>##REF##23612156##28##</sup>, could cause secondary pollution<sup>##UREF##17##29##</sup>, have a short half-life (ozonation) <sup>##UREF##18##30##</sup>, or are complicated to prepare<sup>##REF##24560285##31##</sup>. Therefore, scientists are looking for cheaper, greener, and more efficient materials to modify BC and HC for better results and broad applications. Clay minerals are extensively employed in agriculture, industrial engineering, and for the exploration, extraction, and refinement of fuel. Some of the essential physical and chemical characteristics which contribute to making clay minerals desirable and beneficial are size of particle, particle shape, surface chemistry, and surface area<sup>##REF##28892700##32##</sup>. Kaolinite clay can assist as a potential candidate for this purpose due to its cost-effectiveness and abundance. In kaolinite clay minerals, one tetrahedral sheet is joined to one octahedral alumina sheet via oxygen atoms as layered silicate minerals<sup>##UREF##19##33##</sup>. Kaolinites have surface exchange sites, but no exchange sites exist between the layers<sup>##UREF##20##34##</sup>. Hence, using kaolinite clay minerals to modify the surface BC and HC could create new composites that can improve the performance of such composites for applications of environment<sup>##UREF##16##27##,##UREF##21##35##,##REF##23545188##36##</sup>. Combining BC or HC with kaolinite may improve the porous structure of the composite due to better distribution of the kaolinite particles on BC or HC matrix<sup>##UREF##22##37##</sup>. On the other hand, kaolinite is abundant, cheap, environment-friendly, and chemically more stable. Thus, the kaolinite-composited BC and HC could have superior possibilities for applications as sorbents and amendments to soil<sup>##UREF##23##38##–##UREF##25##40##</sup>. The previous studies showed that biochar composites with kaolinite significantly enhanced the treated soil’s organic matter, ammonical nitrogen content (NH<sub>4</sub>-N), and cation exchange capacity (CEC) compared to the control<sup>##UREF##26##41##</sup>. In addition, the soil's NH<sub>4</sub>-N, organic matter, and CEC increased with an increase kaolinite percentage in the biochar composites<sup>##UREF##26##41##</sup>. Furthermore, the plant root, shoot length, and biomass significantly increased compared to the control<sup>##UREF##26##41##</sup>. Another study by Qiu et al.<sup>##REF##31672351##42##</sup> revealed that the lowest toxic Cd concentration and the highest stable Cd concentration were detected in the treated soil with kaolinite biochar composite. They also mentioned that the kaolinite also enhanced the stability of biochar. Therefore, we hypothesized that compositing biochar/hydrochar with kaolinite clay minerals might combine both materials' benefits, consequently resulting in a stable and applicable material for environmental and agricultural applications.</p>","<p id=\"Par4\">To the best of our knowledge, very limited researches have been attempted to combine and compare the composites of BC and HC with kaolinite. Therefore, the main aims of this research were to: (1) synthesize low-cost composite materials of BC and HC with kaolinite natural deposits via pyrolysis and hydrothermal carbonization, (2) characterize the synthesized materials for chemical, proximate, elemental, and structural properties, and evaluate stability, and C sequestration potential, and (3) explore the potential toxicity of the synthesized materials by analyzing PAHs compounds and investigating impacts on maize (<italic>Zea mays</italic> L<italic>.</italic>) seed germination.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">Burning biomass, including wood, leaves, grass, and other materials, is likely to be a contributor to air pollution in many parts of the world<sup>##REF##27908624##1##</sup>. The transition from a natural to an agricultural ecosystem resulted in the loss of 20–80 tons of carbon (C) per hectare, which had negative effects on the quality of water and soil as well as the productivity of biomass<sup>##REF##15192216##2##</sup>. To maintain sustainable agriculture and environment, actions must be taken to reduce C losses from soil to atmosphere. Thermal carbonization is one of the suitable options for mitigation of climate change and minimize C losses. Pyrolysis and hydrothermal carbonization usually use high-temperature processes for transforming biomass into solid materials such as biochar (BC) and hydrochar (HC)<sup>##UREF##0##3##–##REF##29091840##5##</sup>. The BC and HC have garnered global interest recently because of their considerable potential applications in various sectors, such as soil remediation, wastewater treatment, climate change mitigation, CO<sub>2</sub> capture, and energy storage<sup>##REF##26477952##6##–##UREF##3##10##</sup>.The C storage potential of BC and HC is a result of their high recalcitrance, which reduces the amount at which the photosynthetic processes of biomass fix C and release it into the atmosphere<sup>##UREF##4##11##,##UREF##5##12##</sup>. Also, aromatic ring structures in BC and HC make them more stable and stubborn material against thermal and microbial degradation<sup>##UREF##6##13##,##UREF##7##14##</sup>. Uncontrolled open burning of biomass releases harmful gases, enhancing environmental pollution. Therefore, converting biomass to BC can sequester C for 1000 years <sup>##UREF##8##15##</sup>. On the other hand, HC can sequester C lower than that of BC<sup>##REF##22751047##16##</sup>. It has been estimated that BC can sequester around 50% of the initial C<sup>##UREF##8##15##</sup>. Likewise, HC is also considered to be more stable than biomass<sup>##UREF##9##17##</sup>. The stability and recalcitrance of BC and HC are the most decisive factors that determine their C sequestration potential. Therefore, these properties of produced BC and HC depend on the type of feedstock, pyrolysis temperature, residence time, and pretreatment conditions<sup>##UREF##10##18##</sup>. The literature showed that the pyrolysis temperature, residence time, and heating rate are significantly correlated to pH, yield, surface area, fixed C, volatile matter, and ash content of biochar<sup>##UREF##10##18##</sup>. For instance, pyrolysis temperature is positively correlated with pH, surface area, microporous, and ash content, while having a negative relationship with yield, functional groups, and volatile matter <sup>##UREF##10##18##</sup>. Additionally, BC and HC could improve the water holding capacity, fertility of soil, soil nutrients, and productivity of agriculture, while reducing the greenhouse gases emissions. Current researches have indicated that BC and HC can be employed as inexpensive alternative sorbents to eliminate a range of different organic and inorganic contaminants from environments.</p>\",\n \"<p id=\\\"Par3\\\">Typically, BC is produced from organic waste materials such as agricultural waste, municipal sewage sludge, and manure, etc. under limited oxygen supply during pyrolytic processes<sup>##UREF##11##19##–##UREF##13##22##</sup>, while Pauline and Joseph<sup>##REF##31455552##23##</sup> mentioned that HC is produced by controlling the carbonization of biomass through thermochemical decomposition under pressure in hot compressed water at 180–300 °C for many hours. In terms of their physical and chemical characteristics, BC and HC (both chars) differ from one another, which makes them highly adaptable tools in a wide range of industries and environments. Despite these advantages, some disadvantages are associated with such materials. For instance, degradation of cellulose and lignin may generate phenolic chemicals such as PAHs and dioxins during the thermochemical decomposition of biomass, which could make the resultant material toxic and potentially pose a risk to the soil biota if applied as soil amendment<sup>##UREF##14##24##,##UREF##15##25##</sup>. Hence, to minimize the negative impacts of HC and BC on soil biota, it is crucial to examine the compositions of ecotoxicology of BC and HC before their application as soil amendments. Washing char materials with deionized water is a common practice to minimize toxicity potential<sup>##REF##27567477##26##</sup>. Moreover, due to their surface characteristics and heterogeneous nature, BC and HC are not always successful<sup>##UREF##16##27##</sup>. Therefore, to enhance the physical and chemical characteristics of the charred materials, modification of the charred materials with other substances, such as acids, oxides, polymers etc. has been developed. Combining BC or HC with foreign materials may combine both materials' benefits, subsequently resulting in improved performance. However, in majority of cases, these materials could be either expensive, have a specific use<sup>##REF##23612156##28##</sup>, could cause secondary pollution<sup>##UREF##17##29##</sup>, have a short half-life (ozonation) <sup>##UREF##18##30##</sup>, or are complicated to prepare<sup>##REF##24560285##31##</sup>. Therefore, scientists are looking for cheaper, greener, and more efficient materials to modify BC and HC for better results and broad applications. Clay minerals are extensively employed in agriculture, industrial engineering, and for the exploration, extraction, and refinement of fuel. Some of the essential physical and chemical characteristics which contribute to making clay minerals desirable and beneficial are size of particle, particle shape, surface chemistry, and surface area<sup>##REF##28892700##32##</sup>. Kaolinite clay can assist as a potential candidate for this purpose due to its cost-effectiveness and abundance. In kaolinite clay minerals, one tetrahedral sheet is joined to one octahedral alumina sheet via oxygen atoms as layered silicate minerals<sup>##UREF##19##33##</sup>. Kaolinites have surface exchange sites, but no exchange sites exist between the layers<sup>##UREF##20##34##</sup>. Hence, using kaolinite clay minerals to modify the surface BC and HC could create new composites that can improve the performance of such composites for applications of environment<sup>##UREF##16##27##,##UREF##21##35##,##REF##23545188##36##</sup>. Combining BC or HC with kaolinite may improve the porous structure of the composite due to better distribution of the kaolinite particles on BC or HC matrix<sup>##UREF##22##37##</sup>. On the other hand, kaolinite is abundant, cheap, environment-friendly, and chemically more stable. Thus, the kaolinite-composited BC and HC could have superior possibilities for applications as sorbents and amendments to soil<sup>##UREF##23##38##–##UREF##25##40##</sup>. The previous studies showed that biochar composites with kaolinite significantly enhanced the treated soil’s organic matter, ammonical nitrogen content (NH<sub>4</sub>-N), and cation exchange capacity (CEC) compared to the control<sup>##UREF##26##41##</sup>. In addition, the soil's NH<sub>4</sub>-N, organic matter, and CEC increased with an increase kaolinite percentage in the biochar composites<sup>##UREF##26##41##</sup>. Furthermore, the plant root, shoot length, and biomass significantly increased compared to the control<sup>##UREF##26##41##</sup>. Another study by Qiu et al.<sup>##REF##31672351##42##</sup> revealed that the lowest toxic Cd concentration and the highest stable Cd concentration were detected in the treated soil with kaolinite biochar composite. They also mentioned that the kaolinite also enhanced the stability of biochar. Therefore, we hypothesized that compositing biochar/hydrochar with kaolinite clay minerals might combine both materials' benefits, consequently resulting in a stable and applicable material for environmental and agricultural applications.</p>\",\n \"<p id=\\\"Par4\\\">To the best of our knowledge, very limited researches have been attempted to combine and compare the composites of BC and HC with kaolinite. Therefore, the main aims of this research were to: (1) synthesize low-cost composite materials of BC and HC with kaolinite natural deposits via pyrolysis and hydrothermal carbonization, (2) characterize the synthesized materials for chemical, proximate, elemental, and structural properties, and evaluate stability, and C sequestration potential, and (3) explore the potential toxicity of the synthesized materials by analyzing PAHs compounds and investigating impacts on maize (<italic>Zea mays</italic> L<italic>.</italic>) seed germination.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Materials and methods</title>","<title>Synthesis of materials</title>","<title>Synthesis of kaolinite-biochar composite</title>","<p id=\"Par5\">Conocarpus waste was gathered from the King Saud University Campus in Riyadh, Saudi Arabia. The conocarpus waste was then washed, air-dried, ground, passed through a 1000 μm sieve, and termed as BM. Kaolinite deposits were obtained from the Al-Zobaira region in the Hael governorate (N: 27.916207, E: 43.711223). Kaolinite deposits were dried, ground, and washed with warm deionized water to remove gypsum. Later on, the kaolinite deposits were washed with deionized water and shaken many times for removal of soluble salts. Thereafter, the kaolinite deposits were dried at 105 °C for 4 h, and ground by grinder (Rotary Cup Mill (BICO), Sepor Company, India) to less than 100 μm size. The suspension of kaolinite was made by adding 0, 1, and 2 g of kaolinite powder to 500 mL of deionized water; the mixture was ultrasonically sonicated for 30 min with a frequency of 50 kHz (Ultrasonic sonicator Q700, Qsonica, Newtown, CT, USA). Subsequently, conocarpus BM (10 g) was immersed into the kaolinite suspension and stirred for 1 h. Following separation from the mixture, the kaolinite-BM was dried in an oven at 80 °C. Then, the kaolinite-BM was placed in a tightly sealed stainless-steel container (length: 22 cm and diameter: 7 cm), put in the muffle furnace (WiseTherm; Daihan Scientific, Gangwon-do, South Korea), and pyrolyzed at 600°C for 1 h under limited oxygen supply. The untreated feedstock was also used to prepare BC without kaolinite modification (i.e., pristine BC) with the same conditions of pyrolysis in the furnace. The pristine BC and kaolinite-BC composites were cooled and washed with deionized water many times for impurities removal, then dried in an oven, ground, sieved through a 100 μm screen, and stored in a container for further analyses. The synthesized pristine BC and kaolinite-modified BC composites with 10% and 20% kaolinite were henceforth referred to as BC, BCK10, and BCK20, respectively.</p>","<title>Synthesis of kaolinite-hydrochar composite</title>","<p id=\"Par6\">The dried kaolinite-BM mixture was used for the preparation of kaolinite-HC composite. 60 g of each kaolinite-BM mixture was added to 600 mL (1:10 v/v) DI water, stirred for 1 h, then placed in a tightly sealed stainless-steel container (length: 30 cm and diameter: 7 cm) and put in the muffle furnace (WiseTherm; Daihan Scientific, Gangwon-do, South Korea) at 200 °C for 6 h, then allowed to cool. The untreated feedstock was also used to prepare HC without kaolinite modification (i.e., pristine HC). After oven-drying, the pristine HC and kaolinite-HC composites were washed with deionized water many times for impurities removal, dried in an oven, ground in a mortar, sieved to pass 100 μm and stored in a container for further analyses. The synthesized pristine HC and kaolinite-modified HC composites with 10% and 20% of kaolinite were henceforth referred to as HC, HCK10, and HCK20, respectively.</p>","<title>Characterization of the synthesized materials</title>","<p id=\"Par7\">All synthesized materials, such as BM, BCs (BC, BCK10, and BCK20), and HCs (HC, HCK10, and HCK20) were conducted to various chemical, proximate, and ultimate analyses.</p>","<title>Yield, proximate and chemical analyses</title>","<p id=\"Par8\">Equation (##FORMU##0##1##) was applied to estimate the yield percentage of the synthesized materials:</p>","<p id=\"Par9\">The proximate analysis of synthesized materials was carried out to measure the percentage of moisture content, ash, volatile matter, and fixed carbon<sup>##REF##32105922##43##,##UREF##27##44##</sup>. The pH of the synthesized materials was measured at a 1:25 solid/water ratio<sup>##UREF##28##45##</sup>; EC was determined in the same ratio extraction. The method of ammonium acetate extraction was conducted to determine the CEC values of the synthesized materials<sup>##UREF##29##46##</sup>.</p>","<title>Ultimate analysis</title>","<p id=\"Par10\">The CHNS analyzer (PerkinElmer series II, Walttham, USA) was used for the ultimate analysis (indicated by elemental composition) of the synthesized materials to measure C, nitrogen (N), hydrogen (H), and sulfur (S). Equation (##FORMU##1##2##) was used to calculate the percentage of oxygen (O) in the synthesized materials:</p>","<p id=\"Par11\">According to the obtained results of elemental composition, the aromaticity and polarity index of the synthesized materials were also calculated using the elemental molar ratios of O/C and H/C.</p>","<title>XRD, SEM, FTIR, BET, TGA, zeta potential and hydrodynamic size analyses</title>","<p id=\"Par12\">X-ray diffraction (XRD) (MAXima X XRD-7000, Shimadzu, Japan) was employed to observe the mineralogical composition of the synthesized materials. The surface morphology and structural changes of synthesized materials were analyzed by capturing images on scanning electron microscopy (SEM) (SEM, EFI S50 Inspect, Netherlands). The aluminum stubs coated with adhesive carbon tape (12 mm; PELCO, UK) were used to spread the samples and then coated for 60 s with nano-gold particles using a 108 Auto/SE Sputter Coater (Ted Pella Inc. USA). The images were captured in a high vacuum at an acceleration voltage of 30 kV and a magnification of 3000. To analyze the functional groups of the synthesized materials, a Fourier Transform Infrared Spectrometer (FTIR, Bruker Alpha-Eco ATR-FTIR, Bruker Optics Inc) was employed. The Brunauer-Emmett-Taller (BET) method using a surface area and porosity analyzer (TriStar II 3020, Micromeritics, USA) was used to analyze the surface area, total pore volume, and pore size. Thermogravimetric analysis (TGA) was performed to display the weight loss of the synthesized materials (DTG-60H, Shimadzu, Japan). With temperature increases from 25 to 1000 °C, weight loss of the synthesized materials was observed. The zeta potential of the synthesized materials was measured using the techniques of dynamic light scattering by determining the electrophoretic mobility of 1 g L<sup>−1</sup> for the various particle suspensions with Zetasizer (Zetasizer Nano ZS, Malvern, UK). Using Laser Doppler Velocimetery (Zetasizer Nano ZS, Malvern, UK), the average hydrodynamic size of the synthesized material particles was determined in aqueous suspensions.</p>","<title>Estimation of thermal stability</title>","<p id=\"Par13\">Harvey et al.<sup>##REF##22242866##47##</sup> established the recalcitrance index (R<sub>50</sub>), which they calculated using moisture and ash free TGA analytical data to quantify the materials' relative thermal degradability by following Eq. (##FORMU##2##3##):where <italic>T</italic><sub><italic>50,x</italic></sub> and<italic> T</italic><sub><italic>50,graphite</italic></sub> are the temperatures of the moisture and ash corrected TGA thermograms of the synthesized materials and graphite, respectively (weight loss because of oxidation of C only), of which 50% of the weight is lost by oxidation or volatilization.</p>","<p id=\"Par14\">According to Harvey et al.<sup>##REF##22242866##47##</sup>, Eq. (##FORMU##3##4##) was used to correct the TGA thermograms for contents of moisture and ash:where <italic>W</italic><sub><italic>i,cor</italic></sub> and <italic>W</italic><sub><italic>i,uncor</italic></sub> represent, respectively, the corrected and uncorrected percent weight loss of the initial material, while <italic>W</italic><sub><italic>200,uncor</italic></sub> represents the initial material's weight loss in percentage up to 200 °C (which corresponds to water loss in the material). <italic>W</italic><sub><italic>cutoff,uncor</italic></sub> is the weight loss at the temperature where no more oxidation occurred.</p>","<p id=\"Par15\">Materials can be categorized into three groups based on R<sub>50</sub> values<sup>##REF##22242866##47##</sup>:<list list-type=\"bullet\"><list-item><p id=\"Par16\">R<sub>50</sub> ≥ 0.7 = highly recalcitrant</p></list-item><list-item><p id=\"Par17\">0.7 ˃ R<sub>50</sub> ≥ 0.5 = minimally degradable</p></list-item><list-item><p id=\"Par18\">R<sub>50</sub> < 0.5 = highly degradable</p></list-item></list></p>","<p id=\"Par19\">Equation (##FORMU##4##5##) provided by Zhao et al.<sup>##UREF##30##48##</sup> was used to calculate the percent carbon sequestration potential (CS).where C %<sub>material</sub> and C %<sub>feedstock</sub> are the percent carbon content of the material and feedstock, respectively.</p>","<title>PAHs analysis</title>","<p id=\"Par20\">The special extraction method provided by El-Saeid et al.<sup>##REF##36673970##49##</sup> was used by adding 1 g of material and 1 mL of DI water to a 50 mL centrifuge tube, after a short vortex, the mixture was allowed to homogenize for roughly 10 min. Each material received 6 mL of acetonitrile, and the sample was shaken for 5 min. Individual materials were placed in the centrifuge tube, and the Mylar pouch's citrate salts (ECQUEU750CT-MP) were added. Then, materials were shaken immediately for at least 2 min, followed by a 5 min and then centrifugation at ≥ 3500 rcf. The cleanup of materials was executed by transporting a 1.5 mL aliquot of supernatant to a 2 mL CUMPSC18CT (MgSO<sub>4</sub>, PSA, C18) dSPE tube. Next, vortexing of materials was carried out for 2 min and then centrifuged for 2 min at high rcf (i.e., ≥ 5000). The supernatant solution was immediately transferred into a 1.8 mL GC vial using a 0.2 µm syringe filter. Finally, PAHs in the solution were estimated by GC–MS/MSTQD.</p>","<p id=\"Par21\">The EPA method was applied to adjusted the GC MS/MS conditions (SVOC 8270). For the procedure development of GC MS/MS, the auto SRM was carried out. The improved procedure was divided into quantifier and qualifier ions to acquire good sensitivity. With a maximum of 51 transitions per segment, scanning was successfully completed in each segment (500–700 MS). Prior to each batch of analysis, MS was automatically tuned, while nitrogen and argon were employed as collision gases, and helium gas was employed as a carrier gas from a registered Linde gas (SiGas, Saudi Arabia). A total of 17 PAHs were analyzed from all synthesized materials.</p>","<title>Germination test</title>","<p id=\"Par22\">This study complies with national and international regulation and legislation for the maize (<italic>Zea mays</italic> L.) plant. The methods involved in this study are in accordance with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora. The International Biochar Initiative recommends a quick and easy germination inhibition analysis to determine whether there are any unfavorable substances in the synthesized materials. A germination experiment was conducted by growing plants in Petri plates to measure the toxic impacts of the synthesized materials. Briefly, filter papers (Whatman 42) were used to germinate maize seeds. The phototoxicity of the synthesized materials was assessed by comparing their germination results with those of the control (without any amendment). Briefly, the Petri plate size was (90 mm × 12mm) and the filter papers were cut to fit the Petri plate. After that, the Petri plates were soaked in 5 mL of DI water. Then, 0.2 and 0.4 g of pristine BC, HC, and kaolinite-synthesized BC and HC (BCK10, BCK20, HCK10, and HCK20) were sprayed separately on the particular filter paper. Next, 10 maize seeds were added on each filter paper. Subsequently, the Petri plates were covered and put in the darkness for 48 h at 25°C. Then, for the next 11 days, there were cycles of 16 h light and 8 h darkness. The number of seeds that germinated in each treatment was used to calculate the percentage of germination rate<sup>##REF##31048990##8##</sup>. Moreover, the fresh and dry weights of maize seedlings and shoot and root length were also measured. Triplicates of each treatment were carried out.</p>","<p id=\"Par23\">Equation (##FORMU##5##6##) of the germination index (GI) is as follows:</p>","<title>Statistical analysis</title>","<p id=\"Par24\">Using the Statistics 8.1 program, the obtained data were statistically analyzed<sup>##UREF##31##50##</sup>. Descriptive statistics were used to calculate the average and standard deviation. The least significant difference (LSD) test was applied to compare the treatment means, with a significant level of 0.05.</p>"],"string":"[\n \"<title>Materials and methods</title>\",\n \"<title>Synthesis of materials</title>\",\n \"<title>Synthesis of kaolinite-biochar composite</title>\",\n \"<p id=\\\"Par5\\\">Conocarpus waste was gathered from the King Saud University Campus in Riyadh, Saudi Arabia. The conocarpus waste was then washed, air-dried, ground, passed through a 1000 μm sieve, and termed as BM. Kaolinite deposits were obtained from the Al-Zobaira region in the Hael governorate (N: 27.916207, E: 43.711223). Kaolinite deposits were dried, ground, and washed with warm deionized water to remove gypsum. Later on, the kaolinite deposits were washed with deionized water and shaken many times for removal of soluble salts. Thereafter, the kaolinite deposits were dried at 105 °C for 4 h, and ground by grinder (Rotary Cup Mill (BICO), Sepor Company, India) to less than 100 μm size. The suspension of kaolinite was made by adding 0, 1, and 2 g of kaolinite powder to 500 mL of deionized water; the mixture was ultrasonically sonicated for 30 min with a frequency of 50 kHz (Ultrasonic sonicator Q700, Qsonica, Newtown, CT, USA). Subsequently, conocarpus BM (10 g) was immersed into the kaolinite suspension and stirred for 1 h. Following separation from the mixture, the kaolinite-BM was dried in an oven at 80 °C. Then, the kaolinite-BM was placed in a tightly sealed stainless-steel container (length: 22 cm and diameter: 7 cm), put in the muffle furnace (WiseTherm; Daihan Scientific, Gangwon-do, South Korea), and pyrolyzed at 600°C for 1 h under limited oxygen supply. The untreated feedstock was also used to prepare BC without kaolinite modification (i.e., pristine BC) with the same conditions of pyrolysis in the furnace. The pristine BC and kaolinite-BC composites were cooled and washed with deionized water many times for impurities removal, then dried in an oven, ground, sieved through a 100 μm screen, and stored in a container for further analyses. The synthesized pristine BC and kaolinite-modified BC composites with 10% and 20% kaolinite were henceforth referred to as BC, BCK10, and BCK20, respectively.</p>\",\n \"<title>Synthesis of kaolinite-hydrochar composite</title>\",\n \"<p id=\\\"Par6\\\">The dried kaolinite-BM mixture was used for the preparation of kaolinite-HC composite. 60 g of each kaolinite-BM mixture was added to 600 mL (1:10 v/v) DI water, stirred for 1 h, then placed in a tightly sealed stainless-steel container (length: 30 cm and diameter: 7 cm) and put in the muffle furnace (WiseTherm; Daihan Scientific, Gangwon-do, South Korea) at 200 °C for 6 h, then allowed to cool. The untreated feedstock was also used to prepare HC without kaolinite modification (i.e., pristine HC). After oven-drying, the pristine HC and kaolinite-HC composites were washed with deionized water many times for impurities removal, dried in an oven, ground in a mortar, sieved to pass 100 μm and stored in a container for further analyses. The synthesized pristine HC and kaolinite-modified HC composites with 10% and 20% of kaolinite were henceforth referred to as HC, HCK10, and HCK20, respectively.</p>\",\n \"<title>Characterization of the synthesized materials</title>\",\n \"<p id=\\\"Par7\\\">All synthesized materials, such as BM, BCs (BC, BCK10, and BCK20), and HCs (HC, HCK10, and HCK20) were conducted to various chemical, proximate, and ultimate analyses.</p>\",\n \"<title>Yield, proximate and chemical analyses</title>\",\n \"<p id=\\\"Par8\\\">Equation (##FORMU##0##1##) was applied to estimate the yield percentage of the synthesized materials:</p>\",\n \"<p id=\\\"Par9\\\">The proximate analysis of synthesized materials was carried out to measure the percentage of moisture content, ash, volatile matter, and fixed carbon<sup>##REF##32105922##43##,##UREF##27##44##</sup>. The pH of the synthesized materials was measured at a 1:25 solid/water ratio<sup>##UREF##28##45##</sup>; EC was determined in the same ratio extraction. The method of ammonium acetate extraction was conducted to determine the CEC values of the synthesized materials<sup>##UREF##29##46##</sup>.</p>\",\n \"<title>Ultimate analysis</title>\",\n \"<p id=\\\"Par10\\\">The CHNS analyzer (PerkinElmer series II, Walttham, USA) was used for the ultimate analysis (indicated by elemental composition) of the synthesized materials to measure C, nitrogen (N), hydrogen (H), and sulfur (S). Equation (##FORMU##1##2##) was used to calculate the percentage of oxygen (O) in the synthesized materials:</p>\",\n \"<p id=\\\"Par11\\\">According to the obtained results of elemental composition, the aromaticity and polarity index of the synthesized materials were also calculated using the elemental molar ratios of O/C and H/C.</p>\",\n \"<title>XRD, SEM, FTIR, BET, TGA, zeta potential and hydrodynamic size analyses</title>\",\n \"<p id=\\\"Par12\\\">X-ray diffraction (XRD) (MAXima X XRD-7000, Shimadzu, Japan) was employed to observe the mineralogical composition of the synthesized materials. The surface morphology and structural changes of synthesized materials were analyzed by capturing images on scanning electron microscopy (SEM) (SEM, EFI S50 Inspect, Netherlands). The aluminum stubs coated with adhesive carbon tape (12 mm; PELCO, UK) were used to spread the samples and then coated for 60 s with nano-gold particles using a 108 Auto/SE Sputter Coater (Ted Pella Inc. USA). The images were captured in a high vacuum at an acceleration voltage of 30 kV and a magnification of 3000. To analyze the functional groups of the synthesized materials, a Fourier Transform Infrared Spectrometer (FTIR, Bruker Alpha-Eco ATR-FTIR, Bruker Optics Inc) was employed. The Brunauer-Emmett-Taller (BET) method using a surface area and porosity analyzer (TriStar II 3020, Micromeritics, USA) was used to analyze the surface area, total pore volume, and pore size. Thermogravimetric analysis (TGA) was performed to display the weight loss of the synthesized materials (DTG-60H, Shimadzu, Japan). With temperature increases from 25 to 1000 °C, weight loss of the synthesized materials was observed. The zeta potential of the synthesized materials was measured using the techniques of dynamic light scattering by determining the electrophoretic mobility of 1 g L<sup>−1</sup> for the various particle suspensions with Zetasizer (Zetasizer Nano ZS, Malvern, UK). Using Laser Doppler Velocimetery (Zetasizer Nano ZS, Malvern, UK), the average hydrodynamic size of the synthesized material particles was determined in aqueous suspensions.</p>\",\n \"<title>Estimation of thermal stability</title>\",\n \"<p id=\\\"Par13\\\">Harvey et al.<sup>##REF##22242866##47##</sup> established the recalcitrance index (R<sub>50</sub>), which they calculated using moisture and ash free TGA analytical data to quantify the materials' relative thermal degradability by following Eq. (##FORMU##2##3##):where <italic>T</italic><sub><italic>50,x</italic></sub> and<italic> T</italic><sub><italic>50,graphite</italic></sub> are the temperatures of the moisture and ash corrected TGA thermograms of the synthesized materials and graphite, respectively (weight loss because of oxidation of C only), of which 50% of the weight is lost by oxidation or volatilization.</p>\",\n \"<p id=\\\"Par14\\\">According to Harvey et al.<sup>##REF##22242866##47##</sup>, Eq. (##FORMU##3##4##) was used to correct the TGA thermograms for contents of moisture and ash:where <italic>W</italic><sub><italic>i,cor</italic></sub> and <italic>W</italic><sub><italic>i,uncor</italic></sub> represent, respectively, the corrected and uncorrected percent weight loss of the initial material, while <italic>W</italic><sub><italic>200,uncor</italic></sub> represents the initial material's weight loss in percentage up to 200 °C (which corresponds to water loss in the material). <italic>W</italic><sub><italic>cutoff,uncor</italic></sub> is the weight loss at the temperature where no more oxidation occurred.</p>\",\n \"<p id=\\\"Par15\\\">Materials can be categorized into three groups based on R<sub>50</sub> values<sup>##REF##22242866##47##</sup>:<list list-type=\\\"bullet\\\"><list-item><p id=\\\"Par16\\\">R<sub>50</sub> ≥ 0.7 = highly recalcitrant</p></list-item><list-item><p id=\\\"Par17\\\">0.7 ˃ R<sub>50</sub> ≥ 0.5 = minimally degradable</p></list-item><list-item><p id=\\\"Par18\\\">R<sub>50</sub> < 0.5 = highly degradable</p></list-item></list></p>\",\n \"<p id=\\\"Par19\\\">Equation (##FORMU##4##5##) provided by Zhao et al.<sup>##UREF##30##48##</sup> was used to calculate the percent carbon sequestration potential (CS).where C %<sub>material</sub> and C %<sub>feedstock</sub> are the percent carbon content of the material and feedstock, respectively.</p>\",\n \"<title>PAHs analysis</title>\",\n \"<p id=\\\"Par20\\\">The special extraction method provided by El-Saeid et al.<sup>##REF##36673970##49##</sup> was used by adding 1 g of material and 1 mL of DI water to a 50 mL centrifuge tube, after a short vortex, the mixture was allowed to homogenize for roughly 10 min. Each material received 6 mL of acetonitrile, and the sample was shaken for 5 min. Individual materials were placed in the centrifuge tube, and the Mylar pouch's citrate salts (ECQUEU750CT-MP) were added. Then, materials were shaken immediately for at least 2 min, followed by a 5 min and then centrifugation at ≥ 3500 rcf. The cleanup of materials was executed by transporting a 1.5 mL aliquot of supernatant to a 2 mL CUMPSC18CT (MgSO<sub>4</sub>, PSA, C18) dSPE tube. Next, vortexing of materials was carried out for 2 min and then centrifuged for 2 min at high rcf (i.e., ≥ 5000). The supernatant solution was immediately transferred into a 1.8 mL GC vial using a 0.2 µm syringe filter. Finally, PAHs in the solution were estimated by GC–MS/MSTQD.</p>\",\n \"<p id=\\\"Par21\\\">The EPA method was applied to adjusted the GC MS/MS conditions (SVOC 8270). For the procedure development of GC MS/MS, the auto SRM was carried out. The improved procedure was divided into quantifier and qualifier ions to acquire good sensitivity. With a maximum of 51 transitions per segment, scanning was successfully completed in each segment (500–700 MS). Prior to each batch of analysis, MS was automatically tuned, while nitrogen and argon were employed as collision gases, and helium gas was employed as a carrier gas from a registered Linde gas (SiGas, Saudi Arabia). A total of 17 PAHs were analyzed from all synthesized materials.</p>\",\n \"<title>Germination test</title>\",\n \"<p id=\\\"Par22\\\">This study complies with national and international regulation and legislation for the maize (<italic>Zea mays</italic> L.) plant. The methods involved in this study are in accordance with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora. The International Biochar Initiative recommends a quick and easy germination inhibition analysis to determine whether there are any unfavorable substances in the synthesized materials. A germination experiment was conducted by growing plants in Petri plates to measure the toxic impacts of the synthesized materials. Briefly, filter papers (Whatman 42) were used to germinate maize seeds. The phototoxicity of the synthesized materials was assessed by comparing their germination results with those of the control (without any amendment). Briefly, the Petri plate size was (90 mm × 12mm) and the filter papers were cut to fit the Petri plate. After that, the Petri plates were soaked in 5 mL of DI water. Then, 0.2 and 0.4 g of pristine BC, HC, and kaolinite-synthesized BC and HC (BCK10, BCK20, HCK10, and HCK20) were sprayed separately on the particular filter paper. Next, 10 maize seeds were added on each filter paper. Subsequently, the Petri plates were covered and put in the darkness for 48 h at 25°C. Then, for the next 11 days, there were cycles of 16 h light and 8 h darkness. The number of seeds that germinated in each treatment was used to calculate the percentage of germination rate<sup>##REF##31048990##8##</sup>. Moreover, the fresh and dry weights of maize seedlings and shoot and root length were also measured. Triplicates of each treatment were carried out.</p>\",\n \"<p id=\\\"Par23\\\">Equation (##FORMU##5##6##) of the germination index (GI) is as follows:</p>\",\n \"<title>Statistical analysis</title>\",\n \"<p id=\\\"Par24\\\">Using the Statistics 8.1 program, the obtained data were statistically analyzed<sup>##UREF##31##50##</sup>. Descriptive statistics were used to calculate the average and standard deviation. The least significant difference (LSD) test was applied to compare the treatment means, with a significant level of 0.05.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results and discussion</title>","<title>Characterization</title>","<title>Proximate and chemical analyses</title>","<p id=\"Par25\">The results of the chemical and proximate analyses of the fabricated materials are presented in Table ##TAB##0##1##. The results revealed that the HC materials (HC, HCK10, and HCK20) had a higher yield than the BC materials (BC, BCK10, and BCK20). In addition, the highest yield was found in HCK20, while the lowest yield was found in BC. On the other hand, increased yield was associated with an increasing percentage of kaolinite, which was noticed in BCK20 and HCK20 compared to pristine. The yield order was in the order: HCK20 (64.35%) ˃ HCK10 (62.19%) ˃ HC (56.74%) ˃ BCK20 (36.84%) ˃ BCK10 (32.89%) ˃ BC (24.15%). Therefore, the HC materials showed a higher yield than the BC materials. The lower yield of pristine BC materials can be attributed to the higher volatile matter loss and a higher weight loss from biomass through pyrolysis than those of HC materials, which were made through hydrothermal carbonization in an airtight container. Hence, the higher yield of kaolinite-synthesized materials indicated more thermal stability than pristine. The highest yield of kaolinite-synthesized materials could be related to the highly resistant nature of kaolinite against thermal degradation<sup>##REF##31672351##42##</sup>. The volatiles decreased with an increasing percentage of kaolinite and carbonization. This reduction was 3.94 times for BC, 4.13 times for BCK10, and 4.07 times for BCK20 than that of BM, while 1.44 times for HC, 1.67 times for HCK10, and 2.02 times for HCK20 than that of BM. Likewise, the fixed C was higher in pristine BC and HC as compared to BC and HC-based materials. The highest fixed C was observed in BC (70.20%), while the lowest was in BCK20 (25.59%) among the synthesized materials. In all synthesized materials, BC materials were more recalcitrant than HC materials<sup>##REF##31048990##8##</sup>. The highest ash contents were found in BCK20, while the lowest were in HC. The ash contents increased with increased kaolinite percentage in synthesized BC and HC materials. The ash contents were increased by 2.43, 12.87, 18.15, 0.91, 6.35, and 9.46-folds in BC, BCK10, BCK20, HC, HCK10, and HCK20, respectively, as compared to BM, indicating the formation/condensation of the compounds of minerals in these materials through pyrolysis<sup>##REF##28337620##51##</sup>. In comparison to HC-based materials, the increased ash contents in BC-based materials were related to the thermal oxidation of organic compounds during the pyrolysis process<sup>##REF##31048990##8##</sup>. The BM displayed a higher moisture content of 2.73%, while it was higher in HCs (HC = 2.41%, HCK10 = 2.11%) and HCK20 = 1.46%) as compared to BCs (BC = 0.87%, BCK10 = 1.02%, and BCK20 = 0.41%). The HC materials had a higher moisture content due to being hydrothermally pretreated.</p>","<p id=\"Par26\">The finding displayed that the pH of BC materials elevated with an increasing kaolinite percentage during pyrolysis. In contrast, the pH of HC materials decreased with an increasing kaolinite percentage during hydrothermal carbonization. The highest pH value was 10.97 in BCK20, while the lowest was 4.48 in HCK20. The pH increased by 3.14, 4 and 5.7 units in BC, BCK10 and BCK20, respectively, compared with the BM, indicating the elimination of acidic functional groups and concentration of basic functional groups<sup>##REF##29220749##52##</sup>. Furthermore, with increasing the pyrolysis temperature, the recalcitrant cationic species (Ca<sup>+2</sup>, Mg<sup>+2</sup>, Na<sup>+</sup>) condensed in BC materials, which could also cause increased pH<sup>##REF##20634061##53##,##UREF##32##54##</sup>. On the other hand, the pH values of the HCK10 and HCK20 were decreased by only 0.09 and 0.83 units, respectively, as compared with BM, indicating the minimal removal of basic functional groups<sup>##REF##29220749##52##</sup>. The pH value of the HC increased by 0.91 units compared with the BM. Likewise, the EC of all materials decreased with pyrolysis and hydrothermal carbonization, which could be due to washing all the materials before analysis to remove surface basicity<sup>##UREF##33##55##</sup>, as well as the dissolved salts released into the liquid phase during hydrothermal carbonization<sup>##UREF##34##56##</sup>. The highest cation exchange capacity (CEC) was shown in HC (132.8 cmol kg<sup>−1</sup>), while the lowest CEC was in BC (16.3 cmol kg<sup>−1</sup>). With pyrolysis, the CEC was increased by 34.97% for BCK10 and 38.04% for BCK20 as compared to pristine BC, which is attributed to an increase in surface functional groups<sup>##UREF##35##57##</sup>. Contrarily, with hydrothermal carbonization, the CEC was decreased by 7.38% for HCK10 and 6.02% for HCK20 compared with HC. Nevertheless, the CEC for HC materials was several times higher than that for BC materials due to more oxygen-containing functional groups on the surface of HC materials<sup>##UREF##36##58##–##UREF##39##61##</sup>.</p>","<title>X-ray diffraction, SEM, and FTIR analyses</title>","<p id=\"Par27\">The XRD spectra of the BM, kaolinite deposits, and synthesized materials are shown in Fig. ##FIG##0##1##. The various visible peaks on the spectra of all the synthesized materials demonstrate the presence of crystalline minerals and inorganic materials. The XRD patterns of raw materials, such as kaolinite clay deposits and BM, were identified. In the XRD pattern of kaolinite deposits, the strongest intense peaks of kaolinite in XRD pattern of kaolinite deposits were identified at 2θ = 12.46°; 25.06° and 26.68° (Fig. ##FIG##0##1##a)<sup>##UREF##40##62##–##UREF##42##64##</sup> and lower diffraction kaolinite peaks than previous peaks were revealed at 2θ = 36.7°; 39.50°; 42.56°; 50.3°; 55.16° and 62.4°<sup>##UREF##43##65##–##REF##33567736##70##</sup>. Four peaks of cellulose in BM are displayed at = 21.8; 22.4°; 24.3° and 30°, peak of carbon-containing minerals mellite is at 2θ = 14.8°, and calcite at 2θ = 39.78° (Fig. ##FIG##0##1##a)<sup>##UREF##48##71##–##UREF##51##74##</sup>. The kaolinite peaks were found in BC-based materials and HC-based materials, which endorsed successfully implanted kaolinite onto the BC and HC matrix. Peak shifting is a sign of interactions between kaolinite and BC or HC during synthesizing the composite. Peaks of kaolinite at 2θ = 20.7°; 26.68°; 39.5° and 50.3° in BCK10 and BCK20 were shifted to 20.78°; 26.6°; 39.26° and 50°, respectively, and 2θ = 25.06° in BCK10 was shifted to 25.2°, and 2θ = 36.86° in BCK20 was shifted to 36.48° (Fig. ##FIG##0##1##b)<sup>##UREF##42##64##,##UREF##44##66##,##UREF##46##68##,##UREF##52##75##</sup>. Similarly, peaks of kaolinite at 2θ = 12.46°; 26.68°; 36.1°; 38° and 42.56° in HCK10 and HCK20 were shifted to 12.26°; 26.62°; 36.14°; 38.32° and 42.46°, respectively, and 2θ = 39.5° in HCK10 was shifted to 39.44°, and 2θ = 55.1° was shifted to 54.78° (Fig. ##FIG##0##1##b)<sup>##UREF##42##64##,##UREF##45##67##,##UREF##53##76##,##UREF##54##77##</sup>. The other peaks in BC-based materials were impurities corresponding to calcite and quartz; and cellulose and mellite for HC-based materials. The XRD analysis of BC (Fig. ##FIG##0##1##b) displayed peaks at 2θ = 23.04°, which indicated the presence of graphite<sup>##UREF##55##78##</sup>. Other peaks were identified at 2θ = 29.3°; 39.44°; 43.1°; 47.34° and 48.28°, and indicated the presence of calcite<sup>##REF##29220749##52##,##REF##30149311##79##,##REF##32685816##80##</sup>. Likewise, the peak of cellulose in HC is at 2θ = 22.4°<sup>##UREF##49##72##</sup> and the peak of mellite is at 2θ = 14.8° (Fig. ##FIG##0##1##c). In BC-based materials, mellite was lost during the pyrolysis process of BM, while it still exists with HCs. Therefore, the changes in the results of the kaolinite-synthesized BC and kaolinite-synthesized HC showed that the synthesis method used successfully implanted kaolinite onto the BC and HC matrix.</p>","<p id=\"Par28\">SEM images in Fig. ##FIG##1##2## depict the surface morphology of the synthesized materials. SEM images are highly useful to obtain minute details about the structure of synthesized materials and their modifications. In addition, comparing pristine BC and HC with their modified and raw materials would therefore allow us to make judgments on morphological changes during pyrolysis and hydrothermal carbonization. Pyrolysis and hydrothermal carbonization converted the crystalline surface of BM (Fig. ##FIG##1##2##a) into porous and amorphous materials, as presented in Fig. ##FIG##1##2##b–g. The surface area of the BC-based materials and HC-based materials was generally coated by thin film structures and were more irregular on the BC and HC surfaces (Fig. ##FIG##1##2##c–g), indicating that after zooming in at 3000 × magnification, the kaolinite well onto the surfaces and within the pores of BC and HC<sup>##REF##19150079##81##</sup>. The decomposition and volatilization of biomass caused a small number of pores with different sizes to appear in pristine BC and HC. In addition, the SEM images showed that kaolinite was not entirely covering the surfaces of the BC-based materials<sup>##UREF##56##82##,##UREF##57##83##</sup>, while kaolinite was entirely covering the surfaces of the HC-based materials.</p>","<p id=\"Par29\">According to Li et al.<sup>##REF##30423497##84##</sup>, the surface functional groups, particularly O-containing functional groups, may facilitate the chemical adsorption of BC and HC. Figure ##FIG##2##3## displays the FTIR spectra of synthesized materials in the range of 400–4400 cm<sup>−1</sup>. A broad band has been explored at 3300–3800 cm<sup>−1</sup> in BM, representing the presence of O–H bonding<sup>##UREF##58##85##</sup>, which continued to appear during hydrothermal carbonization and vanished during pyrolysis. The structural–functional groups found in the BC and HC materials included C=C, O–H, C–O, C=O, C–H, Si–O–Al, Si–O–Si, Si–O, N–H, C–OH, CH<sub>2</sub> and C–N. It is evident that some functional groups, such as C–H and C=O (between 1413 and 1462 cm<sup>−1</sup>) were shared by BC and HC materials<sup>##REF##31254497##86##</sup>, C–O and O–H (1033 cm<sup>-1</sup>)<sup>##UREF##59##87##</sup>. For BC-based materials, some peaks appeared with increasing kaolinite, such as; Si–O–Si groups at 465 and 469 cm<sup>−1</sup><sup>##UREF##60##88##</sup>, Si–O at 791 cm<sup>−1</sup><sup>##UREF##61##89##</sup> and C–O, C–N at 1083 cm<sup>−1</sup><sup>##UREF##62##90##</sup><sup>,</sup><sup>##REF##32122867##91##</sup>. Therefore, those peaks indicated that kaolinite was successfully loaded onto the BC matrix<sup>##UREF##63##92##</sup>. The HC materials showed more bands than BC materials, which could be due to minimal losses of functional groups. Likewise, some peaks appeared with increasing kaolinite, such as Si–O–Si at 469 cm<sup>−1</sup><sup>##UREF##58##85##</sup>, Si–O and Al–O vibrations at 762, 696 and 539 cm<sup>−1</sup><sup>##UREF##64##93##</sup>, Si–O at 784 cm<sup>−1</sup><sup>##UREF##65##94##</sup>. On the other hand, the same peaks appeared in HC, HCK10, and HCK20 composites, such as; C–O and O–H at 1033 cm<sup>−1</sup><sup>##UREF##59##87##</sup>, C–O–C at 1111 cm<sup>−1</sup><sup>##UREF##66##95##</sup>, CH<sub>3</sub> at 1440 cm<sup>−1</sup><sup>##UREF##67##96##</sup>, C=O at 1510 cm<sup>−1</sup>, C=O at 1510 cm<sup>−1</sup><sup>##REF##35580789##97##</sup>, COOH at 1700 cm<sup>−1</sup><sup>##UREF##68##98##</sup>, C–H at 2921 cm<sup>−1</sup><sup>##UREF##69##99##</sup>, and O–H (between 3300 and 3800 cm<sup>−1</sup>) <sup>##UREF##56##82##</sup>. Moreover, some bands were not found in BC and HC alone, but when composited with kaolinite, such bands were visible. Therefore, this finding can further assist in predicting the impact of kaolinite-synthesized BCs and HCs on the removal efficiency of these composites for various pollutants.</p>","<title>Size and surface characteristics</title>","<p id=\"Par30\">The BET surface area, pore size, and pore volume of the BM and synthesized materials are shown in Table ##TAB##1##2##. BC materials have a higher surface area than HC materials. The highest surface areas were found in BC (290.89 m<sup>2</sup> g<sup>−1</sup>), and the lowest in HC (5.32 m<sup>2</sup> g<sup>−1</sup>). Moreover, it was detected that the surface areas of the BCK10 and BCK20 (225.14 and 180.40 m<sup>2</sup> g<sup>−1</sup>) were reduced compared to the pristine BC (290.89 m<sup>2</sup> g<sup>−1</sup>), indicating that the pores on the BC might have been covered/clogged by kaolinite<sup>##UREF##24##39##</sup>. Conversely, the pristine HC suffered agglomeration and revealed a lower surface area<sup>##REF##35580789##97##,##REF##30609488##100##</sup>. Therefore, the surface area of the HCK10 and HCK20 was higher than that of HC. The surface area of the HCK10 and HCK20 was 16.11 and 15.44 m<sup>2</sup> g<sup>−1</sup>, which was threefold higher than the pristine HC (5.32 m<sup>2</sup> g<sup>−1</sup>). The composite interposes retained the kaolinite particles, increasing the surface area of kaolinite-HC<sup>##REF##32105922##43##,##REF##33773345##101##</sup>. Contrarily, the BCK10 and BCK20 pore sizes were 38.31 and 28.17 Å, which were higher as compared to BC (28.81Å). On the contrary, the pore sizes of HCK10 and HCK20 were 145.90 and 175.92 Å, which were lower as compared to HC (187.71 Å). The highest pore sizes was appeared in HC (187.71 Å), while the lowest was in BCK20 (28.17 Å). However, with increasing the amount of kaolinite deposit, the surface area decreased by 22.68% in BCK10 and 38% in BCK20 compared with pristine BC, while kaolinite addition increased the surface area of HCK10 by 203% and HCK20 by 190% as compared to pristine HC. Overall, the pore size of the HC materials was more than 10 times larger than that of the BC materials. The previous studies showed that the low surface area of zeolite and silica-composited BC is due to plugging of pores in the existence of minerals<sup>##REF##28337620##51##</sup>. Yao et al.<sup>##UREF##21##35##</sup> also mentioned that the blockage of BC pores by clay minerals particles could be the cause of the decreased surface area of clay biochar composites. The hydrodynamic size of the particles of the synthesized materials in aqueous suspensions was determined (Table ##TAB##1##2##). Any particle aggregate or particle with an equivalent diameter is seen by Dynamic Light Scattering (DLS) to reflect a similar size. The average size of particles (hydrodynamic size) was 2.63 μm for BC, 2.34 μm for BCK10, 2.73 μm for BCK20, 2.19 μm for HC, 2.38 μm for HCK10, and 3.10 μm for HCK20. These results of particle size analyses suggested that HC showed the minimum particle size, while HCK20 showed the maximum particle size.</p>","<p id=\"Par31\">Colloidal dispersions have an electrokinetic potential known as zeta potential, and the value of this potential is influenced by the surface charge of the individual particles. In our study, the zeta potential values of the synthesized materials were determined as a function of the solution pH (Table ##TAB##1##2##). The zeta potential of the pristine BC and BC-based materials ranged from − 25.06 to − 25.08 mV; however, the zeta potential of the pristine HC and HC-based materials ranged from − 21.68 to − 24.98 mV. The highest zeta potential was shown in BCK10 (− 25.08 mV), while the lowest was in HCK20 (− 21.86 mV). Consequently, the negative charge of pristine BC and BC-based materials is slightly higher than that of pristine HC and HC-based materials. Hence, the zeta potential of all the synthesized materials was negative, indicating that all surfaces of the synthesized materials are negatively charged. Therefore, a larger zeta potential means a larger negative charge, which is beneficial for remediation, especially with removing cationic ions.</p>","<title>Elemental composition and carbon stability</title>","<p id=\"Par32\">The elemental composition of the synthesized materials is presented in Table ##TAB##2##3##. In comparison to BM, thermal treatment enhanced the total C contents of BC and HC materials. An increased degree of carbonization may be the cause of rising C contents with pyrolysis and hydrothermal carbonization. The higher contents of C were observed in BC, and the lowest was observed in HCK20 among the synthesized materials. HC materials had higher H contents ranging from 5.33 to 5.54%, while BC materials ranged from 1.08 to 1.41%. Among the synthesized materials, the maximum N contents were in BCK20, while the minimum was in BC. However, the C contents of the BC, BCK10, BCK20, HC, HCK10, and HCK20 were increased by 47.8%, 44.1%, 43.6%, 18.3, 20.9%, and 17.8%, respectively, as compared to BM. The C content was higher in pristine BC, which decreased with kaolinite modification in BC-based materials, such as 6.59% in BCK10 and 7.32% in BCK20. Meanwhile, the C content was increased in HCK10 (3.37%) and slightly decreased in HCK20 (0.52%) compared to HC. The increasing lignin content in biomass has been mentioned to enhance carbonization and increase the content of C in BC<sup>##REF##25957037##102##,##UREF##70##103##,##UREF##72##105##</sup>. Other studies reported that cellulose and hemicelluloses also significantly affect on the C content<sup>##UREF##71##104##,##UREF##72##105##</sup>. On the other hand, a reduction of H contents in BC materials was more than that of HC materials. Compared to BM, the reduction of H contents for BC, BCK10, BCK20, HC, HCK10, and HCK20 was 76.3%, 78.4%, 82.0%, 10.8%, 7.3%, and 9.9%, respectively. According to previous studies, Gai et al. <sup>##REF##25469875##106##</sup> reported that the decrease in H content of BC was due to the loss of water, gaseous H<sub>2</sub>, hydrocarbons, and tarry vapors. The total N contents decreased with pyrolysis and hydrothermal carbonization in BC (27.3%), HC (3.1%), and HCK10 (11.9%), while it increased in BCK10 (21.5%), BCK20 (32.6%), and HCK20 (18.9%) compared to BM. Furthermore, the contents of N increased with increased kaolinite modification of BC and HC, which were higher in BCK20 and HCK20. The total O contents decreased with pyrolysis and hydrothermal carbonization of biomass in all materials. The reduction in O was observed in BC (97.1%), BCK10 (92.5%), BCK20 (94.5%), HC (27.1%), HCK10 (30.8%), and HCK20 (29.6%), compared to BM. Moreover, BC-based materials showed higher O contents compared to pristine BC, while HC-based materials slightly presented lower O contents compared to pristine HC. Dehydration, volatilization, and depolymerization could be the causes of the decrease in O contents with hydrothermal carbonization and pyrolysis<sup>##REF##23376202##107##</sup>.</p>","<p id=\"Par33\">Figure ##FIG##3##4## illustrates the Van Krevelen diagram, frequently used to calculate the molar O/C and H/C ratios to compute the BC and HC material’s recalcitrance. With pyrolysis and thermal carbonization, conocarpus waste biomass is dehydrated and depolymerized to produce smaller dissociation products<sup>##UREF##14##24##,##REF##20099810##108##</sup>. Reduced H/C and O/C molar ratios showed reduced polarity and a greater degree of aromaticity, which in turn increased the stability of BC<sup>##UREF##33##55##</sup>. Also, another research mentioned that the higher stability of BC composites is attributed to greater polyaromatic carbon content compared to HC composites<sup>##UREF##73##109##</sup>. From Table ##TAB##2##3## and Fig. ##FIG##3##4##, the surface polarity index (i.e., indicated by O/C molar ratio) of BC and HC materials decreased with pyrolysis and hydrothermal carbonization compared with BM, indicating a decrease in the hydrophobicity of these materials. Overall, the highest values of O/C and H/C (0.604 and 1.47) were found for the BM. Among the synthesized materials, maximum values of O/C and H/C (0.36 and 1.09) were expressed by HCK20 and HC, respectively. In any case, BC-based materials showed slightly decreased H/C values and increased O/C values compared to pristine BC. In contrast to pristine HC, HC-based materials showed slightly increased H/C values and decreased O/C values. The O/C ratio of BC (0.01), BCK10 (0.025), and BCK20 (0.02) was less than 0.2, suggesting more stability of such materials and a half-life of more than 1000 years<sup>##UREF##74##110##</sup>. In contrast, the O/C ratio of HC (0.360), HCK10 (0.331), and HCK20 (0.350) was between 0.2 and 0.6, suggesting a half-life ranging from 100 to 1000 years<sup>##UREF##74##110##</sup>. For BM, the O/C ratio was 0.604, which will probably possess a half-life of less than 100 years<sup>##UREF##74##110##</sup>). Likewise, low H/C values of BC and HC materials compared with BM indicated high aromaticity and reactivity<sup>##UREF##75##111##</sup>. In addition, the much lower H/C ratio of BC materials compared to HC materials demonstrated that BC materials were heavily carbonized and showed highly aromatic structures. Depending on IBI<sup>##UREF##76##112##</sup> criteria, the H/C < 0.7 showed greater combined aromatic ring structures for the BC materials, while the H/C molar ratio of HC materials is higher than that of BC materials, which was ˃ 0.7. Therefore, BC materials showed higher aromaticity and low polarity than that of HC materials. Hence, the ratios of H/C and O/C in the BC-based materials and HC-based materials indicated more aromatic C and becoming less hydrophilic<sup>##UREF##77##113##</sup>.</p>","<title>TGA and recalcitrance index (R<sub>50</sub>)</title>","<p id=\"Par34\">In this study, TGA thermogravimetric analysis was applied to investigate the long-term stability of synthesized materials. The results of TGA-DTG analysis are shown in Fig. ##FIG##4##5##a,b. Our results indicated that the BM thermally decomposed earlier than the HC and BC materials due to thermal instability. The thermogravimetric curves of the different BC materials and the HC materials exposed similar performances as regards weight loss (%) on a reducing trend with rising temperature. For BC, BCK10, and BCK20, the sudden weight loss began at 650 °C ≈ 700 °C, at 350 °C ≈ 400 °C for HC, HCK10, and HCK20, and at 250 °C ≈ 350 °C for BM. The thermograms displayed two general regions where weight loss has occurred: (i) around 300°C for BM and HC materials because of thermal degradation of cellulose and hemicellulose compounds<sup>##UREF##78##114##</sup> and (ii) around 600–1000 °C because of lignin degradation<sup>##REF##28337620##51##</sup>. The order of materials for degradability is as follows: BCK20 < BCK10 < BC < HCK20 < HCK10 < HC < BM. Therefore, the BC and HC with a 20% kaolinite ratio were more stable, followed by a 10% kaolinite ratio, followed by pristine. Moreover, a higher weight loss was observed in HC materials Than in BC materials.</p>","<p id=\"Par35\">The recalcitrance of the synthesized materials in soil depends on the potential of BC and HC materials to resist thermal, physical, and chemical degradation. The formation of organometallic complexes and the aromatic carbon structure play significant roles in BC and HC materials. Due to its increased aromaticity, the C of BC materials has a higher potential for recalcitrance than that of HC materials and BM; however, the recalcitrance and stability of C in the soil after adding BC and HC materials vary depending on the kind and feedstock composition, the texture and structure of the soil, and other environmental factors. Hence, the recalcitrance index of BC and HC materials is required to be determined in relative to graphite, because one of the most stable C forms is graphite<sup>##REF##35598667##115##</sup>. As a result, TGA thermograms that are marked as R<sub>50</sub> were used by Harvey et al. <sup>##REF##22242866##47##</sup> to create a novel recalcitrance index that predicts the materials' potential for recalcitrance.</p>","<p id=\"Par36\">To calculate the recalcitrant index (R<sub>50</sub>), TGA thermograms for all materials were corrected for moisture and ash contents, which reflect the stability of synthesized materials and the extent of C sequestration<sup>##REF##25203840##116##</sup>. Figure ##FIG##4##5##c,d display moisture and ash-corrected TGA thermograms. Therefore, the R<sub>50</sub> values of BM and HC in this study were 0.40 and 0.42, indicating that these are highly biodegradable and belong to class 3 (Table ##TAB##2##3##). BC, BCK10, and BCK20 were considered in class 1 with R<sub>50</sub> of 0.78, 0.81, and 0.79, respectively, indicating that these have high recalcitrance potential<sup>##REF##22242866##47##</sup>. The R<sub>50</sub> values of HCK10 and HCK20 composites were 0.50 and 0.51, suggesting they were minimally degradable. Increased R<sub>50</sub> in the BCK10 and BCK20 composites could be attributed to kaolinite's presence, which improved BC's oxidation resistance. The previous study by Ahmad et al.<sup>##REF##28337620##51##</sup> demonstrated that the higher R<sub>50</sub> in silica BC composites could be related to the probable protection of C by silica particles, which may be controlled through the pyrolysis process. The reason was also attributed to the change from the aromatic C–C/C=C functionality to the C–O and C–H configuration of the BC surface forming stable organo-mineral complexes (i.e., C–O–Al and C–O–Si) with clay minerals<sup>##REF##34695458##117##</sup>. The previous study by Wang et al.<sup>##REF##35598667##115##</sup> showed that the R<sub>50</sub> increased by 0.89 for kaolinite-composited BC, reflecting that minerals-composited BC could improve the thermal stability of BC. According to Ahmad et al.<sup>##UREF##75##111##</sup>, changes in structural arrangements within the silica-BC complex may be responsible for the high recalcitrance and C sequestration potential of silica-composited BC. Therefore, increased R<sub>50</sub> values of BC materials expect greater recalcitrance and stability because of the particular interactions of kaolinite with the BC matrix.</p>","<p id=\"Par37\">Although it provides a range of recalcitrance relevant to graphite (a highly stable C form), the recalcitrance index (R<sub>50</sub>) does not provide information on the specific timing for C sequestration. Consequently, the potential of C sequestration (CS) of BC and HC materials has been calculated using R<sub>50</sub>, yield, and C contents. A higher CS percentage relies on (i) the yield (%), (ii) the contents of C (%) before and after pyrolysis and hydrothermal carbonization, and (iii) the R<sub>50</sub> value. In our study, the CS of the BC materials ranged from 46.43% to 51.63%, while the HC materials showed CS in the range of 29.16%–39.94%. The highest CS value was found in BCK20 (51.63), while the lowest was in HC. On the other hand, increased CS values were associated with an increasing percentage of kaolinite in BC and HC, which were 51.63% in BCK20 and 39.95% in HCK20 compared to pristine. Ahmad et al. <sup>##UREF##75##111##</sup> revealed that the silica presence in BC-based materials enhanced the C sequestration potential through modifications to the silica-BC complex's structural arrangements. Another study by Sewu et al. <sup>##UREF##79##118##</sup> indicated that the bentonite-BC materials had higher C sequestration potential than the original BC. Therefore, in our study, forming Si–O–C and Al–O–C in BC-based materials and HC-based materials possessed the highest C sequestration potential.</p>","<title>Toxicity evaluation</title>","<title>Contents of PAHs</title>","<p id=\"Par38\">Biomass combustion is one of the primary sources of anthropogenic PAHs<sup>##UREF##80##119##</sup>. The total PAHs quantities and proportional contributions of individual PAHs provide valuable data regarding the quality of synthesized BC and HC materials regarding environmental safety. The contents of PAHs in BC and HC materials were determined to identify the PAHs retention in all materials due to various processes of pyrolysis and hydrothermal carbonization, and the findings are presented in Table ##TAB##3##4##. Pristine HC showed higher amounts of all PAHs than in pristine BC. Acenaphthene content was found to be around 2.6 times greater in pristine HC than pristine BC. Moreover, around 2 times greater phenanthrene, anthracene, and benzo [e] pyrene [BeP] contents, about 1.5 times greater naphthalene and acenaphthylene contents, and 1.3 times greater fluorine, fluoranthene, pyrene, and retene contents were detected in pristine HC in comparison to pristine BC. These findings support previous studies showing that pristine HC contains more PAHs than pristine BC<sup>##REF##31048990##8##,##UREF##81##120##</sup>. Compared to dry pyrolysis, the tar condensation on the surface of HC through hydrothermal carbonization of biomass might have resulted in the retention of PAHs in such materials<sup>##UREF##36##58##</sup>. In contrast, the modification of BC and HC with kaolinite decreased the PAHs contents as compared to pristine BC and HC. The HC-based materials such as HCK10 and HCK20 showed a greater reduction of PAHs contents than the BC-based materials such as BCK10 and BCK20. Similarly, increasing kaolinite modification means more reduction of PAHs, such as in BCK20 and HCK20, which were more PAHs-reducing than BCK10 and HCK10. Generally, PAHs contents in HC-based materials were lower than in BC-based materials. Compared to pristine BC and HC, the reduction for these materials significantly ranged between 1.2 and 6.4 times for most of the PAHs contents. For instance, the reduction for phenanthrene was about 5 times for HCK10 and 6.4 times for BCK20 compared to HC. Similarly, the reduction of naphthalene was about 1.2 times for BCK10 and 2.6 times for BCK20 compared to pristine BC; likewise, about 2.8 times for HCK10 and 3.8 times for HCK20 as compared to pristine HC. Overall, ten forms of seventeen PAHs were detected in the BC and HC materials. The sum of the PAHs total concentrations of BC and HC materials ranged from 739.1 μg kg<sup>−1</sup> in HCK20 to 2770.7 μg kg<sup>−1</sup> in HC. The order of the ∑PAHs contents was HC (2770.7 μg kg<sup>−1</sup>) ˃ BC (1752.9 μg kg<sup>−1</sup>) ˃ BCK10 (1514.1 μg kg<sup>−1</sup>) ˃ HCK10 (972.8 μg kg<sup>−1</sup>) ˃ HCK20 (739.1 μg kg<sup>−1</sup>) ˃ BCK20 (934.4 μg kg<sup>−1</sup>). Nevertheless, ∑ 15 PAHs contents (∑16 US-EPA PAHs, except Benzo[b] Flouranthene) were in the BC (1487.8 μg kg<sup>−1</sup>), BCK10 (1293.3 μg kg<sup>−1</sup>), BCK20 (799.5 μg kg<sup>−1</sup>), HC (2383.2 μg kg<sup>−1</sup>), HCK10 (861.71 μg kg<sup>−1</sup>), and HCK20 (619.5 μg kg<sup>−1</sup>). Therefore, ∑15 PAHs contents in BC and HC materials were lower than the threshold value of ∑16 PAH (6000–20,000 μg kg<sup>−1</sup> = 6000 μg kg<sup>−1</sup>) provided by the IBI<sup>##UREF##76##112##</sup>. Therefore, the PAHs compounds in BC and HC materials are considered safe for use as soil amendments and have the lowest potential risk for PAHs-related effects.</p>","<p id=\"Par39\">Based on the aromatic rings number, PAHs with 3 rings were predominant in BC and HC materials, followed by those with 4, 2, and 5 rings (Fig. ##FIG##5##6##). Large proportion of low-molecular-weight 3 ring PAHs in BC and HC materials may be because of their atmospheric emission during pyrolysis and retention in the condensed tar through hydrothermal carbonization. Comparatively, pristine BC contained higher proportions of 2 and 4 ring PAHs than pristine HC, while pristine HC contained higher proportions of 3 and 5 ring PAHs. In BC and HC-based materials, the proportions of 2 and 5 ring PAHs were 17% and 3% in BCK10, BCK20, HCK10, and HCK20, which were equal (except 2 ring PAHs in BCK20 which, was 13%). The proportions of 3 ring PAHs were ordered as BCK20 (67%) ˃ HCK10 and HCK20 (64%) ˃ BCK10 (61%). Likewise, the proportions of 4 ring PAHs were ordered as BCK10 (19%) ˃ HCK10 and HCK20 (17%) ˃ BCK20 (16%). Generally, the proportion of 3 ring PAHs was the highest (ranging from 59 to 67%) in all synthesized materials, while the proportion of 5 ring PAHs was the lowest (ranging from 2 to 3%). During pyrolysis, they are released into the atmosphere while being retained in the condensed tar during hydrothermal carbonization. Low molecular weight 3 ring PAHs may be present in significant concentrations in HC. Previous studies mentioned that the toxicity and PAHs concentration in BC decreased significantly with increasing temperature, which can be attributed to the evaporation with rising temperature<sup>##UREF##82##121##–##UREF##83##123##</sup>. In addition, the most important factors in the elimination of PAHs compounds are volatility and thermal degradation<sup>##UREF##84##124##</sup>. Washing the BC and HC materials could reduce the negative impact on plant growth due to the reduced PAHs compounds<sup>##REF##35313162##125##</sup>. Hence, kaolinite-composited BC and HC could be better options for amendments to soil and water due to decreasing PAHs contents.</p>","<p id=\"Par40\">Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20).</p>","<title>Maize germination and growth</title>","<p id=\"Par41\">The germination test results of maize (<italic>Zea mays</italic> L.) are presented in Fig. ##FIG##6##7##a. In control (CK) treatment, 75% germination of maize was detected. Seed germination was significantly affected by the application of BC and HC materials. The highest germination was observed in the HC (0.2 g) treatment, which was 85%, while the lowest was for HC (0.4 g) and HCK20 (0.2 g) treatments, which were 55%. In addition, there were no significant differences between 0.2 and 0.4 g applications of BC, BCK10, BCK20, and HCK10. The order of the germination percentages was as follows: HC (0.2 g) ˃ CK, BC (0.2 and 0.4 g), BCK10 (0.2 and 0.4 g), BCK20 (0.2 and 0.4 g), HCK10 (0.2 and 0.4 g), HCK20 (0.4 g) ˃ HC (0.4 g), HCK20 (0.2 g). Our finding showed that the previously mentioned synthesized materials are safe when applied to soil to enhance plant production without phytotoxic effects. Our finding supports the previous studies, which mentioned that the application of HC mixed with kaolinite could mitigate greenhouse gas emissions and might support improved soil retention of C and N for better management of agricultural nutrients<sup>##REF##33773345##101##</sup>. Likewise, another study confirmed that applying a clay-BC composite positively impacted on the yield and quality of blue grass and improved soil properties<sup>##REF##28689123##126##</sup>.</p>","<p id=\"Par42\">To evaluate the effect of various treatments, the shoot and root length of the maize seedling were also determined, and the results are shown in Fig. ##FIG##6##7##b. Compared to CK treatment, all treatments significantly improved the maize growth (except BC, 0.2 g). Shoot length of maize seedlings for BCK20 (0.2 and 0.4 g), HC (0.2 and 0.4 g), HCK10 (0.2 g), and HCK20 (0.2 g) treatments was 54.63%, 60.62%, 65.21%, 61.55%, 53.56%, and 64.20%, respectively, higher than CK. On the other hand, other treatments such as BC (0.4 g), BCK10 (0.2 and 0.4 g), HCK10 (0.4 g), and HCK20 (0.4 g) also increased the length of the shoot of maize seedlings by 36.11%, 25.62%, 40.60%, 24.36%, and 41.18%, respectively, as compared to CK. These findings confirm the potential of the synthesized materials to improve plant growth. Likewise, HC (0.2 and 0.4 g) and HCK10 (0.2 g) significantly increased the root length of maize seedlings by 89.27%, 90.80%, and 88.29%, respectively, compared to CK. Moreover, the root length of the other treatments was lower than previous treatments but higher than CK, which were 27.16% in BC (0.2 g), 38.71% in BC (0.4 g), 62.07% in BCK10 (0.2 g), 51.08% in BCK10 (0.4 g), 41.48% in BCK20 (0.2 g), 53.16% in BCK20 (0.4 g), 51.96% in HCK10 (0.4 g), 60.31% in HCK20 (0.2 g), and 52.06% in HCK20 (0.4 g).</p>","<p id=\"Par43\">Figure ##FIG##6##7##c,d depict maize seedlings’ fresh and dry weights as influenced by synthesized materials. The HC (0.2 g) treatment enhanced fresh and dry maize weights by 112.49% and 105.40%, respectively, compared to CK, while the other treatments were lower than HC (0.2 g) treatment. Hence, all the treatments significantly increased the fresh and dry weights, but in varying proportions compared to the CK treatment. Mumme et al.<sup>##REF##29778049##127##</sup> showed that biochar-zeolite composites increased the germination rate compared to the non-amended treatments. Another study by Medha et al.<sup>##UREF##26##41##</sup> showed that Sorghum grass root and shoot length significantly increased than CK. Furthermore, among the tested materials, with the increase in the bentonite biochar composite and kaolinite fractions, the shoot and root length substantially increased by five and ten folds, respectively. In a study about the effect of biochar on the physiological growth of maize. Cong et al.<sup>##REF##37409290##128##</sup> found that biochar increased the dry biomass of maize by 22.22% against the control condition while up to 58.39% increase in plant height was noted against control with no biochar amendment. In another study, Yang et al.<sup>##REF##26828311##129##</sup> stated that kaolinite composite with walnut shell derived biochar improved its surface and adsorptive characteristics and nutrient release capacity, which ultimately positively impacted plant growth. Fregolente et al.<sup>##UREF##85##130##</sup> found positive impact of hydrochar application on root and shoot development and dry biomass production of maize. Therefore, the parameters of seed germination and seedling growth revealed positive impacts of the synthesized materials with varying proportions. Consequently, it is highly recommended to properly assess the BC and HC materials before applying them to the soil as amendments.</p>","<p id=\"Par44\">The correlation between the characteristics of synthesized materials such as R<sub>50</sub>, CS, O/C, CEC, SA, and ∑ total PAHs toxicity and maize germination and growth were established (Table ##TAB##4##5##). The results of germination and growth parameters were taken by the average of additives rate. The relationship revealed a significant correlation between maize germination and growth and the characteristics of synthesized materials (R<sub>50</sub>, CS, O/C, CEC, SA, and PAHs). The significant relationships (r) between germination and characteristics of synthesized materials of R<sub>50</sub>, O/C, CEC, and SA were 0.75, − 0.78, − 0.78, and 0.81, respectively. Likewise, the r values between shoot length and R<sub>50</sub>, O/C, CEC, and SA were − 0.52, 0.54, 0.55, and − 0.71, respectively. Similarly, the r values between root length and R<sub>50</sub>, CS, O/C, CEC, and SA were − 0.78, − 0.83, 0.76, 0.78, and − 0.80, respectively. On the other hand, the r value between fresh weight and ∑ total PAHs was 0.53, while it was -0.50 between dry weight and SA. According to our findings, the characteristics of the synthesized materials have positive relationships on maize growth and germination.</p>","<title>Implications of the study</title>","<p id=\"Par45\">In recent years, fabricated BC and HC materials have received a lot of interest. Although a few studies have examined the feasibility of clay-supported BC and HC as composites in different applications, evidence from germination and growth parameters is still very limited. In our study, pristine BC, HC, and kaolinite-composited BC and HC were synthesized and characterized. The synthesized materials can be used successfully to sequester C, immobilize inorganic and organic pollutants in water and soil, increase N and phosphorus availability, and increase plant production at the field scale. Our founding materials, BC-based materials, could be used for long-term stability and sequestering C due to their high recalcitrance potential compared to HC-based materials, which had an R<sub>50</sub> > 0.7 and CS > 47.63%. The higher stability and C sequestration of these charred materials could help mitigate climate change. Furthermore, synthesized materials can be used successfully for water and soil remediation; BC-based materials have high surface area and zeta potential, while HC materials have high CEC and surface functional groups. Moreover, kaolinite-composited BC and HC can be used for plant production, which showed no toxicity in the germination test and the lowest potential risk for PAHs-related impacts. On the other hand, HC-based materials typically have advantages such as a low pH and are suitable in arid regions that suffer from high pH; hence, their additions to the alkaline soil can overcome the alkalinity; as a result, the pH value decreases in these areas. In addition, the kaolinite-composited BC and HC materials can enhance soil health by improving the properties of soil through physical structure, increasing porosity, reducing bulk density, enhancing soil aggregation, organic contaminants degradation, water and nutrients retention, as well as climate change mitigation.</p>"],"string":"[\n \"<title>Results and discussion</title>\",\n \"<title>Characterization</title>\",\n \"<title>Proximate and chemical analyses</title>\",\n \"<p id=\\\"Par25\\\">The results of the chemical and proximate analyses of the fabricated materials are presented in Table ##TAB##0##1##. The results revealed that the HC materials (HC, HCK10, and HCK20) had a higher yield than the BC materials (BC, BCK10, and BCK20). In addition, the highest yield was found in HCK20, while the lowest yield was found in BC. On the other hand, increased yield was associated with an increasing percentage of kaolinite, which was noticed in BCK20 and HCK20 compared to pristine. The yield order was in the order: HCK20 (64.35%) ˃ HCK10 (62.19%) ˃ HC (56.74%) ˃ BCK20 (36.84%) ˃ BCK10 (32.89%) ˃ BC (24.15%). Therefore, the HC materials showed a higher yield than the BC materials. The lower yield of pristine BC materials can be attributed to the higher volatile matter loss and a higher weight loss from biomass through pyrolysis than those of HC materials, which were made through hydrothermal carbonization in an airtight container. Hence, the higher yield of kaolinite-synthesized materials indicated more thermal stability than pristine. The highest yield of kaolinite-synthesized materials could be related to the highly resistant nature of kaolinite against thermal degradation<sup>##REF##31672351##42##</sup>. The volatiles decreased with an increasing percentage of kaolinite and carbonization. This reduction was 3.94 times for BC, 4.13 times for BCK10, and 4.07 times for BCK20 than that of BM, while 1.44 times for HC, 1.67 times for HCK10, and 2.02 times for HCK20 than that of BM. Likewise, the fixed C was higher in pristine BC and HC as compared to BC and HC-based materials. The highest fixed C was observed in BC (70.20%), while the lowest was in BCK20 (25.59%) among the synthesized materials. In all synthesized materials, BC materials were more recalcitrant than HC materials<sup>##REF##31048990##8##</sup>. The highest ash contents were found in BCK20, while the lowest were in HC. The ash contents increased with increased kaolinite percentage in synthesized BC and HC materials. The ash contents were increased by 2.43, 12.87, 18.15, 0.91, 6.35, and 9.46-folds in BC, BCK10, BCK20, HC, HCK10, and HCK20, respectively, as compared to BM, indicating the formation/condensation of the compounds of minerals in these materials through pyrolysis<sup>##REF##28337620##51##</sup>. In comparison to HC-based materials, the increased ash contents in BC-based materials were related to the thermal oxidation of organic compounds during the pyrolysis process<sup>##REF##31048990##8##</sup>. The BM displayed a higher moisture content of 2.73%, while it was higher in HCs (HC = 2.41%, HCK10 = 2.11%) and HCK20 = 1.46%) as compared to BCs (BC = 0.87%, BCK10 = 1.02%, and BCK20 = 0.41%). The HC materials had a higher moisture content due to being hydrothermally pretreated.</p>\",\n \"<p id=\\\"Par26\\\">The finding displayed that the pH of BC materials elevated with an increasing kaolinite percentage during pyrolysis. In contrast, the pH of HC materials decreased with an increasing kaolinite percentage during hydrothermal carbonization. The highest pH value was 10.97 in BCK20, while the lowest was 4.48 in HCK20. The pH increased by 3.14, 4 and 5.7 units in BC, BCK10 and BCK20, respectively, compared with the BM, indicating the elimination of acidic functional groups and concentration of basic functional groups<sup>##REF##29220749##52##</sup>. Furthermore, with increasing the pyrolysis temperature, the recalcitrant cationic species (Ca<sup>+2</sup>, Mg<sup>+2</sup>, Na<sup>+</sup>) condensed in BC materials, which could also cause increased pH<sup>##REF##20634061##53##,##UREF##32##54##</sup>. On the other hand, the pH values of the HCK10 and HCK20 were decreased by only 0.09 and 0.83 units, respectively, as compared with BM, indicating the minimal removal of basic functional groups<sup>##REF##29220749##52##</sup>. The pH value of the HC increased by 0.91 units compared with the BM. Likewise, the EC of all materials decreased with pyrolysis and hydrothermal carbonization, which could be due to washing all the materials before analysis to remove surface basicity<sup>##UREF##33##55##</sup>, as well as the dissolved salts released into the liquid phase during hydrothermal carbonization<sup>##UREF##34##56##</sup>. The highest cation exchange capacity (CEC) was shown in HC (132.8 cmol kg<sup>−1</sup>), while the lowest CEC was in BC (16.3 cmol kg<sup>−1</sup>). With pyrolysis, the CEC was increased by 34.97% for BCK10 and 38.04% for BCK20 as compared to pristine BC, which is attributed to an increase in surface functional groups<sup>##UREF##35##57##</sup>. Contrarily, with hydrothermal carbonization, the CEC was decreased by 7.38% for HCK10 and 6.02% for HCK20 compared with HC. Nevertheless, the CEC for HC materials was several times higher than that for BC materials due to more oxygen-containing functional groups on the surface of HC materials<sup>##UREF##36##58##–##UREF##39##61##</sup>.</p>\",\n \"<title>X-ray diffraction, SEM, and FTIR analyses</title>\",\n \"<p id=\\\"Par27\\\">The XRD spectra of the BM, kaolinite deposits, and synthesized materials are shown in Fig. ##FIG##0##1##. The various visible peaks on the spectra of all the synthesized materials demonstrate the presence of crystalline minerals and inorganic materials. The XRD patterns of raw materials, such as kaolinite clay deposits and BM, were identified. In the XRD pattern of kaolinite deposits, the strongest intense peaks of kaolinite in XRD pattern of kaolinite deposits were identified at 2θ = 12.46°; 25.06° and 26.68° (Fig. ##FIG##0##1##a)<sup>##UREF##40##62##–##UREF##42##64##</sup> and lower diffraction kaolinite peaks than previous peaks were revealed at 2θ = 36.7°; 39.50°; 42.56°; 50.3°; 55.16° and 62.4°<sup>##UREF##43##65##–##REF##33567736##70##</sup>. Four peaks of cellulose in BM are displayed at = 21.8; 22.4°; 24.3° and 30°, peak of carbon-containing minerals mellite is at 2θ = 14.8°, and calcite at 2θ = 39.78° (Fig. ##FIG##0##1##a)<sup>##UREF##48##71##–##UREF##51##74##</sup>. The kaolinite peaks were found in BC-based materials and HC-based materials, which endorsed successfully implanted kaolinite onto the BC and HC matrix. Peak shifting is a sign of interactions between kaolinite and BC or HC during synthesizing the composite. Peaks of kaolinite at 2θ = 20.7°; 26.68°; 39.5° and 50.3° in BCK10 and BCK20 were shifted to 20.78°; 26.6°; 39.26° and 50°, respectively, and 2θ = 25.06° in BCK10 was shifted to 25.2°, and 2θ = 36.86° in BCK20 was shifted to 36.48° (Fig. ##FIG##0##1##b)<sup>##UREF##42##64##,##UREF##44##66##,##UREF##46##68##,##UREF##52##75##</sup>. Similarly, peaks of kaolinite at 2θ = 12.46°; 26.68°; 36.1°; 38° and 42.56° in HCK10 and HCK20 were shifted to 12.26°; 26.62°; 36.14°; 38.32° and 42.46°, respectively, and 2θ = 39.5° in HCK10 was shifted to 39.44°, and 2θ = 55.1° was shifted to 54.78° (Fig. ##FIG##0##1##b)<sup>##UREF##42##64##,##UREF##45##67##,##UREF##53##76##,##UREF##54##77##</sup>. The other peaks in BC-based materials were impurities corresponding to calcite and quartz; and cellulose and mellite for HC-based materials. The XRD analysis of BC (Fig. ##FIG##0##1##b) displayed peaks at 2θ = 23.04°, which indicated the presence of graphite<sup>##UREF##55##78##</sup>. Other peaks were identified at 2θ = 29.3°; 39.44°; 43.1°; 47.34° and 48.28°, and indicated the presence of calcite<sup>##REF##29220749##52##,##REF##30149311##79##,##REF##32685816##80##</sup>. Likewise, the peak of cellulose in HC is at 2θ = 22.4°<sup>##UREF##49##72##</sup> and the peak of mellite is at 2θ = 14.8° (Fig. ##FIG##0##1##c). In BC-based materials, mellite was lost during the pyrolysis process of BM, while it still exists with HCs. Therefore, the changes in the results of the kaolinite-synthesized BC and kaolinite-synthesized HC showed that the synthesis method used successfully implanted kaolinite onto the BC and HC matrix.</p>\",\n \"<p id=\\\"Par28\\\">SEM images in Fig. ##FIG##1##2## depict the surface morphology of the synthesized materials. SEM images are highly useful to obtain minute details about the structure of synthesized materials and their modifications. In addition, comparing pristine BC and HC with their modified and raw materials would therefore allow us to make judgments on morphological changes during pyrolysis and hydrothermal carbonization. Pyrolysis and hydrothermal carbonization converted the crystalline surface of BM (Fig. ##FIG##1##2##a) into porous and amorphous materials, as presented in Fig. ##FIG##1##2##b–g. The surface area of the BC-based materials and HC-based materials was generally coated by thin film structures and were more irregular on the BC and HC surfaces (Fig. ##FIG##1##2##c–g), indicating that after zooming in at 3000 × magnification, the kaolinite well onto the surfaces and within the pores of BC and HC<sup>##REF##19150079##81##</sup>. The decomposition and volatilization of biomass caused a small number of pores with different sizes to appear in pristine BC and HC. In addition, the SEM images showed that kaolinite was not entirely covering the surfaces of the BC-based materials<sup>##UREF##56##82##,##UREF##57##83##</sup>, while kaolinite was entirely covering the surfaces of the HC-based materials.</p>\",\n \"<p id=\\\"Par29\\\">According to Li et al.<sup>##REF##30423497##84##</sup>, the surface functional groups, particularly O-containing functional groups, may facilitate the chemical adsorption of BC and HC. Figure ##FIG##2##3## displays the FTIR spectra of synthesized materials in the range of 400–4400 cm<sup>−1</sup>. A broad band has been explored at 3300–3800 cm<sup>−1</sup> in BM, representing the presence of O–H bonding<sup>##UREF##58##85##</sup>, which continued to appear during hydrothermal carbonization and vanished during pyrolysis. The structural–functional groups found in the BC and HC materials included C=C, O–H, C–O, C=O, C–H, Si–O–Al, Si–O–Si, Si–O, N–H, C–OH, CH<sub>2</sub> and C–N. It is evident that some functional groups, such as C–H and C=O (between 1413 and 1462 cm<sup>−1</sup>) were shared by BC and HC materials<sup>##REF##31254497##86##</sup>, C–O and O–H (1033 cm<sup>-1</sup>)<sup>##UREF##59##87##</sup>. For BC-based materials, some peaks appeared with increasing kaolinite, such as; Si–O–Si groups at 465 and 469 cm<sup>−1</sup><sup>##UREF##60##88##</sup>, Si–O at 791 cm<sup>−1</sup><sup>##UREF##61##89##</sup> and C–O, C–N at 1083 cm<sup>−1</sup><sup>##UREF##62##90##</sup><sup>,</sup><sup>##REF##32122867##91##</sup>. Therefore, those peaks indicated that kaolinite was successfully loaded onto the BC matrix<sup>##UREF##63##92##</sup>. The HC materials showed more bands than BC materials, which could be due to minimal losses of functional groups. Likewise, some peaks appeared with increasing kaolinite, such as Si–O–Si at 469 cm<sup>−1</sup><sup>##UREF##58##85##</sup>, Si–O and Al–O vibrations at 762, 696 and 539 cm<sup>−1</sup><sup>##UREF##64##93##</sup>, Si–O at 784 cm<sup>−1</sup><sup>##UREF##65##94##</sup>. On the other hand, the same peaks appeared in HC, HCK10, and HCK20 composites, such as; C–O and O–H at 1033 cm<sup>−1</sup><sup>##UREF##59##87##</sup>, C–O–C at 1111 cm<sup>−1</sup><sup>##UREF##66##95##</sup>, CH<sub>3</sub> at 1440 cm<sup>−1</sup><sup>##UREF##67##96##</sup>, C=O at 1510 cm<sup>−1</sup>, C=O at 1510 cm<sup>−1</sup><sup>##REF##35580789##97##</sup>, COOH at 1700 cm<sup>−1</sup><sup>##UREF##68##98##</sup>, C–H at 2921 cm<sup>−1</sup><sup>##UREF##69##99##</sup>, and O–H (between 3300 and 3800 cm<sup>−1</sup>) <sup>##UREF##56##82##</sup>. Moreover, some bands were not found in BC and HC alone, but when composited with kaolinite, such bands were visible. Therefore, this finding can further assist in predicting the impact of kaolinite-synthesized BCs and HCs on the removal efficiency of these composites for various pollutants.</p>\",\n \"<title>Size and surface characteristics</title>\",\n \"<p id=\\\"Par30\\\">The BET surface area, pore size, and pore volume of the BM and synthesized materials are shown in Table ##TAB##1##2##. BC materials have a higher surface area than HC materials. The highest surface areas were found in BC (290.89 m<sup>2</sup> g<sup>−1</sup>), and the lowest in HC (5.32 m<sup>2</sup> g<sup>−1</sup>). Moreover, it was detected that the surface areas of the BCK10 and BCK20 (225.14 and 180.40 m<sup>2</sup> g<sup>−1</sup>) were reduced compared to the pristine BC (290.89 m<sup>2</sup> g<sup>−1</sup>), indicating that the pores on the BC might have been covered/clogged by kaolinite<sup>##UREF##24##39##</sup>. Conversely, the pristine HC suffered agglomeration and revealed a lower surface area<sup>##REF##35580789##97##,##REF##30609488##100##</sup>. Therefore, the surface area of the HCK10 and HCK20 was higher than that of HC. The surface area of the HCK10 and HCK20 was 16.11 and 15.44 m<sup>2</sup> g<sup>−1</sup>, which was threefold higher than the pristine HC (5.32 m<sup>2</sup> g<sup>−1</sup>). The composite interposes retained the kaolinite particles, increasing the surface area of kaolinite-HC<sup>##REF##32105922##43##,##REF##33773345##101##</sup>. Contrarily, the BCK10 and BCK20 pore sizes were 38.31 and 28.17 Å, which were higher as compared to BC (28.81Å). On the contrary, the pore sizes of HCK10 and HCK20 were 145.90 and 175.92 Å, which were lower as compared to HC (187.71 Å). The highest pore sizes was appeared in HC (187.71 Å), while the lowest was in BCK20 (28.17 Å). However, with increasing the amount of kaolinite deposit, the surface area decreased by 22.68% in BCK10 and 38% in BCK20 compared with pristine BC, while kaolinite addition increased the surface area of HCK10 by 203% and HCK20 by 190% as compared to pristine HC. Overall, the pore size of the HC materials was more than 10 times larger than that of the BC materials. The previous studies showed that the low surface area of zeolite and silica-composited BC is due to plugging of pores in the existence of minerals<sup>##REF##28337620##51##</sup>. Yao et al.<sup>##UREF##21##35##</sup> also mentioned that the blockage of BC pores by clay minerals particles could be the cause of the decreased surface area of clay biochar composites. The hydrodynamic size of the particles of the synthesized materials in aqueous suspensions was determined (Table ##TAB##1##2##). Any particle aggregate or particle with an equivalent diameter is seen by Dynamic Light Scattering (DLS) to reflect a similar size. The average size of particles (hydrodynamic size) was 2.63 μm for BC, 2.34 μm for BCK10, 2.73 μm for BCK20, 2.19 μm for HC, 2.38 μm for HCK10, and 3.10 μm for HCK20. These results of particle size analyses suggested that HC showed the minimum particle size, while HCK20 showed the maximum particle size.</p>\",\n \"<p id=\\\"Par31\\\">Colloidal dispersions have an electrokinetic potential known as zeta potential, and the value of this potential is influenced by the surface charge of the individual particles. In our study, the zeta potential values of the synthesized materials were determined as a function of the solution pH (Table ##TAB##1##2##). The zeta potential of the pristine BC and BC-based materials ranged from − 25.06 to − 25.08 mV; however, the zeta potential of the pristine HC and HC-based materials ranged from − 21.68 to − 24.98 mV. The highest zeta potential was shown in BCK10 (− 25.08 mV), while the lowest was in HCK20 (− 21.86 mV). Consequently, the negative charge of pristine BC and BC-based materials is slightly higher than that of pristine HC and HC-based materials. Hence, the zeta potential of all the synthesized materials was negative, indicating that all surfaces of the synthesized materials are negatively charged. Therefore, a larger zeta potential means a larger negative charge, which is beneficial for remediation, especially with removing cationic ions.</p>\",\n \"<title>Elemental composition and carbon stability</title>\",\n \"<p id=\\\"Par32\\\">The elemental composition of the synthesized materials is presented in Table ##TAB##2##3##. In comparison to BM, thermal treatment enhanced the total C contents of BC and HC materials. An increased degree of carbonization may be the cause of rising C contents with pyrolysis and hydrothermal carbonization. The higher contents of C were observed in BC, and the lowest was observed in HCK20 among the synthesized materials. HC materials had higher H contents ranging from 5.33 to 5.54%, while BC materials ranged from 1.08 to 1.41%. Among the synthesized materials, the maximum N contents were in BCK20, while the minimum was in BC. However, the C contents of the BC, BCK10, BCK20, HC, HCK10, and HCK20 were increased by 47.8%, 44.1%, 43.6%, 18.3, 20.9%, and 17.8%, respectively, as compared to BM. The C content was higher in pristine BC, which decreased with kaolinite modification in BC-based materials, such as 6.59% in BCK10 and 7.32% in BCK20. Meanwhile, the C content was increased in HCK10 (3.37%) and slightly decreased in HCK20 (0.52%) compared to HC. The increasing lignin content in biomass has been mentioned to enhance carbonization and increase the content of C in BC<sup>##REF##25957037##102##,##UREF##70##103##,##UREF##72##105##</sup>. Other studies reported that cellulose and hemicelluloses also significantly affect on the C content<sup>##UREF##71##104##,##UREF##72##105##</sup>. On the other hand, a reduction of H contents in BC materials was more than that of HC materials. Compared to BM, the reduction of H contents for BC, BCK10, BCK20, HC, HCK10, and HCK20 was 76.3%, 78.4%, 82.0%, 10.8%, 7.3%, and 9.9%, respectively. According to previous studies, Gai et al. <sup>##REF##25469875##106##</sup> reported that the decrease in H content of BC was due to the loss of water, gaseous H<sub>2</sub>, hydrocarbons, and tarry vapors. The total N contents decreased with pyrolysis and hydrothermal carbonization in BC (27.3%), HC (3.1%), and HCK10 (11.9%), while it increased in BCK10 (21.5%), BCK20 (32.6%), and HCK20 (18.9%) compared to BM. Furthermore, the contents of N increased with increased kaolinite modification of BC and HC, which were higher in BCK20 and HCK20. The total O contents decreased with pyrolysis and hydrothermal carbonization of biomass in all materials. The reduction in O was observed in BC (97.1%), BCK10 (92.5%), BCK20 (94.5%), HC (27.1%), HCK10 (30.8%), and HCK20 (29.6%), compared to BM. Moreover, BC-based materials showed higher O contents compared to pristine BC, while HC-based materials slightly presented lower O contents compared to pristine HC. Dehydration, volatilization, and depolymerization could be the causes of the decrease in O contents with hydrothermal carbonization and pyrolysis<sup>##REF##23376202##107##</sup>.</p>\",\n \"<p id=\\\"Par33\\\">Figure ##FIG##3##4## illustrates the Van Krevelen diagram, frequently used to calculate the molar O/C and H/C ratios to compute the BC and HC material’s recalcitrance. With pyrolysis and thermal carbonization, conocarpus waste biomass is dehydrated and depolymerized to produce smaller dissociation products<sup>##UREF##14##24##,##REF##20099810##108##</sup>. Reduced H/C and O/C molar ratios showed reduced polarity and a greater degree of aromaticity, which in turn increased the stability of BC<sup>##UREF##33##55##</sup>. Also, another research mentioned that the higher stability of BC composites is attributed to greater polyaromatic carbon content compared to HC composites<sup>##UREF##73##109##</sup>. From Table ##TAB##2##3## and Fig. ##FIG##3##4##, the surface polarity index (i.e., indicated by O/C molar ratio) of BC and HC materials decreased with pyrolysis and hydrothermal carbonization compared with BM, indicating a decrease in the hydrophobicity of these materials. Overall, the highest values of O/C and H/C (0.604 and 1.47) were found for the BM. Among the synthesized materials, maximum values of O/C and H/C (0.36 and 1.09) were expressed by HCK20 and HC, respectively. In any case, BC-based materials showed slightly decreased H/C values and increased O/C values compared to pristine BC. In contrast to pristine HC, HC-based materials showed slightly increased H/C values and decreased O/C values. The O/C ratio of BC (0.01), BCK10 (0.025), and BCK20 (0.02) was less than 0.2, suggesting more stability of such materials and a half-life of more than 1000 years<sup>##UREF##74##110##</sup>. In contrast, the O/C ratio of HC (0.360), HCK10 (0.331), and HCK20 (0.350) was between 0.2 and 0.6, suggesting a half-life ranging from 100 to 1000 years<sup>##UREF##74##110##</sup>. For BM, the O/C ratio was 0.604, which will probably possess a half-life of less than 100 years<sup>##UREF##74##110##</sup>). Likewise, low H/C values of BC and HC materials compared with BM indicated high aromaticity and reactivity<sup>##UREF##75##111##</sup>. In addition, the much lower H/C ratio of BC materials compared to HC materials demonstrated that BC materials were heavily carbonized and showed highly aromatic structures. Depending on IBI<sup>##UREF##76##112##</sup> criteria, the H/C < 0.7 showed greater combined aromatic ring structures for the BC materials, while the H/C molar ratio of HC materials is higher than that of BC materials, which was ˃ 0.7. Therefore, BC materials showed higher aromaticity and low polarity than that of HC materials. Hence, the ratios of H/C and O/C in the BC-based materials and HC-based materials indicated more aromatic C and becoming less hydrophilic<sup>##UREF##77##113##</sup>.</p>\",\n \"<title>TGA and recalcitrance index (R<sub>50</sub>)</title>\",\n \"<p id=\\\"Par34\\\">In this study, TGA thermogravimetric analysis was applied to investigate the long-term stability of synthesized materials. The results of TGA-DTG analysis are shown in Fig. ##FIG##4##5##a,b. Our results indicated that the BM thermally decomposed earlier than the HC and BC materials due to thermal instability. The thermogravimetric curves of the different BC materials and the HC materials exposed similar performances as regards weight loss (%) on a reducing trend with rising temperature. For BC, BCK10, and BCK20, the sudden weight loss began at 650 °C ≈ 700 °C, at 350 °C ≈ 400 °C for HC, HCK10, and HCK20, and at 250 °C ≈ 350 °C for BM. The thermograms displayed two general regions where weight loss has occurred: (i) around 300°C for BM and HC materials because of thermal degradation of cellulose and hemicellulose compounds<sup>##UREF##78##114##</sup> and (ii) around 600–1000 °C because of lignin degradation<sup>##REF##28337620##51##</sup>. The order of materials for degradability is as follows: BCK20 < BCK10 < BC < HCK20 < HCK10 < HC < BM. Therefore, the BC and HC with a 20% kaolinite ratio were more stable, followed by a 10% kaolinite ratio, followed by pristine. Moreover, a higher weight loss was observed in HC materials Than in BC materials.</p>\",\n \"<p id=\\\"Par35\\\">The recalcitrance of the synthesized materials in soil depends on the potential of BC and HC materials to resist thermal, physical, and chemical degradation. The formation of organometallic complexes and the aromatic carbon structure play significant roles in BC and HC materials. Due to its increased aromaticity, the C of BC materials has a higher potential for recalcitrance than that of HC materials and BM; however, the recalcitrance and stability of C in the soil after adding BC and HC materials vary depending on the kind and feedstock composition, the texture and structure of the soil, and other environmental factors. Hence, the recalcitrance index of BC and HC materials is required to be determined in relative to graphite, because one of the most stable C forms is graphite<sup>##REF##35598667##115##</sup>. As a result, TGA thermograms that are marked as R<sub>50</sub> were used by Harvey et al. <sup>##REF##22242866##47##</sup> to create a novel recalcitrance index that predicts the materials' potential for recalcitrance.</p>\",\n \"<p id=\\\"Par36\\\">To calculate the recalcitrant index (R<sub>50</sub>), TGA thermograms for all materials were corrected for moisture and ash contents, which reflect the stability of synthesized materials and the extent of C sequestration<sup>##REF##25203840##116##</sup>. Figure ##FIG##4##5##c,d display moisture and ash-corrected TGA thermograms. Therefore, the R<sub>50</sub> values of BM and HC in this study were 0.40 and 0.42, indicating that these are highly biodegradable and belong to class 3 (Table ##TAB##2##3##). BC, BCK10, and BCK20 were considered in class 1 with R<sub>50</sub> of 0.78, 0.81, and 0.79, respectively, indicating that these have high recalcitrance potential<sup>##REF##22242866##47##</sup>. The R<sub>50</sub> values of HCK10 and HCK20 composites were 0.50 and 0.51, suggesting they were minimally degradable. Increased R<sub>50</sub> in the BCK10 and BCK20 composites could be attributed to kaolinite's presence, which improved BC's oxidation resistance. The previous study by Ahmad et al.<sup>##REF##28337620##51##</sup> demonstrated that the higher R<sub>50</sub> in silica BC composites could be related to the probable protection of C by silica particles, which may be controlled through the pyrolysis process. The reason was also attributed to the change from the aromatic C–C/C=C functionality to the C–O and C–H configuration of the BC surface forming stable organo-mineral complexes (i.e., C–O–Al and C–O–Si) with clay minerals<sup>##REF##34695458##117##</sup>. The previous study by Wang et al.<sup>##REF##35598667##115##</sup> showed that the R<sub>50</sub> increased by 0.89 for kaolinite-composited BC, reflecting that minerals-composited BC could improve the thermal stability of BC. According to Ahmad et al.<sup>##UREF##75##111##</sup>, changes in structural arrangements within the silica-BC complex may be responsible for the high recalcitrance and C sequestration potential of silica-composited BC. Therefore, increased R<sub>50</sub> values of BC materials expect greater recalcitrance and stability because of the particular interactions of kaolinite with the BC matrix.</p>\",\n \"<p id=\\\"Par37\\\">Although it provides a range of recalcitrance relevant to graphite (a highly stable C form), the recalcitrance index (R<sub>50</sub>) does not provide information on the specific timing for C sequestration. Consequently, the potential of C sequestration (CS) of BC and HC materials has been calculated using R<sub>50</sub>, yield, and C contents. A higher CS percentage relies on (i) the yield (%), (ii) the contents of C (%) before and after pyrolysis and hydrothermal carbonization, and (iii) the R<sub>50</sub> value. In our study, the CS of the BC materials ranged from 46.43% to 51.63%, while the HC materials showed CS in the range of 29.16%–39.94%. The highest CS value was found in BCK20 (51.63), while the lowest was in HC. On the other hand, increased CS values were associated with an increasing percentage of kaolinite in BC and HC, which were 51.63% in BCK20 and 39.95% in HCK20 compared to pristine. Ahmad et al. <sup>##UREF##75##111##</sup> revealed that the silica presence in BC-based materials enhanced the C sequestration potential through modifications to the silica-BC complex's structural arrangements. Another study by Sewu et al. <sup>##UREF##79##118##</sup> indicated that the bentonite-BC materials had higher C sequestration potential than the original BC. Therefore, in our study, forming Si–O–C and Al–O–C in BC-based materials and HC-based materials possessed the highest C sequestration potential.</p>\",\n \"<title>Toxicity evaluation</title>\",\n \"<title>Contents of PAHs</title>\",\n \"<p id=\\\"Par38\\\">Biomass combustion is one of the primary sources of anthropogenic PAHs<sup>##UREF##80##119##</sup>. The total PAHs quantities and proportional contributions of individual PAHs provide valuable data regarding the quality of synthesized BC and HC materials regarding environmental safety. The contents of PAHs in BC and HC materials were determined to identify the PAHs retention in all materials due to various processes of pyrolysis and hydrothermal carbonization, and the findings are presented in Table ##TAB##3##4##. Pristine HC showed higher amounts of all PAHs than in pristine BC. Acenaphthene content was found to be around 2.6 times greater in pristine HC than pristine BC. Moreover, around 2 times greater phenanthrene, anthracene, and benzo [e] pyrene [BeP] contents, about 1.5 times greater naphthalene and acenaphthylene contents, and 1.3 times greater fluorine, fluoranthene, pyrene, and retene contents were detected in pristine HC in comparison to pristine BC. These findings support previous studies showing that pristine HC contains more PAHs than pristine BC<sup>##REF##31048990##8##,##UREF##81##120##</sup>. Compared to dry pyrolysis, the tar condensation on the surface of HC through hydrothermal carbonization of biomass might have resulted in the retention of PAHs in such materials<sup>##UREF##36##58##</sup>. In contrast, the modification of BC and HC with kaolinite decreased the PAHs contents as compared to pristine BC and HC. The HC-based materials such as HCK10 and HCK20 showed a greater reduction of PAHs contents than the BC-based materials such as BCK10 and BCK20. Similarly, increasing kaolinite modification means more reduction of PAHs, such as in BCK20 and HCK20, which were more PAHs-reducing than BCK10 and HCK10. Generally, PAHs contents in HC-based materials were lower than in BC-based materials. Compared to pristine BC and HC, the reduction for these materials significantly ranged between 1.2 and 6.4 times for most of the PAHs contents. For instance, the reduction for phenanthrene was about 5 times for HCK10 and 6.4 times for BCK20 compared to HC. Similarly, the reduction of naphthalene was about 1.2 times for BCK10 and 2.6 times for BCK20 compared to pristine BC; likewise, about 2.8 times for HCK10 and 3.8 times for HCK20 as compared to pristine HC. Overall, ten forms of seventeen PAHs were detected in the BC and HC materials. The sum of the PAHs total concentrations of BC and HC materials ranged from 739.1 μg kg<sup>−1</sup> in HCK20 to 2770.7 μg kg<sup>−1</sup> in HC. The order of the ∑PAHs contents was HC (2770.7 μg kg<sup>−1</sup>) ˃ BC (1752.9 μg kg<sup>−1</sup>) ˃ BCK10 (1514.1 μg kg<sup>−1</sup>) ˃ HCK10 (972.8 μg kg<sup>−1</sup>) ˃ HCK20 (739.1 μg kg<sup>−1</sup>) ˃ BCK20 (934.4 μg kg<sup>−1</sup>). Nevertheless, ∑ 15 PAHs contents (∑16 US-EPA PAHs, except Benzo[b] Flouranthene) were in the BC (1487.8 μg kg<sup>−1</sup>), BCK10 (1293.3 μg kg<sup>−1</sup>), BCK20 (799.5 μg kg<sup>−1</sup>), HC (2383.2 μg kg<sup>−1</sup>), HCK10 (861.71 μg kg<sup>−1</sup>), and HCK20 (619.5 μg kg<sup>−1</sup>). Therefore, ∑15 PAHs contents in BC and HC materials were lower than the threshold value of ∑16 PAH (6000–20,000 μg kg<sup>−1</sup> = 6000 μg kg<sup>−1</sup>) provided by the IBI<sup>##UREF##76##112##</sup>. Therefore, the PAHs compounds in BC and HC materials are considered safe for use as soil amendments and have the lowest potential risk for PAHs-related effects.</p>\",\n \"<p id=\\\"Par39\\\">Based on the aromatic rings number, PAHs with 3 rings were predominant in BC and HC materials, followed by those with 4, 2, and 5 rings (Fig. ##FIG##5##6##). Large proportion of low-molecular-weight 3 ring PAHs in BC and HC materials may be because of their atmospheric emission during pyrolysis and retention in the condensed tar through hydrothermal carbonization. Comparatively, pristine BC contained higher proportions of 2 and 4 ring PAHs than pristine HC, while pristine HC contained higher proportions of 3 and 5 ring PAHs. In BC and HC-based materials, the proportions of 2 and 5 ring PAHs were 17% and 3% in BCK10, BCK20, HCK10, and HCK20, which were equal (except 2 ring PAHs in BCK20 which, was 13%). The proportions of 3 ring PAHs were ordered as BCK20 (67%) ˃ HCK10 and HCK20 (64%) ˃ BCK10 (61%). Likewise, the proportions of 4 ring PAHs were ordered as BCK10 (19%) ˃ HCK10 and HCK20 (17%) ˃ BCK20 (16%). Generally, the proportion of 3 ring PAHs was the highest (ranging from 59 to 67%) in all synthesized materials, while the proportion of 5 ring PAHs was the lowest (ranging from 2 to 3%). During pyrolysis, they are released into the atmosphere while being retained in the condensed tar during hydrothermal carbonization. Low molecular weight 3 ring PAHs may be present in significant concentrations in HC. Previous studies mentioned that the toxicity and PAHs concentration in BC decreased significantly with increasing temperature, which can be attributed to the evaporation with rising temperature<sup>##UREF##82##121##–##UREF##83##123##</sup>. In addition, the most important factors in the elimination of PAHs compounds are volatility and thermal degradation<sup>##UREF##84##124##</sup>. Washing the BC and HC materials could reduce the negative impact on plant growth due to the reduced PAHs compounds<sup>##REF##35313162##125##</sup>. Hence, kaolinite-composited BC and HC could be better options for amendments to soil and water due to decreasing PAHs contents.</p>\",\n \"<p id=\\\"Par40\\\">Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20).</p>\",\n \"<title>Maize germination and growth</title>\",\n \"<p id=\\\"Par41\\\">The germination test results of maize (<italic>Zea mays</italic> L.) are presented in Fig. ##FIG##6##7##a. In control (CK) treatment, 75% germination of maize was detected. Seed germination was significantly affected by the application of BC and HC materials. The highest germination was observed in the HC (0.2 g) treatment, which was 85%, while the lowest was for HC (0.4 g) and HCK20 (0.2 g) treatments, which were 55%. In addition, there were no significant differences between 0.2 and 0.4 g applications of BC, BCK10, BCK20, and HCK10. The order of the germination percentages was as follows: HC (0.2 g) ˃ CK, BC (0.2 and 0.4 g), BCK10 (0.2 and 0.4 g), BCK20 (0.2 and 0.4 g), HCK10 (0.2 and 0.4 g), HCK20 (0.4 g) ˃ HC (0.4 g), HCK20 (0.2 g). Our finding showed that the previously mentioned synthesized materials are safe when applied to soil to enhance plant production without phytotoxic effects. Our finding supports the previous studies, which mentioned that the application of HC mixed with kaolinite could mitigate greenhouse gas emissions and might support improved soil retention of C and N for better management of agricultural nutrients<sup>##REF##33773345##101##</sup>. Likewise, another study confirmed that applying a clay-BC composite positively impacted on the yield and quality of blue grass and improved soil properties<sup>##REF##28689123##126##</sup>.</p>\",\n \"<p id=\\\"Par42\\\">To evaluate the effect of various treatments, the shoot and root length of the maize seedling were also determined, and the results are shown in Fig. ##FIG##6##7##b. Compared to CK treatment, all treatments significantly improved the maize growth (except BC, 0.2 g). Shoot length of maize seedlings for BCK20 (0.2 and 0.4 g), HC (0.2 and 0.4 g), HCK10 (0.2 g), and HCK20 (0.2 g) treatments was 54.63%, 60.62%, 65.21%, 61.55%, 53.56%, and 64.20%, respectively, higher than CK. On the other hand, other treatments such as BC (0.4 g), BCK10 (0.2 and 0.4 g), HCK10 (0.4 g), and HCK20 (0.4 g) also increased the length of the shoot of maize seedlings by 36.11%, 25.62%, 40.60%, 24.36%, and 41.18%, respectively, as compared to CK. These findings confirm the potential of the synthesized materials to improve plant growth. Likewise, HC (0.2 and 0.4 g) and HCK10 (0.2 g) significantly increased the root length of maize seedlings by 89.27%, 90.80%, and 88.29%, respectively, compared to CK. Moreover, the root length of the other treatments was lower than previous treatments but higher than CK, which were 27.16% in BC (0.2 g), 38.71% in BC (0.4 g), 62.07% in BCK10 (0.2 g), 51.08% in BCK10 (0.4 g), 41.48% in BCK20 (0.2 g), 53.16% in BCK20 (0.4 g), 51.96% in HCK10 (0.4 g), 60.31% in HCK20 (0.2 g), and 52.06% in HCK20 (0.4 g).</p>\",\n \"<p id=\\\"Par43\\\">Figure ##FIG##6##7##c,d depict maize seedlings’ fresh and dry weights as influenced by synthesized materials. The HC (0.2 g) treatment enhanced fresh and dry maize weights by 112.49% and 105.40%, respectively, compared to CK, while the other treatments were lower than HC (0.2 g) treatment. Hence, all the treatments significantly increased the fresh and dry weights, but in varying proportions compared to the CK treatment. Mumme et al.<sup>##REF##29778049##127##</sup> showed that biochar-zeolite composites increased the germination rate compared to the non-amended treatments. Another study by Medha et al.<sup>##UREF##26##41##</sup> showed that Sorghum grass root and shoot length significantly increased than CK. Furthermore, among the tested materials, with the increase in the bentonite biochar composite and kaolinite fractions, the shoot and root length substantially increased by five and ten folds, respectively. In a study about the effect of biochar on the physiological growth of maize. Cong et al.<sup>##REF##37409290##128##</sup> found that biochar increased the dry biomass of maize by 22.22% against the control condition while up to 58.39% increase in plant height was noted against control with no biochar amendment. In another study, Yang et al.<sup>##REF##26828311##129##</sup> stated that kaolinite composite with walnut shell derived biochar improved its surface and adsorptive characteristics and nutrient release capacity, which ultimately positively impacted plant growth. Fregolente et al.<sup>##UREF##85##130##</sup> found positive impact of hydrochar application on root and shoot development and dry biomass production of maize. Therefore, the parameters of seed germination and seedling growth revealed positive impacts of the synthesized materials with varying proportions. Consequently, it is highly recommended to properly assess the BC and HC materials before applying them to the soil as amendments.</p>\",\n \"<p id=\\\"Par44\\\">The correlation between the characteristics of synthesized materials such as R<sub>50</sub>, CS, O/C, CEC, SA, and ∑ total PAHs toxicity and maize germination and growth were established (Table ##TAB##4##5##). The results of germination and growth parameters were taken by the average of additives rate. The relationship revealed a significant correlation between maize germination and growth and the characteristics of synthesized materials (R<sub>50</sub>, CS, O/C, CEC, SA, and PAHs). The significant relationships (r) between germination and characteristics of synthesized materials of R<sub>50</sub>, O/C, CEC, and SA were 0.75, − 0.78, − 0.78, and 0.81, respectively. Likewise, the r values between shoot length and R<sub>50</sub>, O/C, CEC, and SA were − 0.52, 0.54, 0.55, and − 0.71, respectively. Similarly, the r values between root length and R<sub>50</sub>, CS, O/C, CEC, and SA were − 0.78, − 0.83, 0.76, 0.78, and − 0.80, respectively. On the other hand, the r value between fresh weight and ∑ total PAHs was 0.53, while it was -0.50 between dry weight and SA. According to our findings, the characteristics of the synthesized materials have positive relationships on maize growth and germination.</p>\",\n \"<title>Implications of the study</title>\",\n \"<p id=\\\"Par45\\\">In recent years, fabricated BC and HC materials have received a lot of interest. Although a few studies have examined the feasibility of clay-supported BC and HC as composites in different applications, evidence from germination and growth parameters is still very limited. In our study, pristine BC, HC, and kaolinite-composited BC and HC were synthesized and characterized. The synthesized materials can be used successfully to sequester C, immobilize inorganic and organic pollutants in water and soil, increase N and phosphorus availability, and increase plant production at the field scale. Our founding materials, BC-based materials, could be used for long-term stability and sequestering C due to their high recalcitrance potential compared to HC-based materials, which had an R<sub>50</sub> > 0.7 and CS > 47.63%. The higher stability and C sequestration of these charred materials could help mitigate climate change. Furthermore, synthesized materials can be used successfully for water and soil remediation; BC-based materials have high surface area and zeta potential, while HC materials have high CEC and surface functional groups. Moreover, kaolinite-composited BC and HC can be used for plant production, which showed no toxicity in the germination test and the lowest potential risk for PAHs-related impacts. On the other hand, HC-based materials typically have advantages such as a low pH and are suitable in arid regions that suffer from high pH; hence, their additions to the alkaline soil can overcome the alkalinity; as a result, the pH value decreases in these areas. In addition, the kaolinite-composited BC and HC materials can enhance soil health by improving the properties of soil through physical structure, increasing porosity, reducing bulk density, enhancing soil aggregation, organic contaminants degradation, water and nutrients retention, as well as climate change mitigation.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Results and discussion</title>","<title>Characterization</title>","<title>Proximate and chemical analyses</title>","<p id=\"Par25\">The results of the chemical and proximate analyses of the fabricated materials are presented in Table ##TAB##0##1##. The results revealed that the HC materials (HC, HCK10, and HCK20) had a higher yield than the BC materials (BC, BCK10, and BCK20). In addition, the highest yield was found in HCK20, while the lowest yield was found in BC. On the other hand, increased yield was associated with an increasing percentage of kaolinite, which was noticed in BCK20 and HCK20 compared to pristine. The yield order was in the order: HCK20 (64.35%) ˃ HCK10 (62.19%) ˃ HC (56.74%) ˃ BCK20 (36.84%) ˃ BCK10 (32.89%) ˃ BC (24.15%). Therefore, the HC materials showed a higher yield than the BC materials. The lower yield of pristine BC materials can be attributed to the higher volatile matter loss and a higher weight loss from biomass through pyrolysis than those of HC materials, which were made through hydrothermal carbonization in an airtight container. Hence, the higher yield of kaolinite-synthesized materials indicated more thermal stability than pristine. The highest yield of kaolinite-synthesized materials could be related to the highly resistant nature of kaolinite against thermal degradation<sup>##REF##31672351##42##</sup>. The volatiles decreased with an increasing percentage of kaolinite and carbonization. This reduction was 3.94 times for BC, 4.13 times for BCK10, and 4.07 times for BCK20 than that of BM, while 1.44 times for HC, 1.67 times for HCK10, and 2.02 times for HCK20 than that of BM. Likewise, the fixed C was higher in pristine BC and HC as compared to BC and HC-based materials. The highest fixed C was observed in BC (70.20%), while the lowest was in BCK20 (25.59%) among the synthesized materials. In all synthesized materials, BC materials were more recalcitrant than HC materials<sup>##REF##31048990##8##</sup>. The highest ash contents were found in BCK20, while the lowest were in HC. The ash contents increased with increased kaolinite percentage in synthesized BC and HC materials. The ash contents were increased by 2.43, 12.87, 18.15, 0.91, 6.35, and 9.46-folds in BC, BCK10, BCK20, HC, HCK10, and HCK20, respectively, as compared to BM, indicating the formation/condensation of the compounds of minerals in these materials through pyrolysis<sup>##REF##28337620##51##</sup>. In comparison to HC-based materials, the increased ash contents in BC-based materials were related to the thermal oxidation of organic compounds during the pyrolysis process<sup>##REF##31048990##8##</sup>. The BM displayed a higher moisture content of 2.73%, while it was higher in HCs (HC = 2.41%, HCK10 = 2.11%) and HCK20 = 1.46%) as compared to BCs (BC = 0.87%, BCK10 = 1.02%, and BCK20 = 0.41%). The HC materials had a higher moisture content due to being hydrothermally pretreated.</p>","<p id=\"Par26\">The finding displayed that the pH of BC materials elevated with an increasing kaolinite percentage during pyrolysis. In contrast, the pH of HC materials decreased with an increasing kaolinite percentage during hydrothermal carbonization. The highest pH value was 10.97 in BCK20, while the lowest was 4.48 in HCK20. The pH increased by 3.14, 4 and 5.7 units in BC, BCK10 and BCK20, respectively, compared with the BM, indicating the elimination of acidic functional groups and concentration of basic functional groups<sup>##REF##29220749##52##</sup>. Furthermore, with increasing the pyrolysis temperature, the recalcitrant cationic species (Ca<sup>+2</sup>, Mg<sup>+2</sup>, Na<sup>+</sup>) condensed in BC materials, which could also cause increased pH<sup>##REF##20634061##53##,##UREF##32##54##</sup>. On the other hand, the pH values of the HCK10 and HCK20 were decreased by only 0.09 and 0.83 units, respectively, as compared with BM, indicating the minimal removal of basic functional groups<sup>##REF##29220749##52##</sup>. The pH value of the HC increased by 0.91 units compared with the BM. Likewise, the EC of all materials decreased with pyrolysis and hydrothermal carbonization, which could be due to washing all the materials before analysis to remove surface basicity<sup>##UREF##33##55##</sup>, as well as the dissolved salts released into the liquid phase during hydrothermal carbonization<sup>##UREF##34##56##</sup>. The highest cation exchange capacity (CEC) was shown in HC (132.8 cmol kg<sup>−1</sup>), while the lowest CEC was in BC (16.3 cmol kg<sup>−1</sup>). With pyrolysis, the CEC was increased by 34.97% for BCK10 and 38.04% for BCK20 as compared to pristine BC, which is attributed to an increase in surface functional groups<sup>##UREF##35##57##</sup>. Contrarily, with hydrothermal carbonization, the CEC was decreased by 7.38% for HCK10 and 6.02% for HCK20 compared with HC. Nevertheless, the CEC for HC materials was several times higher than that for BC materials due to more oxygen-containing functional groups on the surface of HC materials<sup>##UREF##36##58##–##UREF##39##61##</sup>.</p>","<title>X-ray diffraction, SEM, and FTIR analyses</title>","<p id=\"Par27\">The XRD spectra of the BM, kaolinite deposits, and synthesized materials are shown in Fig. ##FIG##0##1##. The various visible peaks on the spectra of all the synthesized materials demonstrate the presence of crystalline minerals and inorganic materials. The XRD patterns of raw materials, such as kaolinite clay deposits and BM, were identified. In the XRD pattern of kaolinite deposits, the strongest intense peaks of kaolinite in XRD pattern of kaolinite deposits were identified at 2θ = 12.46°; 25.06° and 26.68° (Fig. ##FIG##0##1##a)<sup>##UREF##40##62##–##UREF##42##64##</sup> and lower diffraction kaolinite peaks than previous peaks were revealed at 2θ = 36.7°; 39.50°; 42.56°; 50.3°; 55.16° and 62.4°<sup>##UREF##43##65##–##REF##33567736##70##</sup>. Four peaks of cellulose in BM are displayed at = 21.8; 22.4°; 24.3° and 30°, peak of carbon-containing minerals mellite is at 2θ = 14.8°, and calcite at 2θ = 39.78° (Fig. ##FIG##0##1##a)<sup>##UREF##48##71##–##UREF##51##74##</sup>. The kaolinite peaks were found in BC-based materials and HC-based materials, which endorsed successfully implanted kaolinite onto the BC and HC matrix. Peak shifting is a sign of interactions between kaolinite and BC or HC during synthesizing the composite. Peaks of kaolinite at 2θ = 20.7°; 26.68°; 39.5° and 50.3° in BCK10 and BCK20 were shifted to 20.78°; 26.6°; 39.26° and 50°, respectively, and 2θ = 25.06° in BCK10 was shifted to 25.2°, and 2θ = 36.86° in BCK20 was shifted to 36.48° (Fig. ##FIG##0##1##b)<sup>##UREF##42##64##,##UREF##44##66##,##UREF##46##68##,##UREF##52##75##</sup>. Similarly, peaks of kaolinite at 2θ = 12.46°; 26.68°; 36.1°; 38° and 42.56° in HCK10 and HCK20 were shifted to 12.26°; 26.62°; 36.14°; 38.32° and 42.46°, respectively, and 2θ = 39.5° in HCK10 was shifted to 39.44°, and 2θ = 55.1° was shifted to 54.78° (Fig. ##FIG##0##1##b)<sup>##UREF##42##64##,##UREF##45##67##,##UREF##53##76##,##UREF##54##77##</sup>. The other peaks in BC-based materials were impurities corresponding to calcite and quartz; and cellulose and mellite for HC-based materials. The XRD analysis of BC (Fig. ##FIG##0##1##b) displayed peaks at 2θ = 23.04°, which indicated the presence of graphite<sup>##UREF##55##78##</sup>. Other peaks were identified at 2θ = 29.3°; 39.44°; 43.1°; 47.34° and 48.28°, and indicated the presence of calcite<sup>##REF##29220749##52##,##REF##30149311##79##,##REF##32685816##80##</sup>. Likewise, the peak of cellulose in HC is at 2θ = 22.4°<sup>##UREF##49##72##</sup> and the peak of mellite is at 2θ = 14.8° (Fig. ##FIG##0##1##c). In BC-based materials, mellite was lost during the pyrolysis process of BM, while it still exists with HCs. Therefore, the changes in the results of the kaolinite-synthesized BC and kaolinite-synthesized HC showed that the synthesis method used successfully implanted kaolinite onto the BC and HC matrix.</p>","<p id=\"Par28\">SEM images in Fig. ##FIG##1##2## depict the surface morphology of the synthesized materials. SEM images are highly useful to obtain minute details about the structure of synthesized materials and their modifications. In addition, comparing pristine BC and HC with their modified and raw materials would therefore allow us to make judgments on morphological changes during pyrolysis and hydrothermal carbonization. Pyrolysis and hydrothermal carbonization converted the crystalline surface of BM (Fig. ##FIG##1##2##a) into porous and amorphous materials, as presented in Fig. ##FIG##1##2##b–g. The surface area of the BC-based materials and HC-based materials was generally coated by thin film structures and were more irregular on the BC and HC surfaces (Fig. ##FIG##1##2##c–g), indicating that after zooming in at 3000 × magnification, the kaolinite well onto the surfaces and within the pores of BC and HC<sup>##REF##19150079##81##</sup>. The decomposition and volatilization of biomass caused a small number of pores with different sizes to appear in pristine BC and HC. In addition, the SEM images showed that kaolinite was not entirely covering the surfaces of the BC-based materials<sup>##UREF##56##82##,##UREF##57##83##</sup>, while kaolinite was entirely covering the surfaces of the HC-based materials.</p>","<p id=\"Par29\">According to Li et al.<sup>##REF##30423497##84##</sup>, the surface functional groups, particularly O-containing functional groups, may facilitate the chemical adsorption of BC and HC. Figure ##FIG##2##3## displays the FTIR spectra of synthesized materials in the range of 400–4400 cm<sup>−1</sup>. A broad band has been explored at 3300–3800 cm<sup>−1</sup> in BM, representing the presence of O–H bonding<sup>##UREF##58##85##</sup>, which continued to appear during hydrothermal carbonization and vanished during pyrolysis. The structural–functional groups found in the BC and HC materials included C=C, O–H, C–O, C=O, C–H, Si–O–Al, Si–O–Si, Si–O, N–H, C–OH, CH<sub>2</sub> and C–N. It is evident that some functional groups, such as C–H and C=O (between 1413 and 1462 cm<sup>−1</sup>) were shared by BC and HC materials<sup>##REF##31254497##86##</sup>, C–O and O–H (1033 cm<sup>-1</sup>)<sup>##UREF##59##87##</sup>. For BC-based materials, some peaks appeared with increasing kaolinite, such as; Si–O–Si groups at 465 and 469 cm<sup>−1</sup><sup>##UREF##60##88##</sup>, Si–O at 791 cm<sup>−1</sup><sup>##UREF##61##89##</sup> and C–O, C–N at 1083 cm<sup>−1</sup><sup>##UREF##62##90##</sup><sup>,</sup><sup>##REF##32122867##91##</sup>. Therefore, those peaks indicated that kaolinite was successfully loaded onto the BC matrix<sup>##UREF##63##92##</sup>. The HC materials showed more bands than BC materials, which could be due to minimal losses of functional groups. Likewise, some peaks appeared with increasing kaolinite, such as Si–O–Si at 469 cm<sup>−1</sup><sup>##UREF##58##85##</sup>, Si–O and Al–O vibrations at 762, 696 and 539 cm<sup>−1</sup><sup>##UREF##64##93##</sup>, Si–O at 784 cm<sup>−1</sup><sup>##UREF##65##94##</sup>. On the other hand, the same peaks appeared in HC, HCK10, and HCK20 composites, such as; C–O and O–H at 1033 cm<sup>−1</sup><sup>##UREF##59##87##</sup>, C–O–C at 1111 cm<sup>−1</sup><sup>##UREF##66##95##</sup>, CH<sub>3</sub> at 1440 cm<sup>−1</sup><sup>##UREF##67##96##</sup>, C=O at 1510 cm<sup>−1</sup>, C=O at 1510 cm<sup>−1</sup><sup>##REF##35580789##97##</sup>, COOH at 1700 cm<sup>−1</sup><sup>##UREF##68##98##</sup>, C–H at 2921 cm<sup>−1</sup><sup>##UREF##69##99##</sup>, and O–H (between 3300 and 3800 cm<sup>−1</sup>) <sup>##UREF##56##82##</sup>. Moreover, some bands were not found in BC and HC alone, but when composited with kaolinite, such bands were visible. Therefore, this finding can further assist in predicting the impact of kaolinite-synthesized BCs and HCs on the removal efficiency of these composites for various pollutants.</p>","<title>Size and surface characteristics</title>","<p id=\"Par30\">The BET surface area, pore size, and pore volume of the BM and synthesized materials are shown in Table ##TAB##1##2##. BC materials have a higher surface area than HC materials. The highest surface areas were found in BC (290.89 m<sup>2</sup> g<sup>−1</sup>), and the lowest in HC (5.32 m<sup>2</sup> g<sup>−1</sup>). Moreover, it was detected that the surface areas of the BCK10 and BCK20 (225.14 and 180.40 m<sup>2</sup> g<sup>−1</sup>) were reduced compared to the pristine BC (290.89 m<sup>2</sup> g<sup>−1</sup>), indicating that the pores on the BC might have been covered/clogged by kaolinite<sup>##UREF##24##39##</sup>. Conversely, the pristine HC suffered agglomeration and revealed a lower surface area<sup>##REF##35580789##97##,##REF##30609488##100##</sup>. Therefore, the surface area of the HCK10 and HCK20 was higher than that of HC. The surface area of the HCK10 and HCK20 was 16.11 and 15.44 m<sup>2</sup> g<sup>−1</sup>, which was threefold higher than the pristine HC (5.32 m<sup>2</sup> g<sup>−1</sup>). The composite interposes retained the kaolinite particles, increasing the surface area of kaolinite-HC<sup>##REF##32105922##43##,##REF##33773345##101##</sup>. Contrarily, the BCK10 and BCK20 pore sizes were 38.31 and 28.17 Å, which were higher as compared to BC (28.81Å). On the contrary, the pore sizes of HCK10 and HCK20 were 145.90 and 175.92 Å, which were lower as compared to HC (187.71 Å). The highest pore sizes was appeared in HC (187.71 Å), while the lowest was in BCK20 (28.17 Å). However, with increasing the amount of kaolinite deposit, the surface area decreased by 22.68% in BCK10 and 38% in BCK20 compared with pristine BC, while kaolinite addition increased the surface area of HCK10 by 203% and HCK20 by 190% as compared to pristine HC. Overall, the pore size of the HC materials was more than 10 times larger than that of the BC materials. The previous studies showed that the low surface area of zeolite and silica-composited BC is due to plugging of pores in the existence of minerals<sup>##REF##28337620##51##</sup>. Yao et al.<sup>##UREF##21##35##</sup> also mentioned that the blockage of BC pores by clay minerals particles could be the cause of the decreased surface area of clay biochar composites. The hydrodynamic size of the particles of the synthesized materials in aqueous suspensions was determined (Table ##TAB##1##2##). Any particle aggregate or particle with an equivalent diameter is seen by Dynamic Light Scattering (DLS) to reflect a similar size. The average size of particles (hydrodynamic size) was 2.63 μm for BC, 2.34 μm for BCK10, 2.73 μm for BCK20, 2.19 μm for HC, 2.38 μm for HCK10, and 3.10 μm for HCK20. These results of particle size analyses suggested that HC showed the minimum particle size, while HCK20 showed the maximum particle size.</p>","<p id=\"Par31\">Colloidal dispersions have an electrokinetic potential known as zeta potential, and the value of this potential is influenced by the surface charge of the individual particles. In our study, the zeta potential values of the synthesized materials were determined as a function of the solution pH (Table ##TAB##1##2##). The zeta potential of the pristine BC and BC-based materials ranged from − 25.06 to − 25.08 mV; however, the zeta potential of the pristine HC and HC-based materials ranged from − 21.68 to − 24.98 mV. The highest zeta potential was shown in BCK10 (− 25.08 mV), while the lowest was in HCK20 (− 21.86 mV). Consequently, the negative charge of pristine BC and BC-based materials is slightly higher than that of pristine HC and HC-based materials. Hence, the zeta potential of all the synthesized materials was negative, indicating that all surfaces of the synthesized materials are negatively charged. Therefore, a larger zeta potential means a larger negative charge, which is beneficial for remediation, especially with removing cationic ions.</p>","<title>Elemental composition and carbon stability</title>","<p id=\"Par32\">The elemental composition of the synthesized materials is presented in Table ##TAB##2##3##. In comparison to BM, thermal treatment enhanced the total C contents of BC and HC materials. An increased degree of carbonization may be the cause of rising C contents with pyrolysis and hydrothermal carbonization. The higher contents of C were observed in BC, and the lowest was observed in HCK20 among the synthesized materials. HC materials had higher H contents ranging from 5.33 to 5.54%, while BC materials ranged from 1.08 to 1.41%. Among the synthesized materials, the maximum N contents were in BCK20, while the minimum was in BC. However, the C contents of the BC, BCK10, BCK20, HC, HCK10, and HCK20 were increased by 47.8%, 44.1%, 43.6%, 18.3, 20.9%, and 17.8%, respectively, as compared to BM. The C content was higher in pristine BC, which decreased with kaolinite modification in BC-based materials, such as 6.59% in BCK10 and 7.32% in BCK20. Meanwhile, the C content was increased in HCK10 (3.37%) and slightly decreased in HCK20 (0.52%) compared to HC. The increasing lignin content in biomass has been mentioned to enhance carbonization and increase the content of C in BC<sup>##REF##25957037##102##,##UREF##70##103##,##UREF##72##105##</sup>. Other studies reported that cellulose and hemicelluloses also significantly affect on the C content<sup>##UREF##71##104##,##UREF##72##105##</sup>. On the other hand, a reduction of H contents in BC materials was more than that of HC materials. Compared to BM, the reduction of H contents for BC, BCK10, BCK20, HC, HCK10, and HCK20 was 76.3%, 78.4%, 82.0%, 10.8%, 7.3%, and 9.9%, respectively. According to previous studies, Gai et al. <sup>##REF##25469875##106##</sup> reported that the decrease in H content of BC was due to the loss of water, gaseous H<sub>2</sub>, hydrocarbons, and tarry vapors. The total N contents decreased with pyrolysis and hydrothermal carbonization in BC (27.3%), HC (3.1%), and HCK10 (11.9%), while it increased in BCK10 (21.5%), BCK20 (32.6%), and HCK20 (18.9%) compared to BM. Furthermore, the contents of N increased with increased kaolinite modification of BC and HC, which were higher in BCK20 and HCK20. The total O contents decreased with pyrolysis and hydrothermal carbonization of biomass in all materials. The reduction in O was observed in BC (97.1%), BCK10 (92.5%), BCK20 (94.5%), HC (27.1%), HCK10 (30.8%), and HCK20 (29.6%), compared to BM. Moreover, BC-based materials showed higher O contents compared to pristine BC, while HC-based materials slightly presented lower O contents compared to pristine HC. Dehydration, volatilization, and depolymerization could be the causes of the decrease in O contents with hydrothermal carbonization and pyrolysis<sup>##REF##23376202##107##</sup>.</p>","<p id=\"Par33\">Figure ##FIG##3##4## illustrates the Van Krevelen diagram, frequently used to calculate the molar O/C and H/C ratios to compute the BC and HC material’s recalcitrance. With pyrolysis and thermal carbonization, conocarpus waste biomass is dehydrated and depolymerized to produce smaller dissociation products<sup>##UREF##14##24##,##REF##20099810##108##</sup>. Reduced H/C and O/C molar ratios showed reduced polarity and a greater degree of aromaticity, which in turn increased the stability of BC<sup>##UREF##33##55##</sup>. Also, another research mentioned that the higher stability of BC composites is attributed to greater polyaromatic carbon content compared to HC composites<sup>##UREF##73##109##</sup>. From Table ##TAB##2##3## and Fig. ##FIG##3##4##, the surface polarity index (i.e., indicated by O/C molar ratio) of BC and HC materials decreased with pyrolysis and hydrothermal carbonization compared with BM, indicating a decrease in the hydrophobicity of these materials. Overall, the highest values of O/C and H/C (0.604 and 1.47) were found for the BM. Among the synthesized materials, maximum values of O/C and H/C (0.36 and 1.09) were expressed by HCK20 and HC, respectively. In any case, BC-based materials showed slightly decreased H/C values and increased O/C values compared to pristine BC. In contrast to pristine HC, HC-based materials showed slightly increased H/C values and decreased O/C values. The O/C ratio of BC (0.01), BCK10 (0.025), and BCK20 (0.02) was less than 0.2, suggesting more stability of such materials and a half-life of more than 1000 years<sup>##UREF##74##110##</sup>. In contrast, the O/C ratio of HC (0.360), HCK10 (0.331), and HCK20 (0.350) was between 0.2 and 0.6, suggesting a half-life ranging from 100 to 1000 years<sup>##UREF##74##110##</sup>. For BM, the O/C ratio was 0.604, which will probably possess a half-life of less than 100 years<sup>##UREF##74##110##</sup>). Likewise, low H/C values of BC and HC materials compared with BM indicated high aromaticity and reactivity<sup>##UREF##75##111##</sup>. In addition, the much lower H/C ratio of BC materials compared to HC materials demonstrated that BC materials were heavily carbonized and showed highly aromatic structures. Depending on IBI<sup>##UREF##76##112##</sup> criteria, the H/C < 0.7 showed greater combined aromatic ring structures for the BC materials, while the H/C molar ratio of HC materials is higher than that of BC materials, which was ˃ 0.7. Therefore, BC materials showed higher aromaticity and low polarity than that of HC materials. Hence, the ratios of H/C and O/C in the BC-based materials and HC-based materials indicated more aromatic C and becoming less hydrophilic<sup>##UREF##77##113##</sup>.</p>","<title>TGA and recalcitrance index (R<sub>50</sub>)</title>","<p id=\"Par34\">In this study, TGA thermogravimetric analysis was applied to investigate the long-term stability of synthesized materials. The results of TGA-DTG analysis are shown in Fig. ##FIG##4##5##a,b. Our results indicated that the BM thermally decomposed earlier than the HC and BC materials due to thermal instability. The thermogravimetric curves of the different BC materials and the HC materials exposed similar performances as regards weight loss (%) on a reducing trend with rising temperature. For BC, BCK10, and BCK20, the sudden weight loss began at 650 °C ≈ 700 °C, at 350 °C ≈ 400 °C for HC, HCK10, and HCK20, and at 250 °C ≈ 350 °C for BM. The thermograms displayed two general regions where weight loss has occurred: (i) around 300°C for BM and HC materials because of thermal degradation of cellulose and hemicellulose compounds<sup>##UREF##78##114##</sup> and (ii) around 600–1000 °C because of lignin degradation<sup>##REF##28337620##51##</sup>. The order of materials for degradability is as follows: BCK20 < BCK10 < BC < HCK20 < HCK10 < HC < BM. Therefore, the BC and HC with a 20% kaolinite ratio were more stable, followed by a 10% kaolinite ratio, followed by pristine. Moreover, a higher weight loss was observed in HC materials Than in BC materials.</p>","<p id=\"Par35\">The recalcitrance of the synthesized materials in soil depends on the potential of BC and HC materials to resist thermal, physical, and chemical degradation. The formation of organometallic complexes and the aromatic carbon structure play significant roles in BC and HC materials. Due to its increased aromaticity, the C of BC materials has a higher potential for recalcitrance than that of HC materials and BM; however, the recalcitrance and stability of C in the soil after adding BC and HC materials vary depending on the kind and feedstock composition, the texture and structure of the soil, and other environmental factors. Hence, the recalcitrance index of BC and HC materials is required to be determined in relative to graphite, because one of the most stable C forms is graphite<sup>##REF##35598667##115##</sup>. As a result, TGA thermograms that are marked as R<sub>50</sub> were used by Harvey et al. <sup>##REF##22242866##47##</sup> to create a novel recalcitrance index that predicts the materials' potential for recalcitrance.</p>","<p id=\"Par36\">To calculate the recalcitrant index (R<sub>50</sub>), TGA thermograms for all materials were corrected for moisture and ash contents, which reflect the stability of synthesized materials and the extent of C sequestration<sup>##REF##25203840##116##</sup>. Figure ##FIG##4##5##c,d display moisture and ash-corrected TGA thermograms. Therefore, the R<sub>50</sub> values of BM and HC in this study were 0.40 and 0.42, indicating that these are highly biodegradable and belong to class 3 (Table ##TAB##2##3##). BC, BCK10, and BCK20 were considered in class 1 with R<sub>50</sub> of 0.78, 0.81, and 0.79, respectively, indicating that these have high recalcitrance potential<sup>##REF##22242866##47##</sup>. The R<sub>50</sub> values of HCK10 and HCK20 composites were 0.50 and 0.51, suggesting they were minimally degradable. Increased R<sub>50</sub> in the BCK10 and BCK20 composites could be attributed to kaolinite's presence, which improved BC's oxidation resistance. The previous study by Ahmad et al.<sup>##REF##28337620##51##</sup> demonstrated that the higher R<sub>50</sub> in silica BC composites could be related to the probable protection of C by silica particles, which may be controlled through the pyrolysis process. The reason was also attributed to the change from the aromatic C–C/C=C functionality to the C–O and C–H configuration of the BC surface forming stable organo-mineral complexes (i.e., C–O–Al and C–O–Si) with clay minerals<sup>##REF##34695458##117##</sup>. The previous study by Wang et al.<sup>##REF##35598667##115##</sup> showed that the R<sub>50</sub> increased by 0.89 for kaolinite-composited BC, reflecting that minerals-composited BC could improve the thermal stability of BC. According to Ahmad et al.<sup>##UREF##75##111##</sup>, changes in structural arrangements within the silica-BC complex may be responsible for the high recalcitrance and C sequestration potential of silica-composited BC. Therefore, increased R<sub>50</sub> values of BC materials expect greater recalcitrance and stability because of the particular interactions of kaolinite with the BC matrix.</p>","<p id=\"Par37\">Although it provides a range of recalcitrance relevant to graphite (a highly stable C form), the recalcitrance index (R<sub>50</sub>) does not provide information on the specific timing for C sequestration. Consequently, the potential of C sequestration (CS) of BC and HC materials has been calculated using R<sub>50</sub>, yield, and C contents. A higher CS percentage relies on (i) the yield (%), (ii) the contents of C (%) before and after pyrolysis and hydrothermal carbonization, and (iii) the R<sub>50</sub> value. In our study, the CS of the BC materials ranged from 46.43% to 51.63%, while the HC materials showed CS in the range of 29.16%–39.94%. The highest CS value was found in BCK20 (51.63), while the lowest was in HC. On the other hand, increased CS values were associated with an increasing percentage of kaolinite in BC and HC, which were 51.63% in BCK20 and 39.95% in HCK20 compared to pristine. Ahmad et al. <sup>##UREF##75##111##</sup> revealed that the silica presence in BC-based materials enhanced the C sequestration potential through modifications to the silica-BC complex's structural arrangements. Another study by Sewu et al. <sup>##UREF##79##118##</sup> indicated that the bentonite-BC materials had higher C sequestration potential than the original BC. Therefore, in our study, forming Si–O–C and Al–O–C in BC-based materials and HC-based materials possessed the highest C sequestration potential.</p>","<title>Toxicity evaluation</title>","<title>Contents of PAHs</title>","<p id=\"Par38\">Biomass combustion is one of the primary sources of anthropogenic PAHs<sup>##UREF##80##119##</sup>. The total PAHs quantities and proportional contributions of individual PAHs provide valuable data regarding the quality of synthesized BC and HC materials regarding environmental safety. The contents of PAHs in BC and HC materials were determined to identify the PAHs retention in all materials due to various processes of pyrolysis and hydrothermal carbonization, and the findings are presented in Table ##TAB##3##4##. Pristine HC showed higher amounts of all PAHs than in pristine BC. Acenaphthene content was found to be around 2.6 times greater in pristine HC than pristine BC. Moreover, around 2 times greater phenanthrene, anthracene, and benzo [e] pyrene [BeP] contents, about 1.5 times greater naphthalene and acenaphthylene contents, and 1.3 times greater fluorine, fluoranthene, pyrene, and retene contents were detected in pristine HC in comparison to pristine BC. These findings support previous studies showing that pristine HC contains more PAHs than pristine BC<sup>##REF##31048990##8##,##UREF##81##120##</sup>. Compared to dry pyrolysis, the tar condensation on the surface of HC through hydrothermal carbonization of biomass might have resulted in the retention of PAHs in such materials<sup>##UREF##36##58##</sup>. In contrast, the modification of BC and HC with kaolinite decreased the PAHs contents as compared to pristine BC and HC. The HC-based materials such as HCK10 and HCK20 showed a greater reduction of PAHs contents than the BC-based materials such as BCK10 and BCK20. Similarly, increasing kaolinite modification means more reduction of PAHs, such as in BCK20 and HCK20, which were more PAHs-reducing than BCK10 and HCK10. Generally, PAHs contents in HC-based materials were lower than in BC-based materials. Compared to pristine BC and HC, the reduction for these materials significantly ranged between 1.2 and 6.4 times for most of the PAHs contents. For instance, the reduction for phenanthrene was about 5 times for HCK10 and 6.4 times for BCK20 compared to HC. Similarly, the reduction of naphthalene was about 1.2 times for BCK10 and 2.6 times for BCK20 compared to pristine BC; likewise, about 2.8 times for HCK10 and 3.8 times for HCK20 as compared to pristine HC. Overall, ten forms of seventeen PAHs were detected in the BC and HC materials. The sum of the PAHs total concentrations of BC and HC materials ranged from 739.1 μg kg<sup>−1</sup> in HCK20 to 2770.7 μg kg<sup>−1</sup> in HC. The order of the ∑PAHs contents was HC (2770.7 μg kg<sup>−1</sup>) ˃ BC (1752.9 μg kg<sup>−1</sup>) ˃ BCK10 (1514.1 μg kg<sup>−1</sup>) ˃ HCK10 (972.8 μg kg<sup>−1</sup>) ˃ HCK20 (739.1 μg kg<sup>−1</sup>) ˃ BCK20 (934.4 μg kg<sup>−1</sup>). Nevertheless, ∑ 15 PAHs contents (∑16 US-EPA PAHs, except Benzo[b] Flouranthene) were in the BC (1487.8 μg kg<sup>−1</sup>), BCK10 (1293.3 μg kg<sup>−1</sup>), BCK20 (799.5 μg kg<sup>−1</sup>), HC (2383.2 μg kg<sup>−1</sup>), HCK10 (861.71 μg kg<sup>−1</sup>), and HCK20 (619.5 μg kg<sup>−1</sup>). Therefore, ∑15 PAHs contents in BC and HC materials were lower than the threshold value of ∑16 PAH (6000–20,000 μg kg<sup>−1</sup> = 6000 μg kg<sup>−1</sup>) provided by the IBI<sup>##UREF##76##112##</sup>. Therefore, the PAHs compounds in BC and HC materials are considered safe for use as soil amendments and have the lowest potential risk for PAHs-related effects.</p>","<p id=\"Par39\">Based on the aromatic rings number, PAHs with 3 rings were predominant in BC and HC materials, followed by those with 4, 2, and 5 rings (Fig. ##FIG##5##6##). Large proportion of low-molecular-weight 3 ring PAHs in BC and HC materials may be because of their atmospheric emission during pyrolysis and retention in the condensed tar through hydrothermal carbonization. Comparatively, pristine BC contained higher proportions of 2 and 4 ring PAHs than pristine HC, while pristine HC contained higher proportions of 3 and 5 ring PAHs. In BC and HC-based materials, the proportions of 2 and 5 ring PAHs were 17% and 3% in BCK10, BCK20, HCK10, and HCK20, which were equal (except 2 ring PAHs in BCK20 which, was 13%). The proportions of 3 ring PAHs were ordered as BCK20 (67%) ˃ HCK10 and HCK20 (64%) ˃ BCK10 (61%). Likewise, the proportions of 4 ring PAHs were ordered as BCK10 (19%) ˃ HCK10 and HCK20 (17%) ˃ BCK20 (16%). Generally, the proportion of 3 ring PAHs was the highest (ranging from 59 to 67%) in all synthesized materials, while the proportion of 5 ring PAHs was the lowest (ranging from 2 to 3%). During pyrolysis, they are released into the atmosphere while being retained in the condensed tar during hydrothermal carbonization. Low molecular weight 3 ring PAHs may be present in significant concentrations in HC. Previous studies mentioned that the toxicity and PAHs concentration in BC decreased significantly with increasing temperature, which can be attributed to the evaporation with rising temperature<sup>##UREF##82##121##–##UREF##83##123##</sup>. In addition, the most important factors in the elimination of PAHs compounds are volatility and thermal degradation<sup>##UREF##84##124##</sup>. Washing the BC and HC materials could reduce the negative impact on plant growth due to the reduced PAHs compounds<sup>##REF##35313162##125##</sup>. Hence, kaolinite-composited BC and HC could be better options for amendments to soil and water due to decreasing PAHs contents.</p>","<p id=\"Par40\">Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20).</p>","<title>Maize germination and growth</title>","<p id=\"Par41\">The germination test results of maize (<italic>Zea mays</italic> L.) are presented in Fig. ##FIG##6##7##a. In control (CK) treatment, 75% germination of maize was detected. Seed germination was significantly affected by the application of BC and HC materials. The highest germination was observed in the HC (0.2 g) treatment, which was 85%, while the lowest was for HC (0.4 g) and HCK20 (0.2 g) treatments, which were 55%. In addition, there were no significant differences between 0.2 and 0.4 g applications of BC, BCK10, BCK20, and HCK10. The order of the germination percentages was as follows: HC (0.2 g) ˃ CK, BC (0.2 and 0.4 g), BCK10 (0.2 and 0.4 g), BCK20 (0.2 and 0.4 g), HCK10 (0.2 and 0.4 g), HCK20 (0.4 g) ˃ HC (0.4 g), HCK20 (0.2 g). Our finding showed that the previously mentioned synthesized materials are safe when applied to soil to enhance plant production without phytotoxic effects. Our finding supports the previous studies, which mentioned that the application of HC mixed with kaolinite could mitigate greenhouse gas emissions and might support improved soil retention of C and N for better management of agricultural nutrients<sup>##REF##33773345##101##</sup>. Likewise, another study confirmed that applying a clay-BC composite positively impacted on the yield and quality of blue grass and improved soil properties<sup>##REF##28689123##126##</sup>.</p>","<p id=\"Par42\">To evaluate the effect of various treatments, the shoot and root length of the maize seedling were also determined, and the results are shown in Fig. ##FIG##6##7##b. Compared to CK treatment, all treatments significantly improved the maize growth (except BC, 0.2 g). Shoot length of maize seedlings for BCK20 (0.2 and 0.4 g), HC (0.2 and 0.4 g), HCK10 (0.2 g), and HCK20 (0.2 g) treatments was 54.63%, 60.62%, 65.21%, 61.55%, 53.56%, and 64.20%, respectively, higher than CK. On the other hand, other treatments such as BC (0.4 g), BCK10 (0.2 and 0.4 g), HCK10 (0.4 g), and HCK20 (0.4 g) also increased the length of the shoot of maize seedlings by 36.11%, 25.62%, 40.60%, 24.36%, and 41.18%, respectively, as compared to CK. These findings confirm the potential of the synthesized materials to improve plant growth. Likewise, HC (0.2 and 0.4 g) and HCK10 (0.2 g) significantly increased the root length of maize seedlings by 89.27%, 90.80%, and 88.29%, respectively, compared to CK. Moreover, the root length of the other treatments was lower than previous treatments but higher than CK, which were 27.16% in BC (0.2 g), 38.71% in BC (0.4 g), 62.07% in BCK10 (0.2 g), 51.08% in BCK10 (0.4 g), 41.48% in BCK20 (0.2 g), 53.16% in BCK20 (0.4 g), 51.96% in HCK10 (0.4 g), 60.31% in HCK20 (0.2 g), and 52.06% in HCK20 (0.4 g).</p>","<p id=\"Par43\">Figure ##FIG##6##7##c,d depict maize seedlings’ fresh and dry weights as influenced by synthesized materials. The HC (0.2 g) treatment enhanced fresh and dry maize weights by 112.49% and 105.40%, respectively, compared to CK, while the other treatments were lower than HC (0.2 g) treatment. Hence, all the treatments significantly increased the fresh and dry weights, but in varying proportions compared to the CK treatment. Mumme et al.<sup>##REF##29778049##127##</sup> showed that biochar-zeolite composites increased the germination rate compared to the non-amended treatments. Another study by Medha et al.<sup>##UREF##26##41##</sup> showed that Sorghum grass root and shoot length significantly increased than CK. Furthermore, among the tested materials, with the increase in the bentonite biochar composite and kaolinite fractions, the shoot and root length substantially increased by five and ten folds, respectively. In a study about the effect of biochar on the physiological growth of maize. Cong et al.<sup>##REF##37409290##128##</sup> found that biochar increased the dry biomass of maize by 22.22% against the control condition while up to 58.39% increase in plant height was noted against control with no biochar amendment. In another study, Yang et al.<sup>##REF##26828311##129##</sup> stated that kaolinite composite with walnut shell derived biochar improved its surface and adsorptive characteristics and nutrient release capacity, which ultimately positively impacted plant growth. Fregolente et al.<sup>##UREF##85##130##</sup> found positive impact of hydrochar application on root and shoot development and dry biomass production of maize. Therefore, the parameters of seed germination and seedling growth revealed positive impacts of the synthesized materials with varying proportions. Consequently, it is highly recommended to properly assess the BC and HC materials before applying them to the soil as amendments.</p>","<p id=\"Par44\">The correlation between the characteristics of synthesized materials such as R<sub>50</sub>, CS, O/C, CEC, SA, and ∑ total PAHs toxicity and maize germination and growth were established (Table ##TAB##4##5##). The results of germination and growth parameters were taken by the average of additives rate. The relationship revealed a significant correlation between maize germination and growth and the characteristics of synthesized materials (R<sub>50</sub>, CS, O/C, CEC, SA, and PAHs). The significant relationships (r) between germination and characteristics of synthesized materials of R<sub>50</sub>, O/C, CEC, and SA were 0.75, − 0.78, − 0.78, and 0.81, respectively. Likewise, the r values between shoot length and R<sub>50</sub>, O/C, CEC, and SA were − 0.52, 0.54, 0.55, and − 0.71, respectively. Similarly, the r values between root length and R<sub>50</sub>, CS, O/C, CEC, and SA were − 0.78, − 0.83, 0.76, 0.78, and − 0.80, respectively. On the other hand, the r value between fresh weight and ∑ total PAHs was 0.53, while it was -0.50 between dry weight and SA. According to our findings, the characteristics of the synthesized materials have positive relationships on maize growth and germination.</p>","<title>Implications of the study</title>","<p id=\"Par45\">In recent years, fabricated BC and HC materials have received a lot of interest. Although a few studies have examined the feasibility of clay-supported BC and HC as composites in different applications, evidence from germination and growth parameters is still very limited. In our study, pristine BC, HC, and kaolinite-composited BC and HC were synthesized and characterized. The synthesized materials can be used successfully to sequester C, immobilize inorganic and organic pollutants in water and soil, increase N and phosphorus availability, and increase plant production at the field scale. Our founding materials, BC-based materials, could be used for long-term stability and sequestering C due to their high recalcitrance potential compared to HC-based materials, which had an R<sub>50</sub> > 0.7 and CS > 47.63%. The higher stability and C sequestration of these charred materials could help mitigate climate change. Furthermore, synthesized materials can be used successfully for water and soil remediation; BC-based materials have high surface area and zeta potential, while HC materials have high CEC and surface functional groups. Moreover, kaolinite-composited BC and HC can be used for plant production, which showed no toxicity in the germination test and the lowest potential risk for PAHs-related impacts. On the other hand, HC-based materials typically have advantages such as a low pH and are suitable in arid regions that suffer from high pH; hence, their additions to the alkaline soil can overcome the alkalinity; as a result, the pH value decreases in these areas. In addition, the kaolinite-composited BC and HC materials can enhance soil health by improving the properties of soil through physical structure, increasing porosity, reducing bulk density, enhancing soil aggregation, organic contaminants degradation, water and nutrients retention, as well as climate change mitigation.</p>"],"string":"[\n \"<title>Results and discussion</title>\",\n \"<title>Characterization</title>\",\n \"<title>Proximate and chemical analyses</title>\",\n \"<p id=\\\"Par25\\\">The results of the chemical and proximate analyses of the fabricated materials are presented in Table ##TAB##0##1##. The results revealed that the HC materials (HC, HCK10, and HCK20) had a higher yield than the BC materials (BC, BCK10, and BCK20). In addition, the highest yield was found in HCK20, while the lowest yield was found in BC. On the other hand, increased yield was associated with an increasing percentage of kaolinite, which was noticed in BCK20 and HCK20 compared to pristine. The yield order was in the order: HCK20 (64.35%) ˃ HCK10 (62.19%) ˃ HC (56.74%) ˃ BCK20 (36.84%) ˃ BCK10 (32.89%) ˃ BC (24.15%). Therefore, the HC materials showed a higher yield than the BC materials. The lower yield of pristine BC materials can be attributed to the higher volatile matter loss and a higher weight loss from biomass through pyrolysis than those of HC materials, which were made through hydrothermal carbonization in an airtight container. Hence, the higher yield of kaolinite-synthesized materials indicated more thermal stability than pristine. The highest yield of kaolinite-synthesized materials could be related to the highly resistant nature of kaolinite against thermal degradation<sup>##REF##31672351##42##</sup>. The volatiles decreased with an increasing percentage of kaolinite and carbonization. This reduction was 3.94 times for BC, 4.13 times for BCK10, and 4.07 times for BCK20 than that of BM, while 1.44 times for HC, 1.67 times for HCK10, and 2.02 times for HCK20 than that of BM. Likewise, the fixed C was higher in pristine BC and HC as compared to BC and HC-based materials. The highest fixed C was observed in BC (70.20%), while the lowest was in BCK20 (25.59%) among the synthesized materials. In all synthesized materials, BC materials were more recalcitrant than HC materials<sup>##REF##31048990##8##</sup>. The highest ash contents were found in BCK20, while the lowest were in HC. The ash contents increased with increased kaolinite percentage in synthesized BC and HC materials. The ash contents were increased by 2.43, 12.87, 18.15, 0.91, 6.35, and 9.46-folds in BC, BCK10, BCK20, HC, HCK10, and HCK20, respectively, as compared to BM, indicating the formation/condensation of the compounds of minerals in these materials through pyrolysis<sup>##REF##28337620##51##</sup>. In comparison to HC-based materials, the increased ash contents in BC-based materials were related to the thermal oxidation of organic compounds during the pyrolysis process<sup>##REF##31048990##8##</sup>. The BM displayed a higher moisture content of 2.73%, while it was higher in HCs (HC = 2.41%, HCK10 = 2.11%) and HCK20 = 1.46%) as compared to BCs (BC = 0.87%, BCK10 = 1.02%, and BCK20 = 0.41%). The HC materials had a higher moisture content due to being hydrothermally pretreated.</p>\",\n \"<p id=\\\"Par26\\\">The finding displayed that the pH of BC materials elevated with an increasing kaolinite percentage during pyrolysis. In contrast, the pH of HC materials decreased with an increasing kaolinite percentage during hydrothermal carbonization. The highest pH value was 10.97 in BCK20, while the lowest was 4.48 in HCK20. The pH increased by 3.14, 4 and 5.7 units in BC, BCK10 and BCK20, respectively, compared with the BM, indicating the elimination of acidic functional groups and concentration of basic functional groups<sup>##REF##29220749##52##</sup>. Furthermore, with increasing the pyrolysis temperature, the recalcitrant cationic species (Ca<sup>+2</sup>, Mg<sup>+2</sup>, Na<sup>+</sup>) condensed in BC materials, which could also cause increased pH<sup>##REF##20634061##53##,##UREF##32##54##</sup>. On the other hand, the pH values of the HCK10 and HCK20 were decreased by only 0.09 and 0.83 units, respectively, as compared with BM, indicating the minimal removal of basic functional groups<sup>##REF##29220749##52##</sup>. The pH value of the HC increased by 0.91 units compared with the BM. Likewise, the EC of all materials decreased with pyrolysis and hydrothermal carbonization, which could be due to washing all the materials before analysis to remove surface basicity<sup>##UREF##33##55##</sup>, as well as the dissolved salts released into the liquid phase during hydrothermal carbonization<sup>##UREF##34##56##</sup>. The highest cation exchange capacity (CEC) was shown in HC (132.8 cmol kg<sup>−1</sup>), while the lowest CEC was in BC (16.3 cmol kg<sup>−1</sup>). With pyrolysis, the CEC was increased by 34.97% for BCK10 and 38.04% for BCK20 as compared to pristine BC, which is attributed to an increase in surface functional groups<sup>##UREF##35##57##</sup>. Contrarily, with hydrothermal carbonization, the CEC was decreased by 7.38% for HCK10 and 6.02% for HCK20 compared with HC. Nevertheless, the CEC for HC materials was several times higher than that for BC materials due to more oxygen-containing functional groups on the surface of HC materials<sup>##UREF##36##58##–##UREF##39##61##</sup>.</p>\",\n \"<title>X-ray diffraction, SEM, and FTIR analyses</title>\",\n \"<p id=\\\"Par27\\\">The XRD spectra of the BM, kaolinite deposits, and synthesized materials are shown in Fig. ##FIG##0##1##. The various visible peaks on the spectra of all the synthesized materials demonstrate the presence of crystalline minerals and inorganic materials. The XRD patterns of raw materials, such as kaolinite clay deposits and BM, were identified. In the XRD pattern of kaolinite deposits, the strongest intense peaks of kaolinite in XRD pattern of kaolinite deposits were identified at 2θ = 12.46°; 25.06° and 26.68° (Fig. ##FIG##0##1##a)<sup>##UREF##40##62##–##UREF##42##64##</sup> and lower diffraction kaolinite peaks than previous peaks were revealed at 2θ = 36.7°; 39.50°; 42.56°; 50.3°; 55.16° and 62.4°<sup>##UREF##43##65##–##REF##33567736##70##</sup>. Four peaks of cellulose in BM are displayed at = 21.8; 22.4°; 24.3° and 30°, peak of carbon-containing minerals mellite is at 2θ = 14.8°, and calcite at 2θ = 39.78° (Fig. ##FIG##0##1##a)<sup>##UREF##48##71##–##UREF##51##74##</sup>. The kaolinite peaks were found in BC-based materials and HC-based materials, which endorsed successfully implanted kaolinite onto the BC and HC matrix. Peak shifting is a sign of interactions between kaolinite and BC or HC during synthesizing the composite. Peaks of kaolinite at 2θ = 20.7°; 26.68°; 39.5° and 50.3° in BCK10 and BCK20 were shifted to 20.78°; 26.6°; 39.26° and 50°, respectively, and 2θ = 25.06° in BCK10 was shifted to 25.2°, and 2θ = 36.86° in BCK20 was shifted to 36.48° (Fig. ##FIG##0##1##b)<sup>##UREF##42##64##,##UREF##44##66##,##UREF##46##68##,##UREF##52##75##</sup>. Similarly, peaks of kaolinite at 2θ = 12.46°; 26.68°; 36.1°; 38° and 42.56° in HCK10 and HCK20 were shifted to 12.26°; 26.62°; 36.14°; 38.32° and 42.46°, respectively, and 2θ = 39.5° in HCK10 was shifted to 39.44°, and 2θ = 55.1° was shifted to 54.78° (Fig. ##FIG##0##1##b)<sup>##UREF##42##64##,##UREF##45##67##,##UREF##53##76##,##UREF##54##77##</sup>. The other peaks in BC-based materials were impurities corresponding to calcite and quartz; and cellulose and mellite for HC-based materials. The XRD analysis of BC (Fig. ##FIG##0##1##b) displayed peaks at 2θ = 23.04°, which indicated the presence of graphite<sup>##UREF##55##78##</sup>. Other peaks were identified at 2θ = 29.3°; 39.44°; 43.1°; 47.34° and 48.28°, and indicated the presence of calcite<sup>##REF##29220749##52##,##REF##30149311##79##,##REF##32685816##80##</sup>. Likewise, the peak of cellulose in HC is at 2θ = 22.4°<sup>##UREF##49##72##</sup> and the peak of mellite is at 2θ = 14.8° (Fig. ##FIG##0##1##c). In BC-based materials, mellite was lost during the pyrolysis process of BM, while it still exists with HCs. Therefore, the changes in the results of the kaolinite-synthesized BC and kaolinite-synthesized HC showed that the synthesis method used successfully implanted kaolinite onto the BC and HC matrix.</p>\",\n \"<p id=\\\"Par28\\\">SEM images in Fig. ##FIG##1##2## depict the surface morphology of the synthesized materials. SEM images are highly useful to obtain minute details about the structure of synthesized materials and their modifications. In addition, comparing pristine BC and HC with their modified and raw materials would therefore allow us to make judgments on morphological changes during pyrolysis and hydrothermal carbonization. Pyrolysis and hydrothermal carbonization converted the crystalline surface of BM (Fig. ##FIG##1##2##a) into porous and amorphous materials, as presented in Fig. ##FIG##1##2##b–g. The surface area of the BC-based materials and HC-based materials was generally coated by thin film structures and were more irregular on the BC and HC surfaces (Fig. ##FIG##1##2##c–g), indicating that after zooming in at 3000 × magnification, the kaolinite well onto the surfaces and within the pores of BC and HC<sup>##REF##19150079##81##</sup>. The decomposition and volatilization of biomass caused a small number of pores with different sizes to appear in pristine BC and HC. In addition, the SEM images showed that kaolinite was not entirely covering the surfaces of the BC-based materials<sup>##UREF##56##82##,##UREF##57##83##</sup>, while kaolinite was entirely covering the surfaces of the HC-based materials.</p>\",\n \"<p id=\\\"Par29\\\">According to Li et al.<sup>##REF##30423497##84##</sup>, the surface functional groups, particularly O-containing functional groups, may facilitate the chemical adsorption of BC and HC. Figure ##FIG##2##3## displays the FTIR spectra of synthesized materials in the range of 400–4400 cm<sup>−1</sup>. A broad band has been explored at 3300–3800 cm<sup>−1</sup> in BM, representing the presence of O–H bonding<sup>##UREF##58##85##</sup>, which continued to appear during hydrothermal carbonization and vanished during pyrolysis. The structural–functional groups found in the BC and HC materials included C=C, O–H, C–O, C=O, C–H, Si–O–Al, Si–O–Si, Si–O, N–H, C–OH, CH<sub>2</sub> and C–N. It is evident that some functional groups, such as C–H and C=O (between 1413 and 1462 cm<sup>−1</sup>) were shared by BC and HC materials<sup>##REF##31254497##86##</sup>, C–O and O–H (1033 cm<sup>-1</sup>)<sup>##UREF##59##87##</sup>. For BC-based materials, some peaks appeared with increasing kaolinite, such as; Si–O–Si groups at 465 and 469 cm<sup>−1</sup><sup>##UREF##60##88##</sup>, Si–O at 791 cm<sup>−1</sup><sup>##UREF##61##89##</sup> and C–O, C–N at 1083 cm<sup>−1</sup><sup>##UREF##62##90##</sup><sup>,</sup><sup>##REF##32122867##91##</sup>. Therefore, those peaks indicated that kaolinite was successfully loaded onto the BC matrix<sup>##UREF##63##92##</sup>. The HC materials showed more bands than BC materials, which could be due to minimal losses of functional groups. Likewise, some peaks appeared with increasing kaolinite, such as Si–O–Si at 469 cm<sup>−1</sup><sup>##UREF##58##85##</sup>, Si–O and Al–O vibrations at 762, 696 and 539 cm<sup>−1</sup><sup>##UREF##64##93##</sup>, Si–O at 784 cm<sup>−1</sup><sup>##UREF##65##94##</sup>. On the other hand, the same peaks appeared in HC, HCK10, and HCK20 composites, such as; C–O and O–H at 1033 cm<sup>−1</sup><sup>##UREF##59##87##</sup>, C–O–C at 1111 cm<sup>−1</sup><sup>##UREF##66##95##</sup>, CH<sub>3</sub> at 1440 cm<sup>−1</sup><sup>##UREF##67##96##</sup>, C=O at 1510 cm<sup>−1</sup>, C=O at 1510 cm<sup>−1</sup><sup>##REF##35580789##97##</sup>, COOH at 1700 cm<sup>−1</sup><sup>##UREF##68##98##</sup>, C–H at 2921 cm<sup>−1</sup><sup>##UREF##69##99##</sup>, and O–H (between 3300 and 3800 cm<sup>−1</sup>) <sup>##UREF##56##82##</sup>. Moreover, some bands were not found in BC and HC alone, but when composited with kaolinite, such bands were visible. Therefore, this finding can further assist in predicting the impact of kaolinite-synthesized BCs and HCs on the removal efficiency of these composites for various pollutants.</p>\",\n \"<title>Size and surface characteristics</title>\",\n \"<p id=\\\"Par30\\\">The BET surface area, pore size, and pore volume of the BM and synthesized materials are shown in Table ##TAB##1##2##. BC materials have a higher surface area than HC materials. The highest surface areas were found in BC (290.89 m<sup>2</sup> g<sup>−1</sup>), and the lowest in HC (5.32 m<sup>2</sup> g<sup>−1</sup>). Moreover, it was detected that the surface areas of the BCK10 and BCK20 (225.14 and 180.40 m<sup>2</sup> g<sup>−1</sup>) were reduced compared to the pristine BC (290.89 m<sup>2</sup> g<sup>−1</sup>), indicating that the pores on the BC might have been covered/clogged by kaolinite<sup>##UREF##24##39##</sup>. Conversely, the pristine HC suffered agglomeration and revealed a lower surface area<sup>##REF##35580789##97##,##REF##30609488##100##</sup>. Therefore, the surface area of the HCK10 and HCK20 was higher than that of HC. The surface area of the HCK10 and HCK20 was 16.11 and 15.44 m<sup>2</sup> g<sup>−1</sup>, which was threefold higher than the pristine HC (5.32 m<sup>2</sup> g<sup>−1</sup>). The composite interposes retained the kaolinite particles, increasing the surface area of kaolinite-HC<sup>##REF##32105922##43##,##REF##33773345##101##</sup>. Contrarily, the BCK10 and BCK20 pore sizes were 38.31 and 28.17 Å, which were higher as compared to BC (28.81Å). On the contrary, the pore sizes of HCK10 and HCK20 were 145.90 and 175.92 Å, which were lower as compared to HC (187.71 Å). The highest pore sizes was appeared in HC (187.71 Å), while the lowest was in BCK20 (28.17 Å). However, with increasing the amount of kaolinite deposit, the surface area decreased by 22.68% in BCK10 and 38% in BCK20 compared with pristine BC, while kaolinite addition increased the surface area of HCK10 by 203% and HCK20 by 190% as compared to pristine HC. Overall, the pore size of the HC materials was more than 10 times larger than that of the BC materials. The previous studies showed that the low surface area of zeolite and silica-composited BC is due to plugging of pores in the existence of minerals<sup>##REF##28337620##51##</sup>. Yao et al.<sup>##UREF##21##35##</sup> also mentioned that the blockage of BC pores by clay minerals particles could be the cause of the decreased surface area of clay biochar composites. The hydrodynamic size of the particles of the synthesized materials in aqueous suspensions was determined (Table ##TAB##1##2##). Any particle aggregate or particle with an equivalent diameter is seen by Dynamic Light Scattering (DLS) to reflect a similar size. The average size of particles (hydrodynamic size) was 2.63 μm for BC, 2.34 μm for BCK10, 2.73 μm for BCK20, 2.19 μm for HC, 2.38 μm for HCK10, and 3.10 μm for HCK20. These results of particle size analyses suggested that HC showed the minimum particle size, while HCK20 showed the maximum particle size.</p>\",\n \"<p id=\\\"Par31\\\">Colloidal dispersions have an electrokinetic potential known as zeta potential, and the value of this potential is influenced by the surface charge of the individual particles. In our study, the zeta potential values of the synthesized materials were determined as a function of the solution pH (Table ##TAB##1##2##). The zeta potential of the pristine BC and BC-based materials ranged from − 25.06 to − 25.08 mV; however, the zeta potential of the pristine HC and HC-based materials ranged from − 21.68 to − 24.98 mV. The highest zeta potential was shown in BCK10 (− 25.08 mV), while the lowest was in HCK20 (− 21.86 mV). Consequently, the negative charge of pristine BC and BC-based materials is slightly higher than that of pristine HC and HC-based materials. Hence, the zeta potential of all the synthesized materials was negative, indicating that all surfaces of the synthesized materials are negatively charged. Therefore, a larger zeta potential means a larger negative charge, which is beneficial for remediation, especially with removing cationic ions.</p>\",\n \"<title>Elemental composition and carbon stability</title>\",\n \"<p id=\\\"Par32\\\">The elemental composition of the synthesized materials is presented in Table ##TAB##2##3##. In comparison to BM, thermal treatment enhanced the total C contents of BC and HC materials. An increased degree of carbonization may be the cause of rising C contents with pyrolysis and hydrothermal carbonization. The higher contents of C were observed in BC, and the lowest was observed in HCK20 among the synthesized materials. HC materials had higher H contents ranging from 5.33 to 5.54%, while BC materials ranged from 1.08 to 1.41%. Among the synthesized materials, the maximum N contents were in BCK20, while the minimum was in BC. However, the C contents of the BC, BCK10, BCK20, HC, HCK10, and HCK20 were increased by 47.8%, 44.1%, 43.6%, 18.3, 20.9%, and 17.8%, respectively, as compared to BM. The C content was higher in pristine BC, which decreased with kaolinite modification in BC-based materials, such as 6.59% in BCK10 and 7.32% in BCK20. Meanwhile, the C content was increased in HCK10 (3.37%) and slightly decreased in HCK20 (0.52%) compared to HC. The increasing lignin content in biomass has been mentioned to enhance carbonization and increase the content of C in BC<sup>##REF##25957037##102##,##UREF##70##103##,##UREF##72##105##</sup>. Other studies reported that cellulose and hemicelluloses also significantly affect on the C content<sup>##UREF##71##104##,##UREF##72##105##</sup>. On the other hand, a reduction of H contents in BC materials was more than that of HC materials. Compared to BM, the reduction of H contents for BC, BCK10, BCK20, HC, HCK10, and HCK20 was 76.3%, 78.4%, 82.0%, 10.8%, 7.3%, and 9.9%, respectively. According to previous studies, Gai et al. <sup>##REF##25469875##106##</sup> reported that the decrease in H content of BC was due to the loss of water, gaseous H<sub>2</sub>, hydrocarbons, and tarry vapors. The total N contents decreased with pyrolysis and hydrothermal carbonization in BC (27.3%), HC (3.1%), and HCK10 (11.9%), while it increased in BCK10 (21.5%), BCK20 (32.6%), and HCK20 (18.9%) compared to BM. Furthermore, the contents of N increased with increased kaolinite modification of BC and HC, which were higher in BCK20 and HCK20. The total O contents decreased with pyrolysis and hydrothermal carbonization of biomass in all materials. The reduction in O was observed in BC (97.1%), BCK10 (92.5%), BCK20 (94.5%), HC (27.1%), HCK10 (30.8%), and HCK20 (29.6%), compared to BM. Moreover, BC-based materials showed higher O contents compared to pristine BC, while HC-based materials slightly presented lower O contents compared to pristine HC. Dehydration, volatilization, and depolymerization could be the causes of the decrease in O contents with hydrothermal carbonization and pyrolysis<sup>##REF##23376202##107##</sup>.</p>\",\n \"<p id=\\\"Par33\\\">Figure ##FIG##3##4## illustrates the Van Krevelen diagram, frequently used to calculate the molar O/C and H/C ratios to compute the BC and HC material’s recalcitrance. With pyrolysis and thermal carbonization, conocarpus waste biomass is dehydrated and depolymerized to produce smaller dissociation products<sup>##UREF##14##24##,##REF##20099810##108##</sup>. Reduced H/C and O/C molar ratios showed reduced polarity and a greater degree of aromaticity, which in turn increased the stability of BC<sup>##UREF##33##55##</sup>. Also, another research mentioned that the higher stability of BC composites is attributed to greater polyaromatic carbon content compared to HC composites<sup>##UREF##73##109##</sup>. From Table ##TAB##2##3## and Fig. ##FIG##3##4##, the surface polarity index (i.e., indicated by O/C molar ratio) of BC and HC materials decreased with pyrolysis and hydrothermal carbonization compared with BM, indicating a decrease in the hydrophobicity of these materials. Overall, the highest values of O/C and H/C (0.604 and 1.47) were found for the BM. Among the synthesized materials, maximum values of O/C and H/C (0.36 and 1.09) were expressed by HCK20 and HC, respectively. In any case, BC-based materials showed slightly decreased H/C values and increased O/C values compared to pristine BC. In contrast to pristine HC, HC-based materials showed slightly increased H/C values and decreased O/C values. The O/C ratio of BC (0.01), BCK10 (0.025), and BCK20 (0.02) was less than 0.2, suggesting more stability of such materials and a half-life of more than 1000 years<sup>##UREF##74##110##</sup>. In contrast, the O/C ratio of HC (0.360), HCK10 (0.331), and HCK20 (0.350) was between 0.2 and 0.6, suggesting a half-life ranging from 100 to 1000 years<sup>##UREF##74##110##</sup>. For BM, the O/C ratio was 0.604, which will probably possess a half-life of less than 100 years<sup>##UREF##74##110##</sup>). Likewise, low H/C values of BC and HC materials compared with BM indicated high aromaticity and reactivity<sup>##UREF##75##111##</sup>. In addition, the much lower H/C ratio of BC materials compared to HC materials demonstrated that BC materials were heavily carbonized and showed highly aromatic structures. Depending on IBI<sup>##UREF##76##112##</sup> criteria, the H/C < 0.7 showed greater combined aromatic ring structures for the BC materials, while the H/C molar ratio of HC materials is higher than that of BC materials, which was ˃ 0.7. Therefore, BC materials showed higher aromaticity and low polarity than that of HC materials. Hence, the ratios of H/C and O/C in the BC-based materials and HC-based materials indicated more aromatic C and becoming less hydrophilic<sup>##UREF##77##113##</sup>.</p>\",\n \"<title>TGA and recalcitrance index (R<sub>50</sub>)</title>\",\n \"<p id=\\\"Par34\\\">In this study, TGA thermogravimetric analysis was applied to investigate the long-term stability of synthesized materials. The results of TGA-DTG analysis are shown in Fig. ##FIG##4##5##a,b. Our results indicated that the BM thermally decomposed earlier than the HC and BC materials due to thermal instability. The thermogravimetric curves of the different BC materials and the HC materials exposed similar performances as regards weight loss (%) on a reducing trend with rising temperature. For BC, BCK10, and BCK20, the sudden weight loss began at 650 °C ≈ 700 °C, at 350 °C ≈ 400 °C for HC, HCK10, and HCK20, and at 250 °C ≈ 350 °C for BM. The thermograms displayed two general regions where weight loss has occurred: (i) around 300°C for BM and HC materials because of thermal degradation of cellulose and hemicellulose compounds<sup>##UREF##78##114##</sup> and (ii) around 600–1000 °C because of lignin degradation<sup>##REF##28337620##51##</sup>. The order of materials for degradability is as follows: BCK20 < BCK10 < BC < HCK20 < HCK10 < HC < BM. Therefore, the BC and HC with a 20% kaolinite ratio were more stable, followed by a 10% kaolinite ratio, followed by pristine. Moreover, a higher weight loss was observed in HC materials Than in BC materials.</p>\",\n \"<p id=\\\"Par35\\\">The recalcitrance of the synthesized materials in soil depends on the potential of BC and HC materials to resist thermal, physical, and chemical degradation. The formation of organometallic complexes and the aromatic carbon structure play significant roles in BC and HC materials. Due to its increased aromaticity, the C of BC materials has a higher potential for recalcitrance than that of HC materials and BM; however, the recalcitrance and stability of C in the soil after adding BC and HC materials vary depending on the kind and feedstock composition, the texture and structure of the soil, and other environmental factors. Hence, the recalcitrance index of BC and HC materials is required to be determined in relative to graphite, because one of the most stable C forms is graphite<sup>##REF##35598667##115##</sup>. As a result, TGA thermograms that are marked as R<sub>50</sub> were used by Harvey et al. <sup>##REF##22242866##47##</sup> to create a novel recalcitrance index that predicts the materials' potential for recalcitrance.</p>\",\n \"<p id=\\\"Par36\\\">To calculate the recalcitrant index (R<sub>50</sub>), TGA thermograms for all materials were corrected for moisture and ash contents, which reflect the stability of synthesized materials and the extent of C sequestration<sup>##REF##25203840##116##</sup>. Figure ##FIG##4##5##c,d display moisture and ash-corrected TGA thermograms. Therefore, the R<sub>50</sub> values of BM and HC in this study were 0.40 and 0.42, indicating that these are highly biodegradable and belong to class 3 (Table ##TAB##2##3##). BC, BCK10, and BCK20 were considered in class 1 with R<sub>50</sub> of 0.78, 0.81, and 0.79, respectively, indicating that these have high recalcitrance potential<sup>##REF##22242866##47##</sup>. The R<sub>50</sub> values of HCK10 and HCK20 composites were 0.50 and 0.51, suggesting they were minimally degradable. Increased R<sub>50</sub> in the BCK10 and BCK20 composites could be attributed to kaolinite's presence, which improved BC's oxidation resistance. The previous study by Ahmad et al.<sup>##REF##28337620##51##</sup> demonstrated that the higher R<sub>50</sub> in silica BC composites could be related to the probable protection of C by silica particles, which may be controlled through the pyrolysis process. The reason was also attributed to the change from the aromatic C–C/C=C functionality to the C–O and C–H configuration of the BC surface forming stable organo-mineral complexes (i.e., C–O–Al and C–O–Si) with clay minerals<sup>##REF##34695458##117##</sup>. The previous study by Wang et al.<sup>##REF##35598667##115##</sup> showed that the R<sub>50</sub> increased by 0.89 for kaolinite-composited BC, reflecting that minerals-composited BC could improve the thermal stability of BC. According to Ahmad et al.<sup>##UREF##75##111##</sup>, changes in structural arrangements within the silica-BC complex may be responsible for the high recalcitrance and C sequestration potential of silica-composited BC. Therefore, increased R<sub>50</sub> values of BC materials expect greater recalcitrance and stability because of the particular interactions of kaolinite with the BC matrix.</p>\",\n \"<p id=\\\"Par37\\\">Although it provides a range of recalcitrance relevant to graphite (a highly stable C form), the recalcitrance index (R<sub>50</sub>) does not provide information on the specific timing for C sequestration. Consequently, the potential of C sequestration (CS) of BC and HC materials has been calculated using R<sub>50</sub>, yield, and C contents. A higher CS percentage relies on (i) the yield (%), (ii) the contents of C (%) before and after pyrolysis and hydrothermal carbonization, and (iii) the R<sub>50</sub> value. In our study, the CS of the BC materials ranged from 46.43% to 51.63%, while the HC materials showed CS in the range of 29.16%–39.94%. The highest CS value was found in BCK20 (51.63), while the lowest was in HC. On the other hand, increased CS values were associated with an increasing percentage of kaolinite in BC and HC, which were 51.63% in BCK20 and 39.95% in HCK20 compared to pristine. Ahmad et al. <sup>##UREF##75##111##</sup> revealed that the silica presence in BC-based materials enhanced the C sequestration potential through modifications to the silica-BC complex's structural arrangements. Another study by Sewu et al. <sup>##UREF##79##118##</sup> indicated that the bentonite-BC materials had higher C sequestration potential than the original BC. Therefore, in our study, forming Si–O–C and Al–O–C in BC-based materials and HC-based materials possessed the highest C sequestration potential.</p>\",\n \"<title>Toxicity evaluation</title>\",\n \"<title>Contents of PAHs</title>\",\n \"<p id=\\\"Par38\\\">Biomass combustion is one of the primary sources of anthropogenic PAHs<sup>##UREF##80##119##</sup>. The total PAHs quantities and proportional contributions of individual PAHs provide valuable data regarding the quality of synthesized BC and HC materials regarding environmental safety. The contents of PAHs in BC and HC materials were determined to identify the PAHs retention in all materials due to various processes of pyrolysis and hydrothermal carbonization, and the findings are presented in Table ##TAB##3##4##. Pristine HC showed higher amounts of all PAHs than in pristine BC. Acenaphthene content was found to be around 2.6 times greater in pristine HC than pristine BC. Moreover, around 2 times greater phenanthrene, anthracene, and benzo [e] pyrene [BeP] contents, about 1.5 times greater naphthalene and acenaphthylene contents, and 1.3 times greater fluorine, fluoranthene, pyrene, and retene contents were detected in pristine HC in comparison to pristine BC. These findings support previous studies showing that pristine HC contains more PAHs than pristine BC<sup>##REF##31048990##8##,##UREF##81##120##</sup>. Compared to dry pyrolysis, the tar condensation on the surface of HC through hydrothermal carbonization of biomass might have resulted in the retention of PAHs in such materials<sup>##UREF##36##58##</sup>. In contrast, the modification of BC and HC with kaolinite decreased the PAHs contents as compared to pristine BC and HC. The HC-based materials such as HCK10 and HCK20 showed a greater reduction of PAHs contents than the BC-based materials such as BCK10 and BCK20. Similarly, increasing kaolinite modification means more reduction of PAHs, such as in BCK20 and HCK20, which were more PAHs-reducing than BCK10 and HCK10. Generally, PAHs contents in HC-based materials were lower than in BC-based materials. Compared to pristine BC and HC, the reduction for these materials significantly ranged between 1.2 and 6.4 times for most of the PAHs contents. For instance, the reduction for phenanthrene was about 5 times for HCK10 and 6.4 times for BCK20 compared to HC. Similarly, the reduction of naphthalene was about 1.2 times for BCK10 and 2.6 times for BCK20 compared to pristine BC; likewise, about 2.8 times for HCK10 and 3.8 times for HCK20 as compared to pristine HC. Overall, ten forms of seventeen PAHs were detected in the BC and HC materials. The sum of the PAHs total concentrations of BC and HC materials ranged from 739.1 μg kg<sup>−1</sup> in HCK20 to 2770.7 μg kg<sup>−1</sup> in HC. The order of the ∑PAHs contents was HC (2770.7 μg kg<sup>−1</sup>) ˃ BC (1752.9 μg kg<sup>−1</sup>) ˃ BCK10 (1514.1 μg kg<sup>−1</sup>) ˃ HCK10 (972.8 μg kg<sup>−1</sup>) ˃ HCK20 (739.1 μg kg<sup>−1</sup>) ˃ BCK20 (934.4 μg kg<sup>−1</sup>). Nevertheless, ∑ 15 PAHs contents (∑16 US-EPA PAHs, except Benzo[b] Flouranthene) were in the BC (1487.8 μg kg<sup>−1</sup>), BCK10 (1293.3 μg kg<sup>−1</sup>), BCK20 (799.5 μg kg<sup>−1</sup>), HC (2383.2 μg kg<sup>−1</sup>), HCK10 (861.71 μg kg<sup>−1</sup>), and HCK20 (619.5 μg kg<sup>−1</sup>). Therefore, ∑15 PAHs contents in BC and HC materials were lower than the threshold value of ∑16 PAH (6000–20,000 μg kg<sup>−1</sup> = 6000 μg kg<sup>−1</sup>) provided by the IBI<sup>##UREF##76##112##</sup>. Therefore, the PAHs compounds in BC and HC materials are considered safe for use as soil amendments and have the lowest potential risk for PAHs-related effects.</p>\",\n \"<p id=\\\"Par39\\\">Based on the aromatic rings number, PAHs with 3 rings were predominant in BC and HC materials, followed by those with 4, 2, and 5 rings (Fig. ##FIG##5##6##). Large proportion of low-molecular-weight 3 ring PAHs in BC and HC materials may be because of their atmospheric emission during pyrolysis and retention in the condensed tar through hydrothermal carbonization. Comparatively, pristine BC contained higher proportions of 2 and 4 ring PAHs than pristine HC, while pristine HC contained higher proportions of 3 and 5 ring PAHs. In BC and HC-based materials, the proportions of 2 and 5 ring PAHs were 17% and 3% in BCK10, BCK20, HCK10, and HCK20, which were equal (except 2 ring PAHs in BCK20 which, was 13%). The proportions of 3 ring PAHs were ordered as BCK20 (67%) ˃ HCK10 and HCK20 (64%) ˃ BCK10 (61%). Likewise, the proportions of 4 ring PAHs were ordered as BCK10 (19%) ˃ HCK10 and HCK20 (17%) ˃ BCK20 (16%). Generally, the proportion of 3 ring PAHs was the highest (ranging from 59 to 67%) in all synthesized materials, while the proportion of 5 ring PAHs was the lowest (ranging from 2 to 3%). During pyrolysis, they are released into the atmosphere while being retained in the condensed tar during hydrothermal carbonization. Low molecular weight 3 ring PAHs may be present in significant concentrations in HC. Previous studies mentioned that the toxicity and PAHs concentration in BC decreased significantly with increasing temperature, which can be attributed to the evaporation with rising temperature<sup>##UREF##82##121##–##UREF##83##123##</sup>. In addition, the most important factors in the elimination of PAHs compounds are volatility and thermal degradation<sup>##UREF##84##124##</sup>. Washing the BC and HC materials could reduce the negative impact on plant growth due to the reduced PAHs compounds<sup>##REF##35313162##125##</sup>. Hence, kaolinite-composited BC and HC could be better options for amendments to soil and water due to decreasing PAHs contents.</p>\",\n \"<p id=\\\"Par40\\\">Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20).</p>\",\n \"<title>Maize germination and growth</title>\",\n \"<p id=\\\"Par41\\\">The germination test results of maize (<italic>Zea mays</italic> L.) are presented in Fig. ##FIG##6##7##a. In control (CK) treatment, 75% germination of maize was detected. Seed germination was significantly affected by the application of BC and HC materials. The highest germination was observed in the HC (0.2 g) treatment, which was 85%, while the lowest was for HC (0.4 g) and HCK20 (0.2 g) treatments, which were 55%. In addition, there were no significant differences between 0.2 and 0.4 g applications of BC, BCK10, BCK20, and HCK10. The order of the germination percentages was as follows: HC (0.2 g) ˃ CK, BC (0.2 and 0.4 g), BCK10 (0.2 and 0.4 g), BCK20 (0.2 and 0.4 g), HCK10 (0.2 and 0.4 g), HCK20 (0.4 g) ˃ HC (0.4 g), HCK20 (0.2 g). Our finding showed that the previously mentioned synthesized materials are safe when applied to soil to enhance plant production without phytotoxic effects. Our finding supports the previous studies, which mentioned that the application of HC mixed with kaolinite could mitigate greenhouse gas emissions and might support improved soil retention of C and N for better management of agricultural nutrients<sup>##REF##33773345##101##</sup>. Likewise, another study confirmed that applying a clay-BC composite positively impacted on the yield and quality of blue grass and improved soil properties<sup>##REF##28689123##126##</sup>.</p>\",\n \"<p id=\\\"Par42\\\">To evaluate the effect of various treatments, the shoot and root length of the maize seedling were also determined, and the results are shown in Fig. ##FIG##6##7##b. Compared to CK treatment, all treatments significantly improved the maize growth (except BC, 0.2 g). Shoot length of maize seedlings for BCK20 (0.2 and 0.4 g), HC (0.2 and 0.4 g), HCK10 (0.2 g), and HCK20 (0.2 g) treatments was 54.63%, 60.62%, 65.21%, 61.55%, 53.56%, and 64.20%, respectively, higher than CK. On the other hand, other treatments such as BC (0.4 g), BCK10 (0.2 and 0.4 g), HCK10 (0.4 g), and HCK20 (0.4 g) also increased the length of the shoot of maize seedlings by 36.11%, 25.62%, 40.60%, 24.36%, and 41.18%, respectively, as compared to CK. These findings confirm the potential of the synthesized materials to improve plant growth. Likewise, HC (0.2 and 0.4 g) and HCK10 (0.2 g) significantly increased the root length of maize seedlings by 89.27%, 90.80%, and 88.29%, respectively, compared to CK. Moreover, the root length of the other treatments was lower than previous treatments but higher than CK, which were 27.16% in BC (0.2 g), 38.71% in BC (0.4 g), 62.07% in BCK10 (0.2 g), 51.08% in BCK10 (0.4 g), 41.48% in BCK20 (0.2 g), 53.16% in BCK20 (0.4 g), 51.96% in HCK10 (0.4 g), 60.31% in HCK20 (0.2 g), and 52.06% in HCK20 (0.4 g).</p>\",\n \"<p id=\\\"Par43\\\">Figure ##FIG##6##7##c,d depict maize seedlings’ fresh and dry weights as influenced by synthesized materials. The HC (0.2 g) treatment enhanced fresh and dry maize weights by 112.49% and 105.40%, respectively, compared to CK, while the other treatments were lower than HC (0.2 g) treatment. Hence, all the treatments significantly increased the fresh and dry weights, but in varying proportions compared to the CK treatment. Mumme et al.<sup>##REF##29778049##127##</sup> showed that biochar-zeolite composites increased the germination rate compared to the non-amended treatments. Another study by Medha et al.<sup>##UREF##26##41##</sup> showed that Sorghum grass root and shoot length significantly increased than CK. Furthermore, among the tested materials, with the increase in the bentonite biochar composite and kaolinite fractions, the shoot and root length substantially increased by five and ten folds, respectively. In a study about the effect of biochar on the physiological growth of maize. Cong et al.<sup>##REF##37409290##128##</sup> found that biochar increased the dry biomass of maize by 22.22% against the control condition while up to 58.39% increase in plant height was noted against control with no biochar amendment. In another study, Yang et al.<sup>##REF##26828311##129##</sup> stated that kaolinite composite with walnut shell derived biochar improved its surface and adsorptive characteristics and nutrient release capacity, which ultimately positively impacted plant growth. Fregolente et al.<sup>##UREF##85##130##</sup> found positive impact of hydrochar application on root and shoot development and dry biomass production of maize. Therefore, the parameters of seed germination and seedling growth revealed positive impacts of the synthesized materials with varying proportions. Consequently, it is highly recommended to properly assess the BC and HC materials before applying them to the soil as amendments.</p>\",\n \"<p id=\\\"Par44\\\">The correlation between the characteristics of synthesized materials such as R<sub>50</sub>, CS, O/C, CEC, SA, and ∑ total PAHs toxicity and maize germination and growth were established (Table ##TAB##4##5##). The results of germination and growth parameters were taken by the average of additives rate. The relationship revealed a significant correlation between maize germination and growth and the characteristics of synthesized materials (R<sub>50</sub>, CS, O/C, CEC, SA, and PAHs). The significant relationships (r) between germination and characteristics of synthesized materials of R<sub>50</sub>, O/C, CEC, and SA were 0.75, − 0.78, − 0.78, and 0.81, respectively. Likewise, the r values between shoot length and R<sub>50</sub>, O/C, CEC, and SA were − 0.52, 0.54, 0.55, and − 0.71, respectively. Similarly, the r values between root length and R<sub>50</sub>, CS, O/C, CEC, and SA were − 0.78, − 0.83, 0.76, 0.78, and − 0.80, respectively. On the other hand, the r value between fresh weight and ∑ total PAHs was 0.53, while it was -0.50 between dry weight and SA. According to our findings, the characteristics of the synthesized materials have positive relationships on maize growth and germination.</p>\",\n \"<title>Implications of the study</title>\",\n \"<p id=\\\"Par45\\\">In recent years, fabricated BC and HC materials have received a lot of interest. Although a few studies have examined the feasibility of clay-supported BC and HC as composites in different applications, evidence from germination and growth parameters is still very limited. In our study, pristine BC, HC, and kaolinite-composited BC and HC were synthesized and characterized. The synthesized materials can be used successfully to sequester C, immobilize inorganic and organic pollutants in water and soil, increase N and phosphorus availability, and increase plant production at the field scale. Our founding materials, BC-based materials, could be used for long-term stability and sequestering C due to their high recalcitrance potential compared to HC-based materials, which had an R<sub>50</sub> > 0.7 and CS > 47.63%. The higher stability and C sequestration of these charred materials could help mitigate climate change. Furthermore, synthesized materials can be used successfully for water and soil remediation; BC-based materials have high surface area and zeta potential, while HC materials have high CEC and surface functional groups. Moreover, kaolinite-composited BC and HC can be used for plant production, which showed no toxicity in the germination test and the lowest potential risk for PAHs-related impacts. On the other hand, HC-based materials typically have advantages such as a low pH and are suitable in arid regions that suffer from high pH; hence, their additions to the alkaline soil can overcome the alkalinity; as a result, the pH value decreases in these areas. In addition, the kaolinite-composited BC and HC materials can enhance soil health by improving the properties of soil through physical structure, increasing porosity, reducing bulk density, enhancing soil aggregation, organic contaminants degradation, water and nutrients retention, as well as climate change mitigation.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":["<title>Conclusion</title>","<p id=\"Par46\">Conocarpus waste-derived BC and HC were composited with kaolinite deposits and characterized for their chemical, proximate, elemental, and structural properties. Moreover, the potential toxicity of the synthesized materials was assessed for their implications as soil amendments. Analyses of the structural, morphological, and chemical properties revealed distinctions between pristine and designed BCs and HCs in terms of their properties. BC-based materials showed greater recalcitrance indices (R<sub>50</sub>: 0.79–0.81) and C sequestration potentials (47.63–51.63%) as compared to HC-based materials (R<sub>50</sub>: 0.50–0.51) and C sequestration (39.32–39.94). Kaolinite particles prevented C particles' thermal degradation, enhancing their stability. The O/C and H/C ratios were in the range of 0.02–0.025 and 0.15–0.15, respectively, in the BC-based materials, while 0.33–0.35 and 1.08–1.09, respectively, in HC-based materials, suggesting more aromaticity in BC-based materials as compared to HC-based materials. In addition, these materials can be utilized to sequester soil C pools for a longer time when used to amend the soil. BC-based materials showed a higher CEC than pristine BC, while HC-based materials showed a higher surface area than pristine HC. The total PAHs content decreased with increasing kaolinite deposits percentage in BC and HC; therefore, a greater reduction has been shown in BCK20 and HCK20 than in pristine chars. Overall, the total PAHs contents in BC and HC materials were below the USEPA's suggested limits. The removal of organic pollutants was mostly affected by the pyrolysis and hydrothermal carbonization processes; therefore, the thermal treatments can be a good way to generate chars with kaolinite deposits that have little or no organic pollutants and could be utilized as a safe soil amendment. Kaolinite-synthesized BC and HC demonstrated a positive impact on maize germination and seedling growth. In the future, kaolinite-synthesized BC and HC can be investigated for their efficacy as inexpensive soil amendments to improve soil health and plant productivity.</p>"],"string":"[\n \"<title>Conclusion</title>\",\n \"<p id=\\\"Par46\\\">Conocarpus waste-derived BC and HC were composited with kaolinite deposits and characterized for their chemical, proximate, elemental, and structural properties. Moreover, the potential toxicity of the synthesized materials was assessed for their implications as soil amendments. Analyses of the structural, morphological, and chemical properties revealed distinctions between pristine and designed BCs and HCs in terms of their properties. BC-based materials showed greater recalcitrance indices (R<sub>50</sub>: 0.79–0.81) and C sequestration potentials (47.63–51.63%) as compared to HC-based materials (R<sub>50</sub>: 0.50–0.51) and C sequestration (39.32–39.94). Kaolinite particles prevented C particles' thermal degradation, enhancing their stability. The O/C and H/C ratios were in the range of 0.02–0.025 and 0.15–0.15, respectively, in the BC-based materials, while 0.33–0.35 and 1.08–1.09, respectively, in HC-based materials, suggesting more aromaticity in BC-based materials as compared to HC-based materials. In addition, these materials can be utilized to sequester soil C pools for a longer time when used to amend the soil. BC-based materials showed a higher CEC than pristine BC, while HC-based materials showed a higher surface area than pristine HC. The total PAHs content decreased with increasing kaolinite deposits percentage in BC and HC; therefore, a greater reduction has been shown in BCK20 and HCK20 than in pristine chars. Overall, the total PAHs contents in BC and HC materials were below the USEPA's suggested limits. The removal of organic pollutants was mostly affected by the pyrolysis and hydrothermal carbonization processes; therefore, the thermal treatments can be a good way to generate chars with kaolinite deposits that have little or no organic pollutants and could be utilized as a safe soil amendment. Kaolinite-synthesized BC and HC demonstrated a positive impact on maize germination and seedling growth. In the future, kaolinite-synthesized BC and HC can be investigated for their efficacy as inexpensive soil amendments to improve soil health and plant productivity.</p>\"\n]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">In this study, biochar (BC) and hydrochar (HC) composites were synthesized with natural kaolinite clay and their properties, stability, carbon (C) sequestration potential, polycyclic aromatic hydrocarbons (PAHs) toxicity, and impacts on maize germination and growth were explored. Conocarpus waste was pretreated with 0%, 10%, and 20% kaolinite and pyrolyzed to produce BCs (BC, BCK10, and BCK20, respectively), while hydrothermalized to produce HCs (HC, HCK10, and HCK20, respectively). The synthesized materials were characterized using X-ray diffraction, scanning electron microscope analyses, Fourier transform infrared, thermogravimetric analysis, surface area, proximate analyses, and chemical analysis to investigate the distinction in physiochemical and structural characteristics. The BCs showed higher C contents (85.73–92.50%) as compared to HCs (58.81–61.11%). The BCs demonstrated a higher thermal stability, aromaticity, and C sequestration potential than HCs. Kaolinite enriched-BCs showed the highest cation exchange capacity than pristine BC (34.97% higher in BCK10 and 38.04% higher in BCK20 than pristine BC), while surface area was the highest in kaolinite composited HCs (202.8% higher in HCK10 and 190.2% higher in HCK20 than pristine HC). The recalcitrance index (R<sub>50</sub>) speculated a higher recalcitrance for BC, BCK10, and BCK20 (R<sub>50</sub> > 0.7), minimal degradability for HCK10 and HCK20 (0.5 < R<sub>50</sub> < 0.7), and higher degradability for biomass and HC (R<sub>50</sub> < 0.5). Overall, increasing the kaolinite enrichment percentage significantly enhanced the thermal stability and C sequestration potential of charred materials, which may be attributed to changes in the structural arrangements. The ∑ total PAHs concentration in the synthesized materials were below the USEPA’s suggested limits, indicating their safe use as soil amendments. Germination indices reflected positive impacts of synthesized charred materials on maize germination and growth. Therefore, we propose that kaolinite-composited BCs and HCs could be considered as efficient and cost-effective soil amendments for improving plant growth.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">In this study, biochar (BC) and hydrochar (HC) composites were synthesized with natural kaolinite clay and their properties, stability, carbon (C) sequestration potential, polycyclic aromatic hydrocarbons (PAHs) toxicity, and impacts on maize germination and growth were explored. Conocarpus waste was pretreated with 0%, 10%, and 20% kaolinite and pyrolyzed to produce BCs (BC, BCK10, and BCK20, respectively), while hydrothermalized to produce HCs (HC, HCK10, and HCK20, respectively). The synthesized materials were characterized using X-ray diffraction, scanning electron microscope analyses, Fourier transform infrared, thermogravimetric analysis, surface area, proximate analyses, and chemical analysis to investigate the distinction in physiochemical and structural characteristics. The BCs showed higher C contents (85.73–92.50%) as compared to HCs (58.81–61.11%). The BCs demonstrated a higher thermal stability, aromaticity, and C sequestration potential than HCs. Kaolinite enriched-BCs showed the highest cation exchange capacity than pristine BC (34.97% higher in BCK10 and 38.04% higher in BCK20 than pristine BC), while surface area was the highest in kaolinite composited HCs (202.8% higher in HCK10 and 190.2% higher in HCK20 than pristine HC). The recalcitrance index (R<sub>50</sub>) speculated a higher recalcitrance for BC, BCK10, and BCK20 (R<sub>50</sub> > 0.7), minimal degradability for HCK10 and HCK20 (0.5 < R<sub>50</sub> < 0.7), and higher degradability for biomass and HC (R<sub>50</sub> < 0.5). Overall, increasing the kaolinite enrichment percentage significantly enhanced the thermal stability and C sequestration potential of charred materials, which may be attributed to changes in the structural arrangements. The ∑ total PAHs concentration in the synthesized materials were below the USEPA’s suggested limits, indicating their safe use as soil amendments. Germination indices reflected positive impacts of synthesized charred materials on maize germination and growth. Therefore, we propose that kaolinite-composited BCs and HCs could be considered as efficient and cost-effective soil amendments for improving plant growth.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":[],"string":"[]","isConversational":false},"back":{"kind":"list like","value":["<title>Acknowledgements</title>","<p>The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project No. (IFKSUOR3-603-1).</p>","<title>Author contributions</title>","<p>Conceptualization, H.A.A.-S. and A.S.A.-F.; methodology—H.A.A.-S., A.S.A.-F. and M.I.A.-W; software, M.A. and A.R.A.U.; validation, H.A.A.-S., A.R.A.U. and M.I.A.-W.; formal analysis, J.A., M.I.R. and M.A.; resources: A.S.A.-F. and M.I.A.-W.; investigation, H.A.A.-S., M.I.R. and M.A.M.; writing—H.A.A.-S.; data curation, H.A.A.-S.; writing—reviewing, editing, M.A, J.A, and M.A.M.; visualization, H.A.A.-S.; supervision, A.S.A.-F. and M.I.A.-W.; funding acquisition, A.S.A.-F.; project administration, M.I.A.-W. and A.S.A.-F. All authors have read and agreed to the published version of the manuscript.</p>","<title>Funding</title>","<p>The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the Project No. (IFKSUOR3-144–2).</p>","<title>Data availability</title>","<p>The data analyzed during the current study are available from the corresponding author on reasonable request.</p>","<title>Competing interests</title>","<p id=\"Par47\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Acknowledgements</title>\",\n \"<p>The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project No. (IFKSUOR3-603-1).</p>\",\n \"<title>Author contributions</title>\",\n \"<p>Conceptualization, H.A.A.-S. and A.S.A.-F.; methodology—H.A.A.-S., A.S.A.-F. and M.I.A.-W; software, M.A. and A.R.A.U.; validation, H.A.A.-S., A.R.A.U. and M.I.A.-W.; formal analysis, J.A., M.I.R. and M.A.; resources: A.S.A.-F. and M.I.A.-W.; investigation, H.A.A.-S., M.I.R. and M.A.M.; writing—H.A.A.-S.; data curation, H.A.A.-S.; writing—reviewing, editing, M.A, J.A, and M.A.M.; visualization, H.A.A.-S.; supervision, A.S.A.-F. and M.I.A.-W.; funding acquisition, A.S.A.-F.; project administration, M.I.A.-W. and A.S.A.-F. All authors have read and agreed to the published version of the manuscript.</p>\",\n \"<title>Funding</title>\",\n \"<p>The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the Project No. (IFKSUOR3-144–2).</p>\",\n \"<title>Data availability</title>\",\n \"<p>The data analyzed during the current study are available from the corresponding author on reasonable request.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par47\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>X-ray diffraction of (<bold>a</bold>) kaolinite and BM, (<bold>b</bold>) BC materials, and (<bold>c</bold>) HC materials. (<italic>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomassBC</italic>: biochar, <italic>BCK10</italic>: biochar composite with 10% of kaolinite, <italic>BCK20</italic>: biochar composite with 20% of kaolinite, <italic>HC</italic>: hydrochar, <italic>HCK10</italic>: hydrochar composite with 10% of kaolinite, <italic>HCK20</italic>: hydrochar composite with 20% of kaolinite, <italic>BM</italic>: biomass, <italic>K</italic>: kaolinite, <italic>Me</italic>: mellite, <italic>Ce</italic>: cellulose, <italic>S</italic>: saponite, <italic>Ca</italic>: calcite, <italic>G</italic>: graphite, <italic>Q</italic>: quartz).</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Scanning electron microscopy images of (<bold>a</bold>) BM (<bold>b</bold>) BC, (<bold>c</bold>) BCK10, (<bold>d</bold>) BCK20, (<bold>e</bold>) HC, (<bold>f</bold>) HCK10 and (<bold>g</bold>) HCK20. (Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass).</p></caption></fig>","<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Fourier Transformation Infrared (FTIR) analysis (<bold>a</bold>) BC materials and (<bold>b</bold>) HC materials and BM (Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass).</p></caption></fig>","<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Van Krevelen diagram of elemental ratios of H/C and O/C of synthesized materials. (Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass).</p></caption></fig>","<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Thermogravimetric analysis (TGA) of (<bold>a</bold>) BC materials, (<bold>b</bold>) HC materials and BM, (<bold>c</bold>) moisture, ash and kaolinite corrected TGA thermograms of BC materials, (<bold>d</bold>) moisture, ash and kaolinite corrected TGA thermograms of HC materials and BM (Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass).</p></caption></fig>","<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Percentage composition of 2-, 3-, 4-, 5-, and 6-rings PAHs in the BC and HC materials (2-ring PAHs include Naphthalene; 3-ring PAHs includes Acenaphthylene, Acenaphthene, Fluorene, Phenanthrene, Anthracene, and Retene; 4-ring PAHs includes Fluoranthene, Pyrene, Benzo[a] Anthracene, and Chrysene; 5-rings PAHs include Benzo[k]Fluoranthene, Benzo[a] Pyrene, and Benzo[e] Pyrene). Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass.</p></caption></fig>","<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Germination test, shoot and root length, and fresh and dry weight of maize (<italic>Zee mays</italic> L.) affected by (Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass).</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>X-ray diffraction of (<bold>a</bold>) kaolinite and BM, (<bold>b</bold>) BC materials, and (<bold>c</bold>) HC materials. (<italic>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomassBC</italic>: biochar, <italic>BCK10</italic>: biochar composite with 10% of kaolinite, <italic>BCK20</italic>: biochar composite with 20% of kaolinite, <italic>HC</italic>: hydrochar, <italic>HCK10</italic>: hydrochar composite with 10% of kaolinite, <italic>HCK20</italic>: hydrochar composite with 20% of kaolinite, <italic>BM</italic>: biomass, <italic>K</italic>: kaolinite, <italic>Me</italic>: mellite, <italic>Ce</italic>: cellulose, <italic>S</italic>: saponite, <italic>Ca</italic>: calcite, <italic>G</italic>: graphite, <italic>Q</italic>: quartz).</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>Scanning electron microscopy images of (<bold>a</bold>) BM (<bold>b</bold>) BC, (<bold>c</bold>) BCK10, (<bold>d</bold>) BCK20, (<bold>e</bold>) HC, (<bold>f</bold>) HCK10 and (<bold>g</bold>) HCK20. (Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass).</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Figure 3</label><caption><p>Fourier Transformation Infrared (FTIR) analysis (<bold>a</bold>) BC materials and (<bold>b</bold>) HC materials and BM (Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass).</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Figure 4</label><caption><p>Van Krevelen diagram of elemental ratios of H/C and O/C of synthesized materials. (Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass).</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Figure 5</label><caption><p>Thermogravimetric analysis (TGA) of (<bold>a</bold>) BC materials, (<bold>b</bold>) HC materials and BM, (<bold>c</bold>) moisture, ash and kaolinite corrected TGA thermograms of BC materials, (<bold>d</bold>) moisture, ash and kaolinite corrected TGA thermograms of HC materials and BM (Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass).</p></caption></fig>\",\n \"<fig id=\\\"Fig6\\\"><label>Figure 6</label><caption><p>Percentage composition of 2-, 3-, 4-, 5-, and 6-rings PAHs in the BC and HC materials (2-ring PAHs include Naphthalene; 3-ring PAHs includes Acenaphthylene, Acenaphthene, Fluorene, Phenanthrene, Anthracene, and Retene; 4-ring PAHs includes Fluoranthene, Pyrene, Benzo[a] Anthracene, and Chrysene; 5-rings PAHs include Benzo[k]Fluoranthene, Benzo[a] Pyrene, and Benzo[e] Pyrene). Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass.</p></caption></fig>\",\n \"<fig id=\\\"Fig7\\\"><label>Figure 7</label><caption><p>Germination test, shoot and root length, and fresh and dry weight of maize (<italic>Zee mays</italic> L.) affected by (Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass).</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":["<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Chemical and proximate analyses of the biomass and synthesized materials.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Material</th><th align=\"left\">Moisture (%)</th><th align=\"left\">Volatiles (%)</th><th align=\"left\">Fixed carbon (%)</th><th align=\"left\">Ash (%)</th><th align=\"left\">pH (1:25)</th><th align=\"left\">EC (dSm<sup>−1</sup>)</th><th align=\"left\">Yield (%)</th><th align=\"left\">CEC (cmol kg<sup>−1</sup>)</th></tr></thead><tbody><tr><td align=\"left\">BM</td><td char=\".\" align=\"char\">2.73</td><td char=\".\" align=\"char\">86.22</td><td char=\".\" align=\"char\">7.13</td><td char=\".\" align=\"char\">2.91</td><td char=\".\" align=\"char\">5.31</td><td char=\".\" align=\"char\">0.98</td><td align=\"left\">–</td><td align=\"left\">–</td></tr><tr><td align=\"left\">BC</td><td char=\".\" align=\"char\">0.87</td><td char=\".\" align=\"char\">21.86</td><td char=\".\" align=\"char\">70.20</td><td char=\".\" align=\"char\">7.07</td><td char=\".\" align=\"char\">8.72</td><td char=\".\" align=\"char\">0.44</td><td align=\"left\">24.15</td><td align=\"left\">16.3</td></tr><tr><td align=\"left\">BCK10</td><td char=\".\" align=\"char\">1.02</td><td char=\".\" align=\"char\">20.87</td><td char=\".\" align=\"char\">40.67</td><td char=\".\" align=\"char\">37.45</td><td char=\".\" align=\"char\">9.35</td><td char=\".\" align=\"char\">0.41</td><td align=\"left\">32.89</td><td align=\"left\">22.0</td></tr><tr><td align=\"left\">BCK20</td><td char=\".\" align=\"char\">0.41</td><td char=\".\" align=\"char\">21.18</td><td char=\".\" align=\"char\">25.59</td><td char=\".\" align=\"char\">52.82</td><td char=\".\" align=\"char\">10.97</td><td char=\".\" align=\"char\">0.66</td><td align=\"left\">36.84</td><td align=\"left\">22.5</td></tr><tr><td align=\"left\">HC</td><td char=\".\" align=\"char\">2.41</td><td char=\".\" align=\"char\">59.67</td><td char=\".\" align=\"char\">35.27</td><td char=\".\" align=\"char\">2.65</td><td char=\".\" align=\"char\">6.22</td><td char=\".\" align=\"char\">0.25</td><td align=\"left\">56.74</td><td align=\"left\">132.8</td></tr><tr><td align=\"left\">HCK10</td><td char=\".\" align=\"char\">2.11</td><td char=\".\" align=\"char\">51.60</td><td char=\".\" align=\"char\">27.80</td><td char=\".\" align=\"char\">18.48</td><td char=\".\" align=\"char\">5.22</td><td char=\".\" align=\"char\">0.31</td><td align=\"left\">62.19</td><td align=\"left\">123.0</td></tr><tr><td align=\"left\">HCK20</td><td char=\".\" align=\"char\">1.46</td><td char=\".\" align=\"char\">42.77</td><td char=\".\" align=\"char\">28.24</td><td char=\".\" align=\"char\">27.53</td><td char=\".\" align=\"char\">4.48</td><td char=\".\" align=\"char\">0.31</td><td align=\"left\">64.35</td><td align=\"left\">124.8</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Surface area, pore volume, pore size, hydrodynamics size and zeta potential analyses result of BC and HC materials.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Material</th><th align=\"left\">Surface area (m<sup>2</sup> g<sup>−1</sup>)</th><th align=\"left\">Pore volume (cm<sup>3</sup> g<sup>−1</sup>)</th><th align=\"left\">Pore size (Å)</th><th align=\"left\">Hydrodynamics size (µm)</th><th align=\"left\">Zeta potential (mV)</th></tr></thead><tbody><tr><td align=\"left\">BC</td><td char=\".\" align=\"char\">290.89</td><td char=\".\" align=\"char\">0.209</td><td char=\".\" align=\"char\">28.81</td><td char=\".\" align=\"char\">2.63</td><td char=\".\" align=\"char\">− 25.06</td></tr><tr><td align=\"left\">BCK10</td><td char=\".\" align=\"char\">225.14</td><td char=\".\" align=\"char\">0.216</td><td char=\".\" align=\"char\">38.31</td><td char=\".\" align=\"char\">2.34</td><td char=\".\" align=\"char\">− 25.08</td></tr><tr><td align=\"left\">BCK20</td><td char=\".\" align=\"char\">180.40</td><td char=\".\" align=\"char\">0.127</td><td char=\".\" align=\"char\">28.17</td><td char=\".\" align=\"char\">2.73</td><td char=\".\" align=\"char\">− 25.06</td></tr><tr><td align=\"left\">HC</td><td char=\".\" align=\"char\">5.32</td><td char=\".\" align=\"char\">0.025</td><td char=\".\" align=\"char\">187.71</td><td char=\".\" align=\"char\">2.19</td><td char=\".\" align=\"char\">− 24.98</td></tr><tr><td align=\"left\">HCK10</td><td char=\".\" align=\"char\">16.11</td><td char=\".\" align=\"char\">0.059</td><td char=\".\" align=\"char\">145.90</td><td char=\".\" align=\"char\">2.38</td><td char=\".\" align=\"char\">− 24.66</td></tr><tr><td align=\"left\">HCK20</td><td char=\".\" align=\"char\">15.44</td><td char=\".\" align=\"char\">0.068</td><td char=\".\" align=\"char\">175.92</td><td char=\".\" align=\"char\">3.10</td><td char=\".\" align=\"char\">− 21.68</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Elemental composition, their molar ratios, recalcitrant index (R<sub>50</sub>), temperature corresponding to 50% weight loss of water and ash-free composites (T<sub>50</sub>), and carbon sequestration potential (CS) of the synthesized materials.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Material</th><th align=\"left\">N (%)</th><th align=\"left\">C (%)</th><th align=\"left\">H (%)</th><th align=\"left\">S (%)</th><th align=\"left\">O (%)</th><th align=\"left\">O/C</th><th align=\"left\">H/C</th><th align=\"left\">T<sub>50</sub></th><th align=\"left\">R<sub>50</sub></th><th align=\"left\">CS (%)</th></tr></thead><tbody><tr><td align=\"left\">BC</td><td char=\".\" align=\"char\">4.96</td><td char=\".\" align=\"char\">92.50</td><td char=\".\" align=\"char\">1.41</td><td char=\".\" align=\"char\">0.00</td><td char=\".\" align=\"char\">1.13</td><td char=\".\" align=\"char\">0.009</td><td char=\".\" align=\"char\">0.182</td><td char=\".\" align=\"char\">690.33</td><td char=\".\" align=\"char\">0.78</td><td align=\"left\">46.43</td></tr><tr><td align=\"left\">BCK10</td><td char=\".\" align=\"char\">8.68</td><td char=\".\" align=\"char\">86.40</td><td char=\".\" align=\"char\">1.29</td><td char=\".\" align=\"char\">0.73</td><td char=\".\" align=\"char\">2.90</td><td char=\".\" align=\"char\">0.025</td><td char=\".\" align=\"char\">0.178</td><td char=\".\" align=\"char\">714</td><td char=\".\" align=\"char\">0.81</td><td align=\"left\">47.63</td></tr><tr><td align=\"left\">BCK20</td><td char=\".\" align=\"char\">10.11</td><td char=\".\" align=\"char\">85.73</td><td char=\".\" align=\"char\">1.08</td><td char=\".\" align=\"char\">0.96</td><td char=\".\" align=\"char\">2.13</td><td char=\".\" align=\"char\">0.019</td><td char=\".\" align=\"char\">0.150</td><td char=\".\" align=\"char\">697.67</td><td char=\".\" align=\"char\">0.79</td><td align=\"left\">51.63</td></tr><tr><td align=\"left\">HC</td><td char=\".\" align=\"char\">6.61</td><td char=\".\" align=\"char\">59.12</td><td char=\".\" align=\"char\">5.33</td><td char=\".\" align=\"char\">0.58</td><td char=\".\" align=\"char\">28.35</td><td char=\".\" align=\"char\">0.360</td><td char=\".\" align=\"char\">1.075</td><td char=\".\" align=\"char\">374</td><td char=\".\" align=\"char\">0.42</td><td align=\"left\">29.16</td></tr><tr><td align=\"left\">HCK10</td><td char=\".\" align=\"char\">6.01</td><td char=\".\" align=\"char\">61.11</td><td char=\".\" align=\"char\">5.54</td><td char=\".\" align=\"char\">0.43</td><td char=\".\" align=\"char\">26.91</td><td char=\".\" align=\"char\">0.331</td><td char=\".\" align=\"char\">1.080</td><td char=\".\" align=\"char\">445.33</td><td char=\".\" align=\"char\">0.50</td><td align=\"left\">39.32</td></tr><tr><td align=\"left\">HCK20</td><td char=\".\" align=\"char\">8.41</td><td char=\".\" align=\"char\">58.81</td><td char=\".\" align=\"char\">5.39</td><td char=\".\" align=\"char\">0.00</td><td char=\".\" align=\"char\">27.39</td><td char=\".\" align=\"char\">0.350</td><td char=\".\" align=\"char\">1.092</td><td char=\".\" align=\"char\">447.67</td><td char=\".\" align=\"char\">0.51</td><td align=\"left\">39.94</td></tr><tr><td align=\"left\">BM</td><td char=\".\" align=\"char\">6.82</td><td char=\".\" align=\"char\">48.32</td><td char=\".\" align=\"char\">5.98</td><td char=\".\" align=\"char\">0.00</td><td char=\".\" align=\"char\">38.88</td><td char=\".\" align=\"char\">0.604</td><td char=\".\" align=\"char\">1.474</td><td char=\".\" align=\"char\">354.33</td><td char=\".\" align=\"char\">0.40</td><td align=\"left\">–</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>The contents of polycyclic aromatic hydrocarbons (PAHs) (μg kg<sup>-1</sup>) in the synthesized materials.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">No</th><th align=\"left\">PAHs</th><th align=\"left\">BC</th><th align=\"left\">BCK10</th><th align=\"left\">BCK20</th><th align=\"left\">HC</th><th align=\"left\">HCK10</th><th align=\"left\">HCK20</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">Naphthalene (Nap)</td><td align=\"left\">318.38</td><td align=\"left\">255.05</td><td align=\"left\">124.77</td><td align=\"left\">466.23</td><td align=\"left\">164.37</td><td align=\"left\">123.9</td></tr><tr><td align=\"left\">2</td><td align=\"left\">Acenaphthylene (Acy)</td><td align=\"left\">302.09</td><td align=\"left\">241.11</td><td align=\"left\">148.18</td><td align=\"left\">438.03</td><td align=\"left\">177.04</td><td align=\"left\">113.6</td></tr><tr><td align=\"left\">3</td><td align=\"left\">Acenaphthene (Ace)</td><td align=\"left\">118.36</td><td align=\"left\">153.67</td><td align=\"left\">107.69</td><td align=\"left\">308.55</td><td align=\"left\">103.22</td><td align=\"left\">96.45</td></tr><tr><td align=\"left\">4</td><td align=\"left\">Fluorene (Flu)</td><td align=\"left\">85.88</td><td align=\"left\">69.44</td><td align=\"left\">83.67</td><td align=\"left\">116.23</td><td align=\"left\">89.45</td><td align=\"left\">44.56</td></tr><tr><td align=\"left\">5</td><td align=\"left\">Phenanthrene (Phe)</td><td align=\"left\">205.56</td><td align=\"left\">205.41</td><td align=\"left\">119.45</td><td align=\"left\">398.49</td><td align=\"left\">77.87</td><td align=\"left\">62.33</td></tr><tr><td align=\"left\">6</td><td align=\"left\">Anthracene (Ant)</td><td align=\"left\">99.46</td><td align=\"left\">73.69</td><td align=\"left\">63.21</td><td align=\"left\">182.66</td><td align=\"left\">88.7</td><td align=\"left\">57.72</td></tr><tr><td align=\"left\">7</td><td align=\"left\">Fluoranthene (Fla)</td><td align=\"left\">152.43</td><td align=\"left\">109.45</td><td align=\"left\">57.38</td><td align=\"left\">198.34</td><td align=\"left\">77.51</td><td align=\"left\">49.44</td></tr><tr><td align=\"left\">8</td><td align=\"left\">Pyrene (Pyr)</td><td align=\"left\">205.67</td><td align=\"left\">185.44</td><td align=\"left\">95.19</td><td align=\"left\">274.67</td><td align=\"left\">83.55</td><td align=\"left\">71.51</td></tr><tr><td align=\"left\">9</td><td align=\"left\">Benzo[a] Anthracene (BaA)</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td></tr><tr><td align=\"left\">10</td><td align=\"left\">Chrysene (Chr)</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td></tr><tr><td align=\"left\">11</td><td align=\"left\">Benzo[k]Fluoranthene (BF)</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td></tr><tr><td align=\"left\">12</td><td align=\"left\">Benzo[e] Pyrene (BeP)</td><td align=\"left\">42.55</td><td align=\"left\">38.21</td><td align=\"left\">31.29</td><td align=\"left\">89.35</td><td align=\"left\">27.23</td><td align=\"left\">24.31</td></tr><tr><td align=\"left\">13</td><td align=\"left\">Benzo[a] Pyrene (BaP)</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td></tr><tr><td align=\"left\">14</td><td align=\"left\">Indeno[123-cd]Pyrene (InP)</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td></tr><tr><td align=\"left\">15</td><td align=\"left\">Benzo [ghi] perylene (BghiP)</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td></tr><tr><td align=\"left\">16</td><td align=\"left\">Dibenzo[ah] Anthracene (DBA)</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">ND</td></tr><tr><td align=\"left\">17</td><td align=\"left\">Retene (Ret)</td><td align=\"left\">222.48</td><td align=\"left\">182.58</td><td align=\"left\">103.55</td><td align=\"left\">298.12</td><td align=\"left\">83.88</td><td align=\"left\">95.23</td></tr><tr><td align=\"left\"/><td align=\"left\">∑ total PAHs</td><td align=\"left\">1752.9</td><td align=\"left\">1514.1</td><td align=\"left\">934.4</td><td align=\"left\">2770.7</td><td align=\"left\">972.82</td><td align=\"left\">739.1</td></tr><tr><td align=\"left\"/><td align=\"left\">∑ 14 PAHs of 16 US-EPA PAHs</td><td align=\"left\">1487.8</td><td align=\"left\">1293.3</td><td align=\"left\">799.5</td><td align=\"left\">2383.2</td><td align=\"left\">861.71</td><td align=\"left\">619.5</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Relationship between the characteristics of synthesized materials and maize germination and growth.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Indices</th><th align=\"left\">R<sub>50</sub></th><th align=\"left\">CS</th><th align=\"left\">O/C</th><th align=\"left\">CEC</th><th align=\"left\">SA</th><th align=\"left\">∑ PAHs</th><th align=\"left\">Germination</th><th align=\"left\">Shoot length</th><th align=\"left\">Root length</th><th align=\"left\">Fresh weight</th><th align=\"left\">Dry weight</th></tr></thead><tbody><tr><td align=\"left\">R<sub>50</sub></td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">CS</td><td char=\".\" align=\"char\">0.94*</td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">O/C</td><td char=\".\" align=\"char\">− 0.99*</td><td char=\".\" align=\"char\">− 0.92*</td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">CEC</td><td char=\".\" align=\"char\">− 0.99*</td><td char=\".\" align=\"char\">− 0.92*</td><td char=\".\" align=\"char\">1.00*</td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">SA</td><td char=\".\" align=\"char\">0.95*</td><td char=\".\" align=\"char\">0.83*</td><td char=\".\" align=\"char\">− 0.96*</td><td char=\".\" align=\"char\">− 0.97*</td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">PAHs</td><td char=\".\" align=\"char\">− 0.18</td><td char=\".\" align=\"char\">− 0.48</td><td char=\".\" align=\"char\">0.10</td><td char=\".\" align=\"char\">0.11</td><td char=\".\" align=\"char\">0.00</td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Germination %</td><td char=\".\" align=\"char\">0.75*</td><td char=\".\" align=\"char\">0.49</td><td char=\".\" align=\"char\">− 0.78*</td><td char=\".\" align=\"char\">− 0.78*</td><td char=\".\" align=\"char\">0.81*</td><td char=\".\" align=\"char\">0.49</td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Shoot length mm</td><td char=\".\" align=\"char\">− 0.52*</td><td char=\".\" align=\"char\">− 0.42</td><td char=\".\" align=\"char\">0.54*</td><td char=\".\" align=\"char\">0.55*</td><td char=\".\" align=\"char\">− 0.71*</td><td char=\".\" align=\"char\">0.03</td><td char=\".\" align=\"char\">− 0.46</td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Root length mm</td><td char=\".\" align=\"char\">− 0.78*</td><td char=\".\" align=\"char\">− 0.83*</td><td char=\".\" align=\"char\">0.76*</td><td char=\".\" align=\"char\">0.78*</td><td char=\".\" align=\"char\">− 0.80*</td><td char=\".\" align=\"char\">0.48</td><td char=\".\" align=\"char\">− 0.31</td><td char=\".\" align=\"char\">0.67*</td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Fresh weight mg</td><td char=\".\" align=\"char\">0.01</td><td char=\".\" align=\"char\">− 0.10</td><td char=\".\" align=\"char\">− 0.04</td><td char=\".\" align=\"char\">− 0.02</td><td char=\".\" align=\"char\">− 0.11</td><td char=\".\" align=\"char\">0.53*</td><td char=\".\" align=\"char\">0.36</td><td char=\".\" align=\"char\">0.58*</td><td char=\".\" align=\"char\">0.57*</td><td char=\".\" align=\"char\">1.00</td><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Dry weight mg</td><td char=\".\" align=\"char\">− 0.39</td><td char=\".\" align=\"char\">− 0.48</td><td char=\".\" align=\"char\">0.39</td><td char=\".\" align=\"char\">0.40</td><td char=\".\" align=\"char\">− 0.50*</td><td char=\".\" align=\"char\">0.48</td><td char=\".\" align=\"char\">0.00</td><td char=\".\" align=\"char\">0.82*</td><td char=\".\" align=\"char\">0.78*</td><td char=\".\" align=\"char\">0.78*</td><td char=\".\" align=\"char\">1.00</td></tr></tbody></table></table-wrap>"],"string":"[\n \"<table-wrap id=\\\"Tab1\\\"><label>Table 1</label><caption><p>Chemical and proximate analyses of the biomass and synthesized materials.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Material</th><th align=\\\"left\\\">Moisture (%)</th><th align=\\\"left\\\">Volatiles (%)</th><th align=\\\"left\\\">Fixed carbon (%)</th><th align=\\\"left\\\">Ash (%)</th><th align=\\\"left\\\">pH (1:25)</th><th align=\\\"left\\\">EC (dSm<sup>−1</sup>)</th><th align=\\\"left\\\">Yield (%)</th><th align=\\\"left\\\">CEC (cmol kg<sup>−1</sup>)</th></tr></thead><tbody><tr><td align=\\\"left\\\">BM</td><td char=\\\".\\\" align=\\\"char\\\">2.73</td><td char=\\\".\\\" align=\\\"char\\\">86.22</td><td char=\\\".\\\" align=\\\"char\\\">7.13</td><td char=\\\".\\\" align=\\\"char\\\">2.91</td><td char=\\\".\\\" align=\\\"char\\\">5.31</td><td char=\\\".\\\" align=\\\"char\\\">0.98</td><td align=\\\"left\\\">–</td><td align=\\\"left\\\">–</td></tr><tr><td align=\\\"left\\\">BC</td><td char=\\\".\\\" align=\\\"char\\\">0.87</td><td char=\\\".\\\" align=\\\"char\\\">21.86</td><td char=\\\".\\\" align=\\\"char\\\">70.20</td><td char=\\\".\\\" align=\\\"char\\\">7.07</td><td char=\\\".\\\" align=\\\"char\\\">8.72</td><td char=\\\".\\\" align=\\\"char\\\">0.44</td><td align=\\\"left\\\">24.15</td><td align=\\\"left\\\">16.3</td></tr><tr><td align=\\\"left\\\">BCK10</td><td char=\\\".\\\" align=\\\"char\\\">1.02</td><td char=\\\".\\\" align=\\\"char\\\">20.87</td><td char=\\\".\\\" align=\\\"char\\\">40.67</td><td char=\\\".\\\" align=\\\"char\\\">37.45</td><td char=\\\".\\\" align=\\\"char\\\">9.35</td><td char=\\\".\\\" align=\\\"char\\\">0.41</td><td align=\\\"left\\\">32.89</td><td align=\\\"left\\\">22.0</td></tr><tr><td align=\\\"left\\\">BCK20</td><td char=\\\".\\\" align=\\\"char\\\">0.41</td><td char=\\\".\\\" align=\\\"char\\\">21.18</td><td char=\\\".\\\" align=\\\"char\\\">25.59</td><td char=\\\".\\\" align=\\\"char\\\">52.82</td><td char=\\\".\\\" align=\\\"char\\\">10.97</td><td char=\\\".\\\" align=\\\"char\\\">0.66</td><td align=\\\"left\\\">36.84</td><td align=\\\"left\\\">22.5</td></tr><tr><td align=\\\"left\\\">HC</td><td char=\\\".\\\" align=\\\"char\\\">2.41</td><td char=\\\".\\\" align=\\\"char\\\">59.67</td><td char=\\\".\\\" align=\\\"char\\\">35.27</td><td char=\\\".\\\" align=\\\"char\\\">2.65</td><td char=\\\".\\\" align=\\\"char\\\">6.22</td><td char=\\\".\\\" align=\\\"char\\\">0.25</td><td align=\\\"left\\\">56.74</td><td align=\\\"left\\\">132.8</td></tr><tr><td align=\\\"left\\\">HCK10</td><td char=\\\".\\\" align=\\\"char\\\">2.11</td><td char=\\\".\\\" align=\\\"char\\\">51.60</td><td char=\\\".\\\" align=\\\"char\\\">27.80</td><td char=\\\".\\\" align=\\\"char\\\">18.48</td><td char=\\\".\\\" align=\\\"char\\\">5.22</td><td char=\\\".\\\" align=\\\"char\\\">0.31</td><td align=\\\"left\\\">62.19</td><td align=\\\"left\\\">123.0</td></tr><tr><td align=\\\"left\\\">HCK20</td><td char=\\\".\\\" align=\\\"char\\\">1.46</td><td char=\\\".\\\" align=\\\"char\\\">42.77</td><td char=\\\".\\\" align=\\\"char\\\">28.24</td><td char=\\\".\\\" align=\\\"char\\\">27.53</td><td char=\\\".\\\" align=\\\"char\\\">4.48</td><td char=\\\".\\\" align=\\\"char\\\">0.31</td><td align=\\\"left\\\">64.35</td><td align=\\\"left\\\">124.8</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab2\\\"><label>Table 2</label><caption><p>Surface area, pore volume, pore size, hydrodynamics size and zeta potential analyses result of BC and HC materials.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Material</th><th align=\\\"left\\\">Surface area (m<sup>2</sup> g<sup>−1</sup>)</th><th align=\\\"left\\\">Pore volume (cm<sup>3</sup> g<sup>−1</sup>)</th><th align=\\\"left\\\">Pore size (Å)</th><th align=\\\"left\\\">Hydrodynamics size (µm)</th><th align=\\\"left\\\">Zeta potential (mV)</th></tr></thead><tbody><tr><td align=\\\"left\\\">BC</td><td char=\\\".\\\" align=\\\"char\\\">290.89</td><td char=\\\".\\\" align=\\\"char\\\">0.209</td><td char=\\\".\\\" align=\\\"char\\\">28.81</td><td char=\\\".\\\" align=\\\"char\\\">2.63</td><td char=\\\".\\\" align=\\\"char\\\">− 25.06</td></tr><tr><td align=\\\"left\\\">BCK10</td><td char=\\\".\\\" align=\\\"char\\\">225.14</td><td char=\\\".\\\" align=\\\"char\\\">0.216</td><td char=\\\".\\\" align=\\\"char\\\">38.31</td><td char=\\\".\\\" align=\\\"char\\\">2.34</td><td char=\\\".\\\" align=\\\"char\\\">− 25.08</td></tr><tr><td align=\\\"left\\\">BCK20</td><td char=\\\".\\\" align=\\\"char\\\">180.40</td><td char=\\\".\\\" align=\\\"char\\\">0.127</td><td char=\\\".\\\" align=\\\"char\\\">28.17</td><td char=\\\".\\\" align=\\\"char\\\">2.73</td><td char=\\\".\\\" align=\\\"char\\\">− 25.06</td></tr><tr><td align=\\\"left\\\">HC</td><td char=\\\".\\\" align=\\\"char\\\">5.32</td><td char=\\\".\\\" align=\\\"char\\\">0.025</td><td char=\\\".\\\" align=\\\"char\\\">187.71</td><td char=\\\".\\\" align=\\\"char\\\">2.19</td><td char=\\\".\\\" align=\\\"char\\\">− 24.98</td></tr><tr><td align=\\\"left\\\">HCK10</td><td char=\\\".\\\" align=\\\"char\\\">16.11</td><td char=\\\".\\\" align=\\\"char\\\">0.059</td><td char=\\\".\\\" align=\\\"char\\\">145.90</td><td char=\\\".\\\" align=\\\"char\\\">2.38</td><td char=\\\".\\\" align=\\\"char\\\">− 24.66</td></tr><tr><td align=\\\"left\\\">HCK20</td><td char=\\\".\\\" align=\\\"char\\\">15.44</td><td char=\\\".\\\" align=\\\"char\\\">0.068</td><td char=\\\".\\\" align=\\\"char\\\">175.92</td><td char=\\\".\\\" align=\\\"char\\\">3.10</td><td char=\\\".\\\" align=\\\"char\\\">− 21.68</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab3\\\"><label>Table 3</label><caption><p>Elemental composition, their molar ratios, recalcitrant index (R<sub>50</sub>), temperature corresponding to 50% weight loss of water and ash-free composites (T<sub>50</sub>), and carbon sequestration potential (CS) of the synthesized materials.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Material</th><th align=\\\"left\\\">N (%)</th><th align=\\\"left\\\">C (%)</th><th align=\\\"left\\\">H (%)</th><th align=\\\"left\\\">S (%)</th><th align=\\\"left\\\">O (%)</th><th align=\\\"left\\\">O/C</th><th align=\\\"left\\\">H/C</th><th align=\\\"left\\\">T<sub>50</sub></th><th align=\\\"left\\\">R<sub>50</sub></th><th align=\\\"left\\\">CS (%)</th></tr></thead><tbody><tr><td align=\\\"left\\\">BC</td><td char=\\\".\\\" align=\\\"char\\\">4.96</td><td char=\\\".\\\" align=\\\"char\\\">92.50</td><td char=\\\".\\\" align=\\\"char\\\">1.41</td><td char=\\\".\\\" align=\\\"char\\\">0.00</td><td char=\\\".\\\" align=\\\"char\\\">1.13</td><td char=\\\".\\\" align=\\\"char\\\">0.009</td><td char=\\\".\\\" align=\\\"char\\\">0.182</td><td char=\\\".\\\" align=\\\"char\\\">690.33</td><td char=\\\".\\\" align=\\\"char\\\">0.78</td><td align=\\\"left\\\">46.43</td></tr><tr><td align=\\\"left\\\">BCK10</td><td char=\\\".\\\" align=\\\"char\\\">8.68</td><td char=\\\".\\\" align=\\\"char\\\">86.40</td><td char=\\\".\\\" align=\\\"char\\\">1.29</td><td char=\\\".\\\" align=\\\"char\\\">0.73</td><td char=\\\".\\\" align=\\\"char\\\">2.90</td><td char=\\\".\\\" align=\\\"char\\\">0.025</td><td char=\\\".\\\" align=\\\"char\\\">0.178</td><td char=\\\".\\\" align=\\\"char\\\">714</td><td char=\\\".\\\" align=\\\"char\\\">0.81</td><td align=\\\"left\\\">47.63</td></tr><tr><td align=\\\"left\\\">BCK20</td><td char=\\\".\\\" align=\\\"char\\\">10.11</td><td char=\\\".\\\" align=\\\"char\\\">85.73</td><td char=\\\".\\\" align=\\\"char\\\">1.08</td><td char=\\\".\\\" align=\\\"char\\\">0.96</td><td char=\\\".\\\" align=\\\"char\\\">2.13</td><td char=\\\".\\\" align=\\\"char\\\">0.019</td><td char=\\\".\\\" align=\\\"char\\\">0.150</td><td char=\\\".\\\" align=\\\"char\\\">697.67</td><td char=\\\".\\\" align=\\\"char\\\">0.79</td><td align=\\\"left\\\">51.63</td></tr><tr><td align=\\\"left\\\">HC</td><td char=\\\".\\\" align=\\\"char\\\">6.61</td><td char=\\\".\\\" align=\\\"char\\\">59.12</td><td char=\\\".\\\" align=\\\"char\\\">5.33</td><td char=\\\".\\\" align=\\\"char\\\">0.58</td><td char=\\\".\\\" align=\\\"char\\\">28.35</td><td char=\\\".\\\" align=\\\"char\\\">0.360</td><td char=\\\".\\\" align=\\\"char\\\">1.075</td><td char=\\\".\\\" align=\\\"char\\\">374</td><td char=\\\".\\\" align=\\\"char\\\">0.42</td><td align=\\\"left\\\">29.16</td></tr><tr><td align=\\\"left\\\">HCK10</td><td char=\\\".\\\" align=\\\"char\\\">6.01</td><td char=\\\".\\\" align=\\\"char\\\">61.11</td><td char=\\\".\\\" align=\\\"char\\\">5.54</td><td char=\\\".\\\" align=\\\"char\\\">0.43</td><td char=\\\".\\\" align=\\\"char\\\">26.91</td><td char=\\\".\\\" align=\\\"char\\\">0.331</td><td char=\\\".\\\" align=\\\"char\\\">1.080</td><td char=\\\".\\\" align=\\\"char\\\">445.33</td><td char=\\\".\\\" align=\\\"char\\\">0.50</td><td align=\\\"left\\\">39.32</td></tr><tr><td align=\\\"left\\\">HCK20</td><td char=\\\".\\\" align=\\\"char\\\">8.41</td><td char=\\\".\\\" align=\\\"char\\\">58.81</td><td char=\\\".\\\" align=\\\"char\\\">5.39</td><td char=\\\".\\\" align=\\\"char\\\">0.00</td><td char=\\\".\\\" align=\\\"char\\\">27.39</td><td char=\\\".\\\" align=\\\"char\\\">0.350</td><td char=\\\".\\\" align=\\\"char\\\">1.092</td><td char=\\\".\\\" align=\\\"char\\\">447.67</td><td char=\\\".\\\" align=\\\"char\\\">0.51</td><td align=\\\"left\\\">39.94</td></tr><tr><td align=\\\"left\\\">BM</td><td char=\\\".\\\" align=\\\"char\\\">6.82</td><td char=\\\".\\\" align=\\\"char\\\">48.32</td><td char=\\\".\\\" align=\\\"char\\\">5.98</td><td char=\\\".\\\" align=\\\"char\\\">0.00</td><td char=\\\".\\\" align=\\\"char\\\">38.88</td><td char=\\\".\\\" align=\\\"char\\\">0.604</td><td char=\\\".\\\" align=\\\"char\\\">1.474</td><td char=\\\".\\\" align=\\\"char\\\">354.33</td><td char=\\\".\\\" align=\\\"char\\\">0.40</td><td align=\\\"left\\\">–</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab4\\\"><label>Table 4</label><caption><p>The contents of polycyclic aromatic hydrocarbons (PAHs) (μg kg<sup>-1</sup>) in the synthesized materials.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">No</th><th align=\\\"left\\\">PAHs</th><th align=\\\"left\\\">BC</th><th align=\\\"left\\\">BCK10</th><th align=\\\"left\\\">BCK20</th><th align=\\\"left\\\">HC</th><th align=\\\"left\\\">HCK10</th><th align=\\\"left\\\">HCK20</th></tr></thead><tbody><tr><td align=\\\"left\\\">1</td><td align=\\\"left\\\">Naphthalene (Nap)</td><td align=\\\"left\\\">318.38</td><td align=\\\"left\\\">255.05</td><td align=\\\"left\\\">124.77</td><td align=\\\"left\\\">466.23</td><td align=\\\"left\\\">164.37</td><td align=\\\"left\\\">123.9</td></tr><tr><td align=\\\"left\\\">2</td><td align=\\\"left\\\">Acenaphthylene (Acy)</td><td align=\\\"left\\\">302.09</td><td align=\\\"left\\\">241.11</td><td align=\\\"left\\\">148.18</td><td align=\\\"left\\\">438.03</td><td align=\\\"left\\\">177.04</td><td align=\\\"left\\\">113.6</td></tr><tr><td align=\\\"left\\\">3</td><td align=\\\"left\\\">Acenaphthene (Ace)</td><td align=\\\"left\\\">118.36</td><td align=\\\"left\\\">153.67</td><td align=\\\"left\\\">107.69</td><td align=\\\"left\\\">308.55</td><td align=\\\"left\\\">103.22</td><td align=\\\"left\\\">96.45</td></tr><tr><td align=\\\"left\\\">4</td><td align=\\\"left\\\">Fluorene (Flu)</td><td align=\\\"left\\\">85.88</td><td align=\\\"left\\\">69.44</td><td align=\\\"left\\\">83.67</td><td align=\\\"left\\\">116.23</td><td align=\\\"left\\\">89.45</td><td align=\\\"left\\\">44.56</td></tr><tr><td align=\\\"left\\\">5</td><td align=\\\"left\\\">Phenanthrene (Phe)</td><td align=\\\"left\\\">205.56</td><td align=\\\"left\\\">205.41</td><td align=\\\"left\\\">119.45</td><td align=\\\"left\\\">398.49</td><td align=\\\"left\\\">77.87</td><td align=\\\"left\\\">62.33</td></tr><tr><td align=\\\"left\\\">6</td><td align=\\\"left\\\">Anthracene (Ant)</td><td align=\\\"left\\\">99.46</td><td align=\\\"left\\\">73.69</td><td align=\\\"left\\\">63.21</td><td align=\\\"left\\\">182.66</td><td align=\\\"left\\\">88.7</td><td align=\\\"left\\\">57.72</td></tr><tr><td align=\\\"left\\\">7</td><td align=\\\"left\\\">Fluoranthene (Fla)</td><td align=\\\"left\\\">152.43</td><td align=\\\"left\\\">109.45</td><td align=\\\"left\\\">57.38</td><td align=\\\"left\\\">198.34</td><td align=\\\"left\\\">77.51</td><td align=\\\"left\\\">49.44</td></tr><tr><td align=\\\"left\\\">8</td><td align=\\\"left\\\">Pyrene (Pyr)</td><td align=\\\"left\\\">205.67</td><td align=\\\"left\\\">185.44</td><td align=\\\"left\\\">95.19</td><td align=\\\"left\\\">274.67</td><td align=\\\"left\\\">83.55</td><td align=\\\"left\\\">71.51</td></tr><tr><td align=\\\"left\\\">9</td><td align=\\\"left\\\">Benzo[a] Anthracene (BaA)</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td></tr><tr><td align=\\\"left\\\">10</td><td align=\\\"left\\\">Chrysene (Chr)</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td></tr><tr><td align=\\\"left\\\">11</td><td align=\\\"left\\\">Benzo[k]Fluoranthene (BF)</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td></tr><tr><td align=\\\"left\\\">12</td><td align=\\\"left\\\">Benzo[e] Pyrene (BeP)</td><td align=\\\"left\\\">42.55</td><td align=\\\"left\\\">38.21</td><td align=\\\"left\\\">31.29</td><td align=\\\"left\\\">89.35</td><td align=\\\"left\\\">27.23</td><td align=\\\"left\\\">24.31</td></tr><tr><td align=\\\"left\\\">13</td><td align=\\\"left\\\">Benzo[a] Pyrene (BaP)</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td></tr><tr><td align=\\\"left\\\">14</td><td align=\\\"left\\\">Indeno[123-cd]Pyrene (InP)</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td></tr><tr><td align=\\\"left\\\">15</td><td align=\\\"left\\\">Benzo [ghi] perylene (BghiP)</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td></tr><tr><td align=\\\"left\\\">16</td><td align=\\\"left\\\">Dibenzo[ah] Anthracene (DBA)</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td><td align=\\\"left\\\">ND</td></tr><tr><td align=\\\"left\\\">17</td><td align=\\\"left\\\">Retene (Ret)</td><td align=\\\"left\\\">222.48</td><td align=\\\"left\\\">182.58</td><td align=\\\"left\\\">103.55</td><td align=\\\"left\\\">298.12</td><td align=\\\"left\\\">83.88</td><td align=\\\"left\\\">95.23</td></tr><tr><td align=\\\"left\\\"/><td align=\\\"left\\\">∑ total PAHs</td><td align=\\\"left\\\">1752.9</td><td align=\\\"left\\\">1514.1</td><td align=\\\"left\\\">934.4</td><td align=\\\"left\\\">2770.7</td><td align=\\\"left\\\">972.82</td><td align=\\\"left\\\">739.1</td></tr><tr><td align=\\\"left\\\"/><td align=\\\"left\\\">∑ 14 PAHs of 16 US-EPA PAHs</td><td align=\\\"left\\\">1487.8</td><td align=\\\"left\\\">1293.3</td><td align=\\\"left\\\">799.5</td><td align=\\\"left\\\">2383.2</td><td align=\\\"left\\\">861.71</td><td align=\\\"left\\\">619.5</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab5\\\"><label>Table 5</label><caption><p>Relationship between the characteristics of synthesized materials and maize germination and growth.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Indices</th><th align=\\\"left\\\">R<sub>50</sub></th><th align=\\\"left\\\">CS</th><th align=\\\"left\\\">O/C</th><th align=\\\"left\\\">CEC</th><th align=\\\"left\\\">SA</th><th align=\\\"left\\\">∑ PAHs</th><th align=\\\"left\\\">Germination</th><th align=\\\"left\\\">Shoot length</th><th align=\\\"left\\\">Root length</th><th align=\\\"left\\\">Fresh weight</th><th align=\\\"left\\\">Dry weight</th></tr></thead><tbody><tr><td align=\\\"left\\\">R<sub>50</sub></td><td char=\\\".\\\" align=\\\"char\\\">1.00</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">CS</td><td char=\\\".\\\" align=\\\"char\\\">0.94*</td><td char=\\\".\\\" align=\\\"char\\\">1.00</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">O/C</td><td char=\\\".\\\" align=\\\"char\\\">− 0.99*</td><td char=\\\".\\\" align=\\\"char\\\">− 0.92*</td><td char=\\\".\\\" align=\\\"char\\\">1.00</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">CEC</td><td char=\\\".\\\" align=\\\"char\\\">− 0.99*</td><td char=\\\".\\\" align=\\\"char\\\">− 0.92*</td><td char=\\\".\\\" align=\\\"char\\\">1.00*</td><td char=\\\".\\\" align=\\\"char\\\">1.00</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">SA</td><td char=\\\".\\\" align=\\\"char\\\">0.95*</td><td char=\\\".\\\" align=\\\"char\\\">0.83*</td><td char=\\\".\\\" align=\\\"char\\\">− 0.96*</td><td char=\\\".\\\" align=\\\"char\\\">− 0.97*</td><td char=\\\".\\\" align=\\\"char\\\">1.00</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">PAHs</td><td char=\\\".\\\" align=\\\"char\\\">− 0.18</td><td char=\\\".\\\" align=\\\"char\\\">− 0.48</td><td char=\\\".\\\" align=\\\"char\\\">0.10</td><td char=\\\".\\\" align=\\\"char\\\">0.11</td><td char=\\\".\\\" align=\\\"char\\\">0.00</td><td char=\\\".\\\" align=\\\"char\\\">1.00</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">Germination %</td><td char=\\\".\\\" align=\\\"char\\\">0.75*</td><td char=\\\".\\\" align=\\\"char\\\">0.49</td><td char=\\\".\\\" align=\\\"char\\\">− 0.78*</td><td char=\\\".\\\" align=\\\"char\\\">− 0.78*</td><td char=\\\".\\\" align=\\\"char\\\">0.81*</td><td char=\\\".\\\" align=\\\"char\\\">0.49</td><td char=\\\".\\\" align=\\\"char\\\">1.00</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">Shoot length mm</td><td char=\\\".\\\" align=\\\"char\\\">− 0.52*</td><td char=\\\".\\\" align=\\\"char\\\">− 0.42</td><td char=\\\".\\\" align=\\\"char\\\">0.54*</td><td char=\\\".\\\" align=\\\"char\\\">0.55*</td><td char=\\\".\\\" align=\\\"char\\\">− 0.71*</td><td char=\\\".\\\" align=\\\"char\\\">0.03</td><td char=\\\".\\\" align=\\\"char\\\">− 0.46</td><td char=\\\".\\\" align=\\\"char\\\">1.00</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">Root length mm</td><td char=\\\".\\\" align=\\\"char\\\">− 0.78*</td><td char=\\\".\\\" align=\\\"char\\\">− 0.83*</td><td char=\\\".\\\" align=\\\"char\\\">0.76*</td><td char=\\\".\\\" align=\\\"char\\\">0.78*</td><td char=\\\".\\\" align=\\\"char\\\">− 0.80*</td><td char=\\\".\\\" align=\\\"char\\\">0.48</td><td char=\\\".\\\" align=\\\"char\\\">− 0.31</td><td char=\\\".\\\" align=\\\"char\\\">0.67*</td><td char=\\\".\\\" align=\\\"char\\\">1.00</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">Fresh weight mg</td><td char=\\\".\\\" align=\\\"char\\\">0.01</td><td char=\\\".\\\" align=\\\"char\\\">− 0.10</td><td char=\\\".\\\" align=\\\"char\\\">− 0.04</td><td char=\\\".\\\" align=\\\"char\\\">− 0.02</td><td char=\\\".\\\" align=\\\"char\\\">− 0.11</td><td char=\\\".\\\" align=\\\"char\\\">0.53*</td><td char=\\\".\\\" align=\\\"char\\\">0.36</td><td char=\\\".\\\" align=\\\"char\\\">0.58*</td><td char=\\\".\\\" align=\\\"char\\\">0.57*</td><td char=\\\".\\\" align=\\\"char\\\">1.00</td><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">Dry weight mg</td><td char=\\\".\\\" align=\\\"char\\\">− 0.39</td><td char=\\\".\\\" align=\\\"char\\\">− 0.48</td><td char=\\\".\\\" align=\\\"char\\\">0.39</td><td char=\\\".\\\" align=\\\"char\\\">0.40</td><td char=\\\".\\\" align=\\\"char\\\">− 0.50*</td><td char=\\\".\\\" align=\\\"char\\\">0.48</td><td char=\\\".\\\" align=\\\"char\\\">0.00</td><td char=\\\".\\\" align=\\\"char\\\">0.82*</td><td char=\\\".\\\" align=\\\"char\\\">0.78*</td><td char=\\\".\\\" align=\\\"char\\\">0.78*</td><td char=\\\".\\\" align=\\\"char\\\">1.00</td></tr></tbody></table></table-wrap>\"\n]","isConversational":false},"formula":{"kind":"list like","value":["<disp-formula 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\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${R}_{50}= {T}_{50,x}/{T}_{50,graphite}$$\\end{document}</tex-math><mml:math id=\"M6\" display=\"block\"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>50</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mn>50</mml:mn><mml:mo>,</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">/</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mn>50</mml:mn><mml:mo>,</mml:mo><mml:mi>g</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>p</mml:mi><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$ W_{{i,cor}} = 100 + [100 \\times \\left( {W_{{i,uncor}} {-}W_{{200,uncor}} } \\right]/\\left( {W_{{200,uncor}} {-}W_{{cutoff,uncor}} } \\right) $$\\end{document}</tex-math><mml:math id=\"M8\" display=\"block\"><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>=</mml:mo><mml:mn>100</mml:mn><mml:mo>+</mml:mo><mml:mo stretchy=\"false\">[</mml:mo><mml:mn>100</mml:mn><mml:mo>×</mml:mo></mml:mrow><mml:mfenced close=\"]\" open=\"(\"><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mn>200</mml:mn><mml:mo>,</mml:mo><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mo stretchy=\"false\">/</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mn>200</mml:mn><mml:mo>,</mml:mo><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ5\"><label>5</label><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{CS }(\\mathrm{\\%})=\\frac{Yield\\, \\left(\\%\\right)\\, x \\,C\\%\\, material \\,x\\, R50}{C\\%\\, feedstock}$$\\end{document}</tex-math><mml:math id=\"M10\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">CS</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>%</mml:mo><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>Y</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mi>d</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mo>%</mml:mo></mml:mfenced><mml:mspace width=\"0.166667em\"/><mml:mi>x</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>C</mml:mi><mml:mo>%</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>i</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>x</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>R</mml:mi><mml:mn>50</mml:mn></mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mo>%</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ6\"><label>6</label><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$GI (\\%)=\\frac{no\\, of\\, germinated\\, seeds}{no\\, of\\, total\\, seeds} \\times 100$$\\end{document}</tex-math><mml:math id=\"M12\" display=\"block\"><mml:mrow><mml:mi>G</mml:mi><mml:mi>I</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mo>%</mml:mo><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mi>t</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:mfrac><mml:mo>×</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:math></alternatives></disp-formula>"],"string":"[\n \"<disp-formula id=\\\"Equ1\\\"><label>1</label><alternatives><tex-math id=\\\"M1\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$Yield \\\\left(\\\\%\\\\right)=\\\\frac{Weight\\\\,of\\\\,Biomass-Weight\\\\,of\\\\,BC\\\\,or\\\\,HC\\\\,composite}{Weight\\\\,of\\\\,Biomass} \\\\times 100$$\\\\end{document}</tex-math><mml:math id=\\\"M2\\\" display=\\\"block\\\"><mml:mrow><mml:mi>Y</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mi>d</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mo>%</mml:mo></mml:mfenced><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>W</mml:mi><mml:mi>e</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:mi>t</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>B</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mo>-</mml:mo><mml:mi>W</mml:mi><mml:mi>e</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:mi>t</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>B</mml:mi><mml:mi>C</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>H</mml:mi><mml:mi>C</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mi>o</mml:mi><mml:mi>s</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>W</mml:mi><mml:mi>e</mml:mi><mml:mi>i</mml:mi><mml:mi>g</mml:mi><mml:mi>h</mml:mi><mml:mi>t</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>B</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:mfrac><mml:mo>×</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ2\\\"><label>2</label><alternatives><tex-math id=\\\"M3\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$ O~\\\\left( \\\\% \\\\right) = ~100~{-}~\\\\left( {{\\\\text{C}}~ + {\\\\text{H}}~ + {\\\\text{N}} + {\\\\text{S}}~ + ~{\\\\text{ash}}\\\\% } \\\\right)~ $$\\\\end{document}</tex-math><mml:math id=\\\"M4\\\" display=\\\"block\\\"><mml:mrow><mml:mi>O</mml:mi><mml:mspace width=\\\"3.33333pt\\\"/><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mo>%</mml:mo></mml:mfenced><mml:mo>=</mml:mo><mml:mspace width=\\\"3.33333pt\\\"/><mml:mn>100</mml:mn><mml:mspace width=\\\"3.33333pt\\\"/><mml:mo>-</mml:mo><mml:mspace width=\\\"3.33333pt\\\"/><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mrow><mml:mtext>C</mml:mtext><mml:mspace width=\\\"3.33333pt\\\"/><mml:mo>+</mml:mo><mml:mtext>H</mml:mtext><mml:mspace width=\\\"3.33333pt\\\"/><mml:mo>+</mml:mo><mml:mtext>N</mml:mtext><mml:mo>+</mml:mo><mml:mtext>S</mml:mtext><mml:mspace width=\\\"3.33333pt\\\"/><mml:mo>+</mml:mo><mml:mspace width=\\\"3.33333pt\\\"/><mml:mtext>ash</mml:mtext><mml:mo>%</mml:mo></mml:mrow></mml:mfenced><mml:mspace width=\\\"3.33333pt\\\"/></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ3\\\"><label>3</label><alternatives><tex-math id=\\\"M5\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${R}_{50}= {T}_{50,x}/{T}_{50,graphite}$$\\\\end{document}</tex-math><mml:math id=\\\"M6\\\" display=\\\"block\\\"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>50</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mn>50</mml:mn><mml:mo>,</mml:mo><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mn>50</mml:mn><mml:mo>,</mml:mo><mml:mi>g</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>p</mml:mi><mml:mi>h</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ4\\\"><label>4</label><alternatives><tex-math id=\\\"M7\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$ W_{{i,cor}} = 100 + [100 \\\\times \\\\left( {W_{{i,uncor}} {-}W_{{200,uncor}} } \\\\right]/\\\\left( {W_{{200,uncor}} {-}W_{{cutoff,uncor}} } \\\\right) $$\\\\end{document}</tex-math><mml:math id=\\\"M8\\\" display=\\\"block\\\"><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>=</mml:mo><mml:mn>100</mml:mn><mml:mo>+</mml:mo><mml:mo stretchy=\\\"false\\\">[</mml:mo><mml:mn>100</mml:mn><mml:mo>×</mml:mo></mml:mrow><mml:mfenced close=\\\"]\\\" open=\\\"(\\\"><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mn>200</mml:mn><mml:mo>,</mml:mo><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mrow><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mn>200</mml:mn><mml:mo>,</mml:mo><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>W</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:mi>u</mml:mi><mml:mi>n</mml:mi><mml:mi>c</mml:mi><mml:mi>o</mml:mi><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ5\\\"><label>5</label><alternatives><tex-math id=\\\"M9\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\mathrm{CS }(\\\\mathrm{\\\\%})=\\\\frac{Yield\\\\, \\\\left(\\\\%\\\\right)\\\\, x \\\\,C\\\\%\\\\, material \\\\,x\\\\, R50}{C\\\\%\\\\, feedstock}$$\\\\end{document}</tex-math><mml:math id=\\\"M10\\\" display=\\\"block\\\"><mml:mrow><mml:mi mathvariant=\\\"normal\\\">CS</mml:mi><mml:mrow><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:mo>%</mml:mo><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>Y</mml:mi><mml:mi>i</mml:mi><mml:mi>e</mml:mi><mml:mi>l</mml:mi><mml:mi>d</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mo>%</mml:mo></mml:mfenced><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>x</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>C</mml:mi><mml:mo>%</mml:mo><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>i</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>x</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>R</mml:mi><mml:mn>50</mml:mn></mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mo>%</mml:mo><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>f</mml:mi><mml:mi>e</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mi>s</mml:mi><mml:mi>t</mml:mi><mml:mi>o</mml:mi><mml:mi>c</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ6\\\"><label>6</label><alternatives><tex-math id=\\\"M11\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$GI (\\\\%)=\\\\frac{no\\\\, of\\\\, germinated\\\\, seeds}{no\\\\, of\\\\, total\\\\, seeds} \\\\times 100$$\\\\end{document}</tex-math><mml:math id=\\\"M12\\\" display=\\\"block\\\"><mml:mrow><mml:mi>G</mml:mi><mml:mi>I</mml:mi><mml:mrow><mml:mo stretchy=\\\"false\\\">(</mml:mo><mml:mo>%</mml:mo><mml:mo stretchy=\\\"false\\\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mspace width=\\\"0.166667em\\\"/><mml:mi>g</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi><mml:mspace 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like","value":[],"string":"[]","isConversational":false},"footnote":{"kind":"list like","value":["<table-wrap-foot><p>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass.</p></table-wrap-foot>","<table-wrap-foot><p>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20).</p></table-wrap-foot>","<table-wrap-foot><p>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass.</p></table-wrap-foot>","<table-wrap-foot><p>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20).</p><p><italic>ND</italic> Non-detectable (below detection limit).</p></table-wrap-foot>","<table-wrap-foot><p>*Significant.</p></table-wrap-foot>","<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"],"string":"[\n \"<table-wrap-foot><p>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20).</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20).</p><p><italic>ND</italic> Non-detectable (below detection limit).</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>*Significant.</p></table-wrap-foot>\",\n \"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41598_2024_51786_Article_Equ1.gif\" position=\"anchor\"/>","<graphic xlink:href=\"41598_2024_51786_Article_Equ2.gif\" 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Surface Sci.\"], \"year\": [\"2015\"], \"volume\": [\"346\"], \"fpage\": [\"223\"], \"lpage\": [\"231\"], \"pub-id\": [\"10.1016/j.apsusc.2015.04.014\"]}, {\"label\": [\"9.\"], \"surname\": [\"Carrasco\", \"Pino-Cort\\u00e9s\", \"Barra-Mar\\u00edn\", \"Fierro-Gallegos\", \"Le\\u00f3n\"], \"given-names\": [\"S\", \"E\", \"A\", \"A\", \"M\"], \"article-title\": [\"Use of hydrochar produced by hydrothermal carbonization of lignocellulosic biomass for thermal power plants in Chile: A techno-economic and environmental study\"], \"source\": [\"Sustainability\"], \"year\": [\"2022\"], \"volume\": [\"14\"], \"issue\": [\"13\"], \"fpage\": [\"8041\"], \"pub-id\": [\"10.3390/su14138041\"]}, {\"label\": [\"10.\"], \"surname\": [\"Kavindi\", \"Lei\", \"Yuan\", \"Shimizu\", \"Zhang\"], \"given-names\": [\"GAG\", \"Z\", \"T\", \"K\", \"Z\"], \"article-title\": [\"Use of hydrochar from hydrothermal co-carbonization of rice straw and sewage sludge for Cr (VI) bioremediation in soil\"], \"source\": [\"Bioresour. 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Taiwan Inst. Chem. Eng.\"], \"year\": [\"2019\"], \"volume\": [\"104\"], \"fpage\": [\"106\"], \"lpage\": [\"113\"], \"pub-id\": [\"10.1016/j.jtice.2019.08.017\"]}, {\"label\": [\"119.\"], \"surname\": [\"Abdel-Shafy\", \"Mansour\"], \"given-names\": [\"HI\", \"MS\"], \"article-title\": [\"A review on polycyclic aromatic hydrocarbons: Source, environmental impact, effect on human health and remediation\"], \"source\": [\"Egypt. J. Pet.\"], \"year\": [\"2016\"], \"volume\": [\"25\"], \"issue\": [\"1\"], \"fpage\": [\"107\"], \"lpage\": [\"123\"], \"pub-id\": [\"10.1016/j.ejpe.2015.03.011\"]}, {\"label\": [\"120.\"], \"surname\": [\"Wiedner\", \"Rumpel\", \"Steiner\", \"Pozzi\", \"Maas\", \"Glaser\"], \"given-names\": [\"K\", \"C\", \"C\", \"A\", \"R\", \"B\"], \"article-title\": [\"Chemical evaluation of chars produced by thermochemical conversion (gasification, pyrolysis and hydrothermal carbonization) of agro-industrial biomass on a commercial scale\"], \"source\": [\"Biomass Bioenergy.\"], \"year\": [\"2013\"], \"volume\": [\"59\"], \"fpage\": [\"264\"], \"lpage\": [\"278\"], \"pub-id\": [\"10.1016/j.biombioe.2013.08.026\"]}, {\"label\": [\"121.\"], \"surname\": [\"Buss\", \"Graham\", \"MacKinnon\", \"Ma\\u0161ek\"], \"given-names\": [\"W\", \"MC\", \"G\", \"O\"], \"article-title\": [\"Strategies for producing biochars with minimum PAH contamination\"], \"source\": [\"J. Anal. Appl. Pyrolysis\"], \"year\": [\"2016\"], \"volume\": [\"119\"], \"fpage\": [\"24\"], \"lpage\": [\"30\"], \"pub-id\": [\"10.1016/j.jaap.2016.04.001\"]}, {\"label\": [\"123.\"], \"surname\": [\"Brown\", \"Kercher\", \"Nguyen\", \"Nagle\", \"Ball\"], \"given-names\": [\"RA\", \"AK\", \"TH\", \"DC\", \"WP\"], \"article-title\": [\"Production and characterization of synthetic wood chars for use as surrogates for natural sorbents\"], \"source\": [\"Org. Geochem.\"], \"year\": [\"2006\"], \"volume\": [\"37\"], \"issue\": [\"3\"], \"fpage\": [\"321\"], \"lpage\": [\"333\"], \"pub-id\": [\"10.1016/j.orggeochem.2005.10.008\"]}, {\"label\": [\"124.\"], \"surname\": [\"Dai\", \"Jiang\", \"Jiang\", \"Jin\", \"Wang\", \"Chi\", \"Yan\"], \"given-names\": [\"Q\", \"X\", \"Y\", \"Y\", \"F\", \"Y\", \"J\"], \"article-title\": [\"Formation of PAHs during the pyrolysis of dry sewage sludge\"], \"source\": [\"Fuel.\"], \"year\": [\"2014\"], \"volume\": [\"130\"], \"fpage\": [\"92\"], \"lpage\": [\"99\"], \"pub-id\": [\"10.1016/j.fuel.2014.04.017\"]}, {\"label\": [\"130.\"], \"surname\": [\"Fregolente\", \"dos Santos\", \"Mazzati\", \"Miguel\", \"de Miguel\", \"Moreira\", \"Ferreira\", \"Bisinoti\"], \"given-names\": [\"LG\", \"JV\", \"FS\", \"TBAR\", \"EC\", \"AB\", \"OP\", \"MC\"], \"article-title\": [\"Hydrochar from sugarcane industry by-products: Assessment of its potential use as a soil conditioner by germination and growth of maize\"], \"source\": [\"Chem. Biol. Technol. Agricult.\"], \"year\": [\"2021\"], \"volume\": [\"8\"], \"fpage\": [\"1\"], \"lpage\": [\"13\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":130,"string":"130"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:56"},"citation":{"kind":"string","value":"Sci Rep. 2024 Jan 13; 14:1259"},"package_file":{"kind":"string","value":"oa_package/e7/aa/PMC10787757.tar.gz"}}},{"rowIdx":88,"cells":{"accession_id":{"kind":"string","value":"PMC10787758"},"pmid":{"kind":"string","value":"38218951"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par2\">For poultry farm owners globally, detecting infertile eggs before incubation is an essential economic concern. Since the embryo at the initial stages of development is too small, fertility detection cannot be carried out by the traditional candling method. On the other hand, the hatchery statistics indicate that about 7–8% of the total number of eggs put into incubation remain unhatched, despite they should have been fertilized<sup>##UREF##0##1##</sup>. Therefore, commercial poultry hatcheries are incubating billions of eggs per year worldwide that are not supposed to hatch. In the long run, the main problem in the incubation of such unproductive infertile eggs is ending up the billions of eggs that could have been used for human consumption. Not only that, loss of energy due to the inefficient use of incubator space, increasing handling costs and decreasing hatchery production, and the risk of contamination of the whole eggs set by exploder eggs are considered as the other bottlenecks in incubating the non-fertile eggs<sup>##UREF##1##2##–##UREF##3##4##</sup>. Therefore, developing a non-destructive and more targeted method for early egg fertility identification, preferably before being passed for incubation, can improve the efficiency of the hatchery industry and promise huge economic returns.</p>","<p id=\"Par3\">Different methods have been introduced so far to separate unfertilized eggs, most of which are applicable in 2–5 days after incubation<sup>##UREF##2##3##</sup>. Conventional candling is the most popular method to assess flock fertility, usually performed 5–10 days after incubation. This method is not only slow and cumbersome but also approximately 5% of the entire egg set is randomly investigated while the remaining 95% have the chance of infertility<sup>##UREF##3##4##</sup>. Other approaches were presented to indirectly monitor the fertility of chicken eggs. In the best conditions, acceptable results for infertility detection were obtained by machine vision after 3 days<sup>##UREF##4##5##</sup>, thermal imaging after 14 days<sup>##UREF##5##6##</sup>, temperature sensors after 21 days<sup>##UREF##6##7##</sup> of incubation, and visible and short-wavelength near-infrared (Vis-SWNIR) transmittance spectroscopy<sup>##UREF##7##8##</sup> and NIR hyperspectral imaging in the day before incubation or day 0<sup>##UREF##1##2##</sup>.</p>","<p id=\"Par4\">Among these methods, the hyperspectral imaging (HSI) technique has recently been applied to detect egg fertility and embryo development<sup>##UREF##0##1##,##UREF##1##2##,##UREF##8##9##</sup>. In general, hyperspectral imaging is a type of spectral imaging that combines spectral data from a part of the electromagnetic spectrum with spatial information from a targeted sample. The main purpose of spectral imaging is to obtain spectral content or signature for every single pixel of the image<sup>##UREF##9##10##</sup>. The spectral signature is unique to different materials, such as each human fingerprint, and as a result, by obtaining this signature, it is possible to identify the amount and spatial distribution of different materials<sup>##REF##24441941##11##</sup>.</p>","<p id=\"Par5\">More specifically, hyperspectral transmission imaging in the spectral range of Vis-SWNIR has been used to detect developing eggs with accuracies of 96%, 92%, 100%, and 100% on days 0, 1, 2, and 3 after incubation, respectively<sup>##UREF##10##12##</sup>. Afterward, in addition to recognizing the infertile eggs after starting the incubation process, Smith, et al.<sup>##UREF##11##13##</sup> tried to detect the infertile eggs on day 0 before incubation. The results presented overall accuracy as 71% on day 0 before incubation, 63% on day 1, 65% on day 2, and 83% on day 3 after incubation. Zhihui, et al.<sup>##UREF##12##14##</sup> utilized the entire spectral data within the wavelength range of 400–760 nm to detect fertile hatching eggs before incubation, achieving the highest accuracy of 93% through the application of a support vector machine classifier.</p>","<p id=\"Par6\">The most notable breakthrough in unraveling this problem was made by Liu and Ngadi<sup>##UREF##1##2##</sup> who reported the detection of infertile eggs on day 0 before incubation, however, thanks to a relatively more expensive HSI system with the NIR spectral range (900–1700 nm). The best overall classification accuracies obtained were 100% on day 0, 79% on day 1, 74% on day 2, 82% on day 3, and 84% on day 4.</p>","<p id=\"Par7\">Despite the ability to detect fertility and monitor embryo development, no promising study, to the best of our knowledge, has been found on extracting effective wavelengths using the HSI systems for diagnosing chicken egg fertility on day 0 before incubation.</p>","<p id=\"Par8\">After collecting the hyperspectral images, it is necessary to extract the desired spatial and spectral features from the images. However, due to the small size of the egg embryo on day 0 and limitations in spatial resolution of hyperspectral cameras, it is difficult to visualize the embryo in hyperspectral images. Therefore, it seems that the spectral analysis in hyperspectral images is superior to spatial analysis, especially when other studies confirmed that the transmission spectra are affected by the presence of the embryo in the egg, causing some absorptions<sup>##UREF##2##3##,##UREF##13##15##</sup>.</p>","<p id=\"Par9\">In spectral analysis, choosing effective wavelengths is a critical step. Opting for the right wavelengths can decrease the dimensionality and complexity of data, ultimately enhancing the predictive capability of the model. The use of effective wavelengths not only reduces analysis time but is also advantageous for implementing the model in an online multispectral imaging system<sup>##UREF##14##16##</sup>. Moreover, given the vast amount of information in hyperspectral images, it becomes crucial to select the most appropriate classification method for discriminating the desired classes.</p>","<p id=\"Par10\">Different classification and wavelength selection approaches have been reported in the literature related to spectral analysis and they resulted in various prediction powers. For example, principal components analysis (PCA) in fertility detection by HSI<sup>##UREF##0##1##,##UREF##1##2##</sup>, naive Bayes classifier<sup>##UREF##2##3##</sup>, and linear discriminant analysis and support vector machine<sup>##UREF##13##15##</sup> in fertility detection of eggs using Vis-SWNIR transmittance spectroscopy, and receiver operating characteristic (ROC) analysis in the wavelength difference of reflectance spectra in bruise detection in apple<sup>##UREF##14##16##</sup>, were examined.</p>","<p id=\"Par11\">In this paper, soft independent modeling of class analogy (SIMCA) was first utilized to investigate the feasibility of Vis-SWNIR spectral data based on the HSI technique for detecting non-fertile eggs. Afterward, the wavelength variables with strong discriminatory powers were retained, while the weaker wavelengths were excluded from the data set. The ability of the selected wavebands was checked after importing the raw and processed spectral data to the nonlinear artificial neural network (ANN) classifier.</p>","<p id=\"Par12\">The objectives of this study were (1) to evaluate the feasibility of hyperspectral transmission imaging in the spectral range of Vis-SWNIR to detect the non-fertile eggs before starting the incubation process, and (2) to determine and compare the potential of different machine learning tools in classifying the fertile and infertile eggs, and (3) selecting the most informative wavebands to develop the predictive models for detecting fertility of unincubated chicken egg. Enhancing the accuracy of fertility detection before the incubation process, through the use of a more cost-effective hyperspectral camera, the selection of the most informative wavelengths and development of the predictive model based on the discrete number of wavelengths, and the comparison of the performance of various machine learning techniques, can be considered the novel aspects of the current study.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">For poultry farm owners globally, detecting infertile eggs before incubation is an essential economic concern. Since the embryo at the initial stages of development is too small, fertility detection cannot be carried out by the traditional candling method. On the other hand, the hatchery statistics indicate that about 7–8% of the total number of eggs put into incubation remain unhatched, despite they should have been fertilized<sup>##UREF##0##1##</sup>. Therefore, commercial poultry hatcheries are incubating billions of eggs per year worldwide that are not supposed to hatch. In the long run, the main problem in the incubation of such unproductive infertile eggs is ending up the billions of eggs that could have been used for human consumption. Not only that, loss of energy due to the inefficient use of incubator space, increasing handling costs and decreasing hatchery production, and the risk of contamination of the whole eggs set by exploder eggs are considered as the other bottlenecks in incubating the non-fertile eggs<sup>##UREF##1##2##–##UREF##3##4##</sup>. Therefore, developing a non-destructive and more targeted method for early egg fertility identification, preferably before being passed for incubation, can improve the efficiency of the hatchery industry and promise huge economic returns.</p>\",\n \"<p id=\\\"Par3\\\">Different methods have been introduced so far to separate unfertilized eggs, most of which are applicable in 2–5 days after incubation<sup>##UREF##2##3##</sup>. Conventional candling is the most popular method to assess flock fertility, usually performed 5–10 days after incubation. This method is not only slow and cumbersome but also approximately 5% of the entire egg set is randomly investigated while the remaining 95% have the chance of infertility<sup>##UREF##3##4##</sup>. Other approaches were presented to indirectly monitor the fertility of chicken eggs. In the best conditions, acceptable results for infertility detection were obtained by machine vision after 3 days<sup>##UREF##4##5##</sup>, thermal imaging after 14 days<sup>##UREF##5##6##</sup>, temperature sensors after 21 days<sup>##UREF##6##7##</sup> of incubation, and visible and short-wavelength near-infrared (Vis-SWNIR) transmittance spectroscopy<sup>##UREF##7##8##</sup> and NIR hyperspectral imaging in the day before incubation or day 0<sup>##UREF##1##2##</sup>.</p>\",\n \"<p id=\\\"Par4\\\">Among these methods, the hyperspectral imaging (HSI) technique has recently been applied to detect egg fertility and embryo development<sup>##UREF##0##1##,##UREF##1##2##,##UREF##8##9##</sup>. In general, hyperspectral imaging is a type of spectral imaging that combines spectral data from a part of the electromagnetic spectrum with spatial information from a targeted sample. The main purpose of spectral imaging is to obtain spectral content or signature for every single pixel of the image<sup>##UREF##9##10##</sup>. The spectral signature is unique to different materials, such as each human fingerprint, and as a result, by obtaining this signature, it is possible to identify the amount and spatial distribution of different materials<sup>##REF##24441941##11##</sup>.</p>\",\n \"<p id=\\\"Par5\\\">More specifically, hyperspectral transmission imaging in the spectral range of Vis-SWNIR has been used to detect developing eggs with accuracies of 96%, 92%, 100%, and 100% on days 0, 1, 2, and 3 after incubation, respectively<sup>##UREF##10##12##</sup>. Afterward, in addition to recognizing the infertile eggs after starting the incubation process, Smith, et al.<sup>##UREF##11##13##</sup> tried to detect the infertile eggs on day 0 before incubation. The results presented overall accuracy as 71% on day 0 before incubation, 63% on day 1, 65% on day 2, and 83% on day 3 after incubation. Zhihui, et al.<sup>##UREF##12##14##</sup> utilized the entire spectral data within the wavelength range of 400–760 nm to detect fertile hatching eggs before incubation, achieving the highest accuracy of 93% through the application of a support vector machine classifier.</p>\",\n \"<p id=\\\"Par6\\\">The most notable breakthrough in unraveling this problem was made by Liu and Ngadi<sup>##UREF##1##2##</sup> who reported the detection of infertile eggs on day 0 before incubation, however, thanks to a relatively more expensive HSI system with the NIR spectral range (900–1700 nm). The best overall classification accuracies obtained were 100% on day 0, 79% on day 1, 74% on day 2, 82% on day 3, and 84% on day 4.</p>\",\n \"<p id=\\\"Par7\\\">Despite the ability to detect fertility and monitor embryo development, no promising study, to the best of our knowledge, has been found on extracting effective wavelengths using the HSI systems for diagnosing chicken egg fertility on day 0 before incubation.</p>\",\n \"<p id=\\\"Par8\\\">After collecting the hyperspectral images, it is necessary to extract the desired spatial and spectral features from the images. However, due to the small size of the egg embryo on day 0 and limitations in spatial resolution of hyperspectral cameras, it is difficult to visualize the embryo in hyperspectral images. Therefore, it seems that the spectral analysis in hyperspectral images is superior to spatial analysis, especially when other studies confirmed that the transmission spectra are affected by the presence of the embryo in the egg, causing some absorptions<sup>##UREF##2##3##,##UREF##13##15##</sup>.</p>\",\n \"<p id=\\\"Par9\\\">In spectral analysis, choosing effective wavelengths is a critical step. Opting for the right wavelengths can decrease the dimensionality and complexity of data, ultimately enhancing the predictive capability of the model. The use of effective wavelengths not only reduces analysis time but is also advantageous for implementing the model in an online multispectral imaging system<sup>##UREF##14##16##</sup>. Moreover, given the vast amount of information in hyperspectral images, it becomes crucial to select the most appropriate classification method for discriminating the desired classes.</p>\",\n \"<p id=\\\"Par10\\\">Different classification and wavelength selection approaches have been reported in the literature related to spectral analysis and they resulted in various prediction powers. For example, principal components analysis (PCA) in fertility detection by HSI<sup>##UREF##0##1##,##UREF##1##2##</sup>, naive Bayes classifier<sup>##UREF##2##3##</sup>, and linear discriminant analysis and support vector machine<sup>##UREF##13##15##</sup> in fertility detection of eggs using Vis-SWNIR transmittance spectroscopy, and receiver operating characteristic (ROC) analysis in the wavelength difference of reflectance spectra in bruise detection in apple<sup>##UREF##14##16##</sup>, were examined.</p>\",\n \"<p id=\\\"Par11\\\">In this paper, soft independent modeling of class analogy (SIMCA) was first utilized to investigate the feasibility of Vis-SWNIR spectral data based on the HSI technique for detecting non-fertile eggs. Afterward, the wavelength variables with strong discriminatory powers were retained, while the weaker wavelengths were excluded from the data set. The ability of the selected wavebands was checked after importing the raw and processed spectral data to the nonlinear artificial neural network (ANN) classifier.</p>\",\n \"<p id=\\\"Par12\\\">The objectives of this study were (1) to evaluate the feasibility of hyperspectral transmission imaging in the spectral range of Vis-SWNIR to detect the non-fertile eggs before starting the incubation process, and (2) to determine and compare the potential of different machine learning tools in classifying the fertile and infertile eggs, and (3) selecting the most informative wavebands to develop the predictive models for detecting fertility of unincubated chicken egg. Enhancing the accuracy of fertility detection before the incubation process, through the use of a more cost-effective hyperspectral camera, the selection of the most informative wavelengths and development of the predictive model based on the discrete number of wavelengths, and the comparison of the performance of various machine learning techniques, can be considered the novel aspects of the current study.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Materials and methods</title>","<title>Sample preparation</title>","<p id=\"Par13\">A total of 227 clean, white-shell, fresh, unwashed eggs were prepared including 131 fertile and 96 infertile eggs. The eggs were collected from a flock of 60 Leghorn laying breeder hens (Hy-Line W-36). The birds were purchased from a commercial laying breeder farm and kept in the poultry farm belonging to the Isfahan University of Technology. Then, the hens were randomly distributed in two sub-flocks of 30 laying breeder hens in each. While the first sub-flock was raised without the rooster to produce non-fertile eggs, 4 roosters were added to the second sub-flock to create as much fertility as possible. All the sub-flocks were fed a similar and standard diet, therefore, the collected fertile and infertile eggs were prepared with similar conditions in terms of hen age, feeding program, and management. These conditions could be helpful to minimize errors and make the presence of the embryo the most important factor in providing the differences between the two groups of samples. In other words, the effect of other influential factors such as variations in egg-shell thickness and internal composition arising from the hen age and diet was diminished remarkably.</p>","<p id=\"Par14\">The egg samples were collected daily from both sub-flocks, then numbered and weighed and their dimensions were measured. After acquiring the hyperspectral images, the eggs were immediately incubated in a commercial incubator (Jam Toyor-504, Iran) under the standard conditions (temperature of 37.5 °C and relative humidity of 60%, turned every hour)<sup>##UREF##1##2##</sup>. After 5 days of incubation, eggs were candled and broken out to determine their fertility status. The infertile eggs were distinguished and related hyperspectral data were transferred to the proper sample group.</p>","<title>Hyperspectral imaging system</title>","<p id=\"Par15\">A line scanning visible and near-infrared (Vis–NIR) HSI system (model V1001, OPTC, Iran) was utilized to acquire spectral images in the full-transmittance mode in the range of 400–1000 nm and average optical spectral resolution of 2 nm (Fig. ##FIG##0##1##). The mirror of HSI camera was connected to a stepper motor that could take both spectral and spatial information of the illuminated egg without moving the sample (Fig. ##FIG##0##1##b). The exposure time was set at 100 ms after trial and error to reach the maximum possible signal-to-noise in the spectral data. To cover the entire width of the egg and achieve maximum spatial resolution, the number of scans was set at 400 with a distance of 100 cm between the camera and the sample. The HSI set-up was covered with a polyurethane cover to prevent ambient light from entering the imaging chamber (Fig. ##FIG##0##1##a).</p>","<p id=\"Par16\">The egg sample was vertically placed in a 5 cm diameter hole drilled in a wooden board between the camera and the light sources (Fig. ##FIG##0##1##c). By this arrangement, transmitted light passed through the egg could enter the camera. The light sources comprised one 150W lamp (at the center) and six 50W lamps (arranged around the center) of tungsten halogen, with color temperatures of 3270 K and 3200 K, respectively. These lamps were positioned beneath the sample, as shown in Fig. ##FIG##0##1##b. Two fans were used in the light exposure chamber with the direction of blowing from outside to inside (Fig. ##FIG##0##1##c). In addition to cooling the light sources and preventing the overheating of the egg samples, the blowing of warm air under the eggs could provide the heat required for the embryo’s survival while capturing the hyperspectral images.</p>","<p id=\"Par17\">The HSI images of samples were originally saved in the raw format, comprising 1279 images in λ direction as spectral dimension and 400 × 400 two spatial dimensions. Therefore, the original output hypercube had dimensions of 400 rows × 400 columns × 1279 bands. However, due to the absorption of eggshell<sup>##UREF##7##8##</sup> and the decrease in the intensity of light sources in wavelengths below 500 nm, as well as the low signal-to-noise ratio of the camera’s detector in wavelengths above 950 nm, the transmission spectral data from 500–950 nm was utilized for predicting the fertility of chicken eggs before incubation. To enhance the signal-to-noise ratio, the averaging method was applied in the selected wavelength region, related to the dispersion of wavelength isolator in different wavelengths. This resulted in the final hypercube of 400 rows × 400 columns × 256 bands.</p>","<p id=\"Par18\">For spectral calibration, a reference image was captured by allowing light to pass through the camera lens while adjusting the exposure time to the minimum<sup>##UREF##1##2##</sup>. The dark image was obtained with an exposure time of 100 ms by turning off the light sources and covering the camera with its cap.</p>","<title>Image processing and spectra extraction</title>","<p id=\"Par19\">In dealing with fertilized eggs on day 0, it should be noted that the embryo has not developed enough to be easily recognized without the egg breaking. The blastoderm, a single layer of embryonic epithelial tissue, in a fertilized egg is a symmetrical circular ring with approximately 3–4 mm in diameter, while the blastodisc in unfertilized eggs can be seen as an asymmetrical solid spot with a smaller diameter of about 2.5 mm. Furthermore, blastoderm in fertile eggs has a lower density of Area Pellucida (AP) and a higher density of Area Opaca (AO) regions around its perimeter<sup>##UREF##3##4##</sup>. In addition to changes in the density of AP and AO, molecular alterations also occur in fertilized eggs, offering an alternative indirect method for detecting unincubated fertile and infertile eggs. For instance, Padliya, et al.<sup>##REF##26073176##17##</sup> observed that 9 proteins increased in abundance in fertilized egg yolk compared to unfertilized egg yolk, while 9 proteins decreased in abundance in fertilized egg yolk relative to unfertilized egg yolk. Qiu, et al.<sup>##REF##23436529##18##</sup> and Qiu, et al.<sup>##UREF##15##19##</sup> investigated alterations in egg white protein and the original albumen differences, revealing more than tenfold differences (P < 0.01) in abundance between fresh unfertilized and fertilized chicken egg whites. Whereas the small variation in size of blastoderm and blastodisc along with limitations in spatial resolution of hyperspectral images confine spatial analysis of fertilized unincubated eggs, the changes in the density of AP and AO and the protein content of egg yolk can make the spectral analysis a more adequate approach toward differentiating between fertilized and unfertilized eggs before incubation.</p>","<p id=\"Par20\">To speed up spectral processing and eliminate irrelevant information, the region of interest (ROI) of hyperspectral images should be confined to the egg region. Based on studies that emphasize the molecular differences between fertilized and unfertilized eggs, particularly the changes in protein content in yolk<sup>##REF##26073176##17##</sup> and albumen<sup>##REF##23436529##18##,##UREF##15##19##</sup>, this research utilized the entire egg area as the ROI for subsequent image processing analysis. For this purpose, the best spectral image with the most contrast between the egg and the background was selected by visual investigation. This image was found around the wavelength of 630 nm (Fig. ##FIG##1##2##a) and was then used to perform background removal operations. The edge detection method was applied to segment the image into the egg and background and the binary image was produced (Zero code for the background and code one for the egg) (Fig. ##FIG##1##2##b). By performing the logical multiplication operation of the binary image on the other wavelength images, the hyperspectral image without background was obtained. This operation was performed on all acquired hyperspectral images.</p>","<p id=\"Par21\">In the next step, the average intensity of spectral data obtained from each image (<italic>T</italic><sub><italic>s</italic></sub>) was calculated from the processed images. The relative transmittance spectrum of the samples (<italic>T</italic><sub><italic>rel</italic></sub>) was then calculated to eliminate the interference by the optical system. This operation was done after collecting the dark and reference images. The intensity of dark (<italic>T</italic><sub><italic>d</italic></sub>) and reference (<italic>T</italic><sub><italic>r</italic></sub>) spectra were calculated by averaging the spectral data in the related images. Then, Eq. (##FORMU##0##1##) was used to calculate the relative spectrum of the samples:where <italic>e</italic><sub><italic>s</italic></sub> and <italic>e</italic><sub><italic>r</italic></sub> are the exposure times of the camera while capturing the sample and reference images, respectively. Image processing and feature extraction approaches were all carried out using “MATLAB” V2014a (The MathWorks, Natick, USA).</p>","<title>Spectral data analysis</title>","<p id=\"Par22\">The proposed framework for detecting the fertile and infertile eggs and selecting the most informative wavelength variables is shown in Fig. ##FIG##2##3##. After extracting the sample spectra, spectral preprocessing was performed to eliminate any undesired and irrelevant information due to the various factors such as light scattering effects and detector anomalies and highlight the differences between spectra for further analysis. The Savitsky-Golay smoothing method was first used to remove noise from the spectroscopy machine. In addition, the intensity of the radiation had a great effect on hyperspectral images. Therefore, methods such as standard normal variate (SNV) and normalization were used to minimize the effect of light intensity<sup>##UREF##16##20##</sup>. Other pretreatments such as baseline correction and multiplicative scatter correction (MSC) were also implemented to resolve the baseline changes and additive and/or multiplicative effects in spectral data, respectively. Finally, Savitzky-Golay first and second derivatives were used to correct the overlapping peaks and baseline variations, leading to broad bands being suppressed to sharp bands<sup>##UREF##17##21##</sup>.</p>","<p id=\"Par23\">Moreover, the principal components analysis (PCA) was used to review the spectral data and remove the outliers. The samples positioned outside the Hoteling T<sup>2</sup> ellipse (with the 95% confidential level) were recognized as potential outliers and eliminated from the data set. Then, the data set was randomly disparted into two subsets, containing the calibration subset (80% of total samples) used for building the models, and an independent test subset (20% of remaining samples) applied to assess the ability of the models in prediction.</p>","<p id=\"Par24\">Generally, model building and feature selection approaches carried out in this study comprised two main steps. In the first step, the ability of different linear and nonlinear classifiers to discriminate the fertile and infertile classes was evaluated using all of the wavelength variables in the Vis-SWNIR region. In the next step, the most effective wavelength variables were selected by analysis of classifiers to reach a discrete number of wavelengths, suitable for multispectral imaging systems (Fig. ##FIG##2##3##).</p>","<title>Classification by whole spectral data</title>","<p id=\"Par25\">The machine learning tools used in this study included soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and nonlinear artificial neural networks (ANN). These approaches are described briefly below, with key references cited for more details.</p>","<p id=\"Par26\">SIMCA is a supervised classifier that describes each class individually in PCA sub-models. The constructed sub-models are then used to evaluate the new sample’s belonging to the class. The result of SIMCA analysis is also very useful for evaluating the discrimination power of each wavelength variable. The discrimination power plot obtained from SIMCA analysis shows us which wavelengths are most effective for discerning between two classes. The variables that resulted in a discrimination power of more than 3 can be regarded as useful in distinguishing between two classes<sup>##UREF##18##22##</sup>.</p>","<p id=\"Par27\">LDA is another supervised pattern recognition method that is used to find the linear combination of features to separate two or more classes. It describes a linear separating hyperplane by calculating the linear discriminant functions. The linear functions are used to determine to which class an unknown sample belongs. In this method, the maximum ratio of the variance between the classes to the variance within the class is obtained in the direction of the normal vector of the separating hyperplane<sup>##UREF##19##23##</sup>.</p>","<p id=\"Par28\">QDA is a more general version of the LDA that classifies observations from different multivariate normal populations. While LDA calculates the Mahalanobis distance from a common covariance matrix considered for all classes, QDA derives the Mahalanobis distance from class-specific covariance matrices<sup>##UREF##20##24##</sup>. The QDA models used quadric surfaces to separate two or more classes based on Gaussian distribution. Then it used posterior distributions to guess the class for a given test sample. The Gaussian parameters are estimated based on maximum likelihood<sup>##UREF##21##25##</sup>. QDA is especially proposed when there is any conjecture about the gross violation from the homogeneity of within-classes variance.</p>","<p id=\"Par29\">In ANN analysis, multi-layer, feed-forward networks with the back-propagation (BP) learning algorithm were used for detecting egg fertility. The constructed ANN consisted of one input layer to transfer the spectral data processed by the best pretreatment into the network, one hidden layer, and one output layer with two nodes for fertile and infertile classes. The optimum number of nods in the hidden layer (NHL) was achieved after a trial and error procedure. For each number of NHL, three networks were developed and their average accuracy was calculated. The plot of average accuracy versus the number of NHL was used to find the optimum NHL and hence the best topology of networks.</p>","<p id=\"Par30\">After finding the best ANN model based on total spectral data, sensitivity analysis was performed to identify the most important input variables or wavelengths in explaining variances in the model output. Generally, for a trained network with specific parameters for each input variable, sensitivity analysis determines the effect of varying the parameters of the network for each variable on the overall network fit<sup>##UREF##22##26##</sup>.</p>","<p id=\"Par31\">The statistical parameters used to evaluate the classifiers comprised sensitivity (Sen.), specificity (Spe.), precision (Precis.), and accuracy (Acc.) defined by Eqs. (##FORMU##1##2##) to (##FORMU##2##5##), respectively<sup>##UREF##23##27##</sup>:where TP (or true positive prediction) and TN (or true negative prediction) are the numbers of fertile and infertile eggs, respectively, that were correctly classified, and FP (or false positive prediction) and FN (or false negative prediction) are the numbers of infertile and fertile eggs that were misclassified as fertile and infertile, respectively.</p>","<p id=\"Par32\">Outlier detection, spectral pre-processing, PCA, and SIMCA, LDA, and QDA classifications were all performed using a statistical software package of ‘The Unscrambler’ V10.4 (CAMO AS, Trondheim, Norway). While, the ANN analysis was carried out by the neural networks package of ‘STATISTICA’ V12 (StatSoft, Inc., CA, USA).</p>","<title>Selecting the effective wavebands</title>","<p id=\"Par33\">Due to the great number of spectral bands in the hyperspectral images, the analysis of data is relatively slow. Therefore, multispectral imaging systems are suggested that work based on a certain number of wavelengths. These wavelengths should be the most informative ones, determined by analysis of hyperspectral images and successful predictive models.</p>","<p id=\"Par34\">Following the acceptable results of the SIMCA classifier accomplished by 1st derivative pretreatment, in the next step, the discrimination power plot was used to select the most influential wavebands. The effectiveness of selected wavebands in detecting fertility was evaluated by employing them as input for the ANN models. However, since these regions were discontinuous, the 1st derivative operation could not be carried out via common algorithms. Therefore, two approaches were selected and tested to overcome this problem. First, the raw spectral data of interested wavebands were used as the input to the ANN classifier, assuming that the preprocessing operation can be done by adjusting the weights in the nonlinear ANN method. In the second approach, the spectral difference between all possible pairs of wavelengths in the obtained effective wavebands was calculated to substitute the 1st derivative of transmission spectra<sup>##UREF##14##16##</sup>. The transmission difference values (<italic>T</italic>(λ<sub>1</sub>) − <italic>T</italic>(λ<sub>2</sub>)) were then used as the input of the ANN. Finally, the performance of constructed ANNs was compared and the best classifier based on the discrete number of wavelengths was presented.</p>","<title>Ethics approval</title>","<p id=\"Par35\">Animal experiments were conducted in accordance with the Guiding Principles for the Care and Use of Research Animals at Isfahan University of Technology. The protocol and methods received approval from the Committee on Animal Experiment Ethics at Isfahan University of Technology (No. 390132). The study adhered to the ARRIVE guidelines for reporting animal research, experimental design, and data reporting<sup>##UREF##24##28##</sup>.</p>"],"string":"[\n \"<title>Materials and methods</title>\",\n \"<title>Sample preparation</title>\",\n \"<p id=\\\"Par13\\\">A total of 227 clean, white-shell, fresh, unwashed eggs were prepared including 131 fertile and 96 infertile eggs. The eggs were collected from a flock of 60 Leghorn laying breeder hens (Hy-Line W-36). The birds were purchased from a commercial laying breeder farm and kept in the poultry farm belonging to the Isfahan University of Technology. Then, the hens were randomly distributed in two sub-flocks of 30 laying breeder hens in each. While the first sub-flock was raised without the rooster to produce non-fertile eggs, 4 roosters were added to the second sub-flock to create as much fertility as possible. All the sub-flocks were fed a similar and standard diet, therefore, the collected fertile and infertile eggs were prepared with similar conditions in terms of hen age, feeding program, and management. These conditions could be helpful to minimize errors and make the presence of the embryo the most important factor in providing the differences between the two groups of samples. In other words, the effect of other influential factors such as variations in egg-shell thickness and internal composition arising from the hen age and diet was diminished remarkably.</p>\",\n \"<p id=\\\"Par14\\\">The egg samples were collected daily from both sub-flocks, then numbered and weighed and their dimensions were measured. After acquiring the hyperspectral images, the eggs were immediately incubated in a commercial incubator (Jam Toyor-504, Iran) under the standard conditions (temperature of 37.5 °C and relative humidity of 60%, turned every hour)<sup>##UREF##1##2##</sup>. After 5 days of incubation, eggs were candled and broken out to determine their fertility status. The infertile eggs were distinguished and related hyperspectral data were transferred to the proper sample group.</p>\",\n \"<title>Hyperspectral imaging system</title>\",\n \"<p id=\\\"Par15\\\">A line scanning visible and near-infrared (Vis–NIR) HSI system (model V1001, OPTC, Iran) was utilized to acquire spectral images in the full-transmittance mode in the range of 400–1000 nm and average optical spectral resolution of 2 nm (Fig. ##FIG##0##1##). The mirror of HSI camera was connected to a stepper motor that could take both spectral and spatial information of the illuminated egg without moving the sample (Fig. ##FIG##0##1##b). The exposure time was set at 100 ms after trial and error to reach the maximum possible signal-to-noise in the spectral data. To cover the entire width of the egg and achieve maximum spatial resolution, the number of scans was set at 400 with a distance of 100 cm between the camera and the sample. The HSI set-up was covered with a polyurethane cover to prevent ambient light from entering the imaging chamber (Fig. ##FIG##0##1##a).</p>\",\n \"<p id=\\\"Par16\\\">The egg sample was vertically placed in a 5 cm diameter hole drilled in a wooden board between the camera and the light sources (Fig. ##FIG##0##1##c). By this arrangement, transmitted light passed through the egg could enter the camera. The light sources comprised one 150W lamp (at the center) and six 50W lamps (arranged around the center) of tungsten halogen, with color temperatures of 3270 K and 3200 K, respectively. These lamps were positioned beneath the sample, as shown in Fig. ##FIG##0##1##b. Two fans were used in the light exposure chamber with the direction of blowing from outside to inside (Fig. ##FIG##0##1##c). In addition to cooling the light sources and preventing the overheating of the egg samples, the blowing of warm air under the eggs could provide the heat required for the embryo’s survival while capturing the hyperspectral images.</p>\",\n \"<p id=\\\"Par17\\\">The HSI images of samples were originally saved in the raw format, comprising 1279 images in λ direction as spectral dimension and 400 × 400 two spatial dimensions. Therefore, the original output hypercube had dimensions of 400 rows × 400 columns × 1279 bands. However, due to the absorption of eggshell<sup>##UREF##7##8##</sup> and the decrease in the intensity of light sources in wavelengths below 500 nm, as well as the low signal-to-noise ratio of the camera’s detector in wavelengths above 950 nm, the transmission spectral data from 500–950 nm was utilized for predicting the fertility of chicken eggs before incubation. To enhance the signal-to-noise ratio, the averaging method was applied in the selected wavelength region, related to the dispersion of wavelength isolator in different wavelengths. This resulted in the final hypercube of 400 rows × 400 columns × 256 bands.</p>\",\n \"<p id=\\\"Par18\\\">For spectral calibration, a reference image was captured by allowing light to pass through the camera lens while adjusting the exposure time to the minimum<sup>##UREF##1##2##</sup>. The dark image was obtained with an exposure time of 100 ms by turning off the light sources and covering the camera with its cap.</p>\",\n \"<title>Image processing and spectra extraction</title>\",\n \"<p id=\\\"Par19\\\">In dealing with fertilized eggs on day 0, it should be noted that the embryo has not developed enough to be easily recognized without the egg breaking. The blastoderm, a single layer of embryonic epithelial tissue, in a fertilized egg is a symmetrical circular ring with approximately 3–4 mm in diameter, while the blastodisc in unfertilized eggs can be seen as an asymmetrical solid spot with a smaller diameter of about 2.5 mm. Furthermore, blastoderm in fertile eggs has a lower density of Area Pellucida (AP) and a higher density of Area Opaca (AO) regions around its perimeter<sup>##UREF##3##4##</sup>. In addition to changes in the density of AP and AO, molecular alterations also occur in fertilized eggs, offering an alternative indirect method for detecting unincubated fertile and infertile eggs. For instance, Padliya, et al.<sup>##REF##26073176##17##</sup> observed that 9 proteins increased in abundance in fertilized egg yolk compared to unfertilized egg yolk, while 9 proteins decreased in abundance in fertilized egg yolk relative to unfertilized egg yolk. Qiu, et al.<sup>##REF##23436529##18##</sup> and Qiu, et al.<sup>##UREF##15##19##</sup> investigated alterations in egg white protein and the original albumen differences, revealing more than tenfold differences (P < 0.01) in abundance between fresh unfertilized and fertilized chicken egg whites. Whereas the small variation in size of blastoderm and blastodisc along with limitations in spatial resolution of hyperspectral images confine spatial analysis of fertilized unincubated eggs, the changes in the density of AP and AO and the protein content of egg yolk can make the spectral analysis a more adequate approach toward differentiating between fertilized and unfertilized eggs before incubation.</p>\",\n \"<p id=\\\"Par20\\\">To speed up spectral processing and eliminate irrelevant information, the region of interest (ROI) of hyperspectral images should be confined to the egg region. Based on studies that emphasize the molecular differences between fertilized and unfertilized eggs, particularly the changes in protein content in yolk<sup>##REF##26073176##17##</sup> and albumen<sup>##REF##23436529##18##,##UREF##15##19##</sup>, this research utilized the entire egg area as the ROI for subsequent image processing analysis. For this purpose, the best spectral image with the most contrast between the egg and the background was selected by visual investigation. This image was found around the wavelength of 630 nm (Fig. ##FIG##1##2##a) and was then used to perform background removal operations. The edge detection method was applied to segment the image into the egg and background and the binary image was produced (Zero code for the background and code one for the egg) (Fig. ##FIG##1##2##b). By performing the logical multiplication operation of the binary image on the other wavelength images, the hyperspectral image without background was obtained. This operation was performed on all acquired hyperspectral images.</p>\",\n \"<p id=\\\"Par21\\\">In the next step, the average intensity of spectral data obtained from each image (<italic>T</italic><sub><italic>s</italic></sub>) was calculated from the processed images. The relative transmittance spectrum of the samples (<italic>T</italic><sub><italic>rel</italic></sub>) was then calculated to eliminate the interference by the optical system. This operation was done after collecting the dark and reference images. The intensity of dark (<italic>T</italic><sub><italic>d</italic></sub>) and reference (<italic>T</italic><sub><italic>r</italic></sub>) spectra were calculated by averaging the spectral data in the related images. Then, Eq. (##FORMU##0##1##) was used to calculate the relative spectrum of the samples:where <italic>e</italic><sub><italic>s</italic></sub> and <italic>e</italic><sub><italic>r</italic></sub> are the exposure times of the camera while capturing the sample and reference images, respectively. Image processing and feature extraction approaches were all carried out using “MATLAB” V2014a (The MathWorks, Natick, USA).</p>\",\n \"<title>Spectral data analysis</title>\",\n \"<p id=\\\"Par22\\\">The proposed framework for detecting the fertile and infertile eggs and selecting the most informative wavelength variables is shown in Fig. ##FIG##2##3##. After extracting the sample spectra, spectral preprocessing was performed to eliminate any undesired and irrelevant information due to the various factors such as light scattering effects and detector anomalies and highlight the differences between spectra for further analysis. The Savitsky-Golay smoothing method was first used to remove noise from the spectroscopy machine. In addition, the intensity of the radiation had a great effect on hyperspectral images. Therefore, methods such as standard normal variate (SNV) and normalization were used to minimize the effect of light intensity<sup>##UREF##16##20##</sup>. Other pretreatments such as baseline correction and multiplicative scatter correction (MSC) were also implemented to resolve the baseline changes and additive and/or multiplicative effects in spectral data, respectively. Finally, Savitzky-Golay first and second derivatives were used to correct the overlapping peaks and baseline variations, leading to broad bands being suppressed to sharp bands<sup>##UREF##17##21##</sup>.</p>\",\n \"<p id=\\\"Par23\\\">Moreover, the principal components analysis (PCA) was used to review the spectral data and remove the outliers. The samples positioned outside the Hoteling T<sup>2</sup> ellipse (with the 95% confidential level) were recognized as potential outliers and eliminated from the data set. Then, the data set was randomly disparted into two subsets, containing the calibration subset (80% of total samples) used for building the models, and an independent test subset (20% of remaining samples) applied to assess the ability of the models in prediction.</p>\",\n \"<p id=\\\"Par24\\\">Generally, model building and feature selection approaches carried out in this study comprised two main steps. In the first step, the ability of different linear and nonlinear classifiers to discriminate the fertile and infertile classes was evaluated using all of the wavelength variables in the Vis-SWNIR region. In the next step, the most effective wavelength variables were selected by analysis of classifiers to reach a discrete number of wavelengths, suitable for multispectral imaging systems (Fig. ##FIG##2##3##).</p>\",\n \"<title>Classification by whole spectral data</title>\",\n \"<p id=\\\"Par25\\\">The machine learning tools used in this study included soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and nonlinear artificial neural networks (ANN). These approaches are described briefly below, with key references cited for more details.</p>\",\n \"<p id=\\\"Par26\\\">SIMCA is a supervised classifier that describes each class individually in PCA sub-models. The constructed sub-models are then used to evaluate the new sample’s belonging to the class. The result of SIMCA analysis is also very useful for evaluating the discrimination power of each wavelength variable. The discrimination power plot obtained from SIMCA analysis shows us which wavelengths are most effective for discerning between two classes. The variables that resulted in a discrimination power of more than 3 can be regarded as useful in distinguishing between two classes<sup>##UREF##18##22##</sup>.</p>\",\n \"<p id=\\\"Par27\\\">LDA is another supervised pattern recognition method that is used to find the linear combination of features to separate two or more classes. It describes a linear separating hyperplane by calculating the linear discriminant functions. The linear functions are used to determine to which class an unknown sample belongs. In this method, the maximum ratio of the variance between the classes to the variance within the class is obtained in the direction of the normal vector of the separating hyperplane<sup>##UREF##19##23##</sup>.</p>\",\n \"<p id=\\\"Par28\\\">QDA is a more general version of the LDA that classifies observations from different multivariate normal populations. While LDA calculates the Mahalanobis distance from a common covariance matrix considered for all classes, QDA derives the Mahalanobis distance from class-specific covariance matrices<sup>##UREF##20##24##</sup>. The QDA models used quadric surfaces to separate two or more classes based on Gaussian distribution. Then it used posterior distributions to guess the class for a given test sample. The Gaussian parameters are estimated based on maximum likelihood<sup>##UREF##21##25##</sup>. QDA is especially proposed when there is any conjecture about the gross violation from the homogeneity of within-classes variance.</p>\",\n \"<p id=\\\"Par29\\\">In ANN analysis, multi-layer, feed-forward networks with the back-propagation (BP) learning algorithm were used for detecting egg fertility. The constructed ANN consisted of one input layer to transfer the spectral data processed by the best pretreatment into the network, one hidden layer, and one output layer with two nodes for fertile and infertile classes. The optimum number of nods in the hidden layer (NHL) was achieved after a trial and error procedure. For each number of NHL, three networks were developed and their average accuracy was calculated. The plot of average accuracy versus the number of NHL was used to find the optimum NHL and hence the best topology of networks.</p>\",\n \"<p id=\\\"Par30\\\">After finding the best ANN model based on total spectral data, sensitivity analysis was performed to identify the most important input variables or wavelengths in explaining variances in the model output. Generally, for a trained network with specific parameters for each input variable, sensitivity analysis determines the effect of varying the parameters of the network for each variable on the overall network fit<sup>##UREF##22##26##</sup>.</p>\",\n \"<p id=\\\"Par31\\\">The statistical parameters used to evaluate the classifiers comprised sensitivity (Sen.), specificity (Spe.), precision (Precis.), and accuracy (Acc.) defined by Eqs. (##FORMU##1##2##) to (##FORMU##2##5##), respectively<sup>##UREF##23##27##</sup>:where TP (or true positive prediction) and TN (or true negative prediction) are the numbers of fertile and infertile eggs, respectively, that were correctly classified, and FP (or false positive prediction) and FN (or false negative prediction) are the numbers of infertile and fertile eggs that were misclassified as fertile and infertile, respectively.</p>\",\n \"<p id=\\\"Par32\\\">Outlier detection, spectral pre-processing, PCA, and SIMCA, LDA, and QDA classifications were all performed using a statistical software package of ‘The Unscrambler’ V10.4 (CAMO AS, Trondheim, Norway). While, the ANN analysis was carried out by the neural networks package of ‘STATISTICA’ V12 (StatSoft, Inc., CA, USA).</p>\",\n \"<title>Selecting the effective wavebands</title>\",\n \"<p id=\\\"Par33\\\">Due to the great number of spectral bands in the hyperspectral images, the analysis of data is relatively slow. Therefore, multispectral imaging systems are suggested that work based on a certain number of wavelengths. These wavelengths should be the most informative ones, determined by analysis of hyperspectral images and successful predictive models.</p>\",\n \"<p id=\\\"Par34\\\">Following the acceptable results of the SIMCA classifier accomplished by 1st derivative pretreatment, in the next step, the discrimination power plot was used to select the most influential wavebands. The effectiveness of selected wavebands in detecting fertility was evaluated by employing them as input for the ANN models. However, since these regions were discontinuous, the 1st derivative operation could not be carried out via common algorithms. Therefore, two approaches were selected and tested to overcome this problem. First, the raw spectral data of interested wavebands were used as the input to the ANN classifier, assuming that the preprocessing operation can be done by adjusting the weights in the nonlinear ANN method. In the second approach, the spectral difference between all possible pairs of wavelengths in the obtained effective wavebands was calculated to substitute the 1st derivative of transmission spectra<sup>##UREF##14##16##</sup>. The transmission difference values (<italic>T</italic>(λ<sub>1</sub>) − <italic>T</italic>(λ<sub>2</sub>)) were then used as the input of the ANN. Finally, the performance of constructed ANNs was compared and the best classifier based on the discrete number of wavelengths was presented.</p>\",\n \"<title>Ethics approval</title>\",\n \"<p id=\\\"Par35\\\">Animal experiments were conducted in accordance with the Guiding Principles for the Care and Use of Research Animals at Isfahan University of Technology. The protocol and methods received approval from the Committee on Animal Experiment Ethics at Isfahan University of Technology (No. 390132). The study adhered to the ARRIVE guidelines for reporting animal research, experimental design, and data reporting<sup>##UREF##24##28##</sup>.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results and discussion</title>","<title>Overview of spectral data</title>","<p id=\"Par36\">The raw and 1st derivative of average transmission spectra of fertile and infertile eggs are shown in Fig. ##FIG##3##4##. As shown, the fertile samples had higher raw transmission values than the infertile ones in all spectral regions (Fig. ##FIG##3##4##a). Differences in chemical substances, such as protein content<sup>##REF##23436529##18##,##UREF##15##19##</sup>, and physical properties, like the egg shape index<sup>##UREF##7##8##</sup>, between fertile and infertile chicken eggs could lead to variations in the transmission spectral behavior. Similar transmission values and trends in the spectra of fertile and infertile eggs were observed in the studies performed by conventional transmission spectroscopy systems<sup>##UREF##7##8##,##UREF##13##15##,##UREF##25##29##</sup>. However, more absorption bands appeared in transmission spectra obtained by the HSI system. As shown in Fig. ##FIG##3##4##, in addition to broad absorption bands, significant absorptions (valleys in transmission spectra) were revealed in the spectra of fertile and infertile eggs around the wavelengths of 580, 634, 665, 730, 770, 830, and 930 nm. Since the raw spectra had relatively broad absorption bands, to improve the quality of presentation, the 1st derivative spectra were calculated (Fig. ##FIG##3##4##b).</p>","<p id=\"Par37\">As shown in Fig. ##FIG##3##4##, extensive absorption bands in the visible regions were observed around 580, 634, and 665 nm wavelengths. Considering the white shell of eggs used in this study, these absorptions cannot be attributed to the shell color pigment of protoporphyrin which produces relatively strong absorption in brown-shelled egg spectra in the mentioned wavelengths<sup>##UREF##13##15##,##UREF##25##29##</sup>. Therefore, these broad absorptions were likely due to the color pigments in the yolk such as carotenoids which are a fat-soluble group of yellow (580 nm), orange (634 nm), and red (665 nm) pigments<sup>##UREF##26##30##</sup>.</p>","<p id=\"Par38\">The wavebands around 730 nm are related to the third overtone of O–H in water<sup>##UREF##27##31##</sup> and the carbohydrates of eggs. Since the embryo consumes carbohydrates, proteins, and fats<sup>##UREF##28##32##</sup>, the higher transmission value of fertile samples around 730 nm is likely due to the relatively lower carbohydrate, protein, and fat contents of fertile samples with respect to the infertile ones (Fig. ##FIG##3##4##a).</p>","<p id=\"Par39\">The relatively similar internal conditions and common compounds between these two groups of samples could result in similar absorption bands and make it difficult to distinguish the two classes based on the visual interpretation of spectra. However, by a close investigation of 1st derivative spectra (Fig. ##FIG##3##4##b), the difference in transmitted light was observed between two classes around the discussed wavelengths. The remarkable deviations between the two classes could be observed around the wavelengths of 730, 770, and 830 nm. While the red-edge wavelengths around 770 nm<sup>##UREF##29##33##</sup> were related to embryo development<sup>##UREF##0##1##</sup>, absorption around 830 nm is triggered by the 3rd overtone of the O–H stretching, related to the water content inside of eggs<sup>##UREF##27##31##</sup>. Finally, the strong absorption around 930 nm can be associated with 3rd overtones of the C-H stretching absorption, which may be related to the carbohydrate content in the egg<sup>##REF##25803186##34##</sup>.</p>","<title>Classification by SIMCA</title>","<p id=\"Par40\">Table ##TAB##0##1## shows the test set validation results of SIMCA analysis performed by using entire spectral data for the detection of infertile and fertile eggs before incubation. The effect of different pre-processing and the optimum number of PCs for modeling each class were also presented in this table. Among the various mathematical pretreatments, the best discrimination accuracy was achieved from the 1st and 2nd derivatives. In both pretreatments, all fertile eggs were correctly classified (sensitivity of 100%) and 6 infertile eggs were misclassified into fertile (specificity of 68.42%), resulting in an accuracy of 86.67%. Both pretreatments led to a similar precision of 81.25%. Due to the higher price of fertile eggs, it is more important to correctly identify the fertile eggs prior to incubation to avoid unwanted elimination and increase the hatchability rate. Therefore, the best SIMCA model had promising performance in the correct identification of this group of eggs.</p>","<p id=\"Par41\">In terms of fertile and infertile detection of eggs before incubation by HSI, our predictions based on the SIMCA methods were much better than those reported by Smith, et al.<sup>##UREF##11##13##</sup> (accuracy of 63% for day 0, before incubation), Lin, et al.<sup>##UREF##5##6##</sup> (accuracy of 96% for day 14, after incubation), and Park, et al.<sup>##UREF##0##1##</sup> (accuracy of 99% for day 14, after incubation). In one case, however, our best SIMCA model was less accurate than that presented by Liu and Ngadi (2013) in which a near-infrared HSI system in the range of 900–1700 nm was used for the detection of fertile and infertile eggs prior to incubation. The model extracted by them was able to correctly classify all fertile and infertile eggs into the right classes with an overall accuracy of 100%. Despite the same performance in the detection of fertile eggs (100%), our best SIMCA model was weaker in the detection of infertile ones (specificity of 68.42%). The limited waveband (430–960 nm) in our study provided by a lower-price hyperspectral camera can be regarded as one of the main reasons for obtaining these results. Nevertheless, in the study of Liu and Ngadi (2013), there was an unequal distribution of samples per class on day 0 (prior to incubation), where a total of 18 infertile eggs were used against 156 fertile ones in the training and validating datasets. There was no justification for solving the imbalanced classification problems. The main challenge here was how much the use of advanced multivariate techniques could compensate the drawback of the shorter spectral range.</p>","<p id=\"Par42\">Figure ##FIG##4##5## shows the discrimination power plot of different wavelengths for the separation of the fertile (day 0) class from the infertile one obtained from the best model by 1st derivative pretreatment. This plot shows which wavelengths were most effective for distinguishing between two classes. As a rule, variables that resulted in a discrimination power of more than 3 are very useful in distinguishing between two classes<sup>##UREF##18##22##</sup>. These variables were specified by distinct regions and relatively narrow bands in Fig. ##FIG##4##5##, represented by R<sub>1</sub> (673–675 nm), R<sub>2</sub> (813–840 nm), and R<sub>3</sub> (865–873 nm). Additionally, the discrimination power values around the wavelengths of 799 nm were close to 3, indicating the relative importance of this wavelength. It is noteworthy that important areas were located mostly at the end of the visible region (R<sub>1</sub>) and NIR region (799 nm, R<sub>2</sub>, and R<sub>3</sub>). The effect of fertility on wavelengths around 799 nm can be related to the possible formation of blood spots due to the initial stages of embryo development. By examining the PCA images, Park, et al.<sup>##UREF##0##1##</sup> observed a distinct blood vessel pattern in the viable eggs and reported a height weighting coefficient value of around 799 nm in the corresponding PCA loading plot.</p>","<p id=\"Par43\">Moreover, it seems that the presence of the embryo could influence the NIR region more significantly than the visible one (Fig. ##FIG##4##5##). It can be due to the possible changes in the egg’s chemical composition because of embryo existence<sup>##REF##26073176##17##,##UREF##26##30##,##UREF##30##35##</sup>, making the NIR region more important in separating fertile eggs from infertile ones. The initial parts of the spectrum, especially the wavelength range of 500–650 nm had a lower degree of importance for separation between the two classes.</p>","<p id=\"Par44\">Among the distinguished regions, the R<sub>2</sub> region, including the important wavelength of 830 nm, resulted in the highest discrimination power values (Fig. ##FIG##4##5##). By referring to Fig. ##FIG##3##4##, the wavelengths around 830 nm provided a relatively strong absorption for both fertile and infertile eggs with the remarkable absorption difference between these two groups of samples. As indicated in the overview of spectral data section, the absorptions around 830 nm can be related to 3rd overtones of the O–H stretching absorption, which according to egg ingredients can be attributed to the carbohydrates and water content of the egg. Since the embryo consumes carbohydrates, proteins, and fats during its development<sup>##UREF##28##32##</sup>, the higher discrimination power values of the R<sub>2</sub> region were likely due to the relatively lower carbohydrate, protein, and fat contents of fertile samples with respect to the infertile ones.</p>","<title>Discrimination by LDA and QDA</title>","<p id=\"Par45\">Table ##TAB##1##2## summarizes the test set validation results of QDA and LDA classifiers obtained by various mathematical pretreatments. Similar to SIMCA analysis, the best discrimination accuracy was achieved by the 1st and 2nd derivatives. In the best QDA and LDA models, all of the fertile eggs were correctly classified (sensitivity of 100%) and 5 infertile eggs were misclassified into fertile (specificity of 73.68%), resulting in an accuracy of 88.90%. The maximum precision of both models reached 83.87%. In comparison with the SIMCA analysis with the accuracy and precision of 86.67% and 81.25% respectively, the best QDA and LDA models resulted in slightly better performance (accuracy of 88.90% and precision of 83.87%), indicating 2.6% and 3.2% improvement in discrimination accuracy and precision, respectively. The relatively similar performance of the LDA and QDA methods, when compared to the SIMCA method, suggested that more advanced approaches, capable of better capturing the potential nonlinear nature of the data, are necessary to develop more robust predictive models.</p>","<title>Linear classification by effective wavelengths</title>","<p id=\"Par46\">Table ##TAB##2##3## summarizes the results of linear classifiers, including SIMCA, LDA, and QDA, for fertility detection using the selected regions (R<sub>1</sub>, R<sub>2</sub>, and R<sub>3</sub>) and the spectral differences between two pairs of wavelengths in these selected regions as input variables. As shown, employing the effective wavelengths in the selected regions did not yield promising performances across all classifiers. In the best-case scenario, SIMCA achieved a sensitivity of 61.54%, specificity of 73.68%, precision of 76.19%, and accuracy of 66.67%. It appears that the wavelength variables situated outside the selected region contained some information to describe the variance of the class variable (fertile or infertile), essential for developing a reliable linear model. Furthermore, the utilization of selected variables in their raw form, combined with their inclusion in linear models lacking the capacity to address nonlinear data, contributes to the challenges encountered in fertility detection using selected regions.</p>","<p id=\"Par47\">However, when utilizing wavelength differences as a simulation of 1st derivative pretreatment, predictability improved, although it remained lower than the models developed using the entire spectra as the input variable. For SIMCA, LDA, and QDA, accuracy values of 71.11%, 84.44%, and 82.22% were obtained, respectively (Table ##TAB##2##3##). In comparison, the corresponding values when using the entire spectral data were 86.67%, 88.89%, and 88.89%, respectively (Tables ##TAB##0##1## and ##TAB##1##2##). It seems that spectral differences can effectively simulate the 1st derivative pretreatment in distinct wavelength variables. Nevertheless, linear classification still does not reach the same level of predictability as when the entire spectral data is utilized.</p>","<title>Classification by ANN</title>","<p id=\"Par48\">Table ##TAB##3##4## shows the results ANN classifier obtained from the total spectral data, effective wavelengths in selected regions (R<sub>1</sub>, R<sub>2</sub>, and R<sub>3</sub>), and spectral differences between two pairs of wavelengths in selected regions, as the input variables. Figure ##FIG##5##6## illustrates the classification accuracy of ANNs with the different number of nodes in the hidden layer varying from 1 to 30. In using the total spectral data as the inputs to networks (Fig. ##FIG##5##6##a), the best prediction accuracy was achieved by 10 nodes in the hidden layer. This network was able to separate the fertile egg from the infertile one with sensitivity, specificity, precision, and accuracy of 96.15%, 84.21%, 89.29%, and 91.11% (Table ##TAB##3##4##). As shown, a noticeable improvement in the prediction power of infertile eggs occurred when the nonlinear ANN method was applied to the whole spectral data. While the best linear model could distinguish 73.68% of infertile eggs, 84.21% of infertile eggs could be successfully discriminated by the best ANN model based on total spectral data. This showed a 14.3% improvement in the specificity of the predictive model.</p>","<p id=\"Par49\">Figure ##FIG##6##7## illustrates the sensitivity plot based on the best ANN model developed by the total spectral data. As shown, the wavelengths around 524, 656, 767, and 860 nm resulted in the highest degree of importance in the sensitivity analysis. These wavelengths were also close to the distinguished ones that appeared in Fig. ##FIG##3##4## as the absorption valleys in transmission spectra. Moreover, the wavelengths around 665 and 865 nm were identified as the efficient wavelengths in the discrimination power plot of SIMCA analysis. The coincidence in the identified wavebands between linear (SIMCA) and nonlinear (ANN) approaches highlighted the importance of the selected regions R<sub>1</sub> and R<sub>3</sub> in detecting fertility in unincubated chicken eggs.</p>","<p id=\"Par50\">In using the selected regions (Fig. ##FIG##5##6##b) and spectral differences (Fig. ##FIG##5##6##c) as the input variables to the ANN classifier, the optimized topologies of networks were attained with 11 and 7 nodes in the hidden layer, respectively. Despite a slight difference in precision values attained by selected regions and their spectral differences (92.59% and 89.65%, respectively), similar accuracies (accuracy of 93.33%) were achieved when the selected regions and spectral differences were used as the input variables (Table ##TAB##3##4##). Interestingly, and in contrast to the linear classifiers, the accuracy of prediction slightly improved when employing fewer variables from effective regions or spectral differences as input for nonlinear ANN methods, compared to using the full range data (accuracy of 91.11%). This improvement may be attributed to the elimination of unnecessary and irrelevant data that could otherwise have a detrimental impact on the ANN models.</p>","<p id=\"Par51\">Among the best networks, the ANN model developed by the effective region was the most successful one in separating the infertile eggs with a specificity of 89.47%. Only 2 infertile eggs were misclassified as fertile. However, the model developed by spectral differences was more efficient in predicting the fertile eggs in which all of them were correctly classified (sensitivity of 100%).</p>","<p id=\"Par52\">On the whole, the ANN method outperformed the other classifiers in detecting fertility. Compared to the best SIMCA and LDA/QDA models, the accuracy was improved by 7.7% and 5% when the best ANN model was used to discriminate the eggs.</p>"],"string":"[\n \"<title>Results and discussion</title>\",\n \"<title>Overview of spectral data</title>\",\n \"<p id=\\\"Par36\\\">The raw and 1st derivative of average transmission spectra of fertile and infertile eggs are shown in Fig. ##FIG##3##4##. As shown, the fertile samples had higher raw transmission values than the infertile ones in all spectral regions (Fig. ##FIG##3##4##a). Differences in chemical substances, such as protein content<sup>##REF##23436529##18##,##UREF##15##19##</sup>, and physical properties, like the egg shape index<sup>##UREF##7##8##</sup>, between fertile and infertile chicken eggs could lead to variations in the transmission spectral behavior. Similar transmission values and trends in the spectra of fertile and infertile eggs were observed in the studies performed by conventional transmission spectroscopy systems<sup>##UREF##7##8##,##UREF##13##15##,##UREF##25##29##</sup>. However, more absorption bands appeared in transmission spectra obtained by the HSI system. As shown in Fig. ##FIG##3##4##, in addition to broad absorption bands, significant absorptions (valleys in transmission spectra) were revealed in the spectra of fertile and infertile eggs around the wavelengths of 580, 634, 665, 730, 770, 830, and 930 nm. Since the raw spectra had relatively broad absorption bands, to improve the quality of presentation, the 1st derivative spectra were calculated (Fig. ##FIG##3##4##b).</p>\",\n \"<p id=\\\"Par37\\\">As shown in Fig. ##FIG##3##4##, extensive absorption bands in the visible regions were observed around 580, 634, and 665 nm wavelengths. Considering the white shell of eggs used in this study, these absorptions cannot be attributed to the shell color pigment of protoporphyrin which produces relatively strong absorption in brown-shelled egg spectra in the mentioned wavelengths<sup>##UREF##13##15##,##UREF##25##29##</sup>. Therefore, these broad absorptions were likely due to the color pigments in the yolk such as carotenoids which are a fat-soluble group of yellow (580 nm), orange (634 nm), and red (665 nm) pigments<sup>##UREF##26##30##</sup>.</p>\",\n \"<p id=\\\"Par38\\\">The wavebands around 730 nm are related to the third overtone of O–H in water<sup>##UREF##27##31##</sup> and the carbohydrates of eggs. Since the embryo consumes carbohydrates, proteins, and fats<sup>##UREF##28##32##</sup>, the higher transmission value of fertile samples around 730 nm is likely due to the relatively lower carbohydrate, protein, and fat contents of fertile samples with respect to the infertile ones (Fig. ##FIG##3##4##a).</p>\",\n \"<p id=\\\"Par39\\\">The relatively similar internal conditions and common compounds between these two groups of samples could result in similar absorption bands and make it difficult to distinguish the two classes based on the visual interpretation of spectra. However, by a close investigation of 1st derivative spectra (Fig. ##FIG##3##4##b), the difference in transmitted light was observed between two classes around the discussed wavelengths. The remarkable deviations between the two classes could be observed around the wavelengths of 730, 770, and 830 nm. While the red-edge wavelengths around 770 nm<sup>##UREF##29##33##</sup> were related to embryo development<sup>##UREF##0##1##</sup>, absorption around 830 nm is triggered by the 3rd overtone of the O–H stretching, related to the water content inside of eggs<sup>##UREF##27##31##</sup>. Finally, the strong absorption around 930 nm can be associated with 3rd overtones of the C-H stretching absorption, which may be related to the carbohydrate content in the egg<sup>##REF##25803186##34##</sup>.</p>\",\n \"<title>Classification by SIMCA</title>\",\n \"<p id=\\\"Par40\\\">Table ##TAB##0##1## shows the test set validation results of SIMCA analysis performed by using entire spectral data for the detection of infertile and fertile eggs before incubation. The effect of different pre-processing and the optimum number of PCs for modeling each class were also presented in this table. Among the various mathematical pretreatments, the best discrimination accuracy was achieved from the 1st and 2nd derivatives. In both pretreatments, all fertile eggs were correctly classified (sensitivity of 100%) and 6 infertile eggs were misclassified into fertile (specificity of 68.42%), resulting in an accuracy of 86.67%. Both pretreatments led to a similar precision of 81.25%. Due to the higher price of fertile eggs, it is more important to correctly identify the fertile eggs prior to incubation to avoid unwanted elimination and increase the hatchability rate. Therefore, the best SIMCA model had promising performance in the correct identification of this group of eggs.</p>\",\n \"<p id=\\\"Par41\\\">In terms of fertile and infertile detection of eggs before incubation by HSI, our predictions based on the SIMCA methods were much better than those reported by Smith, et al.<sup>##UREF##11##13##</sup> (accuracy of 63% for day 0, before incubation), Lin, et al.<sup>##UREF##5##6##</sup> (accuracy of 96% for day 14, after incubation), and Park, et al.<sup>##UREF##0##1##</sup> (accuracy of 99% for day 14, after incubation). In one case, however, our best SIMCA model was less accurate than that presented by Liu and Ngadi (2013) in which a near-infrared HSI system in the range of 900–1700 nm was used for the detection of fertile and infertile eggs prior to incubation. The model extracted by them was able to correctly classify all fertile and infertile eggs into the right classes with an overall accuracy of 100%. Despite the same performance in the detection of fertile eggs (100%), our best SIMCA model was weaker in the detection of infertile ones (specificity of 68.42%). The limited waveband (430–960 nm) in our study provided by a lower-price hyperspectral camera can be regarded as one of the main reasons for obtaining these results. Nevertheless, in the study of Liu and Ngadi (2013), there was an unequal distribution of samples per class on day 0 (prior to incubation), where a total of 18 infertile eggs were used against 156 fertile ones in the training and validating datasets. There was no justification for solving the imbalanced classification problems. The main challenge here was how much the use of advanced multivariate techniques could compensate the drawback of the shorter spectral range.</p>\",\n \"<p id=\\\"Par42\\\">Figure ##FIG##4##5## shows the discrimination power plot of different wavelengths for the separation of the fertile (day 0) class from the infertile one obtained from the best model by 1st derivative pretreatment. This plot shows which wavelengths were most effective for distinguishing between two classes. As a rule, variables that resulted in a discrimination power of more than 3 are very useful in distinguishing between two classes<sup>##UREF##18##22##</sup>. These variables were specified by distinct regions and relatively narrow bands in Fig. ##FIG##4##5##, represented by R<sub>1</sub> (673–675 nm), R<sub>2</sub> (813–840 nm), and R<sub>3</sub> (865–873 nm). Additionally, the discrimination power values around the wavelengths of 799 nm were close to 3, indicating the relative importance of this wavelength. It is noteworthy that important areas were located mostly at the end of the visible region (R<sub>1</sub>) and NIR region (799 nm, R<sub>2</sub>, and R<sub>3</sub>). The effect of fertility on wavelengths around 799 nm can be related to the possible formation of blood spots due to the initial stages of embryo development. By examining the PCA images, Park, et al.<sup>##UREF##0##1##</sup> observed a distinct blood vessel pattern in the viable eggs and reported a height weighting coefficient value of around 799 nm in the corresponding PCA loading plot.</p>\",\n \"<p id=\\\"Par43\\\">Moreover, it seems that the presence of the embryo could influence the NIR region more significantly than the visible one (Fig. ##FIG##4##5##). It can be due to the possible changes in the egg’s chemical composition because of embryo existence<sup>##REF##26073176##17##,##UREF##26##30##,##UREF##30##35##</sup>, making the NIR region more important in separating fertile eggs from infertile ones. The initial parts of the spectrum, especially the wavelength range of 500–650 nm had a lower degree of importance for separation between the two classes.</p>\",\n \"<p id=\\\"Par44\\\">Among the distinguished regions, the R<sub>2</sub> region, including the important wavelength of 830 nm, resulted in the highest discrimination power values (Fig. ##FIG##4##5##). By referring to Fig. ##FIG##3##4##, the wavelengths around 830 nm provided a relatively strong absorption for both fertile and infertile eggs with the remarkable absorption difference between these two groups of samples. As indicated in the overview of spectral data section, the absorptions around 830 nm can be related to 3rd overtones of the O–H stretching absorption, which according to egg ingredients can be attributed to the carbohydrates and water content of the egg. Since the embryo consumes carbohydrates, proteins, and fats during its development<sup>##UREF##28##32##</sup>, the higher discrimination power values of the R<sub>2</sub> region were likely due to the relatively lower carbohydrate, protein, and fat contents of fertile samples with respect to the infertile ones.</p>\",\n \"<title>Discrimination by LDA and QDA</title>\",\n \"<p id=\\\"Par45\\\">Table ##TAB##1##2## summarizes the test set validation results of QDA and LDA classifiers obtained by various mathematical pretreatments. Similar to SIMCA analysis, the best discrimination accuracy was achieved by the 1st and 2nd derivatives. In the best QDA and LDA models, all of the fertile eggs were correctly classified (sensitivity of 100%) and 5 infertile eggs were misclassified into fertile (specificity of 73.68%), resulting in an accuracy of 88.90%. The maximum precision of both models reached 83.87%. In comparison with the SIMCA analysis with the accuracy and precision of 86.67% and 81.25% respectively, the best QDA and LDA models resulted in slightly better performance (accuracy of 88.90% and precision of 83.87%), indicating 2.6% and 3.2% improvement in discrimination accuracy and precision, respectively. The relatively similar performance of the LDA and QDA methods, when compared to the SIMCA method, suggested that more advanced approaches, capable of better capturing the potential nonlinear nature of the data, are necessary to develop more robust predictive models.</p>\",\n \"<title>Linear classification by effective wavelengths</title>\",\n \"<p id=\\\"Par46\\\">Table ##TAB##2##3## summarizes the results of linear classifiers, including SIMCA, LDA, and QDA, for fertility detection using the selected regions (R<sub>1</sub>, R<sub>2</sub>, and R<sub>3</sub>) and the spectral differences between two pairs of wavelengths in these selected regions as input variables. As shown, employing the effective wavelengths in the selected regions did not yield promising performances across all classifiers. In the best-case scenario, SIMCA achieved a sensitivity of 61.54%, specificity of 73.68%, precision of 76.19%, and accuracy of 66.67%. It appears that the wavelength variables situated outside the selected region contained some information to describe the variance of the class variable (fertile or infertile), essential for developing a reliable linear model. Furthermore, the utilization of selected variables in their raw form, combined with their inclusion in linear models lacking the capacity to address nonlinear data, contributes to the challenges encountered in fertility detection using selected regions.</p>\",\n \"<p id=\\\"Par47\\\">However, when utilizing wavelength differences as a simulation of 1st derivative pretreatment, predictability improved, although it remained lower than the models developed using the entire spectra as the input variable. For SIMCA, LDA, and QDA, accuracy values of 71.11%, 84.44%, and 82.22% were obtained, respectively (Table ##TAB##2##3##). In comparison, the corresponding values when using the entire spectral data were 86.67%, 88.89%, and 88.89%, respectively (Tables ##TAB##0##1## and ##TAB##1##2##). It seems that spectral differences can effectively simulate the 1st derivative pretreatment in distinct wavelength variables. Nevertheless, linear classification still does not reach the same level of predictability as when the entire spectral data is utilized.</p>\",\n \"<title>Classification by ANN</title>\",\n \"<p id=\\\"Par48\\\">Table ##TAB##3##4## shows the results ANN classifier obtained from the total spectral data, effective wavelengths in selected regions (R<sub>1</sub>, R<sub>2</sub>, and R<sub>3</sub>), and spectral differences between two pairs of wavelengths in selected regions, as the input variables. Figure ##FIG##5##6## illustrates the classification accuracy of ANNs with the different number of nodes in the hidden layer varying from 1 to 30. In using the total spectral data as the inputs to networks (Fig. ##FIG##5##6##a), the best prediction accuracy was achieved by 10 nodes in the hidden layer. This network was able to separate the fertile egg from the infertile one with sensitivity, specificity, precision, and accuracy of 96.15%, 84.21%, 89.29%, and 91.11% (Table ##TAB##3##4##). As shown, a noticeable improvement in the prediction power of infertile eggs occurred when the nonlinear ANN method was applied to the whole spectral data. While the best linear model could distinguish 73.68% of infertile eggs, 84.21% of infertile eggs could be successfully discriminated by the best ANN model based on total spectral data. This showed a 14.3% improvement in the specificity of the predictive model.</p>\",\n \"<p id=\\\"Par49\\\">Figure ##FIG##6##7## illustrates the sensitivity plot based on the best ANN model developed by the total spectral data. As shown, the wavelengths around 524, 656, 767, and 860 nm resulted in the highest degree of importance in the sensitivity analysis. These wavelengths were also close to the distinguished ones that appeared in Fig. ##FIG##3##4## as the absorption valleys in transmission spectra. Moreover, the wavelengths around 665 and 865 nm were identified as the efficient wavelengths in the discrimination power plot of SIMCA analysis. The coincidence in the identified wavebands between linear (SIMCA) and nonlinear (ANN) approaches highlighted the importance of the selected regions R<sub>1</sub> and R<sub>3</sub> in detecting fertility in unincubated chicken eggs.</p>\",\n \"<p id=\\\"Par50\\\">In using the selected regions (Fig. ##FIG##5##6##b) and spectral differences (Fig. ##FIG##5##6##c) as the input variables to the ANN classifier, the optimized topologies of networks were attained with 11 and 7 nodes in the hidden layer, respectively. Despite a slight difference in precision values attained by selected regions and their spectral differences (92.59% and 89.65%, respectively), similar accuracies (accuracy of 93.33%) were achieved when the selected regions and spectral differences were used as the input variables (Table ##TAB##3##4##). Interestingly, and in contrast to the linear classifiers, the accuracy of prediction slightly improved when employing fewer variables from effective regions or spectral differences as input for nonlinear ANN methods, compared to using the full range data (accuracy of 91.11%). This improvement may be attributed to the elimination of unnecessary and irrelevant data that could otherwise have a detrimental impact on the ANN models.</p>\",\n \"<p id=\\\"Par51\\\">Among the best networks, the ANN model developed by the effective region was the most successful one in separating the infertile eggs with a specificity of 89.47%. Only 2 infertile eggs were misclassified as fertile. However, the model developed by spectral differences was more efficient in predicting the fertile eggs in which all of them were correctly classified (sensitivity of 100%).</p>\",\n \"<p id=\\\"Par52\\\">On the whole, the ANN method outperformed the other classifiers in detecting fertility. Compared to the best SIMCA and LDA/QDA models, the accuracy was improved by 7.7% and 5% when the best ANN model was used to discriminate the eggs.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Results and discussion</title>","<title>Overview of spectral data</title>","<p id=\"Par36\">The raw and 1st derivative of average transmission spectra of fertile and infertile eggs are shown in Fig. ##FIG##3##4##. As shown, the fertile samples had higher raw transmission values than the infertile ones in all spectral regions (Fig. ##FIG##3##4##a). Differences in chemical substances, such as protein content<sup>##REF##23436529##18##,##UREF##15##19##</sup>, and physical properties, like the egg shape index<sup>##UREF##7##8##</sup>, between fertile and infertile chicken eggs could lead to variations in the transmission spectral behavior. Similar transmission values and trends in the spectra of fertile and infertile eggs were observed in the studies performed by conventional transmission spectroscopy systems<sup>##UREF##7##8##,##UREF##13##15##,##UREF##25##29##</sup>. However, more absorption bands appeared in transmission spectra obtained by the HSI system. As shown in Fig. ##FIG##3##4##, in addition to broad absorption bands, significant absorptions (valleys in transmission spectra) were revealed in the spectra of fertile and infertile eggs around the wavelengths of 580, 634, 665, 730, 770, 830, and 930 nm. Since the raw spectra had relatively broad absorption bands, to improve the quality of presentation, the 1st derivative spectra were calculated (Fig. ##FIG##3##4##b).</p>","<p id=\"Par37\">As shown in Fig. ##FIG##3##4##, extensive absorption bands in the visible regions were observed around 580, 634, and 665 nm wavelengths. Considering the white shell of eggs used in this study, these absorptions cannot be attributed to the shell color pigment of protoporphyrin which produces relatively strong absorption in brown-shelled egg spectra in the mentioned wavelengths<sup>##UREF##13##15##,##UREF##25##29##</sup>. Therefore, these broad absorptions were likely due to the color pigments in the yolk such as carotenoids which are a fat-soluble group of yellow (580 nm), orange (634 nm), and red (665 nm) pigments<sup>##UREF##26##30##</sup>.</p>","<p id=\"Par38\">The wavebands around 730 nm are related to the third overtone of O–H in water<sup>##UREF##27##31##</sup> and the carbohydrates of eggs. Since the embryo consumes carbohydrates, proteins, and fats<sup>##UREF##28##32##</sup>, the higher transmission value of fertile samples around 730 nm is likely due to the relatively lower carbohydrate, protein, and fat contents of fertile samples with respect to the infertile ones (Fig. ##FIG##3##4##a).</p>","<p id=\"Par39\">The relatively similar internal conditions and common compounds between these two groups of samples could result in similar absorption bands and make it difficult to distinguish the two classes based on the visual interpretation of spectra. However, by a close investigation of 1st derivative spectra (Fig. ##FIG##3##4##b), the difference in transmitted light was observed between two classes around the discussed wavelengths. The remarkable deviations between the two classes could be observed around the wavelengths of 730, 770, and 830 nm. While the red-edge wavelengths around 770 nm<sup>##UREF##29##33##</sup> were related to embryo development<sup>##UREF##0##1##</sup>, absorption around 830 nm is triggered by the 3rd overtone of the O–H stretching, related to the water content inside of eggs<sup>##UREF##27##31##</sup>. Finally, the strong absorption around 930 nm can be associated with 3rd overtones of the C-H stretching absorption, which may be related to the carbohydrate content in the egg<sup>##REF##25803186##34##</sup>.</p>","<title>Classification by SIMCA</title>","<p id=\"Par40\">Table ##TAB##0##1## shows the test set validation results of SIMCA analysis performed by using entire spectral data for the detection of infertile and fertile eggs before incubation. The effect of different pre-processing and the optimum number of PCs for modeling each class were also presented in this table. Among the various mathematical pretreatments, the best discrimination accuracy was achieved from the 1st and 2nd derivatives. In both pretreatments, all fertile eggs were correctly classified (sensitivity of 100%) and 6 infertile eggs were misclassified into fertile (specificity of 68.42%), resulting in an accuracy of 86.67%. Both pretreatments led to a similar precision of 81.25%. Due to the higher price of fertile eggs, it is more important to correctly identify the fertile eggs prior to incubation to avoid unwanted elimination and increase the hatchability rate. Therefore, the best SIMCA model had promising performance in the correct identification of this group of eggs.</p>","<p id=\"Par41\">In terms of fertile and infertile detection of eggs before incubation by HSI, our predictions based on the SIMCA methods were much better than those reported by Smith, et al.<sup>##UREF##11##13##</sup> (accuracy of 63% for day 0, before incubation), Lin, et al.<sup>##UREF##5##6##</sup> (accuracy of 96% for day 14, after incubation), and Park, et al.<sup>##UREF##0##1##</sup> (accuracy of 99% for day 14, after incubation). In one case, however, our best SIMCA model was less accurate than that presented by Liu and Ngadi (2013) in which a near-infrared HSI system in the range of 900–1700 nm was used for the detection of fertile and infertile eggs prior to incubation. The model extracted by them was able to correctly classify all fertile and infertile eggs into the right classes with an overall accuracy of 100%. Despite the same performance in the detection of fertile eggs (100%), our best SIMCA model was weaker in the detection of infertile ones (specificity of 68.42%). The limited waveband (430–960 nm) in our study provided by a lower-price hyperspectral camera can be regarded as one of the main reasons for obtaining these results. Nevertheless, in the study of Liu and Ngadi (2013), there was an unequal distribution of samples per class on day 0 (prior to incubation), where a total of 18 infertile eggs were used against 156 fertile ones in the training and validating datasets. There was no justification for solving the imbalanced classification problems. The main challenge here was how much the use of advanced multivariate techniques could compensate the drawback of the shorter spectral range.</p>","<p id=\"Par42\">Figure ##FIG##4##5## shows the discrimination power plot of different wavelengths for the separation of the fertile (day 0) class from the infertile one obtained from the best model by 1st derivative pretreatment. This plot shows which wavelengths were most effective for distinguishing between two classes. As a rule, variables that resulted in a discrimination power of more than 3 are very useful in distinguishing between two classes<sup>##UREF##18##22##</sup>. These variables were specified by distinct regions and relatively narrow bands in Fig. ##FIG##4##5##, represented by R<sub>1</sub> (673–675 nm), R<sub>2</sub> (813–840 nm), and R<sub>3</sub> (865–873 nm). Additionally, the discrimination power values around the wavelengths of 799 nm were close to 3, indicating the relative importance of this wavelength. It is noteworthy that important areas were located mostly at the end of the visible region (R<sub>1</sub>) and NIR region (799 nm, R<sub>2</sub>, and R<sub>3</sub>). The effect of fertility on wavelengths around 799 nm can be related to the possible formation of blood spots due to the initial stages of embryo development. By examining the PCA images, Park, et al.<sup>##UREF##0##1##</sup> observed a distinct blood vessel pattern in the viable eggs and reported a height weighting coefficient value of around 799 nm in the corresponding PCA loading plot.</p>","<p id=\"Par43\">Moreover, it seems that the presence of the embryo could influence the NIR region more significantly than the visible one (Fig. ##FIG##4##5##). It can be due to the possible changes in the egg’s chemical composition because of embryo existence<sup>##REF##26073176##17##,##UREF##26##30##,##UREF##30##35##</sup>, making the NIR region more important in separating fertile eggs from infertile ones. The initial parts of the spectrum, especially the wavelength range of 500–650 nm had a lower degree of importance for separation between the two classes.</p>","<p id=\"Par44\">Among the distinguished regions, the R<sub>2</sub> region, including the important wavelength of 830 nm, resulted in the highest discrimination power values (Fig. ##FIG##4##5##). By referring to Fig. ##FIG##3##4##, the wavelengths around 830 nm provided a relatively strong absorption for both fertile and infertile eggs with the remarkable absorption difference between these two groups of samples. As indicated in the overview of spectral data section, the absorptions around 830 nm can be related to 3rd overtones of the O–H stretching absorption, which according to egg ingredients can be attributed to the carbohydrates and water content of the egg. Since the embryo consumes carbohydrates, proteins, and fats during its development<sup>##UREF##28##32##</sup>, the higher discrimination power values of the R<sub>2</sub> region were likely due to the relatively lower carbohydrate, protein, and fat contents of fertile samples with respect to the infertile ones.</p>","<title>Discrimination by LDA and QDA</title>","<p id=\"Par45\">Table ##TAB##1##2## summarizes the test set validation results of QDA and LDA classifiers obtained by various mathematical pretreatments. Similar to SIMCA analysis, the best discrimination accuracy was achieved by the 1st and 2nd derivatives. In the best QDA and LDA models, all of the fertile eggs were correctly classified (sensitivity of 100%) and 5 infertile eggs were misclassified into fertile (specificity of 73.68%), resulting in an accuracy of 88.90%. The maximum precision of both models reached 83.87%. In comparison with the SIMCA analysis with the accuracy and precision of 86.67% and 81.25% respectively, the best QDA and LDA models resulted in slightly better performance (accuracy of 88.90% and precision of 83.87%), indicating 2.6% and 3.2% improvement in discrimination accuracy and precision, respectively. The relatively similar performance of the LDA and QDA methods, when compared to the SIMCA method, suggested that more advanced approaches, capable of better capturing the potential nonlinear nature of the data, are necessary to develop more robust predictive models.</p>","<title>Linear classification by effective wavelengths</title>","<p id=\"Par46\">Table ##TAB##2##3## summarizes the results of linear classifiers, including SIMCA, LDA, and QDA, for fertility detection using the selected regions (R<sub>1</sub>, R<sub>2</sub>, and R<sub>3</sub>) and the spectral differences between two pairs of wavelengths in these selected regions as input variables. As shown, employing the effective wavelengths in the selected regions did not yield promising performances across all classifiers. In the best-case scenario, SIMCA achieved a sensitivity of 61.54%, specificity of 73.68%, precision of 76.19%, and accuracy of 66.67%. It appears that the wavelength variables situated outside the selected region contained some information to describe the variance of the class variable (fertile or infertile), essential for developing a reliable linear model. Furthermore, the utilization of selected variables in their raw form, combined with their inclusion in linear models lacking the capacity to address nonlinear data, contributes to the challenges encountered in fertility detection using selected regions.</p>","<p id=\"Par47\">However, when utilizing wavelength differences as a simulation of 1st derivative pretreatment, predictability improved, although it remained lower than the models developed using the entire spectra as the input variable. For SIMCA, LDA, and QDA, accuracy values of 71.11%, 84.44%, and 82.22% were obtained, respectively (Table ##TAB##2##3##). In comparison, the corresponding values when using the entire spectral data were 86.67%, 88.89%, and 88.89%, respectively (Tables ##TAB##0##1## and ##TAB##1##2##). It seems that spectral differences can effectively simulate the 1st derivative pretreatment in distinct wavelength variables. Nevertheless, linear classification still does not reach the same level of predictability as when the entire spectral data is utilized.</p>","<title>Classification by ANN</title>","<p id=\"Par48\">Table ##TAB##3##4## shows the results ANN classifier obtained from the total spectral data, effective wavelengths in selected regions (R<sub>1</sub>, R<sub>2</sub>, and R<sub>3</sub>), and spectral differences between two pairs of wavelengths in selected regions, as the input variables. Figure ##FIG##5##6## illustrates the classification accuracy of ANNs with the different number of nodes in the hidden layer varying from 1 to 30. In using the total spectral data as the inputs to networks (Fig. ##FIG##5##6##a), the best prediction accuracy was achieved by 10 nodes in the hidden layer. This network was able to separate the fertile egg from the infertile one with sensitivity, specificity, precision, and accuracy of 96.15%, 84.21%, 89.29%, and 91.11% (Table ##TAB##3##4##). As shown, a noticeable improvement in the prediction power of infertile eggs occurred when the nonlinear ANN method was applied to the whole spectral data. While the best linear model could distinguish 73.68% of infertile eggs, 84.21% of infertile eggs could be successfully discriminated by the best ANN model based on total spectral data. This showed a 14.3% improvement in the specificity of the predictive model.</p>","<p id=\"Par49\">Figure ##FIG##6##7## illustrates the sensitivity plot based on the best ANN model developed by the total spectral data. As shown, the wavelengths around 524, 656, 767, and 860 nm resulted in the highest degree of importance in the sensitivity analysis. These wavelengths were also close to the distinguished ones that appeared in Fig. ##FIG##3##4## as the absorption valleys in transmission spectra. Moreover, the wavelengths around 665 and 865 nm were identified as the efficient wavelengths in the discrimination power plot of SIMCA analysis. The coincidence in the identified wavebands between linear (SIMCA) and nonlinear (ANN) approaches highlighted the importance of the selected regions R<sub>1</sub> and R<sub>3</sub> in detecting fertility in unincubated chicken eggs.</p>","<p id=\"Par50\">In using the selected regions (Fig. ##FIG##5##6##b) and spectral differences (Fig. ##FIG##5##6##c) as the input variables to the ANN classifier, the optimized topologies of networks were attained with 11 and 7 nodes in the hidden layer, respectively. Despite a slight difference in precision values attained by selected regions and their spectral differences (92.59% and 89.65%, respectively), similar accuracies (accuracy of 93.33%) were achieved when the selected regions and spectral differences were used as the input variables (Table ##TAB##3##4##). Interestingly, and in contrast to the linear classifiers, the accuracy of prediction slightly improved when employing fewer variables from effective regions or spectral differences as input for nonlinear ANN methods, compared to using the full range data (accuracy of 91.11%). This improvement may be attributed to the elimination of unnecessary and irrelevant data that could otherwise have a detrimental impact on the ANN models.</p>","<p id=\"Par51\">Among the best networks, the ANN model developed by the effective region was the most successful one in separating the infertile eggs with a specificity of 89.47%. Only 2 infertile eggs were misclassified as fertile. However, the model developed by spectral differences was more efficient in predicting the fertile eggs in which all of them were correctly classified (sensitivity of 100%).</p>","<p id=\"Par52\">On the whole, the ANN method outperformed the other classifiers in detecting fertility. Compared to the best SIMCA and LDA/QDA models, the accuracy was improved by 7.7% and 5% when the best ANN model was used to discriminate the eggs.</p>"],"string":"[\n \"<title>Results and discussion</title>\",\n \"<title>Overview of spectral data</title>\",\n \"<p id=\\\"Par36\\\">The raw and 1st derivative of average transmission spectra of fertile and infertile eggs are shown in Fig. ##FIG##3##4##. As shown, the fertile samples had higher raw transmission values than the infertile ones in all spectral regions (Fig. ##FIG##3##4##a). Differences in chemical substances, such as protein content<sup>##REF##23436529##18##,##UREF##15##19##</sup>, and physical properties, like the egg shape index<sup>##UREF##7##8##</sup>, between fertile and infertile chicken eggs could lead to variations in the transmission spectral behavior. Similar transmission values and trends in the spectra of fertile and infertile eggs were observed in the studies performed by conventional transmission spectroscopy systems<sup>##UREF##7##8##,##UREF##13##15##,##UREF##25##29##</sup>. However, more absorption bands appeared in transmission spectra obtained by the HSI system. As shown in Fig. ##FIG##3##4##, in addition to broad absorption bands, significant absorptions (valleys in transmission spectra) were revealed in the spectra of fertile and infertile eggs around the wavelengths of 580, 634, 665, 730, 770, 830, and 930 nm. Since the raw spectra had relatively broad absorption bands, to improve the quality of presentation, the 1st derivative spectra were calculated (Fig. ##FIG##3##4##b).</p>\",\n \"<p id=\\\"Par37\\\">As shown in Fig. ##FIG##3##4##, extensive absorption bands in the visible regions were observed around 580, 634, and 665 nm wavelengths. Considering the white shell of eggs used in this study, these absorptions cannot be attributed to the shell color pigment of protoporphyrin which produces relatively strong absorption in brown-shelled egg spectra in the mentioned wavelengths<sup>##UREF##13##15##,##UREF##25##29##</sup>. Therefore, these broad absorptions were likely due to the color pigments in the yolk such as carotenoids which are a fat-soluble group of yellow (580 nm), orange (634 nm), and red (665 nm) pigments<sup>##UREF##26##30##</sup>.</p>\",\n \"<p id=\\\"Par38\\\">The wavebands around 730 nm are related to the third overtone of O–H in water<sup>##UREF##27##31##</sup> and the carbohydrates of eggs. Since the embryo consumes carbohydrates, proteins, and fats<sup>##UREF##28##32##</sup>, the higher transmission value of fertile samples around 730 nm is likely due to the relatively lower carbohydrate, protein, and fat contents of fertile samples with respect to the infertile ones (Fig. ##FIG##3##4##a).</p>\",\n \"<p id=\\\"Par39\\\">The relatively similar internal conditions and common compounds between these two groups of samples could result in similar absorption bands and make it difficult to distinguish the two classes based on the visual interpretation of spectra. However, by a close investigation of 1st derivative spectra (Fig. ##FIG##3##4##b), the difference in transmitted light was observed between two classes around the discussed wavelengths. The remarkable deviations between the two classes could be observed around the wavelengths of 730, 770, and 830 nm. While the red-edge wavelengths around 770 nm<sup>##UREF##29##33##</sup> were related to embryo development<sup>##UREF##0##1##</sup>, absorption around 830 nm is triggered by the 3rd overtone of the O–H stretching, related to the water content inside of eggs<sup>##UREF##27##31##</sup>. Finally, the strong absorption around 930 nm can be associated with 3rd overtones of the C-H stretching absorption, which may be related to the carbohydrate content in the egg<sup>##REF##25803186##34##</sup>.</p>\",\n \"<title>Classification by SIMCA</title>\",\n \"<p id=\\\"Par40\\\">Table ##TAB##0##1## shows the test set validation results of SIMCA analysis performed by using entire spectral data for the detection of infertile and fertile eggs before incubation. The effect of different pre-processing and the optimum number of PCs for modeling each class were also presented in this table. Among the various mathematical pretreatments, the best discrimination accuracy was achieved from the 1st and 2nd derivatives. In both pretreatments, all fertile eggs were correctly classified (sensitivity of 100%) and 6 infertile eggs were misclassified into fertile (specificity of 68.42%), resulting in an accuracy of 86.67%. Both pretreatments led to a similar precision of 81.25%. Due to the higher price of fertile eggs, it is more important to correctly identify the fertile eggs prior to incubation to avoid unwanted elimination and increase the hatchability rate. Therefore, the best SIMCA model had promising performance in the correct identification of this group of eggs.</p>\",\n \"<p id=\\\"Par41\\\">In terms of fertile and infertile detection of eggs before incubation by HSI, our predictions based on the SIMCA methods were much better than those reported by Smith, et al.<sup>##UREF##11##13##</sup> (accuracy of 63% for day 0, before incubation), Lin, et al.<sup>##UREF##5##6##</sup> (accuracy of 96% for day 14, after incubation), and Park, et al.<sup>##UREF##0##1##</sup> (accuracy of 99% for day 14, after incubation). In one case, however, our best SIMCA model was less accurate than that presented by Liu and Ngadi (2013) in which a near-infrared HSI system in the range of 900–1700 nm was used for the detection of fertile and infertile eggs prior to incubation. The model extracted by them was able to correctly classify all fertile and infertile eggs into the right classes with an overall accuracy of 100%. Despite the same performance in the detection of fertile eggs (100%), our best SIMCA model was weaker in the detection of infertile ones (specificity of 68.42%). The limited waveband (430–960 nm) in our study provided by a lower-price hyperspectral camera can be regarded as one of the main reasons for obtaining these results. Nevertheless, in the study of Liu and Ngadi (2013), there was an unequal distribution of samples per class on day 0 (prior to incubation), where a total of 18 infertile eggs were used against 156 fertile ones in the training and validating datasets. There was no justification for solving the imbalanced classification problems. The main challenge here was how much the use of advanced multivariate techniques could compensate the drawback of the shorter spectral range.</p>\",\n \"<p id=\\\"Par42\\\">Figure ##FIG##4##5## shows the discrimination power plot of different wavelengths for the separation of the fertile (day 0) class from the infertile one obtained from the best model by 1st derivative pretreatment. This plot shows which wavelengths were most effective for distinguishing between two classes. As a rule, variables that resulted in a discrimination power of more than 3 are very useful in distinguishing between two classes<sup>##UREF##18##22##</sup>. These variables were specified by distinct regions and relatively narrow bands in Fig. ##FIG##4##5##, represented by R<sub>1</sub> (673–675 nm), R<sub>2</sub> (813–840 nm), and R<sub>3</sub> (865–873 nm). Additionally, the discrimination power values around the wavelengths of 799 nm were close to 3, indicating the relative importance of this wavelength. It is noteworthy that important areas were located mostly at the end of the visible region (R<sub>1</sub>) and NIR region (799 nm, R<sub>2</sub>, and R<sub>3</sub>). The effect of fertility on wavelengths around 799 nm can be related to the possible formation of blood spots due to the initial stages of embryo development. By examining the PCA images, Park, et al.<sup>##UREF##0##1##</sup> observed a distinct blood vessel pattern in the viable eggs and reported a height weighting coefficient value of around 799 nm in the corresponding PCA loading plot.</p>\",\n \"<p id=\\\"Par43\\\">Moreover, it seems that the presence of the embryo could influence the NIR region more significantly than the visible one (Fig. ##FIG##4##5##). It can be due to the possible changes in the egg’s chemical composition because of embryo existence<sup>##REF##26073176##17##,##UREF##26##30##,##UREF##30##35##</sup>, making the NIR region more important in separating fertile eggs from infertile ones. The initial parts of the spectrum, especially the wavelength range of 500–650 nm had a lower degree of importance for separation between the two classes.</p>\",\n \"<p id=\\\"Par44\\\">Among the distinguished regions, the R<sub>2</sub> region, including the important wavelength of 830 nm, resulted in the highest discrimination power values (Fig. ##FIG##4##5##). By referring to Fig. ##FIG##3##4##, the wavelengths around 830 nm provided a relatively strong absorption for both fertile and infertile eggs with the remarkable absorption difference between these two groups of samples. As indicated in the overview of spectral data section, the absorptions around 830 nm can be related to 3rd overtones of the O–H stretching absorption, which according to egg ingredients can be attributed to the carbohydrates and water content of the egg. Since the embryo consumes carbohydrates, proteins, and fats during its development<sup>##UREF##28##32##</sup>, the higher discrimination power values of the R<sub>2</sub> region were likely due to the relatively lower carbohydrate, protein, and fat contents of fertile samples with respect to the infertile ones.</p>\",\n \"<title>Discrimination by LDA and QDA</title>\",\n \"<p id=\\\"Par45\\\">Table ##TAB##1##2## summarizes the test set validation results of QDA and LDA classifiers obtained by various mathematical pretreatments. Similar to SIMCA analysis, the best discrimination accuracy was achieved by the 1st and 2nd derivatives. In the best QDA and LDA models, all of the fertile eggs were correctly classified (sensitivity of 100%) and 5 infertile eggs were misclassified into fertile (specificity of 73.68%), resulting in an accuracy of 88.90%. The maximum precision of both models reached 83.87%. In comparison with the SIMCA analysis with the accuracy and precision of 86.67% and 81.25% respectively, the best QDA and LDA models resulted in slightly better performance (accuracy of 88.90% and precision of 83.87%), indicating 2.6% and 3.2% improvement in discrimination accuracy and precision, respectively. The relatively similar performance of the LDA and QDA methods, when compared to the SIMCA method, suggested that more advanced approaches, capable of better capturing the potential nonlinear nature of the data, are necessary to develop more robust predictive models.</p>\",\n \"<title>Linear classification by effective wavelengths</title>\",\n \"<p id=\\\"Par46\\\">Table ##TAB##2##3## summarizes the results of linear classifiers, including SIMCA, LDA, and QDA, for fertility detection using the selected regions (R<sub>1</sub>, R<sub>2</sub>, and R<sub>3</sub>) and the spectral differences between two pairs of wavelengths in these selected regions as input variables. As shown, employing the effective wavelengths in the selected regions did not yield promising performances across all classifiers. In the best-case scenario, SIMCA achieved a sensitivity of 61.54%, specificity of 73.68%, precision of 76.19%, and accuracy of 66.67%. It appears that the wavelength variables situated outside the selected region contained some information to describe the variance of the class variable (fertile or infertile), essential for developing a reliable linear model. Furthermore, the utilization of selected variables in their raw form, combined with their inclusion in linear models lacking the capacity to address nonlinear data, contributes to the challenges encountered in fertility detection using selected regions.</p>\",\n \"<p id=\\\"Par47\\\">However, when utilizing wavelength differences as a simulation of 1st derivative pretreatment, predictability improved, although it remained lower than the models developed using the entire spectra as the input variable. For SIMCA, LDA, and QDA, accuracy values of 71.11%, 84.44%, and 82.22% were obtained, respectively (Table ##TAB##2##3##). In comparison, the corresponding values when using the entire spectral data were 86.67%, 88.89%, and 88.89%, respectively (Tables ##TAB##0##1## and ##TAB##1##2##). It seems that spectral differences can effectively simulate the 1st derivative pretreatment in distinct wavelength variables. Nevertheless, linear classification still does not reach the same level of predictability as when the entire spectral data is utilized.</p>\",\n \"<title>Classification by ANN</title>\",\n \"<p id=\\\"Par48\\\">Table ##TAB##3##4## shows the results ANN classifier obtained from the total spectral data, effective wavelengths in selected regions (R<sub>1</sub>, R<sub>2</sub>, and R<sub>3</sub>), and spectral differences between two pairs of wavelengths in selected regions, as the input variables. Figure ##FIG##5##6## illustrates the classification accuracy of ANNs with the different number of nodes in the hidden layer varying from 1 to 30. In using the total spectral data as the inputs to networks (Fig. ##FIG##5##6##a), the best prediction accuracy was achieved by 10 nodes in the hidden layer. This network was able to separate the fertile egg from the infertile one with sensitivity, specificity, precision, and accuracy of 96.15%, 84.21%, 89.29%, and 91.11% (Table ##TAB##3##4##). As shown, a noticeable improvement in the prediction power of infertile eggs occurred when the nonlinear ANN method was applied to the whole spectral data. While the best linear model could distinguish 73.68% of infertile eggs, 84.21% of infertile eggs could be successfully discriminated by the best ANN model based on total spectral data. This showed a 14.3% improvement in the specificity of the predictive model.</p>\",\n \"<p id=\\\"Par49\\\">Figure ##FIG##6##7## illustrates the sensitivity plot based on the best ANN model developed by the total spectral data. As shown, the wavelengths around 524, 656, 767, and 860 nm resulted in the highest degree of importance in the sensitivity analysis. These wavelengths were also close to the distinguished ones that appeared in Fig. ##FIG##3##4## as the absorption valleys in transmission spectra. Moreover, the wavelengths around 665 and 865 nm were identified as the efficient wavelengths in the discrimination power plot of SIMCA analysis. The coincidence in the identified wavebands between linear (SIMCA) and nonlinear (ANN) approaches highlighted the importance of the selected regions R<sub>1</sub> and R<sub>3</sub> in detecting fertility in unincubated chicken eggs.</p>\",\n \"<p id=\\\"Par50\\\">In using the selected regions (Fig. ##FIG##5##6##b) and spectral differences (Fig. ##FIG##5##6##c) as the input variables to the ANN classifier, the optimized topologies of networks were attained with 11 and 7 nodes in the hidden layer, respectively. Despite a slight difference in precision values attained by selected regions and their spectral differences (92.59% and 89.65%, respectively), similar accuracies (accuracy of 93.33%) were achieved when the selected regions and spectral differences were used as the input variables (Table ##TAB##3##4##). Interestingly, and in contrast to the linear classifiers, the accuracy of prediction slightly improved when employing fewer variables from effective regions or spectral differences as input for nonlinear ANN methods, compared to using the full range data (accuracy of 91.11%). This improvement may be attributed to the elimination of unnecessary and irrelevant data that could otherwise have a detrimental impact on the ANN models.</p>\",\n \"<p id=\\\"Par51\\\">Among the best networks, the ANN model developed by the effective region was the most successful one in separating the infertile eggs with a specificity of 89.47%. Only 2 infertile eggs were misclassified as fertile. However, the model developed by spectral differences was more efficient in predicting the fertile eggs in which all of them were correctly classified (sensitivity of 100%).</p>\",\n \"<p id=\\\"Par52\\\">On the whole, the ANN method outperformed the other classifiers in detecting fertility. Compared to the best SIMCA and LDA/QDA models, the accuracy was improved by 7.7% and 5% when the best ANN model was used to discriminate the eggs.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":["<title>Conclusion</title>","<p id=\"Par53\">In this study, the feasibility of a line-scan hyperspectral imaging system in the Vis-SWNIR region was assessed for early detection of non-fertile eggs on day 0 before incubation. After capturing hyperspectral images of eggs, the edge detection method was used to segment ROI, and the image spectral information was extracted from the ROI. Then various pretreatment methods as well as different classifiers including SIMCA, LDA, QDA, and ANN classifiers were applied to eliminate the unwanted information and extract the predictive models. The following conclusions can be drawn from our results:<list list-type=\"order\"><list-item><p id=\"Par54\">Following the acceptable results of SIMCA analysis along with 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands in discerning the fertile and non-fertile classes.</p></list-item><list-item><p id=\"Par55\">The best QDA and LDA models resulted in slightly better performances in comparison with the SIMCA analysis. Surprisingly, all the fertile eggs were correctly classified in these models, resulting in a sensitivity of 100%.</p></list-item><list-item><p id=\"Par56\">The best ANN model based on total spectral data was able to separate the fertile eggs from the infertile ones with precision and accuracy of 89.29% and 91.11%, respectively. Whereas, by using the fewer and most informative wavelengths obtained by SIMCA analysis as the input variable to ANN models, an improvement in prediction power occurred. Both wavelength differences and raw spectral data in selected wavebands led to a satisfactory classification accuracy of 93.33%. None of the infertile eggs were misclassified as fertile in the models based on wavelength differences (a sensitivity of 100%).</p></list-item></list></p>"],"string":"[\n \"<title>Conclusion</title>\",\n \"<p id=\\\"Par53\\\">In this study, the feasibility of a line-scan hyperspectral imaging system in the Vis-SWNIR region was assessed for early detection of non-fertile eggs on day 0 before incubation. After capturing hyperspectral images of eggs, the edge detection method was used to segment ROI, and the image spectral information was extracted from the ROI. Then various pretreatment methods as well as different classifiers including SIMCA, LDA, QDA, and ANN classifiers were applied to eliminate the unwanted information and extract the predictive models. The following conclusions can be drawn from our results:<list list-type=\\\"order\\\"><list-item><p id=\\\"Par54\\\">Following the acceptable results of SIMCA analysis along with 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands in discerning the fertile and non-fertile classes.</p></list-item><list-item><p id=\\\"Par55\\\">The best QDA and LDA models resulted in slightly better performances in comparison with the SIMCA analysis. Surprisingly, all the fertile eggs were correctly classified in these models, resulting in a sensitivity of 100%.</p></list-item><list-item><p id=\\\"Par56\\\">The best ANN model based on total spectral data was able to separate the fertile eggs from the infertile ones with precision and accuracy of 89.29% and 91.11%, respectively. Whereas, by using the fewer and most informative wavelengths obtained by SIMCA analysis as the input variable to ANN models, an improvement in prediction power occurred. Both wavelength differences and raw spectral data in selected wavebands led to a satisfactory classification accuracy of 93.33%. None of the infertile eggs were misclassified as fertile in the models based on wavelength differences (a sensitivity of 100%).</p></list-item></list></p>\"\n]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-infrared region was assessed for early detection of non-fertile eggs on day 0 before incubation. A total of 227 white-shell eggs including 131 fertile and 96 infertile eggs were collected from a flock with similar conditions in terms of hen age, feeding, and management. Hyperspectral images of eggs were captured on day 0 before incubation in a transmittance mode of illumination and then the eggs were incubated in a commercial incubator. The edge detection method was used to segment the egg, including both the white and yolk, from the background, and the image spectral information was extracted from the egg region. After applying various pretreatment methods, different classifiers including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and artificial neural networks (ANN) classifiers were utilized to extract the predictive models. Following the acceptable results of SIMCA analysis accomplished by 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands. The results showed that by using fewer variables in effective wavebands better performance (precision and accuracy of 92.59% and 93.33%, respectively) could be obtained in comparison with the ANN classifier based on the whole spectral data (precision and accuracy of 89.29% and 91.11%, respectively). This study revealed the potential application of hyperspectral transmittance imaging in the Vis-SWNIR region to discern the fertile and infertile eggs before starting the incubation process.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-infrared region was assessed for early detection of non-fertile eggs on day 0 before incubation. A total of 227 white-shell eggs including 131 fertile and 96 infertile eggs were collected from a flock with similar conditions in terms of hen age, feeding, and management. Hyperspectral images of eggs were captured on day 0 before incubation in a transmittance mode of illumination and then the eggs were incubated in a commercial incubator. The edge detection method was used to segment the egg, including both the white and yolk, from the background, and the image spectral information was extracted from the egg region. After applying various pretreatment methods, different classifiers including soft independent modeling of class analogy (SIMCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and artificial neural networks (ANN) classifiers were utilized to extract the predictive models. Following the acceptable results of SIMCA analysis accomplished by 1st derivative pretreatment (accuracy of 86.67%), the discrimination power plot was used to select the most informative wavebands. The results showed that by using fewer variables in effective wavebands better performance (precision and accuracy of 92.59% and 93.33%, respectively) could be obtained in comparison with the ANN classifier based on the whole spectral data (precision and accuracy of 89.29% and 91.11%, respectively). This study revealed the potential application of hyperspectral transmittance imaging in the Vis-SWNIR region to discern the fertile and infertile eggs before starting the incubation process.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":[],"string":"[]","isConversational":false},"back":{"kind":"list like","value":["<title>Acknowledgements</title>","<p>The authors gratefully appreciate the financial support from Isfahan University of Technology, Isfahan, Iran.</p>","<title>Author contributions</title>","<p>M.G.: Methodology, validation, formal analysis, investigation, data curation, writing—original draft, visualization. S.A.M.: supervision, conceptualization, formal analysis, investigation, data curation, writing—review & editing, funding acquisition. A.A.M.: supervision, conceptualization, writing—review & editing, funding acquisition. M.S.: methodology, investigation, writing—review & editing. M.N.: methodology, resources, writing—review & editing, funding acquisition.</p>","<title>Data availability</title>","<p>The datasets generated and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.</p>","<title>Competing interests</title>","<p id=\"Par57\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Acknowledgements</title>\",\n \"<p>The authors gratefully appreciate the financial support from Isfahan University of Technology, Isfahan, Iran.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>M.G.: Methodology, validation, formal analysis, investigation, data curation, writing—original draft, visualization. S.A.M.: supervision, conceptualization, formal analysis, investigation, data curation, writing—review & editing, funding acquisition. A.A.M.: supervision, conceptualization, writing—review & editing, funding acquisition. M.S.: methodology, investigation, writing—review & editing. M.N.: methodology, resources, writing—review & editing, funding acquisition.</p>\",\n \"<title>Data availability</title>\",\n \"<p>The datasets generated and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par57\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Hyperspectral imaging system for acquiring the egg pictures in full-transmittance mode (<bold>a</bold>) system set-up with main and secondary incubators, (<bold>b</bold>) The schematic of the hyperspectral imaging system, (<bold>c</bold>) light sources, and (<bold>d</bold>) the RGB image of the sample after illumination.</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Background removal operations (<bold>a</bold>) the 630 nm raw image, (<bold>b</bold>) generated binary image, and (<bold>c</bold>) the 630 nm image without the background.</p></caption></fig>","<fig id=\"Fig3\"><label>Figure 3</label><caption><p>The proposed framework for detecting the fertile and infertile eggs and selecting the most relevant features.</p></caption></fig>","<fig id=\"Fig4\"><label>Figure 4</label><caption><p>The average transmission spectra of fertile and infertile eggs (<bold>a</bold>) raw spectra and (<bold>b</bold>) 1st derivative spectra.</p></caption></fig>","<fig id=\"Fig5\"><label>Figure 5</label><caption><p>The discrimination power plot of wavelength variables obtained by 1st derivative pretreatment and projecting the data from the fertile model onto the infertile ones.</p></caption></fig>","<fig id=\"Fig6\"><label>Figure 6</label><caption><p>The prediction power of ANN models with a different number of nodes in the hidden layer for detecting fertile and infertile eggs (<bold>a</bold>) total spectra, (<bold>b</bold>) selected regions, and (<bold>c</bold>) spectral difference.</p></caption></fig>","<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Sensitivity analysis plot based on best ANN model obtained from total spectral data.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>Hyperspectral imaging system for acquiring the egg pictures in full-transmittance mode (<bold>a</bold>) system set-up with main and secondary incubators, (<bold>b</bold>) The schematic of the hyperspectral imaging system, (<bold>c</bold>) light sources, and (<bold>d</bold>) the RGB image of the sample after illumination.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>Background removal operations (<bold>a</bold>) the 630 nm raw image, (<bold>b</bold>) generated binary image, and (<bold>c</bold>) the 630 nm image without the background.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Figure 3</label><caption><p>The proposed framework for detecting the fertile and infertile eggs and selecting the most relevant features.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Figure 4</label><caption><p>The average transmission spectra of fertile and infertile eggs (<bold>a</bold>) raw spectra and (<bold>b</bold>) 1st derivative spectra.</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Figure 5</label><caption><p>The discrimination power plot of wavelength variables obtained by 1st derivative pretreatment and projecting the data from the fertile model onto the infertile ones.</p></caption></fig>\",\n \"<fig id=\\\"Fig6\\\"><label>Figure 6</label><caption><p>The prediction power of ANN models with a different number of nodes in the hidden layer for detecting fertile and infertile eggs (<bold>a</bold>) total spectra, (<bold>b</bold>) selected regions, and (<bold>c</bold>) spectral difference.</p></caption></fig>\",\n \"<fig id=\\\"Fig7\\\"><label>Figure 7</label><caption><p>Sensitivity analysis plot based on best ANN model obtained from total spectral data.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":["<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Test set validation results of SIMCA analysis for the detection of infertile and fertile eggs before incubation or day 0.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Preprocess</th><th align=\"left\" rowspan=\"2\">Class</th><th align=\"left\" rowspan=\"2\">No. of PCs</th><th align=\"left\" colspan=\"2\">Classified into</th><th align=\"left\" rowspan=\"2\">Sen. (%)</th><th align=\"left\" rowspan=\"2\">Spe. (%)</th><th align=\"left\" rowspan=\"2\">Precis. (%)</th><th align=\"left\" rowspan=\"2\">Acc. (%)</th></tr><tr><th align=\"left\">Infertile</th><th align=\"left\">Fertile</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Raw</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">1</td><td align=\"left\">8</td><td align=\"left\">11</td><td char=\".\" align=\"char\">100.00</td><td char=\".\" align=\"char\">42.11</td><td char=\".\" align=\"char\">70.27</td><td char=\".\" align=\"char\">75.56</td></tr><tr><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">26</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" rowspan=\"2\">Norm</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">4</td><td align=\"left\">12</td><td align=\"left\">7</td><td char=\".\" align=\"char\">96.15</td><td char=\".\" align=\"char\">63.16</td><td char=\".\" align=\"char\">78.13</td><td char=\".\" align=\"char\">82.22</td></tr><tr><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">4</td><td align=\"left\">1</td><td align=\"left\">25</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" rowspan=\"2\">MSC</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">4</td><td align=\"left\">12</td><td align=\"left\">7</td><td char=\".\" align=\"char\">96.15</td><td char=\".\" align=\"char\">63.16</td><td char=\".\" align=\"char\">78.13</td><td char=\".\" align=\"char\">82.22</td></tr><tr><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">4</td><td align=\"left\">1</td><td align=\"left\">25</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" rowspan=\"2\">SNV</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">4</td><td align=\"left\">12</td><td align=\"left\">7</td><td char=\".\" align=\"char\">92.31</td><td char=\".\" align=\"char\">63.16</td><td char=\".\" align=\"char\">77.42</td><td char=\".\" align=\"char\">80.00</td></tr><tr><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">5</td><td align=\"left\">2</td><td align=\"left\">24</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" rowspan=\"2\">BC</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">1</td><td align=\"left\">9</td><td align=\"left\">10</td><td char=\".\" align=\"char\">100.00</td><td char=\".\" align=\"char\">47.37</td><td char=\".\" align=\"char\">72.22</td><td char=\".\" align=\"char\">77.78</td></tr><tr><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">1</td><td align=\"left\">0</td><td align=\"left\">26</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" rowspan=\"2\">1st Der</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">4</td><td align=\"left\">13</td><td align=\"left\">6</td><td char=\".\" align=\"char\">100.00</td><td char=\".\" align=\"char\">68.42</td><td char=\".\" align=\"char\">81.25</td><td char=\".\" align=\"char\">86.67</td></tr><tr><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">4</td><td align=\"left\">0</td><td align=\"left\">26</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" rowspan=\"2\">2nd Der</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">8</td><td align=\"left\">13</td><td align=\"left\">6</td><td char=\".\" align=\"char\">100.00</td><td char=\".\" align=\"char\">68.42</td><td char=\".\" align=\"char\">81.25</td><td char=\".\" align=\"char\">86.67</td></tr><tr><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">5</td><td align=\"left\">0</td><td align=\"left\">26</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Test set validation results obtained by LDA and QDA analysis for the detection of infertile and fertile eggs before incubation or day 0.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Classifier</th><th align=\"left\">Preprocess</th><th align=\"left\">Sen. (%)</th><th align=\"left\">Spe. (%)</th><th align=\"left\">Precis. (%)</th><th align=\"left\">Acc. (%)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"7\">LDA</td><td align=\"left\">Raw</td><td char=\".\" align=\"char\">92.31</td><td char=\".\" align=\"char\">68.42</td><td char=\".\" align=\"char\">80.00</td><td char=\".\" align=\"char\">82.22</td></tr><tr><td align=\"left\">Normalized</td><td char=\".\" align=\"char\">88.46</td><td char=\".\" align=\"char\">68.42</td><td char=\".\" align=\"char\">79.31</td><td char=\".\" align=\"char\">80.00</td></tr><tr><td align=\"left\">MSC</td><td char=\".\" align=\"char\">92.31</td><td char=\".\" align=\"char\">68.42</td><td char=\".\" align=\"char\">80.00</td><td char=\".\" align=\"char\">82.22</td></tr><tr><td align=\"left\">SNV</td><td char=\".\" align=\"char\">92.31</td><td char=\".\" align=\"char\">68.42</td><td char=\".\" align=\"char\">80.00</td><td char=\".\" align=\"char\">82.22</td></tr><tr><td align=\"left\">BC</td><td char=\".\" align=\"char\">76.92</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">80.00</td><td char=\".\" align=\"char\">75.56</td></tr><tr><td align=\"left\">1st Der</td><td char=\".\" align=\"char\">100.00</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">83.87</td><td char=\".\" align=\"char\">88.89</td></tr><tr><td align=\"left\">2nd Der</td><td char=\".\" align=\"char\">100.00</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">83.87</td><td char=\".\" align=\"char\">88.89</td></tr><tr><td align=\"left\" rowspan=\"7\">QDA</td><td align=\"left\">Raw</td><td char=\".\" align=\"char\">80.79</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">80.77</td><td char=\".\" align=\"char\">77.78</td></tr><tr><td align=\"left\">Normalized</td><td char=\".\" align=\"char\">73.1</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">79.16</td><td char=\".\" align=\"char\">73.33</td></tr><tr><td align=\"left\">MSC</td><td char=\".\" align=\"char\">80.77</td><td char=\".\" align=\"char\">68.42</td><td char=\".\" align=\"char\">77.78</td><td char=\".\" align=\"char\">75.56</td></tr><tr><td align=\"left\">SNV</td><td char=\".\" align=\"char\">80.77</td><td char=\".\" align=\"char\">68.42</td><td char=\".\" align=\"char\">77.78</td><td char=\".\" align=\"char\">75.56</td></tr><tr><td align=\"left\">BC</td><td char=\".\" align=\"char\">84.62</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">81.48</td><td char=\".\" align=\"char\">80.00</td></tr><tr><td align=\"left\">1st Der</td><td char=\".\" align=\"char\">100.00</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">83.87</td><td char=\".\" align=\"char\">88.89</td></tr><tr><td align=\"left\">2nd Der</td><td char=\".\" align=\"char\">100.00</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">83.87</td><td char=\".\" align=\"char\">88.89</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Linear classification results using effective wavelengths in selected regions and spectral differences between two pairs of wavelengths in selected regions for detection of infertile and fertile eggs on day 0.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Classifier</th><th align=\"left\" rowspan=\"2\">Input variables</th><th align=\"left\" rowspan=\"2\">Class</th><th align=\"left\" colspan=\"2\">Classified to</th><th align=\"left\" rowspan=\"2\">Sen. (%)</th><th align=\"left\" rowspan=\"2\">Spe. (%)</th><th align=\"left\" rowspan=\"2\">Percis. (%)</th><th align=\"left\" rowspan=\"2\">Acc. (%)</th></tr><tr><th align=\"left\">Infertile</th><th align=\"left\">Fertile</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"4\">SIMCA</td><td align=\"left\">R<sub>1</sub> + R<sub>2</sub> + R<sub>3</sub></td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">14</td><td align=\"left\">5</td><td char=\".\" align=\"char\">61.54</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">76.19</td><td char=\".\" align=\"char\">66.67</td></tr><tr><td align=\"left\"/><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">10</td><td align=\"left\">16</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Spectral difference</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">16</td><td align=\"left\">3</td><td char=\".\" align=\"char\">61.54</td><td char=\".\" align=\"char\">84.21</td><td char=\".\" align=\"char\">84.21</td><td char=\".\" align=\"char\">71.11</td></tr><tr><td align=\"left\"/><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">10</td><td align=\"left\">16</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" rowspan=\"4\">LDA</td><td align=\"left\">R<sub>1</sub> + R<sub>2</sub> + R<sub>3</sub></td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">14</td><td align=\"left\">5</td><td char=\".\" align=\"char\">53.85</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">62.22</td></tr><tr><td align=\"left\"/><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">12</td><td align=\"left\">14</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Spectral difference</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">17</td><td align=\"left\">2</td><td char=\".\" align=\"char\">80.77</td><td char=\".\" align=\"char\">89.47</td><td char=\".\" align=\"char\">91.30</td><td char=\".\" align=\"char\">84.44</td></tr><tr><td align=\"left\"/><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">5</td><td align=\"left\">21</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" rowspan=\"4\">QDA</td><td align=\"left\">R<sub>1</sub> + R<sub>2</sub> + R<sub>3</sub></td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">14</td><td align=\"left\">5</td><td char=\".\" align=\"char\">53.85</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">73.68</td><td char=\".\" align=\"char\">62.22</td></tr><tr><td align=\"left\"/><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">12</td><td align=\"left\">14</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\">Spectral difference</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">15</td><td align=\"left\">4</td><td char=\".\" align=\"char\">84.62</td><td char=\".\" align=\"char\">78.95</td><td char=\".\" align=\"char\">84.62</td><td char=\".\" align=\"char\">82.22</td></tr><tr><td align=\"left\"/><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">4</td><td align=\"left\">22</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Nonlinear classification results for the detection of infertile and fertile eggs on day 0.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Input variables</th><th align=\"left\" rowspan=\"2\">ANN topology</th><th align=\"left\" rowspan=\"2\">Class</th><th align=\"left\" colspan=\"2\">Classified to</th><th align=\"left\" rowspan=\"2\">Sen. (%)</th><th align=\"left\" rowspan=\"2\">Spe. (%)</th><th align=\"left\" rowspan=\"2\">Percis. (%)</th><th align=\"left\" rowspan=\"2\">Acc. (%)</th></tr><tr><th align=\"left\">Infertile</th><th align=\"left\">Fertile</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Total spectral data</td><td align=\"left\">256–10-2</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">16</td><td align=\"left\">3</td><td char=\".\" align=\"char\">96.15</td><td char=\".\" align=\"char\">84.21</td><td char=\".\" align=\"char\">89.29</td><td char=\".\" align=\"char\">91.11</td></tr><tr><td align=\"left\"/><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">1</td><td align=\"left\">25</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" rowspan=\"2\">R<sub>1</sub> + R<sub>2</sub> + R<sub>3</sub></td><td align=\"left\">38–11-2</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">17</td><td align=\"left\">2</td><td char=\".\" align=\"char\">96.15</td><td char=\".\" align=\"char\">89.47</td><td char=\".\" align=\"char\">92.59</td><td char=\".\" align=\"char\">93.33</td></tr><tr><td align=\"left\"/><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">1</td><td align=\"left\">25</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" rowspan=\"2\">Spectral difference</td><td align=\"left\">667–7-2</td><td align=\"left\">Infertile (n = 19)</td><td align=\"left\">16</td><td align=\"left\">3</td><td char=\".\" align=\"char\">100.00</td><td char=\".\" align=\"char\">84.21</td><td char=\".\" align=\"char\">89.65</td><td char=\".\" align=\"char\">93.33</td></tr><tr><td align=\"left\"/><td align=\"left\">Fertile (n = 26)</td><td align=\"left\">0</td><td align=\"left\">26</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr></tbody></table></table-wrap>"],"string":"[\n \"<table-wrap id=\\\"Tab1\\\"><label>Table 1</label><caption><p>Test set validation results of SIMCA analysis for the detection of infertile and fertile eggs before incubation or day 0.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" rowspan=\\\"2\\\">Preprocess</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Class</th><th align=\\\"left\\\" rowspan=\\\"2\\\">No. of PCs</th><th align=\\\"left\\\" colspan=\\\"2\\\">Classified into</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Sen. (%)</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Spe. (%)</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Precis. (%)</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Acc. (%)</th></tr><tr><th align=\\\"left\\\">Infertile</th><th align=\\\"left\\\">Fertile</th></tr></thead><tbody><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Raw</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">8</td><td align=\\\"left\\\">11</td><td char=\\\".\\\" align=\\\"char\\\">100.00</td><td char=\\\".\\\" align=\\\"char\\\">42.11</td><td char=\\\".\\\" align=\\\"char\\\">70.27</td><td char=\\\".\\\" align=\\\"char\\\">75.56</td></tr><tr><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">26</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Norm</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">4</td><td align=\\\"left\\\">12</td><td align=\\\"left\\\">7</td><td char=\\\".\\\" align=\\\"char\\\">96.15</td><td char=\\\".\\\" align=\\\"char\\\">63.16</td><td char=\\\".\\\" align=\\\"char\\\">78.13</td><td char=\\\".\\\" align=\\\"char\\\">82.22</td></tr><tr><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">4</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">25</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">MSC</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">4</td><td align=\\\"left\\\">12</td><td align=\\\"left\\\">7</td><td char=\\\".\\\" align=\\\"char\\\">96.15</td><td char=\\\".\\\" align=\\\"char\\\">63.16</td><td char=\\\".\\\" align=\\\"char\\\">78.13</td><td char=\\\".\\\" align=\\\"char\\\">82.22</td></tr><tr><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">4</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">25</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">SNV</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">4</td><td align=\\\"left\\\">12</td><td align=\\\"left\\\">7</td><td char=\\\".\\\" align=\\\"char\\\">92.31</td><td char=\\\".\\\" align=\\\"char\\\">63.16</td><td char=\\\".\\\" align=\\\"char\\\">77.42</td><td char=\\\".\\\" align=\\\"char\\\">80.00</td></tr><tr><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">5</td><td align=\\\"left\\\">2</td><td align=\\\"left\\\">24</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">BC</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">9</td><td align=\\\"left\\\">10</td><td char=\\\".\\\" align=\\\"char\\\">100.00</td><td char=\\\".\\\" align=\\\"char\\\">47.37</td><td char=\\\".\\\" align=\\\"char\\\">72.22</td><td char=\\\".\\\" align=\\\"char\\\">77.78</td></tr><tr><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">26</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">1st Der</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">4</td><td align=\\\"left\\\">13</td><td align=\\\"left\\\">6</td><td char=\\\".\\\" align=\\\"char\\\">100.00</td><td char=\\\".\\\" align=\\\"char\\\">68.42</td><td char=\\\".\\\" align=\\\"char\\\">81.25</td><td char=\\\".\\\" align=\\\"char\\\">86.67</td></tr><tr><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">4</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">26</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">2nd Der</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">8</td><td align=\\\"left\\\">13</td><td align=\\\"left\\\">6</td><td char=\\\".\\\" align=\\\"char\\\">100.00</td><td char=\\\".\\\" align=\\\"char\\\">68.42</td><td char=\\\".\\\" align=\\\"char\\\">81.25</td><td char=\\\".\\\" align=\\\"char\\\">86.67</td></tr><tr><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">5</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">26</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab2\\\"><label>Table 2</label><caption><p>Test set validation results obtained by LDA and QDA analysis for the detection of infertile and fertile eggs before incubation or day 0.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Classifier</th><th align=\\\"left\\\">Preprocess</th><th align=\\\"left\\\">Sen. (%)</th><th align=\\\"left\\\">Spe. (%)</th><th align=\\\"left\\\">Precis. (%)</th><th align=\\\"left\\\">Acc. (%)</th></tr></thead><tbody><tr><td align=\\\"left\\\" rowspan=\\\"7\\\">LDA</td><td align=\\\"left\\\">Raw</td><td char=\\\".\\\" align=\\\"char\\\">92.31</td><td char=\\\".\\\" align=\\\"char\\\">68.42</td><td char=\\\".\\\" align=\\\"char\\\">80.00</td><td char=\\\".\\\" align=\\\"char\\\">82.22</td></tr><tr><td align=\\\"left\\\">Normalized</td><td char=\\\".\\\" align=\\\"char\\\">88.46</td><td char=\\\".\\\" align=\\\"char\\\">68.42</td><td char=\\\".\\\" align=\\\"char\\\">79.31</td><td char=\\\".\\\" align=\\\"char\\\">80.00</td></tr><tr><td align=\\\"left\\\">MSC</td><td char=\\\".\\\" align=\\\"char\\\">92.31</td><td char=\\\".\\\" align=\\\"char\\\">68.42</td><td char=\\\".\\\" align=\\\"char\\\">80.00</td><td char=\\\".\\\" align=\\\"char\\\">82.22</td></tr><tr><td align=\\\"left\\\">SNV</td><td char=\\\".\\\" align=\\\"char\\\">92.31</td><td char=\\\".\\\" align=\\\"char\\\">68.42</td><td char=\\\".\\\" align=\\\"char\\\">80.00</td><td char=\\\".\\\" align=\\\"char\\\">82.22</td></tr><tr><td align=\\\"left\\\">BC</td><td char=\\\".\\\" align=\\\"char\\\">76.92</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">80.00</td><td char=\\\".\\\" align=\\\"char\\\">75.56</td></tr><tr><td align=\\\"left\\\">1st Der</td><td char=\\\".\\\" align=\\\"char\\\">100.00</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">83.87</td><td char=\\\".\\\" align=\\\"char\\\">88.89</td></tr><tr><td align=\\\"left\\\">2nd Der</td><td char=\\\".\\\" align=\\\"char\\\">100.00</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">83.87</td><td char=\\\".\\\" align=\\\"char\\\">88.89</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"7\\\">QDA</td><td align=\\\"left\\\">Raw</td><td char=\\\".\\\" align=\\\"char\\\">80.79</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">80.77</td><td char=\\\".\\\" align=\\\"char\\\">77.78</td></tr><tr><td align=\\\"left\\\">Normalized</td><td char=\\\".\\\" align=\\\"char\\\">73.1</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">79.16</td><td char=\\\".\\\" align=\\\"char\\\">73.33</td></tr><tr><td align=\\\"left\\\">MSC</td><td char=\\\".\\\" align=\\\"char\\\">80.77</td><td char=\\\".\\\" align=\\\"char\\\">68.42</td><td char=\\\".\\\" align=\\\"char\\\">77.78</td><td char=\\\".\\\" align=\\\"char\\\">75.56</td></tr><tr><td align=\\\"left\\\">SNV</td><td char=\\\".\\\" align=\\\"char\\\">80.77</td><td char=\\\".\\\" align=\\\"char\\\">68.42</td><td char=\\\".\\\" align=\\\"char\\\">77.78</td><td char=\\\".\\\" align=\\\"char\\\">75.56</td></tr><tr><td align=\\\"left\\\">BC</td><td char=\\\".\\\" align=\\\"char\\\">84.62</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">81.48</td><td char=\\\".\\\" align=\\\"char\\\">80.00</td></tr><tr><td align=\\\"left\\\">1st Der</td><td char=\\\".\\\" align=\\\"char\\\">100.00</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">83.87</td><td char=\\\".\\\" align=\\\"char\\\">88.89</td></tr><tr><td align=\\\"left\\\">2nd Der</td><td char=\\\".\\\" align=\\\"char\\\">100.00</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">83.87</td><td char=\\\".\\\" align=\\\"char\\\">88.89</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab3\\\"><label>Table 3</label><caption><p>Linear classification results using effective wavelengths in selected regions and spectral differences between two pairs of wavelengths in selected regions for detection of infertile and fertile eggs on day 0.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" rowspan=\\\"2\\\">Classifier</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Input variables</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Class</th><th align=\\\"left\\\" colspan=\\\"2\\\">Classified to</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Sen. (%)</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Spe. (%)</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Percis. (%)</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Acc. (%)</th></tr><tr><th align=\\\"left\\\">Infertile</th><th align=\\\"left\\\">Fertile</th></tr></thead><tbody><tr><td align=\\\"left\\\" rowspan=\\\"4\\\">SIMCA</td><td align=\\\"left\\\">R<sub>1</sub> + R<sub>2</sub> + R<sub>3</sub></td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">14</td><td align=\\\"left\\\">5</td><td char=\\\".\\\" align=\\\"char\\\">61.54</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">76.19</td><td char=\\\".\\\" align=\\\"char\\\">66.67</td></tr><tr><td align=\\\"left\\\"/><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">10</td><td align=\\\"left\\\">16</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">Spectral difference</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">16</td><td align=\\\"left\\\">3</td><td char=\\\".\\\" align=\\\"char\\\">61.54</td><td char=\\\".\\\" align=\\\"char\\\">84.21</td><td char=\\\".\\\" align=\\\"char\\\">84.21</td><td char=\\\".\\\" align=\\\"char\\\">71.11</td></tr><tr><td align=\\\"left\\\"/><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">10</td><td align=\\\"left\\\">16</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"4\\\">LDA</td><td align=\\\"left\\\">R<sub>1</sub> + R<sub>2</sub> + R<sub>3</sub></td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">14</td><td align=\\\"left\\\">5</td><td char=\\\".\\\" align=\\\"char\\\">53.85</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">62.22</td></tr><tr><td align=\\\"left\\\"/><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">12</td><td align=\\\"left\\\">14</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">Spectral difference</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">17</td><td align=\\\"left\\\">2</td><td char=\\\".\\\" align=\\\"char\\\">80.77</td><td char=\\\".\\\" align=\\\"char\\\">89.47</td><td char=\\\".\\\" align=\\\"char\\\">91.30</td><td char=\\\".\\\" align=\\\"char\\\">84.44</td></tr><tr><td align=\\\"left\\\"/><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">5</td><td align=\\\"left\\\">21</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"4\\\">QDA</td><td align=\\\"left\\\">R<sub>1</sub> + R<sub>2</sub> + R<sub>3</sub></td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">14</td><td align=\\\"left\\\">5</td><td char=\\\".\\\" align=\\\"char\\\">53.85</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">73.68</td><td char=\\\".\\\" align=\\\"char\\\">62.22</td></tr><tr><td align=\\\"left\\\"/><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">12</td><td align=\\\"left\\\">14</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\">Spectral difference</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">15</td><td align=\\\"left\\\">4</td><td char=\\\".\\\" align=\\\"char\\\">84.62</td><td char=\\\".\\\" align=\\\"char\\\">78.95</td><td char=\\\".\\\" align=\\\"char\\\">84.62</td><td char=\\\".\\\" align=\\\"char\\\">82.22</td></tr><tr><td align=\\\"left\\\"/><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">4</td><td align=\\\"left\\\">22</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab4\\\"><label>Table 4</label><caption><p>Nonlinear classification results for the detection of infertile and fertile eggs on day 0.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" rowspan=\\\"2\\\">Input variables</th><th align=\\\"left\\\" rowspan=\\\"2\\\">ANN topology</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Class</th><th align=\\\"left\\\" colspan=\\\"2\\\">Classified to</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Sen. (%)</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Spe. (%)</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Percis. (%)</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Acc. (%)</th></tr><tr><th align=\\\"left\\\">Infertile</th><th align=\\\"left\\\">Fertile</th></tr></thead><tbody><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Total spectral data</td><td align=\\\"left\\\">256–10-2</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">16</td><td align=\\\"left\\\">3</td><td char=\\\".\\\" align=\\\"char\\\">96.15</td><td char=\\\".\\\" align=\\\"char\\\">84.21</td><td char=\\\".\\\" align=\\\"char\\\">89.29</td><td char=\\\".\\\" align=\\\"char\\\">91.11</td></tr><tr><td align=\\\"left\\\"/><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">25</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">R<sub>1</sub> + R<sub>2</sub> + R<sub>3</sub></td><td align=\\\"left\\\">38–11-2</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">17</td><td align=\\\"left\\\">2</td><td char=\\\".\\\" align=\\\"char\\\">96.15</td><td char=\\\".\\\" align=\\\"char\\\">89.47</td><td char=\\\".\\\" align=\\\"char\\\">92.59</td><td char=\\\".\\\" align=\\\"char\\\">93.33</td></tr><tr><td align=\\\"left\\\"/><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">1</td><td align=\\\"left\\\">25</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"2\\\">Spectral difference</td><td align=\\\"left\\\">667–7-2</td><td align=\\\"left\\\">Infertile (n = 19)</td><td align=\\\"left\\\">16</td><td align=\\\"left\\\">3</td><td char=\\\".\\\" align=\\\"char\\\">100.00</td><td char=\\\".\\\" align=\\\"char\\\">84.21</td><td char=\\\".\\\" align=\\\"char\\\">89.65</td><td char=\\\".\\\" align=\\\"char\\\">93.33</td></tr><tr><td align=\\\"left\\\"/><td align=\\\"left\\\">Fertile (n = 26)</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">26</td><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/><td char=\\\".\\\" align=\\\"char\\\"/></tr></tbody></table></table-wrap>\"\n]","isConversational":false},"formula":{"kind":"list like","value":["<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$T_{rel} = \\frac{{T_{s} /e_{s} - T_{d} /e_{s} }}{{T_{r} /e_{r} - T_{d} /e_{s} }} \\times 100$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mi 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\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Spe. = \\frac{TN}{{FP + TN}}$$\\end{document}</tex-math><mml:math id=\"M6\" display=\"block\"><mml:mrow><mml:mi>S</mml:mi><mml:mi>p</mml:mi><mml:mi>e</mml:mi><mml:mo>.</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi mathvariant=\"italic\">TN</mml:mi></mml:mrow><mml:mrow><mml:mi>F</mml:mi><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mi>T</mml:mi><mml:mi>N</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Precis. = \\frac{TP}{{TP + FP}}$$\\end{document}</tex-math><mml:math id=\"M8\" display=\"block\"><mml:mrow><mml:mi>P</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mi>c</mml:mi><mml:mi>i</mml:mi><mml:mi>s</mml:mi><mml:mo>.</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi mathvariant=\"italic\">TP</mml:mi></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mi>F</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ5\"><label>5</label><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Acc. = \\frac{TP + TN}{{TP + FN + FP + TN}}$$\\end{document}</tex-math><mml:math id=\"M10\" display=\"block\"><mml:mrow><mml:mi>A</mml:mi><mml:mi>c</mml:mi><mml:mi>c</mml:mi><mml:mo>.</mml:mo><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>T</mml:mi><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mi>T</mml:mi><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi>T</mml:mi><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mi>F</mml:mi><mml:mi>N</mml:mi><mml:mo>+</mml:mo><mml:mi>F</mml:mi><mml:mi>P</mml:mi><mml:mo>+</mml:mo><mml:mi>T</mml:mi><mml:mi>N</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>"],"string":"[\n \"<disp-formula id=\\\"Equ1\\\"><label>1</label><alternatives><tex-math id=\\\"M1\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$T_{rel} = \\\\frac{{T_{s} /e_{s} - T_{d} /e_{s} }}{{T_{r} /e_{r} - T_{d} /e_{s} }} \\\\times 100$$\\\\end{document}</tex-math><mml:math id=\\\"M2\\\" display=\\\"block\\\"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mi mathvariant=\\\"italic\\\">rel</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi>s</mml:mi></mml:msub><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:msub><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mi>d</mml:mi></mml:msub><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:msub><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:msub><mml:mi>e</mml:mi><mml:mi>r</mml:mi></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>T</mml:mi><mml:mi>d</mml:mi></mml:msub><mml:mo stretchy=\\\"false\\\">/</mml:mo><mml:msub><mml:mi>e</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:mrow></mml:mfrac><mml:mo>×</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ2\\\"><label>2</label><alternatives><tex-math id=\\\"M3\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} 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discriminant analysis; Sen.: Sensitivity; SNV: Standard normal variate; Spe.: Specificity.</p></table-wrap-foot>","<table-wrap-foot><p>Acc.: Accuracy; LDA: Linear discriminant analysis; Percis.: Precision; QDA: Quadratic discriminant analysis; Sen.: Sensitivity; Spe.: Specificity.</p></table-wrap-foot>","<table-wrap-foot><p>Acc.: Accuracy; Percis.: Precision; Sen.: Sensitivity; Spe.: Specificity.</p></table-wrap-foot>","<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"],"string":"[\n \"<table-wrap-foot><p>1st Der.: First derivative; 2nd Der.: Second derivative; Acc.: Accuracy; BC: Baseline correction; MSC: Multiplicative scatter correction; Norm.: Normalization; PCs: Principal components; Percis.: Precision; Preprocess.: Preprocessing; Sen.: Sensitivity; SNV: Standard normal variate; Spe.: Specificity.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>1st Der.: First derivative; 2nd Der.: Second derivative; Acc.: Accuracy; BC: Baseline correction; LDA: Linear discriminant analysis; MSC: Multiplicative scatter correction; Norm.: Normalization; PCs: Principal components; Percis.: Precision; Preprocess.: Preprocessing; QDA: Quadratic discriminant analysis; Sen.: Sensitivity; SNV: Standard normal variate; Spe.: Specificity.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>Acc.: Accuracy; LDA: Linear discriminant analysis; Percis.: Precision; QDA: Quadratic discriminant analysis; Sen.: Sensitivity; Spe.: Specificity.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>Acc.: Accuracy; Percis.: Precision; Sen.: Sensitivity; Spe.: Specificity.</p></table-wrap-foot>\",\n \"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list 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Actuat. B Chem.\"], \"year\": [\"2019\"], \"volume\": [\"281\"], \"fpage\": [\"204\"], \"lpage\": [\"211\"], \"pub-id\": [\"10.1016/j.snb.2018.10.109\"]}, {\"label\": [\"2.\"], \"surname\": [\"Liu\", \"Ngadi\"], \"given-names\": [\"L\", \"MO\"], \"article-title\": [\"Detecting fertility and early embryo development of chicken eggs using near-infrared hyperspectral imaging\"], \"source\": [\"Food Bioprocess Technol.\"], \"year\": [\"2013\"], \"volume\": [\"6\"], \"fpage\": [\"2503\"], \"lpage\": [\"2513\"], \"pub-id\": [\"10.1007/s11947-012-0933-3\"]}, {\"label\": [\"3.\"], \"surname\": [\"Dong\"], \"given-names\": [\"J\"], \"article-title\": [\"Identification of unfertilized duck eggs before hatching using visible/near infrared transmittance spectroscopy\"], \"source\": [\"Comput. Electron. 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Agric.\"], \"year\": [\"2013\"], \"volume\": [\"91\"], \"fpage\": [\"94\"], \"lpage\": [\"105\"], \"pub-id\": [\"10.1016/j.compag.2012.12.004\"]}, {\"label\": [\"7.\"], \"surname\": [\"Tong\"], \"given-names\": [\"Q\"], \"article-title\": [\"Detection of embryo mortality and hatch using thermal differences among incubated chicken eggs\"], \"source\": [\"Livestock Sci.\"], \"year\": [\"2016\"], \"volume\": [\"183\"], \"fpage\": [\"19\"], \"lpage\": [\"23\"], \"pub-id\": [\"10.1016/j.livsci.2015.11.004\"]}, {\"label\": [\"8.\"], \"surname\": [\"Dong\"], \"given-names\": [\"J\"], \"article-title\": [\"Prediction of infertile chicken eggs before hatching by the Na\\u00efve-Bayes method combined with visible near infrared transmission spectroscopy\"], \"source\": [\"Spectrosc. 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W. & Park, B. in \"], \"italic\": [\"Optics for Natural Resources, Agriculture, and Foods.\"]}, {\"label\": [\"13.\"], \"surname\": [\"Smith\", \"Lawrence\", \"Heitschmidt\"], \"given-names\": [\"D\", \"KC\", \"G\"], \"article-title\": [\"Fertility and embryo development of broiler hatching eggs evaluated with a hyperspectral imaging and predictive modeling system\"], \"source\": [\"Int. J. Poult. Sci.\"], \"year\": [\"2008\"], \"volume\": [\"7\"], \"fpage\": [\"1001\"], \"lpage\": [\"1004\"]}, {\"label\": [\"14.\"], \"surname\": [\"Zhihui\", \"Ting\", \"Dejun\", \"Qiaohua\", \"Meihu\"], \"given-names\": [\"Z\", \"L\", \"X\", \"W\", \"M\"], \"article-title\": [\"Nondestructive detection of infertile hatching eggs based on spectral and imaging information\"], \"source\": [\"Int. J. Agric. Biol. 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B. in \"], \"italic\": [\"Robust Data Mining\"]}, {\"label\": [\"24.\"], \"surname\": [\"Dixon\", \"Brereton\"], \"given-names\": [\"SJ\", \"RG\"], \"article-title\": [\"Comparison of performance of five common classifiers represented as boundary methods: Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure\"], \"source\": [\"Chemomet. Intell. Lab. Syst.\"], \"year\": [\"2009\"], \"volume\": [\"95\"], \"fpage\": [\"1\"], \"lpage\": [\"17\"], \"pub-id\": [\"10.1016/j.chemolab.2008.07.010\"]}, {\"label\": [\"25.\"], \"mixed-citation\": [\"Srivastava, S., Gupta, M. R. & Frigyik, B. A. Bayesian quadratic discriminant analysis. \"], \"italic\": [\"Journal of Machine Learning Research\"], \"bold\": [\"8\"]}, {\"label\": [\"26.\"], \"mixed-citation\": [\"Saltelli, A., Tarantola, S., Campolongo, F. & Ratto, M. \"], \"italic\": [\"Sensitivity analysis in practice: a guide to assessing scientific models\"]}, {\"label\": [\"27.\"], \"surname\": [\"Kautz\", \"Eskofier\", \"Pasluosta\"], \"given-names\": [\"T\", \"BM\", \"CF\"], \"article-title\": [\"Generic performance measure for multiclass-classifiers\"], \"source\": [\"Pattern Recognit.\"], \"year\": [\"2017\"], \"volume\": [\"68\"], \"fpage\": [\"111\"], \"lpage\": [\"125\"], \"pub-id\": [\"10.1016/j.patcog.2017.03.008\"]}, {\"label\": [\"28.\"], \"collab\": [\"Updated guidelines for reporting animal research\"], \"article-title\": [\"Percie du Sert, N.\"], \"italic\": [\" et al.\"], \"source\": [\"J. Cerebral Blood Flow Metab.\"], \"year\": [\"2020\"], \"volume\": [\"40\"], \"fpage\": [\"1769\"], \"lpage\": [\"1777\"]}, {\"label\": [\"29.\"], \"surname\": [\"Dong\", \"Dong\", \"Li\", \"Xu\", \"Tang\"], \"given-names\": [\"X\", \"J\", \"Y\", \"H\", \"X\"], \"article-title\": [\"Maintaining the predictive abilities of egg freshness models on new variety based on VIS-NIR spectroscopy technique\"], \"source\": [\"Comput. Electron. Agric.\"], \"year\": [\"2019\"], \"volume\": [\"156\"], \"fpage\": [\"669\"], \"lpage\": [\"676\"], \"pub-id\": [\"10.1016/j.compag.2018.12.012\"]}, {\"label\": [\"30.\"], \"surname\": [\"Tiwari\", \"Yawale\", \"Upadhyay\"], \"given-names\": [\"S\", \"P\", \"N\"], \"article-title\": [\"Carotenoids: Extraction strategies and potential applications for valorization of under-utilized waste biomass\"], \"source\": [\"Food Biosci.\"], \"year\": [\"2022\"], \"volume\": [\"48\"], \"fpage\": [\"101812\"], \"pub-id\": [\"10.1016/j.fbio.2022.101812\"]}, {\"label\": [\"31.\"], \"surname\": [\"Kuroki\", \"Kanoo\", \"Itoh\", \"Kamisoyama\"], \"given-names\": [\"S\", \"T\", \"H\", \"H\"], \"article-title\": [\"Nondestructive VIS/NIR spectroscopy estimation of intravitelline vitamin E and cholesterol concentration in hen shell eggs\"], \"source\": [\"J. Food Meas. Character.\"], \"year\": [\"2020\"], \"volume\": [\"14\"], \"fpage\": [\"1116\"], \"lpage\": [\"1124\"], \"pub-id\": [\"10.1007/s11694-019-00361-8\"]}, {\"label\": [\"32.\"], \"surname\": [\"Uni\", \"Yadgary\", \"Yair\"], \"given-names\": [\"Z\", \"L\", \"R\"], \"article-title\": [\"Nutritional limitations during poultry embryonic development\"], \"source\": [\"J. Appl. Poult. Res.\"], \"year\": [\"2012\"], \"volume\": [\"21\"], \"fpage\": [\"175\"], \"lpage\": [\"184\"], \"pub-id\": [\"10.3382/japr.2011-00478\"]}, {\"label\": [\"33.\"], \"mixed-citation\": [\"Kang, Y., Meng, Q., Liu, M., Zou, Y. & Wang, X. Crop classification based on red edge features analysis of GF-6 WFV data. \"], \"italic\": [\"Sensors (Basel)\"], \"bold\": [\"21\"]}, {\"label\": [\"35.\"], \"surname\": [\"Tian\"], \"given-names\": [\"X\"], \"article-title\": [\"Nondestructive determining the soluble solids content of citrus using near infrared transmittance technology combined with the variable selection algorithm\"], \"source\": [\"Artif. Intell. Agric.\"], \"year\": [\"2020\"], \"volume\": [\"4\"], \"fpage\": [\"48\"], \"lpage\": [\"57\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":35,"string":"35"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:56"},"citation":{"kind":"string","value":"Sci Rep. 2024 Jan 14; 14:1289"},"package_file":{"kind":"string","value":"oa_package/fe/21/PMC10787758.tar.gz"}}},{"rowIdx":89,"cells":{"accession_id":{"kind":"string","value":"PMC10787759"},"pmid":{"kind":"string","value":"38218949"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par3\">The combustion of fossil fuels for industrialization and transportation, which accompanies the rapid growth of cities, has resulted in the release of massive volumes of CO<sub>2</sub> gas into the atmosphere<sup>##REF##24886955##1##</sup>. However, the excessive emissions of CO<sub>2</sub> will result in global warming, ocean acidification, and other environmental issues<sup>##UREF##0##2##,##REF##31836709##3##</sup>. Therefore, the question of dealing with the CO<sub>2</sub> emitted during industrial manufacturing has become an urgent concern<sup>##REF##31695213##4##</sup>. Meanwhile, the fabrication of affordable, reliable, and sustainable chemicals as one of the sustainable development goals has attracted increasing attention<sup>##UREF##1##5##</sup>. A feasible and promising solution for long-term sustainable development is the highly selective catalysis of CO<sub>2</sub> into valuable chemicals<sup>##REF##31106785##6##–##UREF##8##16##</sup>, such as light olefins and liquefied petroleum (LPG)<sup>##REF##31999391##17##–##UREF##11##20##</sup>. As important intermediates in the manufacture of organic products, light olefins are one of the most productive chemicals all over the world, with an amount exceeding 250 million tons per year<sup>##REF##26941306##21##–##UREF##12##23##</sup>. Meanwhile, with the worldwide population continuously increasing, the LPG production amount is boosted every year. It is estimated that annual worldwide LPG production will reach 350 million metric tons in 2030, while keep leveling up to 400 million metric tons in 2050<sup>##UREF##13##24##</sup>. Based on this background, the carbon-neutral production of light olefin and LPG has a great impact and a bright future.</p>","<p id=\"Par4\">CO<sub>2</sub> hydrogenation, which is a crucial catalytic CO<sub>2</sub> conversion reaction, can occur through the methanol intermediate or Fischer-Tropsch synthesis (FTS) routes. To our knowledge, almost all the LPG synthesis methods by C1 chemistry until now, regardless of whether they used syngas (CO/H<sub>2</sub>) or CO<sub>2</sub>/H<sub>2</sub>, employed a methanol-intermediated route by combining methanol synthesis catalysts with zeolites<sup>##REF##31999391##17##–##UREF##11##20##</sup>. Almost no Fischer-Tropsch route was reported for LPG synthesis, especially from CO<sub>2</sub>/H<sub>2</sub>. Although methanol as an intermediate pathway can break the Anderson-Schulz-Flory (ASF) distribution and obtain a target product with high selectivity, it often suffers from low CO<sub>2</sub> conversion (10<bold>–</bold>35%) and high CO selectivity (20<bold>–</bold>75%) due to the thermodynamic equilibrium limitation and thus does not meet the needs of industrial production<sup>##UREF##0##2##</sup>. On the other hand, CO<sub>2</sub> hydrogenation to light olefins is still a hot area, but it faces challenges in increasing CO<sub>2</sub> conversion, suppressing CO by-product selectivity, and enhancing light olefin selectivity. Therefore, a modified Fischer-Tropsch route for light olefin and LPG synthesis that simultaneously maintains a high reaction rate and breaks the ASF distribution is urgently needed.</p>","<p id=\"Par5\">Iron-based catalysts are the most commonly used catalysts for the FTS due to their high reaction activities in both reverse water gas shift (RWGS) and chain growth reactions<sup>##UREF##14##25##–##UREF##16##27##</sup>. However, an unmodified iron-based catalyst typically exhibits poor activity and high by-product (CO, CH<sub>4</sub>, C<sub>2</sub>H<sub>6</sub>, etc.) selectivity<sup>##UREF##17##28##</sup>. To overcome this issue, alkali metal ions, such as K and Na, were added to boost the CO<sub>2</sub> adsorption and the contents of active phases<sup>##UREF##18##29##,##UREF##19##30##</sup>. Indeed, these modified iron-based catalysts without the use of zeolites presented comparable catalytic performances to those of the zeolite-containing composite catalysts<sup>##UREF##14##25##,##REF##29956535##31##</sup>.</p>","<p id=\"Par6\">Besides, works on bimetallic catalysts that combined Fe with other active metal components (Co, Cu, Ni, etc.) have also been investigated. Among them, the incorporation of Co to Fe-based catalysts has been proven to enhance the reactivity and target product selectivity<sup>##UREF##15##26##,##UREF##17##28##,##UREF##18##29##,##UREF##20##32##,##UREF##21##33##</sup>. Deo et al. discovered that the addition of controlled amounts of Co to Fe resulted in high yields of methane<sup>##UREF##22##34##</sup>. Furthermore, Xu et al. proposed that the generation of active iron-cobalt carbides originating from a ternary ZnCo<sub>x</sub>Fe<sub>2-x</sub>O<sub>4</sub> catalyst was conducive to the formation of light olefins<sup>##UREF##23##35##</sup>. Recently, our group reported a spinal-like ZnFe<sub>2</sub>O<sub>4</sub> with a small amount of cobalt incorporation for CO<sub>2</sub> conversion and found that the presence of Co<sub>3</sub>Fe<sub>7</sub> sites could facilitate a high-yield production of liquid fuels (26.7% for C<sub>5</sub><sup>+</sup>)<sup>##UREF##24##36##</sup>. Similarly, Zhang et al. detected that the Na-promoted CoFe alloy benefited the formation of jet fuel<sup>##REF##34704085##37##</sup>. These reports manifest that the combination of Fe and Co can be used as a powerful and efficient catalyst for the selective conversion of CO<sub>2</sub>, and the intimate interaction between cobalt and iron species is able to tune the product distribution. To our knowledge, the supported iron-based catalysts could merely generate one type of hydrocarbon during direct CO<sub>2</sub> hydrogenation. However, given the different intrinsic properties of Fe and Co in the formation of hydrocarbon products, where iron contributes to the alkene production and cobalt contributes to the saturated alkane production<sup>##UREF##25##38##,##UREF##26##39##</sup>, the rational regulation of Fe and Co active site distribution may play a role in transforming the product types. Furthermore, the introduction of support has been revealed to significantly influence the local environment of the active sites. Even a three-dimensional encapsulation structure of graphene led to a fascinating result<sup>##UREF##27##40##,##UREF##28##41##</sup>. Based on the above assumptions, the particles of Fe and Co with rational spatial distributions regulated by the graphene support may achieve integrated production of different types of hydrocarbons.</p>","<p id=\"Par7\">Herein, we report a graphene-fence engineering approach to regulating multiple active sites of Fe-Co bimetallic catalysts for product-switchable CO<sub>2</sub> hydrogenation. Taking advantage of the structural transformation of graphene during the reduction process, a series of graphene-supported Fe-Co bimetallic catalysts with different internal and surface distributions of active sites were successfully synthesized. The Fe-Co active sites tuned the demand for carbon chain growth and olefin secondary hydrogenation, leading to an integrated and switchable process for selective CO<sub>2</sub> hydrogenation to light olefins or LPG. Iron carbides combined with metallic cobalt on the surface of graphene fences could catalyze CO<sub>2</sub> hydrogenation to light olefins (50.1% for C<sub>2</sub><sup>=</sup>–C<sub>4</sub><sup>=</sup>) at a conversion of 55.4%. Whereas the scattered spatial active sites of iron carbides and metallic cobalt, separated by graphene fences, achieved LPG (C<sub>3</sub><sup>P</sup>–C<sub>4</sub><sup>P</sup>) selectivity of 43.6% at a conversion of 46%. Meanwhile, it created a precedent for CO<sub>2</sub> hydrogenation to LPG via a Fischer-Tropsch pathway and exhibited an ultra-high STY (space-time yield) of LPG (151.0 g kg<sub>cat</sub><sup>–1</sup> h<sup>–1</sup>), which was much higher than any other previously reported composite methanol-intermediate catalysts (Supplementary Fig. ##SUPPL##0##1##). In addition, the graphene fences could also protect the metal particles from being deactivated by agglomeration, thus maintaining high activity for a long time in a continuous test. Our research offers methodologies for manipulating the graphene material as fences to divide active nanoparticles and switch product types and sheds light on the rational design of multiple active sites for the synthesis of target chemicals (Supplementary Fig. ##SUPPL##0##2##).</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par3\\\">The combustion of fossil fuels for industrialization and transportation, which accompanies the rapid growth of cities, has resulted in the release of massive volumes of CO<sub>2</sub> gas into the atmosphere<sup>##REF##24886955##1##</sup>. However, the excessive emissions of CO<sub>2</sub> will result in global warming, ocean acidification, and other environmental issues<sup>##UREF##0##2##,##REF##31836709##3##</sup>. Therefore, the question of dealing with the CO<sub>2</sub> emitted during industrial manufacturing has become an urgent concern<sup>##REF##31695213##4##</sup>. Meanwhile, the fabrication of affordable, reliable, and sustainable chemicals as one of the sustainable development goals has attracted increasing attention<sup>##UREF##1##5##</sup>. A feasible and promising solution for long-term sustainable development is the highly selective catalysis of CO<sub>2</sub> into valuable chemicals<sup>##REF##31106785##6##–##UREF##8##16##</sup>, such as light olefins and liquefied petroleum (LPG)<sup>##REF##31999391##17##–##UREF##11##20##</sup>. As important intermediates in the manufacture of organic products, light olefins are one of the most productive chemicals all over the world, with an amount exceeding 250 million tons per year<sup>##REF##26941306##21##–##UREF##12##23##</sup>. Meanwhile, with the worldwide population continuously increasing, the LPG production amount is boosted every year. It is estimated that annual worldwide LPG production will reach 350 million metric tons in 2030, while keep leveling up to 400 million metric tons in 2050<sup>##UREF##13##24##</sup>. Based on this background, the carbon-neutral production of light olefin and LPG has a great impact and a bright future.</p>\",\n \"<p id=\\\"Par4\\\">CO<sub>2</sub> hydrogenation, which is a crucial catalytic CO<sub>2</sub> conversion reaction, can occur through the methanol intermediate or Fischer-Tropsch synthesis (FTS) routes. To our knowledge, almost all the LPG synthesis methods by C1 chemistry until now, regardless of whether they used syngas (CO/H<sub>2</sub>) or CO<sub>2</sub>/H<sub>2</sub>, employed a methanol-intermediated route by combining methanol synthesis catalysts with zeolites<sup>##REF##31999391##17##–##UREF##11##20##</sup>. Almost no Fischer-Tropsch route was reported for LPG synthesis, especially from CO<sub>2</sub>/H<sub>2</sub>. Although methanol as an intermediate pathway can break the Anderson-Schulz-Flory (ASF) distribution and obtain a target product with high selectivity, it often suffers from low CO<sub>2</sub> conversion (10<bold>–</bold>35%) and high CO selectivity (20<bold>–</bold>75%) due to the thermodynamic equilibrium limitation and thus does not meet the needs of industrial production<sup>##UREF##0##2##</sup>. On the other hand, CO<sub>2</sub> hydrogenation to light olefins is still a hot area, but it faces challenges in increasing CO<sub>2</sub> conversion, suppressing CO by-product selectivity, and enhancing light olefin selectivity. Therefore, a modified Fischer-Tropsch route for light olefin and LPG synthesis that simultaneously maintains a high reaction rate and breaks the ASF distribution is urgently needed.</p>\",\n \"<p id=\\\"Par5\\\">Iron-based catalysts are the most commonly used catalysts for the FTS due to their high reaction activities in both reverse water gas shift (RWGS) and chain growth reactions<sup>##UREF##14##25##–##UREF##16##27##</sup>. However, an unmodified iron-based catalyst typically exhibits poor activity and high by-product (CO, CH<sub>4</sub>, C<sub>2</sub>H<sub>6</sub>, etc.) selectivity<sup>##UREF##17##28##</sup>. To overcome this issue, alkali metal ions, such as K and Na, were added to boost the CO<sub>2</sub> adsorption and the contents of active phases<sup>##UREF##18##29##,##UREF##19##30##</sup>. Indeed, these modified iron-based catalysts without the use of zeolites presented comparable catalytic performances to those of the zeolite-containing composite catalysts<sup>##UREF##14##25##,##REF##29956535##31##</sup>.</p>\",\n \"<p id=\\\"Par6\\\">Besides, works on bimetallic catalysts that combined Fe with other active metal components (Co, Cu, Ni, etc.) have also been investigated. Among them, the incorporation of Co to Fe-based catalysts has been proven to enhance the reactivity and target product selectivity<sup>##UREF##15##26##,##UREF##17##28##,##UREF##18##29##,##UREF##20##32##,##UREF##21##33##</sup>. Deo et al. discovered that the addition of controlled amounts of Co to Fe resulted in high yields of methane<sup>##UREF##22##34##</sup>. Furthermore, Xu et al. proposed that the generation of active iron-cobalt carbides originating from a ternary ZnCo<sub>x</sub>Fe<sub>2-x</sub>O<sub>4</sub> catalyst was conducive to the formation of light olefins<sup>##UREF##23##35##</sup>. Recently, our group reported a spinal-like ZnFe<sub>2</sub>O<sub>4</sub> with a small amount of cobalt incorporation for CO<sub>2</sub> conversion and found that the presence of Co<sub>3</sub>Fe<sub>7</sub> sites could facilitate a high-yield production of liquid fuels (26.7% for C<sub>5</sub><sup>+</sup>)<sup>##UREF##24##36##</sup>. Similarly, Zhang et al. detected that the Na-promoted CoFe alloy benefited the formation of jet fuel<sup>##REF##34704085##37##</sup>. These reports manifest that the combination of Fe and Co can be used as a powerful and efficient catalyst for the selective conversion of CO<sub>2</sub>, and the intimate interaction between cobalt and iron species is able to tune the product distribution. To our knowledge, the supported iron-based catalysts could merely generate one type of hydrocarbon during direct CO<sub>2</sub> hydrogenation. However, given the different intrinsic properties of Fe and Co in the formation of hydrocarbon products, where iron contributes to the alkene production and cobalt contributes to the saturated alkane production<sup>##UREF##25##38##,##UREF##26##39##</sup>, the rational regulation of Fe and Co active site distribution may play a role in transforming the product types. Furthermore, the introduction of support has been revealed to significantly influence the local environment of the active sites. Even a three-dimensional encapsulation structure of graphene led to a fascinating result<sup>##UREF##27##40##,##UREF##28##41##</sup>. Based on the above assumptions, the particles of Fe and Co with rational spatial distributions regulated by the graphene support may achieve integrated production of different types of hydrocarbons.</p>\",\n \"<p id=\\\"Par7\\\">Herein, we report a graphene-fence engineering approach to regulating multiple active sites of Fe-Co bimetallic catalysts for product-switchable CO<sub>2</sub> hydrogenation. Taking advantage of the structural transformation of graphene during the reduction process, a series of graphene-supported Fe-Co bimetallic catalysts with different internal and surface distributions of active sites were successfully synthesized. The Fe-Co active sites tuned the demand for carbon chain growth and olefin secondary hydrogenation, leading to an integrated and switchable process for selective CO<sub>2</sub> hydrogenation to light olefins or LPG. Iron carbides combined with metallic cobalt on the surface of graphene fences could catalyze CO<sub>2</sub> hydrogenation to light olefins (50.1% for C<sub>2</sub><sup>=</sup>–C<sub>4</sub><sup>=</sup>) at a conversion of 55.4%. Whereas the scattered spatial active sites of iron carbides and metallic cobalt, separated by graphene fences, achieved LPG (C<sub>3</sub><sup>P</sup>–C<sub>4</sub><sup>P</sup>) selectivity of 43.6% at a conversion of 46%. Meanwhile, it created a precedent for CO<sub>2</sub> hydrogenation to LPG via a Fischer-Tropsch pathway and exhibited an ultra-high STY (space-time yield) of LPG (151.0 g kg<sub>cat</sub><sup>–1</sup> h<sup>–1</sup>), which was much higher than any other previously reported composite methanol-intermediate catalysts (Supplementary Fig. ##SUPPL##0##1##). In addition, the graphene fences could also protect the metal particles from being deactivated by agglomeration, thus maintaining high activity for a long time in a continuous test. Our research offers methodologies for manipulating the graphene material as fences to divide active nanoparticles and switch product types and sheds light on the rational design of multiple active sites for the synthesis of target chemicals (Supplementary Fig. ##SUPPL##0##2##).</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Methods</title>","<title>Catalyst preparation</title>","<title>Graphene oxide (GO)</title>","<p id=\"Par34\">K<sub>2</sub>S<sub>2</sub>O<sub>8</sub> (7.5 g, Rgent Chemical Reagent Co.) and P<sub>2</sub>O<sub>5</sub> (7.5 g, Damao Chemical Reagent Co.) were added to a round-bottomed flask under the conditions of an 80 °C water bath and then combined with concentrated H<sub>2</sub>SO<sub>4</sub> (Fuchen Chemical Reagent Co.) by stirring for 15 min. Graphite powder (10 g, Sinopharm Chemical Reagent Co.) was subsequently added to the solution. After stirring for 4.5 h, the mixture was filtered and rinsed until the pH of the supernatant reached 7, before being dried overnight. The dried pre-oxidized graphite was then transferred into a three-necked flask with concentrated H<sub>2</sub>SO<sub>4</sub> in an ice-water bath. Under the aforementioned conditions, KMnO<sub>4</sub> (50 g, Sinopharm Chemical Reagent Co.) was added in these preparation phases. The solution was stirred at 35°C for 3 h before being progressively combined with deionized water and 30% H<sub>2</sub>O<sub>2</sub> until no bubbles occurred, and then aged overnight. The bottom slurry of the solution was transferred to the 3% HCl (Fuchen Chemical Reagent Co.) for acidification treatment. After filtering and washing to neutrality, GO was then moved to deionized water and ultrasonically agitated for 5 h. Finally, dried graphene oxide was obtained using a freeze-drying method.</p>","<title>Graphene-supported Fe-Co bimetallic catalysts</title>","<p id=\"Par35\">Graphene-supported K-Fe-Co bimetallic catalysts were synthesized using one-pot hydrothermal synthesis and impregnation. The target loadings for the prepared catalysts were 20% Fe, 4% Co, and 1% K, respectively. The ingredients of the obtained catalysts were investigated by inductively coupled plasma optical emission spectrometer (ICP-OES) tests, and the results are shown in Supplementary Table ##SUPPL##0##1##.</p>","<p id=\"Par36\">In detail, GO (2.0 g), urea (2.0 g, Sinopharm Chemical Reagent Co.), and Fe(NO<sub>3</sub>)<sub>3</sub>·9H<sub>2</sub>O (2.4 g, Damao Chemical Reagent Co.) were dissolved in a mixture of ethylene glycol (40 mL, Hengxing Chemical Preparation Co.) and deionized water (290 mL) and then stirred and ultra-sounded for 2 h. The obtained liquid was transferred into a Teflon-lined stainless-steel autoclave, followed by one-pot hydrothermal synthesis at 180 °C for 12 h with rotation. The products were washed and filtered until neutral, then frozen to dry before calcining at 500 °C in a nitrogen atmosphere for 4 h (unless otherwise stated, all drying methods employed were freezing drying methods to maintain the graphene structure). Cobalt and potassium were loaded by impregnating C<sub>10</sub>H<sub>16</sub>CoO<sub>4</sub> (Macklin Biochemical Co.) and K<sub>2</sub>CO<sub>3</sub> (Damao Chemical Reagent Co.) as cobalt and potassium sources, respectively. The amount of the impregnated material was calculated according to the given contents. The obtained catalyst was dried and calcined at 500 °C in a nitrogen atmosphere for 4 h. The as-prepared catalyst was designated as GO-Fe/K-Co. It should be noted that the portion before the slash represented the elements loaded by the one-pot hydrothermal method, while the portion after the slash represented the elements loaded by the impregnation method.</p>","<p id=\"Par37\">GO/K-Fe-Co was prepared in the following steps: Hydrothermal treatment of GO and urea was first performed, followed by impregnating K<sub>2</sub>CO<sub>3</sub>, Fe(NO<sub>3</sub>)<sub>3</sub>·9H<sub>2</sub>O, and C<sub>10</sub>H<sub>16</sub>CoO<sub>4</sub> as K, Fe, and Co sources onto the calcined catalyst. The drying and calcination processes after the hydrothermal synthesis and the impregnation remained unchanged.</p>","<p id=\"Par38\">In the same way, GO-Co/K-Fe and GO-Fe-Co/K catalysts were synthesized with the given loadings by changing the orders of material addition via the hydrothermal or impregnation methods without altering any other preparation techniques or stages.</p>","<p id=\"Par39\">To examine the influence of Fe contents, GO-Fe/K-Co with target Fe loadings of 25% and 30% were prepared and denoted as GO-25Fe/K-Co and GO-30Fe/K-Co, respectively. In addition, GO/K-10Fe-20Co was synthesized by loading 10% Fe and 20% Co with an impregnation process. Unlike the GO/K-Fe-Co catalyst mentioned above, Fe here was first impregnated onto the graphene surface, and then Co was loaded.</p>","<title>Graphene-supported Fe catalysts</title>","<p id=\"Par40\">GO-Fe/K and GO/K-Fe catalysts without the Co addition were also fabricated, in which the loadings for Fe and K were 20 wt% and 1 wt%, respectively.</p>","<title>Other carbon materials supported Fe-Co bimetallic catalysts</title>","<p id=\"Par41\">By adding the same amounts of K<sub>2</sub>CO<sub>3</sub>, Fe(NO<sub>3</sub>)<sub>3</sub>·9H<sub>2</sub>O, and C<sub>15</sub>H<sub>21</sub>CoO<sub>6</sub> to those of the GO-Fe/K-Co catalyst for comparison, carbon nanotubes (CNTs, Macklin Biochemical Co.), carbon black (CB, Macklin Biochemical Co.), and activated carbon (AC, Damao Chemical Co.) were utilized as supports for the preparation of Fe-Co bimetallic catalysts. These catalysts were designated as CNTs-Fe/K-Co, CB-Fe/K-Co, and AC-Fe/K-Co, respectively.</p>","<title>rGO</title>","<p id=\"Par42\">Only GO (2.0 g) and urea (2.0 g) were dissolved in the mixture of ethylene glycol (40 mL) and deionized water (290 mL), agitated, and transferred into a Teflon-lined stainless steel autoclave, where a one-pot hydrothermal synthesis was carried out for 12 h at 180 °C. The resulting material was washed, dried, and calcined, the resultant graphene was labeled as rGO.</p>","<title>rGO-Fe/K-Co</title>","<p id=\"Par43\">The same synthetic process as for GO-Fe/K-Co was applied to the synthesis of rGO-Fe/K-Co, but the raw ingredient was rGO.</p>","<title>Characterization</title>","<p id=\"Par44\">The actual total loadings of Fe, Co, and K in different catalysts were established using ICP-OES, which was performed on an Agilent 5110 (OES). The test procedures were as follows: a 10 mg sample was dissolved in a mix solution (2 mL HNO<sub>3</sub> + 6 mL HCl+2 mL HF) overnight. The dissolved sample was then added to a flask and diluted to the scale line. Five internal concentration standard solutions (0.5 mg/L, 1 mg/L, 3 mg/L, 5 mg/L, and 10 mg/L) were analyzed, and a standard curve was formed.</p>","<p id=\"Par45\">To acquire diffraction patterns, an X-ray diffractometer (XRD) with Cu Kα radiation was employed over a Rigaku RINT 2400 instrument (Scan angle: 5°–90°; scan speed: 2°/min; voltage and current: 40 kV and 40 mA). In situ XRD measurement was conducted on a SmartLab-TD diffraction system using a Cu Kα source with an XRK 900 heater. The reduction was carried out under the conditions of pure hydrogen and a temperature range of 25<bold>–</bold>400 °C. The carbonization process was performed on the reaction gas (CO<sub>2</sub>/H<sub>2</sub>) at 320 °C. Surface morphologies of the catalysts were examined using scanning electron microscopy (SEM, JEOL JSM-IT700HR), and transmission electron microscopy (TEM, JEOL JEM-2100F) was utilized to observe the morphologies and elemental mapping of the catalysts at an acceleration voltage of 100 kV. FIB (Focused ion beam)-SEM was performed using a double-beam electron microscope (Helios G4 PFIB CXe). The surface area of the catalysts was determined using N<sub>2</sub> adsorption-desorption experiments at –196 °C (Micromeritics 3Flex ASAP 2460). Prior to the tests, the samples were vacuum-degassed for 8 h at 240 °C. The M-H properties of the fresh catalysts were measured with a vibrating sample magnetometer (VSM, LakeShore 7404).</p>","<p id=\"Par46\">H<sub>2</sub> temperature-programmed reduction (H<sub>2</sub>-TPR) tests were performed using a BELCAT-II-T-SP analyzer with a thermal conductivity detector (TCD). Helium was used as a pretreatment gas for the sample of 30 mg for 1 h at 300 °C. A gas mixture (5% H<sub>2</sub>/Ar) was then delivered to the reactor at a rate of 30 mL/min when the temperature was decreased to 50 °C. Finally, H<sub>2</sub>-TPR curves were obtained at temperatures ranging from 50 to 900 °C with a heating rate of 10 °C per minute. CO<sub>2</sub> or H<sub>2</sub> temperature-programmed desorption (TPD) tests were also investigated using the same apparatus. 30 mg of the sample was reduced for 2 h at 400 °C under a 100% H<sub>2</sub> gas flow (30 mL/min). The temperature of the reactor was reduced to 50 °C under a He gas flow (30 mL/min) after reduction. The reactor was subsequently filled with a 100% CO<sub>2</sub> or 5% H<sub>2</sub>/Ar gas mixture for 1 h. He gas was then introduced into the reactor to remove the physically-adsorbed CO<sub>2</sub> or H<sub>2</sub>. The CO<sub>2</sub>-TPD and H<sub>2</sub>-TPD curves were recorded from 50 to 900 °C with a heating rate of 10 °C per minute. C<sub>3</sub>H<sub>6</sub>-pulse transient hydrogenation experiments were performed on the spent catalysts. Before experiments, the spent catalysts were pretreated in pure H<sub>2</sub> at 350 °C for 2 h to activate the surface. And then the system was cooled to 320 °C in the Ar stream. After that, the samples were exposed to pure H<sub>2</sub>. As the 10% C<sub>3</sub>H<sub>6</sub>/90% Ar gas was pulsed into the reactor, CH<sub>4</sub> (<italic>m</italic>/<italic>z</italic> = 16), C<sub>3</sub>H<sub>6</sub> (<italic>m</italic>/<italic>z</italic> = 42), and C<sub>3</sub>H<sub>8</sub> (<italic>m</italic>/<italic>z</italic> = 44) transient signals were detected by a mass spectrometer.</p>","<p id=\"Par47\">For the X-ray photoelectron spectroscopy (XPS) analyses, an X-ray photoelectron spectrometer (KRATOS, Axis Ultra DLD) instrument was utilized, equipped with a catalyst pretreatment chamber for altering the gas composition. The excitation source was Al Kα ray (hv =1486.6 eV).</p>","<p id=\"Par48\">The <sup>57</sup>Fe Mössbauer spectra were recorded on an SEE Co W304 Mössbauer spectrometer, using a <sup>57</sup>Co/Rh source in transmission geometry. The data were fitted using the MossWinn 4.0 software. Fourier transform infrared spectroscopy (FTIR) was conducted on a Thermo Scientific Nicolet iS20 IR spectrometer. The samples were finely milled, evenly combined with KBr, and pelletized. The spectral resolution was 4 cm<sup><bold>–</bold>1</sup>, and 32 scans were recorded for each spectrum. The Raman spectra were recorded at room temperature on a HORIBA Scientific LabRAM HR Evolution Raman spectrometer.</p>","<p id=\"Par49\">Co K-edge analyses were carried out with Si (111) crystal monochromators at the BL11B beamlines at the Shanghai Synchrotron Radiation Facility (SSRF) (Shanghai, China). Before the examination at the beamline, samples were compressed into thin sheets of 1 cm in diameter and sealed with Kapton tape film. The EXAFS spectra were captured using a 4-channel Silicon Drift Detector (SDD) Bruker 5040 at room temperature. The Co K-edge extended X-ray absorption fine structure (EXAFS) spectra were recorded in the transmission mode. Two scans were conducted for each sample, and negligible changes in the line shape and peak position of the Co K-edge XANES spectra were observed between the two scans. The EXAFS spectra of these standard samples (Co, CoO, and Co<sub>3</sub>O<sub>4</sub>) were also recorded in the transmission mode. The spectra were processed and analyzed using the software codes Athena and Artemis.</p>","<title>Catalyst tests</title>","<p id=\"Par50\">Granular catalysts of 0.12 g (20–40 meshes) mixed with quartz sand of 0.5 g were used to evaluate the catalytic performance in a fixed-bed reactor. On top of the catalyst bed, 1 g glass beads are applied to adjust the bed height and preheat the reaction gas. The two ends of the catalyst bed and glass beads were separated by quartz cotton. Prior to the reaction, the catalyst was reduced for 8 h at 400 °C with pure H<sub>2</sub> of 30 mL/min. After reduction, the reactor was cooled to room temperature. The reactor was then filled with CO<sub>2</sub>/H<sub>2</sub>/Ar (27.0/68.0/5.0) reactant gas, and the temperature and pressure of the system were gradually raised to 320 °C and 3.0 MPa, respectively, and W<sub>cat</sub>/F<sub>CO2+H2</sub> was 4.5 g h mol<sup>–1</sup>.</p>","<p id=\"Par51\">To collect the heavy hydrocarbons and eliminate the water generated by the reaction, an ice trap was placed between the reactor and the back pressure valve, and an octane of 2 g was added to the ice trap in order to absorb heavy hydrocarbons. At the end of the reaction, the product in the ice trap was collected, and dodecane of 0.1 g and 2-butanol of 0.1 g were added as internal standards to the oil and water phases, respectively. An off-line gas chromatograph (Shimadzu GC-2014) equipped with a flame ionization detector (FID) and a DB-1 capillary column was used to examine the heavy hydrocarbons and water phase product. Two online gas chromatography systems (GL Sciences GC320 and Shimadzu GC-2014) were used to identify the gas-phase products: one had a thermal conductive detector (TCD, GC320) and an active charcoal column for analyzing Ar, CO, CH<sub>4</sub>, and CO<sub>2</sub>, while the other had an FID (GC-2014) and a GS-ALUMINA capillary column for analyzing light hydrocarbons.</p>","<title>Statistics and reproducibility</title>","<p id=\"Par52\">We repeated the main catalysts for the catalyst test. All the experimental results can be reproduced within a small margin of error. No statistical method was used to predetermine the sample size. No data were excluded from the analyses. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.</p>"],"string":"[\n \"<title>Methods</title>\",\n \"<title>Catalyst preparation</title>\",\n \"<title>Graphene oxide (GO)</title>\",\n \"<p id=\\\"Par34\\\">K<sub>2</sub>S<sub>2</sub>O<sub>8</sub> (7.5 g, Rgent Chemical Reagent Co.) and P<sub>2</sub>O<sub>5</sub> (7.5 g, Damao Chemical Reagent Co.) were added to a round-bottomed flask under the conditions of an 80 °C water bath and then combined with concentrated H<sub>2</sub>SO<sub>4</sub> (Fuchen Chemical Reagent Co.) by stirring for 15 min. Graphite powder (10 g, Sinopharm Chemical Reagent Co.) was subsequently added to the solution. After stirring for 4.5 h, the mixture was filtered and rinsed until the pH of the supernatant reached 7, before being dried overnight. The dried pre-oxidized graphite was then transferred into a three-necked flask with concentrated H<sub>2</sub>SO<sub>4</sub> in an ice-water bath. Under the aforementioned conditions, KMnO<sub>4</sub> (50 g, Sinopharm Chemical Reagent Co.) was added in these preparation phases. The solution was stirred at 35°C for 3 h before being progressively combined with deionized water and 30% H<sub>2</sub>O<sub>2</sub> until no bubbles occurred, and then aged overnight. The bottom slurry of the solution was transferred to the 3% HCl (Fuchen Chemical Reagent Co.) for acidification treatment. After filtering and washing to neutrality, GO was then moved to deionized water and ultrasonically agitated for 5 h. Finally, dried graphene oxide was obtained using a freeze-drying method.</p>\",\n \"<title>Graphene-supported Fe-Co bimetallic catalysts</title>\",\n \"<p id=\\\"Par35\\\">Graphene-supported K-Fe-Co bimetallic catalysts were synthesized using one-pot hydrothermal synthesis and impregnation. The target loadings for the prepared catalysts were 20% Fe, 4% Co, and 1% K, respectively. The ingredients of the obtained catalysts were investigated by inductively coupled plasma optical emission spectrometer (ICP-OES) tests, and the results are shown in Supplementary Table ##SUPPL##0##1##.</p>\",\n \"<p id=\\\"Par36\\\">In detail, GO (2.0 g), urea (2.0 g, Sinopharm Chemical Reagent Co.), and Fe(NO<sub>3</sub>)<sub>3</sub>·9H<sub>2</sub>O (2.4 g, Damao Chemical Reagent Co.) were dissolved in a mixture of ethylene glycol (40 mL, Hengxing Chemical Preparation Co.) and deionized water (290 mL) and then stirred and ultra-sounded for 2 h. The obtained liquid was transferred into a Teflon-lined stainless-steel autoclave, followed by one-pot hydrothermal synthesis at 180 °C for 12 h with rotation. The products were washed and filtered until neutral, then frozen to dry before calcining at 500 °C in a nitrogen atmosphere for 4 h (unless otherwise stated, all drying methods employed were freezing drying methods to maintain the graphene structure). Cobalt and potassium were loaded by impregnating C<sub>10</sub>H<sub>16</sub>CoO<sub>4</sub> (Macklin Biochemical Co.) and K<sub>2</sub>CO<sub>3</sub> (Damao Chemical Reagent Co.) as cobalt and potassium sources, respectively. The amount of the impregnated material was calculated according to the given contents. The obtained catalyst was dried and calcined at 500 °C in a nitrogen atmosphere for 4 h. The as-prepared catalyst was designated as GO-Fe/K-Co. It should be noted that the portion before the slash represented the elements loaded by the one-pot hydrothermal method, while the portion after the slash represented the elements loaded by the impregnation method.</p>\",\n \"<p id=\\\"Par37\\\">GO/K-Fe-Co was prepared in the following steps: Hydrothermal treatment of GO and urea was first performed, followed by impregnating K<sub>2</sub>CO<sub>3</sub>, Fe(NO<sub>3</sub>)<sub>3</sub>·9H<sub>2</sub>O, and C<sub>10</sub>H<sub>16</sub>CoO<sub>4</sub> as K, Fe, and Co sources onto the calcined catalyst. The drying and calcination processes after the hydrothermal synthesis and the impregnation remained unchanged.</p>\",\n \"<p id=\\\"Par38\\\">In the same way, GO-Co/K-Fe and GO-Fe-Co/K catalysts were synthesized with the given loadings by changing the orders of material addition via the hydrothermal or impregnation methods without altering any other preparation techniques or stages.</p>\",\n \"<p id=\\\"Par39\\\">To examine the influence of Fe contents, GO-Fe/K-Co with target Fe loadings of 25% and 30% were prepared and denoted as GO-25Fe/K-Co and GO-30Fe/K-Co, respectively. In addition, GO/K-10Fe-20Co was synthesized by loading 10% Fe and 20% Co with an impregnation process. Unlike the GO/K-Fe-Co catalyst mentioned above, Fe here was first impregnated onto the graphene surface, and then Co was loaded.</p>\",\n \"<title>Graphene-supported Fe catalysts</title>\",\n \"<p id=\\\"Par40\\\">GO-Fe/K and GO/K-Fe catalysts without the Co addition were also fabricated, in which the loadings for Fe and K were 20 wt% and 1 wt%, respectively.</p>\",\n \"<title>Other carbon materials supported Fe-Co bimetallic catalysts</title>\",\n \"<p id=\\\"Par41\\\">By adding the same amounts of K<sub>2</sub>CO<sub>3</sub>, Fe(NO<sub>3</sub>)<sub>3</sub>·9H<sub>2</sub>O, and C<sub>15</sub>H<sub>21</sub>CoO<sub>6</sub> to those of the GO-Fe/K-Co catalyst for comparison, carbon nanotubes (CNTs, Macklin Biochemical Co.), carbon black (CB, Macklin Biochemical Co.), and activated carbon (AC, Damao Chemical Co.) were utilized as supports for the preparation of Fe-Co bimetallic catalysts. These catalysts were designated as CNTs-Fe/K-Co, CB-Fe/K-Co, and AC-Fe/K-Co, respectively.</p>\",\n \"<title>rGO</title>\",\n \"<p id=\\\"Par42\\\">Only GO (2.0 g) and urea (2.0 g) were dissolved in the mixture of ethylene glycol (40 mL) and deionized water (290 mL), agitated, and transferred into a Teflon-lined stainless steel autoclave, where a one-pot hydrothermal synthesis was carried out for 12 h at 180 °C. The resulting material was washed, dried, and calcined, the resultant graphene was labeled as rGO.</p>\",\n \"<title>rGO-Fe/K-Co</title>\",\n \"<p id=\\\"Par43\\\">The same synthetic process as for GO-Fe/K-Co was applied to the synthesis of rGO-Fe/K-Co, but the raw ingredient was rGO.</p>\",\n \"<title>Characterization</title>\",\n \"<p id=\\\"Par44\\\">The actual total loadings of Fe, Co, and K in different catalysts were established using ICP-OES, which was performed on an Agilent 5110 (OES). The test procedures were as follows: a 10 mg sample was dissolved in a mix solution (2 mL HNO<sub>3</sub> + 6 mL HCl+2 mL HF) overnight. The dissolved sample was then added to a flask and diluted to the scale line. Five internal concentration standard solutions (0.5 mg/L, 1 mg/L, 3 mg/L, 5 mg/L, and 10 mg/L) were analyzed, and a standard curve was formed.</p>\",\n \"<p id=\\\"Par45\\\">To acquire diffraction patterns, an X-ray diffractometer (XRD) with Cu Kα radiation was employed over a Rigaku RINT 2400 instrument (Scan angle: 5°–90°; scan speed: 2°/min; voltage and current: 40 kV and 40 mA). In situ XRD measurement was conducted on a SmartLab-TD diffraction system using a Cu Kα source with an XRK 900 heater. The reduction was carried out under the conditions of pure hydrogen and a temperature range of 25<bold>–</bold>400 °C. The carbonization process was performed on the reaction gas (CO<sub>2</sub>/H<sub>2</sub>) at 320 °C. Surface morphologies of the catalysts were examined using scanning electron microscopy (SEM, JEOL JSM-IT700HR), and transmission electron microscopy (TEM, JEOL JEM-2100F) was utilized to observe the morphologies and elemental mapping of the catalysts at an acceleration voltage of 100 kV. FIB (Focused ion beam)-SEM was performed using a double-beam electron microscope (Helios G4 PFIB CXe). The surface area of the catalysts was determined using N<sub>2</sub> adsorption-desorption experiments at –196 °C (Micromeritics 3Flex ASAP 2460). Prior to the tests, the samples were vacuum-degassed for 8 h at 240 °C. The M-H properties of the fresh catalysts were measured with a vibrating sample magnetometer (VSM, LakeShore 7404).</p>\",\n \"<p id=\\\"Par46\\\">H<sub>2</sub> temperature-programmed reduction (H<sub>2</sub>-TPR) tests were performed using a BELCAT-II-T-SP analyzer with a thermal conductivity detector (TCD). Helium was used as a pretreatment gas for the sample of 30 mg for 1 h at 300 °C. A gas mixture (5% H<sub>2</sub>/Ar) was then delivered to the reactor at a rate of 30 mL/min when the temperature was decreased to 50 °C. Finally, H<sub>2</sub>-TPR curves were obtained at temperatures ranging from 50 to 900 °C with a heating rate of 10 °C per minute. CO<sub>2</sub> or H<sub>2</sub> temperature-programmed desorption (TPD) tests were also investigated using the same apparatus. 30 mg of the sample was reduced for 2 h at 400 °C under a 100% H<sub>2</sub> gas flow (30 mL/min). The temperature of the reactor was reduced to 50 °C under a He gas flow (30 mL/min) after reduction. The reactor was subsequently filled with a 100% CO<sub>2</sub> or 5% H<sub>2</sub>/Ar gas mixture for 1 h. He gas was then introduced into the reactor to remove the physically-adsorbed CO<sub>2</sub> or H<sub>2</sub>. The CO<sub>2</sub>-TPD and H<sub>2</sub>-TPD curves were recorded from 50 to 900 °C with a heating rate of 10 °C per minute. C<sub>3</sub>H<sub>6</sub>-pulse transient hydrogenation experiments were performed on the spent catalysts. Before experiments, the spent catalysts were pretreated in pure H<sub>2</sub> at 350 °C for 2 h to activate the surface. And then the system was cooled to 320 °C in the Ar stream. After that, the samples were exposed to pure H<sub>2</sub>. As the 10% C<sub>3</sub>H<sub>6</sub>/90% Ar gas was pulsed into the reactor, CH<sub>4</sub> (<italic>m</italic>/<italic>z</italic> = 16), C<sub>3</sub>H<sub>6</sub> (<italic>m</italic>/<italic>z</italic> = 42), and C<sub>3</sub>H<sub>8</sub> (<italic>m</italic>/<italic>z</italic> = 44) transient signals were detected by a mass spectrometer.</p>\",\n \"<p id=\\\"Par47\\\">For the X-ray photoelectron spectroscopy (XPS) analyses, an X-ray photoelectron spectrometer (KRATOS, Axis Ultra DLD) instrument was utilized, equipped with a catalyst pretreatment chamber for altering the gas composition. The excitation source was Al Kα ray (hv =1486.6 eV).</p>\",\n \"<p id=\\\"Par48\\\">The <sup>57</sup>Fe Mössbauer spectra were recorded on an SEE Co W304 Mössbauer spectrometer, using a <sup>57</sup>Co/Rh source in transmission geometry. The data were fitted using the MossWinn 4.0 software. Fourier transform infrared spectroscopy (FTIR) was conducted on a Thermo Scientific Nicolet iS20 IR spectrometer. The samples were finely milled, evenly combined with KBr, and pelletized. The spectral resolution was 4 cm<sup><bold>–</bold>1</sup>, and 32 scans were recorded for each spectrum. The Raman spectra were recorded at room temperature on a HORIBA Scientific LabRAM HR Evolution Raman spectrometer.</p>\",\n \"<p id=\\\"Par49\\\">Co K-edge analyses were carried out with Si (111) crystal monochromators at the BL11B beamlines at the Shanghai Synchrotron Radiation Facility (SSRF) (Shanghai, China). Before the examination at the beamline, samples were compressed into thin sheets of 1 cm in diameter and sealed with Kapton tape film. The EXAFS spectra were captured using a 4-channel Silicon Drift Detector (SDD) Bruker 5040 at room temperature. The Co K-edge extended X-ray absorption fine structure (EXAFS) spectra were recorded in the transmission mode. Two scans were conducted for each sample, and negligible changes in the line shape and peak position of the Co K-edge XANES spectra were observed between the two scans. The EXAFS spectra of these standard samples (Co, CoO, and Co<sub>3</sub>O<sub>4</sub>) were also recorded in the transmission mode. The spectra were processed and analyzed using the software codes Athena and Artemis.</p>\",\n \"<title>Catalyst tests</title>\",\n \"<p id=\\\"Par50\\\">Granular catalysts of 0.12 g (20–40 meshes) mixed with quartz sand of 0.5 g were used to evaluate the catalytic performance in a fixed-bed reactor. On top of the catalyst bed, 1 g glass beads are applied to adjust the bed height and preheat the reaction gas. The two ends of the catalyst bed and glass beads were separated by quartz cotton. Prior to the reaction, the catalyst was reduced for 8 h at 400 °C with pure H<sub>2</sub> of 30 mL/min. After reduction, the reactor was cooled to room temperature. The reactor was then filled with CO<sub>2</sub>/H<sub>2</sub>/Ar (27.0/68.0/5.0) reactant gas, and the temperature and pressure of the system were gradually raised to 320 °C and 3.0 MPa, respectively, and W<sub>cat</sub>/F<sub>CO2+H2</sub> was 4.5 g h mol<sup>–1</sup>.</p>\",\n \"<p id=\\\"Par51\\\">To collect the heavy hydrocarbons and eliminate the water generated by the reaction, an ice trap was placed between the reactor and the back pressure valve, and an octane of 2 g was added to the ice trap in order to absorb heavy hydrocarbons. At the end of the reaction, the product in the ice trap was collected, and dodecane of 0.1 g and 2-butanol of 0.1 g were added as internal standards to the oil and water phases, respectively. An off-line gas chromatograph (Shimadzu GC-2014) equipped with a flame ionization detector (FID) and a DB-1 capillary column was used to examine the heavy hydrocarbons and water phase product. Two online gas chromatography systems (GL Sciences GC320 and Shimadzu GC-2014) were used to identify the gas-phase products: one had a thermal conductive detector (TCD, GC320) and an active charcoal column for analyzing Ar, CO, CH<sub>4</sub>, and CO<sub>2</sub>, while the other had an FID (GC-2014) and a GS-ALUMINA capillary column for analyzing light hydrocarbons.</p>\",\n \"<title>Statistics and reproducibility</title>\",\n \"<p id=\\\"Par52\\\">We repeated the main catalysts for the catalyst test. All the experimental results can be reproduced within a small margin of error. No statistical method was used to predetermine the sample size. No data were excluded from the analyses. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<title>Adjustable spatial distribution of multiple sites</title>","<p id=\"Par8\">A series of graphene-supported Fe-Co bimetallic catalysts with identical total contents of Fe, Co, and K were synthesized by varying the addition order of Fe and Co during the hydrothermal and impregnation processes (Supplementary Fig. ##SUPPL##0##3##). Upon examining the as-prepared catalysts, only the characteristic diffraction peaks ascribed to rGO and Fe<sub>2</sub>O<sub>3</sub> were observed in XRD (Fig. ##FIG##0##1a##). There were no peaks associated with Co detected in the four graphene-supported catalysts. To estimate the total contents of different metal elements, we carried out the inductively coupled plasma-optical emission spectrometer (ICP-OES) tests and found that the contents of Fe, Co, and K were close to the theoretical values of 20 wt%, 4 wt%, and 1 wt%, respectively (Supplementary Table ##SUPPL##0##1##). Although the total contents of each metal were roughly the same for different catalysts, the unique structure separated by graphene fences formed in the hydrothermal process led to different spatial distributions of Fe and Co sites in the inner and surface layers.</p>","<p id=\"Par9\">Throughout the hydrothermal process, the decrease in graphene layer distances and the cross-linking of the graphene layers led to the dynamic transformation of GO from the 2D lamellar structure to the 3D stereoscopic structure<sup>##UREF##27##40##,##UREF##28##41##</sup>. To demonstrate the dynamic evolution, we employed in situ XRD to detect the diffraction peak shift of GO during the temperature-programmed reduction process. With the temperature increasing and H<sub>2</sub> introduction, diffraction peaks gradually shifted to the higher direction (Supplementary Fig. ##SUPPL##0##4##), representing the decrease in graphene layer spacings as determined by Bragg’s law<sup>##UREF##29##42##</sup>. Similarly, the rGO obtained by hydrothermal treatment also exhibited a higher peak and a smaller layer spacing compared with GO (Fig. ##FIG##0##1b##). Meanwhile, the specific surface area significantly decreased (Supplementary Table ##SUPPL##0##2##). These phenomena corresponded to the folding and bending of the graphene layers, as observed in SEM images (Fig. ##FIG##0##1c##).</p>","<p id=\"Par10\">Accordingly, due to the cross-linking effect of the graphene layers during the hydrothermal process, the metals added together with GO were partially encapsulated in the folded inner layers and in situ replaced the oxygen-containing groups of the graphene layers. Consequently, the “graphene fences” were formed by the reduced graphene layers, which encased metal nanoparticles. After the hydrothermal treatment, the metals introduced by impregnation were more easily loaded onto the surface of the folding graphene layers instead of the internal layers, owing to the separation effects of the graphene fences. These unique structures were reflected in the molecular vibration spectra, surface and internal element contents, and morphological characterizations of the catalysts.</p>","<p id=\"Par11\">We employed the FTIR spectra to identify the loss of oxygen-containing groups replaced by metal sites (Fig. ##FIG##0##1d##). The stretching vibrations of O − H (3400 cm<sup>–1</sup>), C = O (1722 cm<sup>–1</sup>), aromatic C = C (1620 cm<sup>–1</sup>), carboxyl O = C − O (1356 cm<sup>–1</sup>), epoxyl C − O (1220 cm<sup>–1</sup>), and alkoxyl C − O (1050 cm<sup>−1</sup>), which were recorded as references, were all observed in the FTIR spectrum of GO. After hydrothermal synthesis, the oxygen-containing group peaks of rGO were significantly diminished, and two broad peaks at 1550 cm<sup>–1</sup> and 1175 cm<sup>–1</sup> appeared, which were assigned to C = C and C–O, respectively<sup>##UREF##30##43##</sup>. Intriguingly, the vibration peaks of metal-supported catalysts displayed smaller areas compared to those of rGO, especially in GO-Fe/K-Co and GO-Fe-Co/K, which could be interpreted as more substitutions of oxygen-containing groups by metals in the reduction process, caused by the metals loading the graphene inner layers. However, the metals in GO/K-Fe-Co and GO-Co/K-Fe were unable to adequately replace the oxygen-containing groups inside the layers due to the protection of graphene fences, thus resulting in a larger peak area.</p>","<p id=\"Par12\">Furthermore, the thickness of the graphene-supported Fe-Co bimetallic catalysts could also reveal the various structures. The thicknesses of the graphene were detected by the second-order peak positions of the Raman spectra, which appear near 2700 cm<sup>–1</sup>. In general, a higher peak position represents a bigger graphene thickness and more graphene layers<sup>##UREF##31##44##,##UREF##32##45##</sup>. GO and rGO displayed the maximum and minimum graphene thicknesses of graphene layers, respectively (Fig. ##FIG##0##1e##). With the incorporation of Fe and Co, the thicknesses of graphene increased compared with rGO, among which the GO-Fe-Co/K showed the largest thickness, revealing that the presence of both Fe and Co on the inner layers hindered the compression of the graphene layers during the hydrothermal process. As inferred, GO/K-Fe-Co showed the smallest thickness of all the graphene-supported catalysts. <italic>I</italic><sub><italic>D</italic></sub>/<italic>I</italic><sub><italic>G</italic></sub> values displayed the reverse order as the thicknesses, indicating that the disorders increased when the graphene thicknesses were compressed (Fig. ##FIG##0##1e##).</p>","<p id=\"Par13\">The varying Fe-Co distributions were further demonstrated by the different metal contents between the surface and interior. Based on the XPS results, the Fe surface contents in GO-Co/K-Fe (12.3%) and GO/K-Fe-Co (11.7%) catalysts whose Fe was introduced by impregnation were distinctly higher than those of the catalysts whose Fe was loaded by hydrothermal incorporation, such as GO-Fe-Co/K (3.4%) and GO-Fe/K-Co (4.1%) (Supplementary Table ##SUPPL##0##3##). Meanwhile, the GO/K-Fe with a higher surface Fe content (Supplementary Table ##SUPPL##0##3##) exhibited a stronger magnetism intensity in the M-H loop compared to the GO-Fe/K (Supplementary Fig. ##SUPPL##0##5##), which ulteriorly supported the conclusion that Fe added by the hydrothermal process was partially encased in the graphene layers, resulting in a lower surface content and a weaker magnetism intensity. Furthermore, the Fe/Co values depicted by SEM mapping, which also reflected the surface element contents, exhibited the same trend as those measured by XPS (Supplementary Fig. ##SUPPL##0##6## and Supplementary Table ##SUPPL##0##4##). In addition to the surface content characterizations, we applied FIB-SEM to directly investigate the metal distributions in the graphene inner layers. In the cross-sectional SEM images of GO-Fe/K-Co, the metal nanoparticles were found to be loaded between the graphene layers (Supplementary Fig. ##SUPPL##0##7a##), which corresponded to the oxide and iron mapping distributions (Supplementary Fig. ##SUPPL##0##7b##, ##SUPPL##0##c##), while only a little cobalt distribution was observed on the internal metal particles (Supplementary Fig. ##SUPPL##0##7d##). Meanwhile, the Fe/Co value of the cross-section (8.42) obtained by the SEM mapping (Supplementary Table ##SUPPL##0##5##) was significantly higher than that of the catalyst surface (0.82) (Supplementary Table ##SUPPL##0##4##). The various metal distributions on the surface and interior effectively corroborated the reconstruction of the graphene-supported catalysts, forming a unique structure with different spatial distributions of multiple active sites.</p>","<p id=\"Par14\">Additionally, as shown in Fig. ##FIG##0##1f##, the yellow circles displayed the same Fe and Co distributions in GO-Fe/K-Co, while in the red circles only the Fe distribution was clearly observed. This could be explained by the difficulty of impregnating Co in the folded interior graphene layers as compared to Fe loaded by hydrothermal synthesis, thus forming the active sites with different metal compositions. As a result, the yellow and red circles represented the surface and internal metal sites of the graphene layers, respectively. By contrast, Fe and Co in GO/K-Fe-Co exhibited a uniform and well-dispersed distribution over the outer surface of graphene layers (Fig. ##FIG##0##1f##). These findings convincingly verified that the spatial distributions of Fe-Co active sites could be regulated by the graphene fences, forming scattered sites (Fe+CoFe) or uniform assemblage sites (FeCo) (Fig. ##FIG##0##1f##).</p>","<p id=\"Par15\">In response to the descriptions mentioned above, a detailed schematic diagram of dynamic evolution regarding the synthesis of GO/K-Fe-Co and GO-Fe/K-Co was drawn in Fig. ##FIG##0##1g##. In terms of the GO/K-Fe-Co catalyst, GO was first reduced by the hydrothermal process, leading the graphene layers to be twisted and folded, thus forming graphene fences. Subsequently, Fe and Co were incorporated through impregnation and primarily dispersed on the surface of the graphene fences with a benign distribution, on account of the resistance of the folded interior layers. In the case of the GO-Fe/K-Co catalyst, Fe was introduced during the hydrothermal process, which would in situ replace the oxygen-containing groups and be partially encapsulated in the interior graphene layers. Then the impregnated Co was loaded on the exterior graphene layers because of the inaccessible accesses controlled by the graphene fences, forming scattered sites with a small amount of Fe on the outer surface of the graphene layers. The two different spatial distribution structures described above could be directly observed by the SEM images. Figure ##FIG##0##1h## clearly showed Fe-Co homogeneity active sites arranged uniformly on the surface of graphene layers (GO/K-Fe-Co) and the metal sites separated by graphene fences (GO-Fe/K-Co), in which the yellow circle represented the sites on the surface layer and the red circles represented the sites on the internal layer. Therefore, with the assistance of developed graphene fences, graphene-supported Fe-Co bimetallic catalysts with adjustable metal spatial distributions in the internal and external layers were successfully synthesized.</p>","<title>Phase composition characterizations of Fe-Co catalysts</title>","<p id=\"Par16\">Several characterizations were first applied to determine the surface metal phase compositions of the as-prepared catalysts. The HR-TEM images (Supplementary Fig. ##SUPPL##0##8##) revealed the lattice spacings of 0.25 nm that corresponded to the (119) plane of the Fe<sub>2</sub>O<sub>3</sub> species in the four graphene-supported catalysts, which was consistent with the XRD results shown in Fig. ##FIG##0##1a##<sup>##UREF##33##46##</sup>. The XPS Fe 2<italic>p</italic> spectra (Supplementary Fig. ##SUPPL##0##9##) showed binding energy peaks at 710.8 eV and 712.8 eV, which were ascribed to the Fe(II) and Fe(III) phases, respectively<sup>##UREF##34##47##</sup>. Moreover, according to the <sup>57</sup>Fe Mössbauer spectra (Supplementary Fig. ##SUPPL##0##10## and Supplementary Table ##SUPPL##0##6##), Fe was predominantly present in the form of Fe<sub>3</sub>O<sub>4</sub>, which was the combination of Fe<sup>2+</sup> and Fe<sup>3+</sup> species. Due to the low contents or benign distributions, there were no diffraction peaks attributed to Co in XRD patterns, whereas two peaks assigned to Co<sup>2+</sup>/Co<sup>3+</sup> and Co<sup>0</sup> appeared in the Co 2<italic>p</italic> XPS spectra (Supplementary Fig. ##SUPPL##0##11##)<sup>##UREF##35##48##</sup>. To further ascertain the Co proportion on the catalytic surface, XANES spectra with fitting curves were recorded. As pictured in Supplementary Fig. ##SUPPL##0##12##, CoO was identified as the predominant phase of Co species regardless of their spatial distributions. Besides, the Co K-edge EXAFS curve fitting results and parameters were also displayed in Supplementary Fig. ##SUPPL##0##13## and Supplementary Table ##SUPPL##0##7##, disclosing the existence of the Co–Co and Co–O bonds.</p>","<p id=\"Par17\">Regarding the spent catalysts, the Fe 2<italic>p</italic> spectra showed binding energy peaks at 708.5 eV, which were assigned to the Fe-C bonds (Fig. ##FIG##1##2b##), indicating the presence of iron carbides<sup>##UREF##36##49##,##UREF##37##50##</sup>. Based on the results of XRD patterns, diffraction peaks attributed to Fe<sub>5</sub>C<sub>2</sub> were observed in all spent catalysts (Fig. ##FIG##1##2a##). Among them, the diffraction peaks with the strongest intensities corresponded to Fe<sub>5</sub>C<sub>2</sub> (510) facets, which were also revealed by HR-TEM images with lattice spacings of 0.20 nm (Supplementary Fig. ##SUPPL##0##14##)<sup>##UREF##38##51##</sup>. Furthermore, the coexistence of Fe<sub>5</sub>C<sub>2</sub> (A) and Fe<sub>5</sub>C<sub>2</sub> (B) species was determined from two overlapping sextuplets in the <sup>57</sup>Fe Mössbauer spectra (Fig. ##FIG##1##2c## and Supplementary Table ##SUPPL##0##8##), which represented the different occupied sites of Fe in the crystallographic structure of Fe<sub>5</sub>C<sub>2</sub><sup>##UREF##39##52##</sup>. The aforementioned findings demonstrated that iron carbide, an active phase for the chain growth reaction, was mainly presented as the Fe<sub>5</sub>C<sub>2</sub> (510) phase in the spent catalysts<sup>##REF##26941306##21##</sup>. Co K-edge XANES tests were also applied to identify the Co phases in the spent catalysts. In contrast to the as-prepared catalysts, the majority of cobalt existed in metallic states (Fig. ##FIG##1##2d##), demonstrating the good reduction abilities of Co in the graphene-supported catalysts. Moreover, the EXAFS fitting results illustrated the presence of only Co–Co bonds, providing further evidence that cobalt existed in the metallic phases (Supplementary Fig. ##SUPPL##0##15## and Supplementary Table ##SUPPL##0##9##).</p>","<title>Catalytic performances and stabilities</title>","<p id=\"Par18\">The catalysts were tested under the conditions of 320 °C, 3.0 MPa, and W/F = 4.5 g h mol<sup>–1</sup>. The main products of both the GO-Fe/K and GO/K-Fe catalysts, without the addition of Co, were methane and light olefins instead of saturated light paraffins (Fig. ##FIG##2##3a##). However, as shown in Fig. ##FIG##2##3b##, with the Co incorporation, the Fe-Co bimetallic catalysts displayed various types of hydrocarbon product selectivities. GO-Co/K-Fe and GO/K-Fe-Co mainly produced light olefins, especially for the GO/K-Fe-Co catalyst, where 50.1% C<sub>2</sub><sup>=</sup>–C<sub>4</sub><sup>=</sup> selectivity was obtained at a CO<sub>2</sub> conversion of 55.4%. Whereas for GO-Fe-Co/K, more alkanes, particularly methane and ethane, were produced (Fig. ##FIG##2##3b##). However, GO-Fe/K-Co exhibited a higher selectivity of propane and butane (43.6%) compared to the GO-Fe-Co/K (Fig. ##FIG##2##3b##). In comparison with GO-Fe/K, the CoFe sites of GO-Fe/K-Co were formed by impregnating Co onto the surface Fe sites of GO-Fe/K with the assistance of graphene fences. GO/K-10Fe-20Co was also employed to simulate the external CoFe sites of GO-Fe/K-Co which had similar surface Fe/Co ratios (0.6 and 0.7) (Supplementary Table ##SUPPL##0##3##). The high methane selectivity confirmed that the external CoFe sites with a high Co content had a strong hydrogenation capacity (Fig. ##FIG##2##3a##), which facilitated the original light olefins produced on the internal Fe sites hydrogenating into saturated alkanes, thus transforming the products from light olefins to LPG (Fig. ##FIG##2##3g##). In this process, the reaction equilibrium was shifted in the positive direction, leading to an increase in the CO<sub>2</sub> conversion from 33.2% (GO-Fe/K) to 46.0% (GO-Fe/K-Co). This can also be observed in the detailed product distributions of GO-Fe/K and GO-Fe/K-Co (Supplementary Fig. ##SUPPL##0##16##). Before the Co addition, the product selectivity of C2 and C3 in GO-Fe/K was roughly the same. However, after the introduction of Co, C3 products occupied the highest selectivity, which further indicated that the diffusion and hydrogenation effects on the internal-active-site products shifted the chemical equilibrium in a positive direction.</p>","<p id=\"Par19\">In contrast to the GO-Fe/K-Co, the products of the rGO-Fe/K-Co were mainly light olefins rather than alkanes (Fig. ##FIG##2##3a##). This was because the absence of the spatial dual active sites separated by the graphene fences (Supplementary Fig. ##SUPPL##0##17##), which is proven by Supplementary Fig. ##SUPPL##0##18## (same distributions of Fe and Co in TEM elemental mapping images), resulted in a higher Fe/Co ratio (Supplementary Table ##SUPPL##0##3##), thus weakening the hydrogenation ability. Such a result further proved that the unique spatial distributions of Fe-Co dual active sites tuned by graphene fences could efficiently control product types.</p>","<p id=\"Par20\">By comparing the TEM images (Fig. ##FIG##0##1f## and Supplementary Figs. ##SUPPL##0##19##–##SUPPL##0##21##), we observed that the fresh GO-Fe-Co/K and GO/K-Fe-Co catalysts presented smaller particle sizes compared to the other catalysts, indicating that the simultaneous addition of Fe and Co constrained the aggregation of Fe nanoparticles and enhanced the dispersions of Fe species. CO<sub>2</sub> hydrogenation is a structurally sensitive reaction, and higher dispersions are beneficial for improving the carbonization ability of Fe and thus enhancing the catalytic performance (Fig. ##FIG##2##3g##)<sup>##UREF##40##53##</sup>. As seen, according to the <sup>57</sup>Fe Mössbauer spectra of the spent catalysts (Fig. ##FIG##1##2c## and Supplementary Table ##SUPPL##0##8##), GO/K-Fe-Co exhibited the largest proportion of Fe<sub>5</sub>C<sub>2</sub> (72%), including both Fe<sub>5</sub>C<sub>2</sub> (A) and Fe<sub>5</sub>C<sub>2</sub> (B). Accordingly, GO/K-Fe-Co also manifested the highest CO<sub>2</sub> conversion (55.4%) and rather high light olefin selectivity (50.1%) (Fig. ##FIG##2##3b##). The GO-Fe-Co/K catalyst, however, displayed the highest temperature of peaks in the H<sub>2</sub>-TPR (peak γ) and CO<sub>2</sub>-TPD profiles (Supplementary Figs. ##SUPPL##0##22## and ##SUPPL##0##23##). This phenomenon could be explained by the fact that smaller particles would enter or penetrate the interlayers more easily during the hydrothermal process, which was proved by the smaller surface metal loadings, as shown in Supplementary Table ##SUPPL##0##3##. As a result, the metal nanoparticles tightly covered by graphene layers showed strong metal-support interaction (SMSI), which made the reduction and carbonization more challenging<sup>##UREF##40##53##</sup>. This view was intuitively illustrated by in situ XRD. When the temperature rose to 400 °C, a peak of metallic iron appeared on the GO-Fe-Co/K catalyst. Obviously, this temperature was the highest of all the catalysts. Correspondingly, in the carbonization process, GO-Fe-Co/K also showed the lowest peak strength of Fe<sub>5</sub>C<sub>2</sub> (Supplementary Fig. ##SUPPL##0##24##). Meanwhile, previous research has demonstrated that strong chemical adsorption of CO<sub>2</sub> is not easily triggered for hydrogenation, and the coating of the surface Fe species formed by the strong chemical adsorption of CO<sub>2</sub> may potentially lower the catalytic performance<sup>##UREF##25##38##</sup>. Consequently, these reasons led to a slightly lower Fe<sub>5</sub>C<sub>2</sub> content (44.6%) and CO<sub>2</sub> conversion of GO-Fe-Co/K (42.1%) than those of GO/K-Fe-Co (72% and 55.4%) (Fig. ##FIG##2##3b## and Supplementary Table ##SUPPL##0##8##).</p>","<p id=\"Par21\">In contrast to GO-Fe-Co/K, the GO-Fe/K-Co catalyst displayed appropriate metal-support interaction (MSI) and CO<sub>2</sub> adsorption strengths (Supplementary Fig. ##SUPPL##0##22## and Supplementary Fig. ##SUPPL##0##23##), leading to a CO<sub>2</sub> conversion of up to 46% (Fig. ##FIG##2##3b##). Given that cobalt was inactive in the RWGS reaction<sup>##UREF##26##39##,##UREF##41##54##</sup>, the Co surrounding the Fe<sub>5</sub>C<sub>2</sub> on the surface layers would further consume CO without producing it<sup>##UREF##42##55##</sup>. Accordingly, GO/K-10Fe-20Co, which was used to simulate the external Fe/Co sites, also exhibited a low CO selectivity (2.7%). It was significantly lowered compared to that of the GO-Fe/K (15.6%), which consisted of Fe active sites (Fig. ##FIG##2##3a##). This finding further suggested that the CoFe active sites with a low Fe/Co ratio (0.82) (Supplementary Table ##SUPPL##0##4##) located on the external surface of GO-Fe/K-Co could consume the CO produced by the internal Fe sites, thus keeping the CO selectivity at a low level. As a result, GO-Fe/K-Co showed an ultra-low CO selectivity (2.2%) among all the catalysts (Fig. ##FIG##2##3b##). To the best of our knowledge, this is the lowest value level reported for the current methanol intermediate route as well as a modified FTS pathway (refer to Fe-containing catalysts). Furthermore, thick carbon deposition around metal particles would reduce the catalytic activity<sup>##UREF##43##56##,##UREF##44##57##</sup> and this phenomenon can be observed in TEM images of the spent catalysts (Supplementary Fig. ##SUPPL##0##14##). Clearly, as determined by TEM mapping, the ratio of Fe/Co in the spent GO-Co/K-Fe was much lower than that of other catalysts and the fresh GO-Co/K-Fe (Supplementary Fig. ##SUPPL##0##25##, Supplementary Table ##SUPPL##0##10## and Supplementary Table ##SUPPL##0##11##), which can be explained by the large amount of amorphous carbon deposited on the surface of Fe sites affecting the determination of element contents. As a result, it exhibited a relatively low CO<sub>2</sub> conversion (32.8%) (Fig. ##FIG##2##3b##).</p>","<p id=\"Par22\">To further investigate the influence of the Fe amount, the total Fe content in the GO-nFe/K-Co catalysts was altered from 20 wt% to 30 wt%. Intuitively, as Fe content rose, the selectivity of LPG (C<sub>3</sub><sup>P</sup><bold>–</bold>C<sub>4</sub><sup>P</sup>) declined, while the selectivities of light olefins (C<sub>2</sub><sup>=</sup><bold>–</bold>C<sub>4</sub><sup>=</sup>) and C<sub>5</sub><sup>+</sup> products increased (Fig. ##FIG##2##3c##), revealing that the extra Fe introduced to the interior and external graphene fences resulted in an enhancement of the carbon chain growth capacity and an inhibition of the olefin secondary hydrogenation, respectively. In addition, to compare the performances of GO-Fe/K-Co with other carbon materials (CNTs, AC, and CB) supported catalysts, the same preparation procedures and addition amounts of GO-Fe/K-Co were performed. The LPG selectivities of AC-Fe/K-Co (15.8%) and CB-Fe/K-Co (3.9%) were significantly lower than those of GO-Fe/K-Co (43.6%) due to the excessive formation of by-product methane and ethane. Besides, the main products of CNTs-Fe/K-Co were light olefins (35.5%) rather than C<sub>3</sub>-C<sub>4</sub> saturated alkanes (7.3%) (Fig. ##FIG##2##3d##). Meanwhile, the metal distributions of these catalysts were determined by TEM elemental mapping images (Supplementary Fig. ##SUPPL##0##26##). Co distributions were found in all the Fe distribution areas in these three catalysts, indicating that these three carbon materials lacked the function of separating metals as graphene. These findings further demonstrated the superior performance of graphene-fence-separated dual active sites in regulating carbon chain growth and olefin secondary hydrogenation.</p>","<p id=\"Par23\">We further investigated the stabilities of GO-Fe/K-Co and GO/K-Fe-Co under 3.0 MPa with a W/F of 4.5 g h mol<sup>–1</sup> at 320 °C. For GO/K-Fe-Co, the CO<sub>2</sub> conversion decreased and the selectivity of CO as a byproduct increased continuously within 40 h on stream. By contrast, GO-Fe/K-Co remained stable during the 100-hour stability test (Fig. ##FIG##2##3e, f##). Interestingly, as depicted in TEM images (Supplementary Fig. ##SUPPL##0##19a##), the metal nanoparticles of the spent GO/K-Fe-Co catalyst aggregated dramatically in comparison to the fresh catalyst with the prolongation of the reaction process. Contrarily, the metal particles of the spent GO-Fe/K-Co did not agglomerate, and the particle sizes maintained a stable range throughout the reaction (Supplementary Fig. ##SUPPL##0##19a##) due to the protection of the graphene fences. Previous studies have demonstrated that the graphene loaded with iron nanoparticles by the hydrothermal method has a role in anchoring iron particles<sup>##UREF##28##41##</sup>. Combined with our results described above, the graphene fences that separated the dual active sites in the GO-Fe/K-Co catalyst could also spatially confine the aggregation of metal particles during the reaction, thus preventing the deactivation and maintaining a high activity (Supplementary Fig. ##SUPPL##0##19b##). Besides, TEM mapping images of the spent GO-Fe/K-Co catalysts were also tested to explore the distributions of Fe and Co after the reaction, and enlarged images are shown in Supplementary Fig. ##SUPPL##0##27##. The red circles represent the area with different distributions of Fe and Co (obvious Fe distributions but few Co distributions). As observed, after the reaction process, graphene fences still maintain the effect of separating Fe and Co active sites.</p>","<title>Mechanistic studies</title>","<p id=\"Par24\">According to the local micro-environments of supported metal catalysts reported in the previous work<sup>##REF##29956535##31##</sup>, in this study, we employed density functional theory (DFT) calculations to explore the influence of the Fe-Co dual sites separated by graphene fences. To precisely identify the micro-environment of each active site, we first constructed a Fe<sub>5</sub>C<sub>2</sub> (510) surface model, which was denoted as Model 1. Afterwards, Model 2 was built by adding a Co<sub>10</sub> cluster to Model 1 in consideration of surface cobalt incorporation of GO-Fe/K-Co (Fig. ##FIG##3##4b##). Thereinto, Model 1 corresponded to the Fe<sub>5</sub>C<sub>2</sub> active sites inside the graphene fences of GO-Fe/K-Co, whereas Model 2 represented the Fe<sub>5</sub>C<sub>2</sub>/Co active sites on the surface of graphene fences (Fig. ##FIG##3##4b##). In order to compare the adsorption capacities of Co carbide and metallic Co, Co<sub>10</sub> in Model 2 was substituted by Co<sub>8</sub>C<sub>4</sub>, and it was dubbed “Model 3” (Fig. ##FIG##3##4b##). According to the adsorption energy results, the adsorption of both the H<sub>2</sub> and light olefins on the surface cobalt sites in Model 2 and Model 3 was more stable than that on the interfacial sites, illustrating that H<sub>2</sub> and light olefins were primarily adsorbed on the surface cobalt sites of Fe<sub>5</sub>C<sub>2</sub>/Co due to the electron transfer between cobalt and iron carbide (Fig. ##FIG##3##4c, d##, and Supplementary Fig. ##SUPPL##0##28##). The electron transfer between iron carbide and cobalt on Model 2 could be intuitively observed by the charge density difference analysis, which was shown in Supplementary Fig. ##SUPPL##0##29##. The red and green colors represented electron accumulation and loss, respectively. Obviously, after being loaded onto Fe<sub>5</sub>C<sub>2</sub>, electrons were transferred from cobalt to Fe<sub>5</sub>C<sub>2</sub>. Since electron-deficient metals are more favorable to adsorb hydrogen<sup>##UREF##45##58##</sup>, it was the electron transfer that enhanced hydrogen adsorption on the cobalt site, while hydrogen was not likely to be adsorbed on the interface of Fe<sub>5</sub>C<sub>2</sub>-Co due to the electron accumulation.</p>","<p id=\"Par25\">Regarding the hydrogen adsorption, the hydrogen adsorption over Model 2, which adsorbed at the surface metallic Co sites, had a lower adsorption energy (–0.84 eV) than that over iron carbide in Model 1 (–0.81 eV), revealing that the Fe<sub>5</sub>C<sub>2</sub>/Co sites on the external graphene fences had a stronger H<sub>2</sub> adsorption effect than that of the Fe<sub>5</sub>C<sub>2</sub> sites inside the graphene fences (Fig. ##FIG##3##4c##). Accordingly, the H<sub>2</sub> temperature-programmed desorption profile (H<sub>2</sub>-TPD) of GO-Fe/K-Co exhibited two distinct peaks (I and II) (Fig. ##FIG##3##4e##), which corresponded to the weak chemical adsorption and strong chemical adsorption of hydrogen, respectively. rGO-Fe/K-Co displayed a desorption peak near 600 °C resembling that of GO-Fe/K-Co. However, unlike GO-Fe/K-Co, the strong chemical adsorption peak around 650 °C was not obvious (Supplementary Fig. ##SUPPL##0##30##), and this was because partial reduction of rGO-Fe/K-Co had already been accomplished when Fe was introduced, making graphene fences ineffective at dividing the dual active sites. Moreover, the GO-Fe/K catalyst also displayed a single weak hydrogen chemisorption peak before Co was added (Supplementary Fig. ##SUPPL##0##30##). This evidence strongly proved that the different hydrogen adsorption capacities between the internal Fe<sub>5</sub>C<sub>2</sub> sites and the surface Fe<sub>5</sub>C<sub>2</sub>/Co sites after the Co addition were the main reasons for the formation of the various hydrogen desorption peaks of GO-Fe/K-Co in H<sub>2</sub>-TPD. Wherein Fe<sub>5</sub>C<sub>2</sub> corresponded to the weak hydrogen chemisorption peak (peak I), while Fe<sub>5</sub>C<sub>2</sub>/Co corresponded to the strong hydrogen chemisorption peak (peak II) (Fig. ##FIG##3##4b, c, and e##).</p>","<p id=\"Par26\">Meanwhile, GO-Co/K-Fe presented one peak in the H<sub>2</sub>-TPD profile as well, which could be explained by the fact that a large quantity of Fe was loaded on the surface of the metallic Co, thus inhibiting the hydrogen adsorption on the Co sites. Furthermore, Fe and Co were distributed evenly and compactly on the graphene fences in GO-Fe-Co/K and GO/K-Fe-Co due to their simultaneous addition, hence they also presented a single chemisorption peak (Fig. ##FIG##3##4e##). Among them, GO-Fe-Co/K exhibited the highest Co valence states in the XANES results (Supplementary Fig. ##SUPPL##0##12##), indicating that Co lost the most electrons in GO-Fe-Co/K, while GO-Fe/K-Co, whose Co was supported on the surface Fe, showed the lowest valence states (Supplementary Fig. ##SUPPL##0##12##), which can be interpreted as the strong metal-support interaction of GO-Fe-Co/K enhancing the electron transfers between Co and graphene (Supplementary Fig. ##SUPPL##0##22##). Past studies have revealed that electron-deficient metals are more likely to absorb hydrogen<sup>##UREF##45##58##</sup>. Therefore, GO-Fe-Co/K displayed the strongest hydrogen adsorption peak. On the contrary, GO/K-Fe-Co, which was loaded with Fe and Co by impregnation, showed a lower-strength hydrogen adsorption peak (Fig. ##FIG##3##4e##) due to the weaker metal-support interaction (Supplementary Fig. ##SUPPL##0##22##) and fewer electron transfers between metals and graphene (Supplementary Fig. ##SUPPL##0##12##). Besides, the H<sub>2</sub>-TPD test performed on graphene oxide (GO) was applied to exclude the effect of carbon material decomposition. No obvious peaks were observed in the profile, indicating that GO remained stable in the helium atmosphere below 800 °C (Supplementary Fig. ##SUPPL##0##31##).</p>","<p id=\"Par27\">These hydrogen adsorption characteristics (Fig. ##FIG##3##4e##) were consistent with the catalytic performances (Fig. ##FIG##2##3b##): weakly chemisorbed hydrogen was easier to be activated and therefore more inclined to hydrogenate CO<sub>2</sub> to extend the carbon chains than olefin secondary hydrogenation, thus generating more olefins. Consequently, the primary products of the GO/K-Fe-Co and the GO-Co/K-Fe were light olefins (Figs. ##FIG##2##3b## and ##FIG##3##4e##). Among them, GO/K-Fe-Co exhibited a higher C<sub>2</sub><sup>=</sup><bold>–</bold>C<sub>4</sub><sup>=</sup> selectivity (50.1%) (Fig. ##FIG##2##3b##) because of its lower adsorption peak position (Fig. ##FIG##3##4e##). On the contrary, strongly chemisorbed H<sub>2</sub>, which was not activated, tended to hydrogenate olefins to manufacture alkanes<sup>##UREF##45##58##–##REF##35530804##60##</sup>, so GO-Fe-Co/K produced more paraffins, especially methane and ethane (Fig. ##FIG##2##3b##). However, unlike other catalysts, the GO-Fe/K-Co, which presented double peaks in the H<sub>2</sub>-TPD profile (Fig. ##FIG##3##4e##), demonstrated that both the carbon chain growth ability and the olefin secondary hydrogenation ability were attained as mentioned above. As a result, unlike GO-Fe-Co/K, whose main products were methane and ethane, the products in GO-Fe/K-Co were extended and concentrated in propane and butane (43.6%) (Fig. ##FIG##2##3b##).</p>","<p id=\"Par28\">In order to clearly verify the differences in the difficulties of the olefin hydrogenation reaction over the spatial dual active sites, on the basis of the structures of Model 1 and Model 2 constructed above, DFT calculations for potential reaction pathways and intermediates of propylene hydrogenation to propane were conducted and summarized (Fig. ##FIG##3##4f##, Supplementary Figs. ##SUPPL##0##32## and ##SUPPL##0##33##)<sup>##UREF##47##61##</sup>. Fe<sub>5</sub>C<sub>2</sub> sites needed to overcome an energy barrier of 0.78 eV to convert the *C<sub>3</sub>H<sub>6</sub> intermediate into *C<sub>3</sub>H<sub>7</sub>. However, for the Fe<sub>5</sub>C<sub>2</sub>/Co site, the rate-determining step of the whole process was changed to the step of *C<sub>3</sub>H<sub>7</sub> to *C<sub>3</sub>H<sub>8</sub>. The lower rate-determining energy barrier (0.43 eV) of the Fe<sub>5</sub>C<sub>2</sub>/Co site indicated a higher propylene hydrogenation activity compared to the Fe<sub>5</sub>C<sub>2</sub> site (Fig. ##FIG##3##4f##)<sup>##REF##36689604##62##</sup>. In addition, C<sub>3</sub>H<sub>6</sub>-pulse transient hydrogenation experiments performed on the spent GO-Fe/K-Co, GO/K-Fe-Co, and GO/K-10Fe-20Co catalysts were applied to realistically examine the propylene secondary hydrogenation capacities on different active sites. As calculated by DFT, with the same amount of C<sub>3</sub>H<sub>6</sub> being pulsed into the reactor, GO-Fe/K-Co exhibited a higher C<sub>3</sub>H<sub>8</sub> signal compared to GO/K-Fe-Co, which demonstrated a stronger propylene secondary hydrogenation capacity. Furthermore, GO/K-10Fe-20Co showed the highest C<sub>3</sub>H<sub>8</sub> signal, and this result further revealed that the external Fe/Co sites were the main active sites for the propylene hydrogenation reaction to obtain propane (Fig. ##FIG##3##4g##). We didn’t find pulse peaks attributed to the methane signal in the three spent catalysts (Supplementary Fig. ##SUPPL##0##34##), illustrating that no hydrocracking reaction occurred.</p>","<p id=\"Par29\">Consequently, a reaction path for selective CO<sub>2</sub> hydrogenation over the GO-Fe/K-Co catalyst was proposed in Fig. ##FIG##3##4a## by analyzing the reaction results and the mechanism characterizations. Initially, CO<sub>2</sub> was converted into CO via the RWGS reaction at the internal Fe active sites, followed by the FTS process to produce light olefins. The diffusion effect could easily transport light olefins to the CoFe active sites on the external surface of the graphene fences, where the CoFe sites had a relatively high cobalt content. Due to their higher hydrogen adsorption capacities, the olefins were hydrogenated to the light alkanes, producing the high selectivity of LPG. Notably, the adsorption strengths of the propylene and 1-butene adsorbed on the metallic Co surface in Model 2 were stronger than those absorbed on Co<sub>2</sub>C in Model 3 (Fig. ##FIG##3##4d##), verifying a higher olefin hydrogenation efficiency of the metallic Co over Co<sub>2</sub>C in this unique CO<sub>2</sub> hydrogenation reaction system. Meanwhile, due to the inactivity of Co in the RWGS reaction, cobalt loaded on the outer surface would consume a large amount of CO produced by the internal Fe active sites, resulting in an ultra-low CO selectivity (2.2%). However, the high H<sub>2</sub> concentration resulting from the lack of RWGS reaction compelled CO<sub>2</sub> hydrogenation directly to the by-products methane and ethane at the Fe<sub>5</sub>C<sub>2</sub>/Co sites on the outer surface of the graphene fences, as demonstrated by the catalytic performance of GO/K-10Fe-20Co (Fig. ##FIG##2##3a##)<sup>##UREF##26##39##</sup>.</p>","<p id=\"Par30\">In order to clearly explore the synergistic effect of the dual active sites in the reaction process, we performed DFT calculations for the chain growth and the olefin hydrogenation reactions from ethylene to butane over the Fe<sub>5</sub>C<sub>2</sub> and Fe<sub>5</sub>C<sub>2</sub>-Co sites (Fig. ##FIG##4##5##, Supplementary Figs. ##SUPPL##0##35##–##SUPPL##0##37##). At the Fe<sub>5</sub>C<sub>2</sub> site, alkenes are more likely to undergo C–C coupling reactions to achieve carbon chain growth than secondary hydrogenation reactions due to their lower free energy barriers. Therefore, more long-chain alkenes would be obtained. For the Fe<sub>5</sub>C<sub>2</sub>-Co site, the hydrogenation of alkenes to alkanes is easier than the chain growth reaction, so there would be more ethane than propane and butane produced. However, once the propylene and butene products diffused from the Fe<sub>5</sub>C<sub>2</sub> sites to the Fe<sub>5</sub>C<sub>2</sub>-Co sites, due to the low energy barriers of the propylene and butene hydrogenation reactions (0.52 and 0.65 eV), propylene and butene would be easily hydrogenated to propane and butane, resulting in a high selectivity of LPG products.</p>","<p id=\"Par31\">These calculation results also revealed the difficulty of producing LPG from CO<sub>2</sub> hydrogenation via a Fischer-Tropsch pathway, that is, the contradiction between the carbon chain growth and the olefin secondary hydrogenation. For the active sites with weak hydrogen adsorption capacity, such as Fe<sub>5</sub>C<sub>2</sub>, it is difficult for alkenes to be hydrogenated to alkanes, leading to low alkane selectivity. Whereas for the sites with strong hydrogen adsorption capacity, such as Fe<sub>5</sub>C<sub>2</sub>-Co, on the other hand, it is also difficult to achieve carbon growth, and excessive methane and ethane products reduce the LPG selectivity (Figs. ##FIG##3##4c## and ##FIG##4##5##). In this situation, the proposed graphene-fence-separated dual active sites could simultaneously meet the demands of carbon chain growth and olefin hydrogenation, thus overcoming this difficulty.</p>","<p id=\"Par32\">As summarized above, as a catalyst for producing olefins, GO/K-Fe-Co had a surface with a significant number of iron carbide active sites combined with a small number of metallic cobalt sites. The intimate contact (Fig. ##FIG##0##1f##) and electron transfers between Fe and Co make their hydrogen adsorption capacity tend to be uniform. Meanwhile, due to the lack of separation effects, the higher surface Fe/Co value (Supplementary Table ##SUPPL##0##3##) also reduced their hydrogen adsorption capacity<sup>##UREF##48##63##–##UREF##50##65##</sup>. Obviously, GO/K-Fe-Co did not exhibit a strong hydrogen chemisorption peak (Fig. ##FIG##3##4e##) in the condition of such a high Fe/Co ratio on the surface (Supplementary Table ##SUPPL##0##3##). Furthermore, as observed in the TEM images (Fig. ##FIG##0##1##f), K was uniformly distributed on the nanoparticles of Fe and Co due to the well dispersion. Potassium has been proven to restrain H<sub>2</sub> chemisorption and increase olefin selectivity<sup>##UREF##51##66##–##UREF##53##68##</sup>. Thus, without the assistance of graphene fences, the formed light olefins were easily diffused into the gas flow and carried out due to the weak olefin secondary hydrogenation capacities (Fig. ##FIG##3##4e##), and exhibited a higher light olefin selectivity (50.1%) (Fig. ##FIG##2##3b##).</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>Adjustable spatial distribution of multiple sites</title>\",\n \"<p id=\\\"Par8\\\">A series of graphene-supported Fe-Co bimetallic catalysts with identical total contents of Fe, Co, and K were synthesized by varying the addition order of Fe and Co during the hydrothermal and impregnation processes (Supplementary Fig. ##SUPPL##0##3##). Upon examining the as-prepared catalysts, only the characteristic diffraction peaks ascribed to rGO and Fe<sub>2</sub>O<sub>3</sub> were observed in XRD (Fig. ##FIG##0##1a##). There were no peaks associated with Co detected in the four graphene-supported catalysts. To estimate the total contents of different metal elements, we carried out the inductively coupled plasma-optical emission spectrometer (ICP-OES) tests and found that the contents of Fe, Co, and K were close to the theoretical values of 20 wt%, 4 wt%, and 1 wt%, respectively (Supplementary Table ##SUPPL##0##1##). Although the total contents of each metal were roughly the same for different catalysts, the unique structure separated by graphene fences formed in the hydrothermal process led to different spatial distributions of Fe and Co sites in the inner and surface layers.</p>\",\n \"<p id=\\\"Par9\\\">Throughout the hydrothermal process, the decrease in graphene layer distances and the cross-linking of the graphene layers led to the dynamic transformation of GO from the 2D lamellar structure to the 3D stereoscopic structure<sup>##UREF##27##40##,##UREF##28##41##</sup>. To demonstrate the dynamic evolution, we employed in situ XRD to detect the diffraction peak shift of GO during the temperature-programmed reduction process. With the temperature increasing and H<sub>2</sub> introduction, diffraction peaks gradually shifted to the higher direction (Supplementary Fig. ##SUPPL##0##4##), representing the decrease in graphene layer spacings as determined by Bragg’s law<sup>##UREF##29##42##</sup>. Similarly, the rGO obtained by hydrothermal treatment also exhibited a higher peak and a smaller layer spacing compared with GO (Fig. ##FIG##0##1b##). Meanwhile, the specific surface area significantly decreased (Supplementary Table ##SUPPL##0##2##). These phenomena corresponded to the folding and bending of the graphene layers, as observed in SEM images (Fig. ##FIG##0##1c##).</p>\",\n \"<p id=\\\"Par10\\\">Accordingly, due to the cross-linking effect of the graphene layers during the hydrothermal process, the metals added together with GO were partially encapsulated in the folded inner layers and in situ replaced the oxygen-containing groups of the graphene layers. Consequently, the “graphene fences” were formed by the reduced graphene layers, which encased metal nanoparticles. After the hydrothermal treatment, the metals introduced by impregnation were more easily loaded onto the surface of the folding graphene layers instead of the internal layers, owing to the separation effects of the graphene fences. These unique structures were reflected in the molecular vibration spectra, surface and internal element contents, and morphological characterizations of the catalysts.</p>\",\n \"<p id=\\\"Par11\\\">We employed the FTIR spectra to identify the loss of oxygen-containing groups replaced by metal sites (Fig. ##FIG##0##1d##). The stretching vibrations of O − H (3400 cm<sup>–1</sup>), C = O (1722 cm<sup>–1</sup>), aromatic C = C (1620 cm<sup>–1</sup>), carboxyl O = C − O (1356 cm<sup>–1</sup>), epoxyl C − O (1220 cm<sup>–1</sup>), and alkoxyl C − O (1050 cm<sup>−1</sup>), which were recorded as references, were all observed in the FTIR spectrum of GO. After hydrothermal synthesis, the oxygen-containing group peaks of rGO were significantly diminished, and two broad peaks at 1550 cm<sup>–1</sup> and 1175 cm<sup>–1</sup> appeared, which were assigned to C = C and C–O, respectively<sup>##UREF##30##43##</sup>. Intriguingly, the vibration peaks of metal-supported catalysts displayed smaller areas compared to those of rGO, especially in GO-Fe/K-Co and GO-Fe-Co/K, which could be interpreted as more substitutions of oxygen-containing groups by metals in the reduction process, caused by the metals loading the graphene inner layers. However, the metals in GO/K-Fe-Co and GO-Co/K-Fe were unable to adequately replace the oxygen-containing groups inside the layers due to the protection of graphene fences, thus resulting in a larger peak area.</p>\",\n \"<p id=\\\"Par12\\\">Furthermore, the thickness of the graphene-supported Fe-Co bimetallic catalysts could also reveal the various structures. The thicknesses of the graphene were detected by the second-order peak positions of the Raman spectra, which appear near 2700 cm<sup>–1</sup>. In general, a higher peak position represents a bigger graphene thickness and more graphene layers<sup>##UREF##31##44##,##UREF##32##45##</sup>. GO and rGO displayed the maximum and minimum graphene thicknesses of graphene layers, respectively (Fig. ##FIG##0##1e##). With the incorporation of Fe and Co, the thicknesses of graphene increased compared with rGO, among which the GO-Fe-Co/K showed the largest thickness, revealing that the presence of both Fe and Co on the inner layers hindered the compression of the graphene layers during the hydrothermal process. As inferred, GO/K-Fe-Co showed the smallest thickness of all the graphene-supported catalysts. <italic>I</italic><sub><italic>D</italic></sub>/<italic>I</italic><sub><italic>G</italic></sub> values displayed the reverse order as the thicknesses, indicating that the disorders increased when the graphene thicknesses were compressed (Fig. ##FIG##0##1e##).</p>\",\n \"<p id=\\\"Par13\\\">The varying Fe-Co distributions were further demonstrated by the different metal contents between the surface and interior. Based on the XPS results, the Fe surface contents in GO-Co/K-Fe (12.3%) and GO/K-Fe-Co (11.7%) catalysts whose Fe was introduced by impregnation were distinctly higher than those of the catalysts whose Fe was loaded by hydrothermal incorporation, such as GO-Fe-Co/K (3.4%) and GO-Fe/K-Co (4.1%) (Supplementary Table ##SUPPL##0##3##). Meanwhile, the GO/K-Fe with a higher surface Fe content (Supplementary Table ##SUPPL##0##3##) exhibited a stronger magnetism intensity in the M-H loop compared to the GO-Fe/K (Supplementary Fig. ##SUPPL##0##5##), which ulteriorly supported the conclusion that Fe added by the hydrothermal process was partially encased in the graphene layers, resulting in a lower surface content and a weaker magnetism intensity. Furthermore, the Fe/Co values depicted by SEM mapping, which also reflected the surface element contents, exhibited the same trend as those measured by XPS (Supplementary Fig. ##SUPPL##0##6## and Supplementary Table ##SUPPL##0##4##). In addition to the surface content characterizations, we applied FIB-SEM to directly investigate the metal distributions in the graphene inner layers. In the cross-sectional SEM images of GO-Fe/K-Co, the metal nanoparticles were found to be loaded between the graphene layers (Supplementary Fig. ##SUPPL##0##7a##), which corresponded to the oxide and iron mapping distributions (Supplementary Fig. ##SUPPL##0##7b##, ##SUPPL##0##c##), while only a little cobalt distribution was observed on the internal metal particles (Supplementary Fig. ##SUPPL##0##7d##). Meanwhile, the Fe/Co value of the cross-section (8.42) obtained by the SEM mapping (Supplementary Table ##SUPPL##0##5##) was significantly higher than that of the catalyst surface (0.82) (Supplementary Table ##SUPPL##0##4##). The various metal distributions on the surface and interior effectively corroborated the reconstruction of the graphene-supported catalysts, forming a unique structure with different spatial distributions of multiple active sites.</p>\",\n \"<p id=\\\"Par14\\\">Additionally, as shown in Fig. ##FIG##0##1f##, the yellow circles displayed the same Fe and Co distributions in GO-Fe/K-Co, while in the red circles only the Fe distribution was clearly observed. This could be explained by the difficulty of impregnating Co in the folded interior graphene layers as compared to Fe loaded by hydrothermal synthesis, thus forming the active sites with different metal compositions. As a result, the yellow and red circles represented the surface and internal metal sites of the graphene layers, respectively. By contrast, Fe and Co in GO/K-Fe-Co exhibited a uniform and well-dispersed distribution over the outer surface of graphene layers (Fig. ##FIG##0##1f##). These findings convincingly verified that the spatial distributions of Fe-Co active sites could be regulated by the graphene fences, forming scattered sites (Fe+CoFe) or uniform assemblage sites (FeCo) (Fig. ##FIG##0##1f##).</p>\",\n \"<p id=\\\"Par15\\\">In response to the descriptions mentioned above, a detailed schematic diagram of dynamic evolution regarding the synthesis of GO/K-Fe-Co and GO-Fe/K-Co was drawn in Fig. ##FIG##0##1g##. In terms of the GO/K-Fe-Co catalyst, GO was first reduced by the hydrothermal process, leading the graphene layers to be twisted and folded, thus forming graphene fences. Subsequently, Fe and Co were incorporated through impregnation and primarily dispersed on the surface of the graphene fences with a benign distribution, on account of the resistance of the folded interior layers. In the case of the GO-Fe/K-Co catalyst, Fe was introduced during the hydrothermal process, which would in situ replace the oxygen-containing groups and be partially encapsulated in the interior graphene layers. Then the impregnated Co was loaded on the exterior graphene layers because of the inaccessible accesses controlled by the graphene fences, forming scattered sites with a small amount of Fe on the outer surface of the graphene layers. The two different spatial distribution structures described above could be directly observed by the SEM images. Figure ##FIG##0##1h## clearly showed Fe-Co homogeneity active sites arranged uniformly on the surface of graphene layers (GO/K-Fe-Co) and the metal sites separated by graphene fences (GO-Fe/K-Co), in which the yellow circle represented the sites on the surface layer and the red circles represented the sites on the internal layer. Therefore, with the assistance of developed graphene fences, graphene-supported Fe-Co bimetallic catalysts with adjustable metal spatial distributions in the internal and external layers were successfully synthesized.</p>\",\n \"<title>Phase composition characterizations of Fe-Co catalysts</title>\",\n \"<p id=\\\"Par16\\\">Several characterizations were first applied to determine the surface metal phase compositions of the as-prepared catalysts. The HR-TEM images (Supplementary Fig. ##SUPPL##0##8##) revealed the lattice spacings of 0.25 nm that corresponded to the (119) plane of the Fe<sub>2</sub>O<sub>3</sub> species in the four graphene-supported catalysts, which was consistent with the XRD results shown in Fig. ##FIG##0##1a##<sup>##UREF##33##46##</sup>. The XPS Fe 2<italic>p</italic> spectra (Supplementary Fig. ##SUPPL##0##9##) showed binding energy peaks at 710.8 eV and 712.8 eV, which were ascribed to the Fe(II) and Fe(III) phases, respectively<sup>##UREF##34##47##</sup>. Moreover, according to the <sup>57</sup>Fe Mössbauer spectra (Supplementary Fig. ##SUPPL##0##10## and Supplementary Table ##SUPPL##0##6##), Fe was predominantly present in the form of Fe<sub>3</sub>O<sub>4</sub>, which was the combination of Fe<sup>2+</sup> and Fe<sup>3+</sup> species. Due to the low contents or benign distributions, there were no diffraction peaks attributed to Co in XRD patterns, whereas two peaks assigned to Co<sup>2+</sup>/Co<sup>3+</sup> and Co<sup>0</sup> appeared in the Co 2<italic>p</italic> XPS spectra (Supplementary Fig. ##SUPPL##0##11##)<sup>##UREF##35##48##</sup>. To further ascertain the Co proportion on the catalytic surface, XANES spectra with fitting curves were recorded. As pictured in Supplementary Fig. ##SUPPL##0##12##, CoO was identified as the predominant phase of Co species regardless of their spatial distributions. Besides, the Co K-edge EXAFS curve fitting results and parameters were also displayed in Supplementary Fig. ##SUPPL##0##13## and Supplementary Table ##SUPPL##0##7##, disclosing the existence of the Co–Co and Co–O bonds.</p>\",\n \"<p id=\\\"Par17\\\">Regarding the spent catalysts, the Fe 2<italic>p</italic> spectra showed binding energy peaks at 708.5 eV, which were assigned to the Fe-C bonds (Fig. ##FIG##1##2b##), indicating the presence of iron carbides<sup>##UREF##36##49##,##UREF##37##50##</sup>. Based on the results of XRD patterns, diffraction peaks attributed to Fe<sub>5</sub>C<sub>2</sub> were observed in all spent catalysts (Fig. ##FIG##1##2a##). Among them, the diffraction peaks with the strongest intensities corresponded to Fe<sub>5</sub>C<sub>2</sub> (510) facets, which were also revealed by HR-TEM images with lattice spacings of 0.20 nm (Supplementary Fig. ##SUPPL##0##14##)<sup>##UREF##38##51##</sup>. Furthermore, the coexistence of Fe<sub>5</sub>C<sub>2</sub> (A) and Fe<sub>5</sub>C<sub>2</sub> (B) species was determined from two overlapping sextuplets in the <sup>57</sup>Fe Mössbauer spectra (Fig. ##FIG##1##2c## and Supplementary Table ##SUPPL##0##8##), which represented the different occupied sites of Fe in the crystallographic structure of Fe<sub>5</sub>C<sub>2</sub><sup>##UREF##39##52##</sup>. The aforementioned findings demonstrated that iron carbide, an active phase for the chain growth reaction, was mainly presented as the Fe<sub>5</sub>C<sub>2</sub> (510) phase in the spent catalysts<sup>##REF##26941306##21##</sup>. Co K-edge XANES tests were also applied to identify the Co phases in the spent catalysts. In contrast to the as-prepared catalysts, the majority of cobalt existed in metallic states (Fig. ##FIG##1##2d##), demonstrating the good reduction abilities of Co in the graphene-supported catalysts. Moreover, the EXAFS fitting results illustrated the presence of only Co–Co bonds, providing further evidence that cobalt existed in the metallic phases (Supplementary Fig. ##SUPPL##0##15## and Supplementary Table ##SUPPL##0##9##).</p>\",\n \"<title>Catalytic performances and stabilities</title>\",\n \"<p id=\\\"Par18\\\">The catalysts were tested under the conditions of 320 °C, 3.0 MPa, and W/F = 4.5 g h mol<sup>–1</sup>. The main products of both the GO-Fe/K and GO/K-Fe catalysts, without the addition of Co, were methane and light olefins instead of saturated light paraffins (Fig. ##FIG##2##3a##). However, as shown in Fig. ##FIG##2##3b##, with the Co incorporation, the Fe-Co bimetallic catalysts displayed various types of hydrocarbon product selectivities. GO-Co/K-Fe and GO/K-Fe-Co mainly produced light olefins, especially for the GO/K-Fe-Co catalyst, where 50.1% C<sub>2</sub><sup>=</sup>–C<sub>4</sub><sup>=</sup> selectivity was obtained at a CO<sub>2</sub> conversion of 55.4%. Whereas for GO-Fe-Co/K, more alkanes, particularly methane and ethane, were produced (Fig. ##FIG##2##3b##). However, GO-Fe/K-Co exhibited a higher selectivity of propane and butane (43.6%) compared to the GO-Fe-Co/K (Fig. ##FIG##2##3b##). In comparison with GO-Fe/K, the CoFe sites of GO-Fe/K-Co were formed by impregnating Co onto the surface Fe sites of GO-Fe/K with the assistance of graphene fences. GO/K-10Fe-20Co was also employed to simulate the external CoFe sites of GO-Fe/K-Co which had similar surface Fe/Co ratios (0.6 and 0.7) (Supplementary Table ##SUPPL##0##3##). The high methane selectivity confirmed that the external CoFe sites with a high Co content had a strong hydrogenation capacity (Fig. ##FIG##2##3a##), which facilitated the original light olefins produced on the internal Fe sites hydrogenating into saturated alkanes, thus transforming the products from light olefins to LPG (Fig. ##FIG##2##3g##). In this process, the reaction equilibrium was shifted in the positive direction, leading to an increase in the CO<sub>2</sub> conversion from 33.2% (GO-Fe/K) to 46.0% (GO-Fe/K-Co). This can also be observed in the detailed product distributions of GO-Fe/K and GO-Fe/K-Co (Supplementary Fig. ##SUPPL##0##16##). Before the Co addition, the product selectivity of C2 and C3 in GO-Fe/K was roughly the same. However, after the introduction of Co, C3 products occupied the highest selectivity, which further indicated that the diffusion and hydrogenation effects on the internal-active-site products shifted the chemical equilibrium in a positive direction.</p>\",\n \"<p id=\\\"Par19\\\">In contrast to the GO-Fe/K-Co, the products of the rGO-Fe/K-Co were mainly light olefins rather than alkanes (Fig. ##FIG##2##3a##). This was because the absence of the spatial dual active sites separated by the graphene fences (Supplementary Fig. ##SUPPL##0##17##), which is proven by Supplementary Fig. ##SUPPL##0##18## (same distributions of Fe and Co in TEM elemental mapping images), resulted in a higher Fe/Co ratio (Supplementary Table ##SUPPL##0##3##), thus weakening the hydrogenation ability. Such a result further proved that the unique spatial distributions of Fe-Co dual active sites tuned by graphene fences could efficiently control product types.</p>\",\n \"<p id=\\\"Par20\\\">By comparing the TEM images (Fig. ##FIG##0##1f## and Supplementary Figs. ##SUPPL##0##19##–##SUPPL##0##21##), we observed that the fresh GO-Fe-Co/K and GO/K-Fe-Co catalysts presented smaller particle sizes compared to the other catalysts, indicating that the simultaneous addition of Fe and Co constrained the aggregation of Fe nanoparticles and enhanced the dispersions of Fe species. CO<sub>2</sub> hydrogenation is a structurally sensitive reaction, and higher dispersions are beneficial for improving the carbonization ability of Fe and thus enhancing the catalytic performance (Fig. ##FIG##2##3g##)<sup>##UREF##40##53##</sup>. As seen, according to the <sup>57</sup>Fe Mössbauer spectra of the spent catalysts (Fig. ##FIG##1##2c## and Supplementary Table ##SUPPL##0##8##), GO/K-Fe-Co exhibited the largest proportion of Fe<sub>5</sub>C<sub>2</sub> (72%), including both Fe<sub>5</sub>C<sub>2</sub> (A) and Fe<sub>5</sub>C<sub>2</sub> (B). Accordingly, GO/K-Fe-Co also manifested the highest CO<sub>2</sub> conversion (55.4%) and rather high light olefin selectivity (50.1%) (Fig. ##FIG##2##3b##). The GO-Fe-Co/K catalyst, however, displayed the highest temperature of peaks in the H<sub>2</sub>-TPR (peak γ) and CO<sub>2</sub>-TPD profiles (Supplementary Figs. ##SUPPL##0##22## and ##SUPPL##0##23##). This phenomenon could be explained by the fact that smaller particles would enter or penetrate the interlayers more easily during the hydrothermal process, which was proved by the smaller surface metal loadings, as shown in Supplementary Table ##SUPPL##0##3##. As a result, the metal nanoparticles tightly covered by graphene layers showed strong metal-support interaction (SMSI), which made the reduction and carbonization more challenging<sup>##UREF##40##53##</sup>. This view was intuitively illustrated by in situ XRD. When the temperature rose to 400 °C, a peak of metallic iron appeared on the GO-Fe-Co/K catalyst. Obviously, this temperature was the highest of all the catalysts. Correspondingly, in the carbonization process, GO-Fe-Co/K also showed the lowest peak strength of Fe<sub>5</sub>C<sub>2</sub> (Supplementary Fig. ##SUPPL##0##24##). Meanwhile, previous research has demonstrated that strong chemical adsorption of CO<sub>2</sub> is not easily triggered for hydrogenation, and the coating of the surface Fe species formed by the strong chemical adsorption of CO<sub>2</sub> may potentially lower the catalytic performance<sup>##UREF##25##38##</sup>. Consequently, these reasons led to a slightly lower Fe<sub>5</sub>C<sub>2</sub> content (44.6%) and CO<sub>2</sub> conversion of GO-Fe-Co/K (42.1%) than those of GO/K-Fe-Co (72% and 55.4%) (Fig. ##FIG##2##3b## and Supplementary Table ##SUPPL##0##8##).</p>\",\n \"<p id=\\\"Par21\\\">In contrast to GO-Fe-Co/K, the GO-Fe/K-Co catalyst displayed appropriate metal-support interaction (MSI) and CO<sub>2</sub> adsorption strengths (Supplementary Fig. ##SUPPL##0##22## and Supplementary Fig. ##SUPPL##0##23##), leading to a CO<sub>2</sub> conversion of up to 46% (Fig. ##FIG##2##3b##). Given that cobalt was inactive in the RWGS reaction<sup>##UREF##26##39##,##UREF##41##54##</sup>, the Co surrounding the Fe<sub>5</sub>C<sub>2</sub> on the surface layers would further consume CO without producing it<sup>##UREF##42##55##</sup>. Accordingly, GO/K-10Fe-20Co, which was used to simulate the external Fe/Co sites, also exhibited a low CO selectivity (2.7%). It was significantly lowered compared to that of the GO-Fe/K (15.6%), which consisted of Fe active sites (Fig. ##FIG##2##3a##). This finding further suggested that the CoFe active sites with a low Fe/Co ratio (0.82) (Supplementary Table ##SUPPL##0##4##) located on the external surface of GO-Fe/K-Co could consume the CO produced by the internal Fe sites, thus keeping the CO selectivity at a low level. As a result, GO-Fe/K-Co showed an ultra-low CO selectivity (2.2%) among all the catalysts (Fig. ##FIG##2##3b##). To the best of our knowledge, this is the lowest value level reported for the current methanol intermediate route as well as a modified FTS pathway (refer to Fe-containing catalysts). Furthermore, thick carbon deposition around metal particles would reduce the catalytic activity<sup>##UREF##43##56##,##UREF##44##57##</sup> and this phenomenon can be observed in TEM images of the spent catalysts (Supplementary Fig. ##SUPPL##0##14##). Clearly, as determined by TEM mapping, the ratio of Fe/Co in the spent GO-Co/K-Fe was much lower than that of other catalysts and the fresh GO-Co/K-Fe (Supplementary Fig. ##SUPPL##0##25##, Supplementary Table ##SUPPL##0##10## and Supplementary Table ##SUPPL##0##11##), which can be explained by the large amount of amorphous carbon deposited on the surface of Fe sites affecting the determination of element contents. As a result, it exhibited a relatively low CO<sub>2</sub> conversion (32.8%) (Fig. ##FIG##2##3b##).</p>\",\n \"<p id=\\\"Par22\\\">To further investigate the influence of the Fe amount, the total Fe content in the GO-nFe/K-Co catalysts was altered from 20 wt% to 30 wt%. Intuitively, as Fe content rose, the selectivity of LPG (C<sub>3</sub><sup>P</sup><bold>–</bold>C<sub>4</sub><sup>P</sup>) declined, while the selectivities of light olefins (C<sub>2</sub><sup>=</sup><bold>–</bold>C<sub>4</sub><sup>=</sup>) and C<sub>5</sub><sup>+</sup> products increased (Fig. ##FIG##2##3c##), revealing that the extra Fe introduced to the interior and external graphene fences resulted in an enhancement of the carbon chain growth capacity and an inhibition of the olefin secondary hydrogenation, respectively. In addition, to compare the performances of GO-Fe/K-Co with other carbon materials (CNTs, AC, and CB) supported catalysts, the same preparation procedures and addition amounts of GO-Fe/K-Co were performed. The LPG selectivities of AC-Fe/K-Co (15.8%) and CB-Fe/K-Co (3.9%) were significantly lower than those of GO-Fe/K-Co (43.6%) due to the excessive formation of by-product methane and ethane. Besides, the main products of CNTs-Fe/K-Co were light olefins (35.5%) rather than C<sub>3</sub>-C<sub>4</sub> saturated alkanes (7.3%) (Fig. ##FIG##2##3d##). Meanwhile, the metal distributions of these catalysts were determined by TEM elemental mapping images (Supplementary Fig. ##SUPPL##0##26##). Co distributions were found in all the Fe distribution areas in these three catalysts, indicating that these three carbon materials lacked the function of separating metals as graphene. These findings further demonstrated the superior performance of graphene-fence-separated dual active sites in regulating carbon chain growth and olefin secondary hydrogenation.</p>\",\n \"<p id=\\\"Par23\\\">We further investigated the stabilities of GO-Fe/K-Co and GO/K-Fe-Co under 3.0 MPa with a W/F of 4.5 g h mol<sup>–1</sup> at 320 °C. For GO/K-Fe-Co, the CO<sub>2</sub> conversion decreased and the selectivity of CO as a byproduct increased continuously within 40 h on stream. By contrast, GO-Fe/K-Co remained stable during the 100-hour stability test (Fig. ##FIG##2##3e, f##). Interestingly, as depicted in TEM images (Supplementary Fig. ##SUPPL##0##19a##), the metal nanoparticles of the spent GO/K-Fe-Co catalyst aggregated dramatically in comparison to the fresh catalyst with the prolongation of the reaction process. Contrarily, the metal particles of the spent GO-Fe/K-Co did not agglomerate, and the particle sizes maintained a stable range throughout the reaction (Supplementary Fig. ##SUPPL##0##19a##) due to the protection of the graphene fences. Previous studies have demonstrated that the graphene loaded with iron nanoparticles by the hydrothermal method has a role in anchoring iron particles<sup>##UREF##28##41##</sup>. Combined with our results described above, the graphene fences that separated the dual active sites in the GO-Fe/K-Co catalyst could also spatially confine the aggregation of metal particles during the reaction, thus preventing the deactivation and maintaining a high activity (Supplementary Fig. ##SUPPL##0##19b##). Besides, TEM mapping images of the spent GO-Fe/K-Co catalysts were also tested to explore the distributions of Fe and Co after the reaction, and enlarged images are shown in Supplementary Fig. ##SUPPL##0##27##. The red circles represent the area with different distributions of Fe and Co (obvious Fe distributions but few Co distributions). As observed, after the reaction process, graphene fences still maintain the effect of separating Fe and Co active sites.</p>\",\n \"<title>Mechanistic studies</title>\",\n \"<p id=\\\"Par24\\\">According to the local micro-environments of supported metal catalysts reported in the previous work<sup>##REF##29956535##31##</sup>, in this study, we employed density functional theory (DFT) calculations to explore the influence of the Fe-Co dual sites separated by graphene fences. To precisely identify the micro-environment of each active site, we first constructed a Fe<sub>5</sub>C<sub>2</sub> (510) surface model, which was denoted as Model 1. Afterwards, Model 2 was built by adding a Co<sub>10</sub> cluster to Model 1 in consideration of surface cobalt incorporation of GO-Fe/K-Co (Fig. ##FIG##3##4b##). Thereinto, Model 1 corresponded to the Fe<sub>5</sub>C<sub>2</sub> active sites inside the graphene fences of GO-Fe/K-Co, whereas Model 2 represented the Fe<sub>5</sub>C<sub>2</sub>/Co active sites on the surface of graphene fences (Fig. ##FIG##3##4b##). In order to compare the adsorption capacities of Co carbide and metallic Co, Co<sub>10</sub> in Model 2 was substituted by Co<sub>8</sub>C<sub>4</sub>, and it was dubbed “Model 3” (Fig. ##FIG##3##4b##). According to the adsorption energy results, the adsorption of both the H<sub>2</sub> and light olefins on the surface cobalt sites in Model 2 and Model 3 was more stable than that on the interfacial sites, illustrating that H<sub>2</sub> and light olefins were primarily adsorbed on the surface cobalt sites of Fe<sub>5</sub>C<sub>2</sub>/Co due to the electron transfer between cobalt and iron carbide (Fig. ##FIG##3##4c, d##, and Supplementary Fig. ##SUPPL##0##28##). The electron transfer between iron carbide and cobalt on Model 2 could be intuitively observed by the charge density difference analysis, which was shown in Supplementary Fig. ##SUPPL##0##29##. The red and green colors represented electron accumulation and loss, respectively. Obviously, after being loaded onto Fe<sub>5</sub>C<sub>2</sub>, electrons were transferred from cobalt to Fe<sub>5</sub>C<sub>2</sub>. Since electron-deficient metals are more favorable to adsorb hydrogen<sup>##UREF##45##58##</sup>, it was the electron transfer that enhanced hydrogen adsorption on the cobalt site, while hydrogen was not likely to be adsorbed on the interface of Fe<sub>5</sub>C<sub>2</sub>-Co due to the electron accumulation.</p>\",\n \"<p id=\\\"Par25\\\">Regarding the hydrogen adsorption, the hydrogen adsorption over Model 2, which adsorbed at the surface metallic Co sites, had a lower adsorption energy (–0.84 eV) than that over iron carbide in Model 1 (–0.81 eV), revealing that the Fe<sub>5</sub>C<sub>2</sub>/Co sites on the external graphene fences had a stronger H<sub>2</sub> adsorption effect than that of the Fe<sub>5</sub>C<sub>2</sub> sites inside the graphene fences (Fig. ##FIG##3##4c##). Accordingly, the H<sub>2</sub> temperature-programmed desorption profile (H<sub>2</sub>-TPD) of GO-Fe/K-Co exhibited two distinct peaks (I and II) (Fig. ##FIG##3##4e##), which corresponded to the weak chemical adsorption and strong chemical adsorption of hydrogen, respectively. rGO-Fe/K-Co displayed a desorption peak near 600 °C resembling that of GO-Fe/K-Co. However, unlike GO-Fe/K-Co, the strong chemical adsorption peak around 650 °C was not obvious (Supplementary Fig. ##SUPPL##0##30##), and this was because partial reduction of rGO-Fe/K-Co had already been accomplished when Fe was introduced, making graphene fences ineffective at dividing the dual active sites. Moreover, the GO-Fe/K catalyst also displayed a single weak hydrogen chemisorption peak before Co was added (Supplementary Fig. ##SUPPL##0##30##). This evidence strongly proved that the different hydrogen adsorption capacities between the internal Fe<sub>5</sub>C<sub>2</sub> sites and the surface Fe<sub>5</sub>C<sub>2</sub>/Co sites after the Co addition were the main reasons for the formation of the various hydrogen desorption peaks of GO-Fe/K-Co in H<sub>2</sub>-TPD. Wherein Fe<sub>5</sub>C<sub>2</sub> corresponded to the weak hydrogen chemisorption peak (peak I), while Fe<sub>5</sub>C<sub>2</sub>/Co corresponded to the strong hydrogen chemisorption peak (peak II) (Fig. ##FIG##3##4b, c, and e##).</p>\",\n \"<p id=\\\"Par26\\\">Meanwhile, GO-Co/K-Fe presented one peak in the H<sub>2</sub>-TPD profile as well, which could be explained by the fact that a large quantity of Fe was loaded on the surface of the metallic Co, thus inhibiting the hydrogen adsorption on the Co sites. Furthermore, Fe and Co were distributed evenly and compactly on the graphene fences in GO-Fe-Co/K and GO/K-Fe-Co due to their simultaneous addition, hence they also presented a single chemisorption peak (Fig. ##FIG##3##4e##). Among them, GO-Fe-Co/K exhibited the highest Co valence states in the XANES results (Supplementary Fig. ##SUPPL##0##12##), indicating that Co lost the most electrons in GO-Fe-Co/K, while GO-Fe/K-Co, whose Co was supported on the surface Fe, showed the lowest valence states (Supplementary Fig. ##SUPPL##0##12##), which can be interpreted as the strong metal-support interaction of GO-Fe-Co/K enhancing the electron transfers between Co and graphene (Supplementary Fig. ##SUPPL##0##22##). Past studies have revealed that electron-deficient metals are more likely to absorb hydrogen<sup>##UREF##45##58##</sup>. Therefore, GO-Fe-Co/K displayed the strongest hydrogen adsorption peak. On the contrary, GO/K-Fe-Co, which was loaded with Fe and Co by impregnation, showed a lower-strength hydrogen adsorption peak (Fig. ##FIG##3##4e##) due to the weaker metal-support interaction (Supplementary Fig. ##SUPPL##0##22##) and fewer electron transfers between metals and graphene (Supplementary Fig. ##SUPPL##0##12##). Besides, the H<sub>2</sub>-TPD test performed on graphene oxide (GO) was applied to exclude the effect of carbon material decomposition. No obvious peaks were observed in the profile, indicating that GO remained stable in the helium atmosphere below 800 °C (Supplementary Fig. ##SUPPL##0##31##).</p>\",\n \"<p id=\\\"Par27\\\">These hydrogen adsorption characteristics (Fig. ##FIG##3##4e##) were consistent with the catalytic performances (Fig. ##FIG##2##3b##): weakly chemisorbed hydrogen was easier to be activated and therefore more inclined to hydrogenate CO<sub>2</sub> to extend the carbon chains than olefin secondary hydrogenation, thus generating more olefins. Consequently, the primary products of the GO/K-Fe-Co and the GO-Co/K-Fe were light olefins (Figs. ##FIG##2##3b## and ##FIG##3##4e##). Among them, GO/K-Fe-Co exhibited a higher C<sub>2</sub><sup>=</sup><bold>–</bold>C<sub>4</sub><sup>=</sup> selectivity (50.1%) (Fig. ##FIG##2##3b##) because of its lower adsorption peak position (Fig. ##FIG##3##4e##). On the contrary, strongly chemisorbed H<sub>2</sub>, which was not activated, tended to hydrogenate olefins to manufacture alkanes<sup>##UREF##45##58##–##REF##35530804##60##</sup>, so GO-Fe-Co/K produced more paraffins, especially methane and ethane (Fig. ##FIG##2##3b##). However, unlike other catalysts, the GO-Fe/K-Co, which presented double peaks in the H<sub>2</sub>-TPD profile (Fig. ##FIG##3##4e##), demonstrated that both the carbon chain growth ability and the olefin secondary hydrogenation ability were attained as mentioned above. As a result, unlike GO-Fe-Co/K, whose main products were methane and ethane, the products in GO-Fe/K-Co were extended and concentrated in propane and butane (43.6%) (Fig. ##FIG##2##3b##).</p>\",\n \"<p id=\\\"Par28\\\">In order to clearly verify the differences in the difficulties of the olefin hydrogenation reaction over the spatial dual active sites, on the basis of the structures of Model 1 and Model 2 constructed above, DFT calculations for potential reaction pathways and intermediates of propylene hydrogenation to propane were conducted and summarized (Fig. ##FIG##3##4f##, Supplementary Figs. ##SUPPL##0##32## and ##SUPPL##0##33##)<sup>##UREF##47##61##</sup>. Fe<sub>5</sub>C<sub>2</sub> sites needed to overcome an energy barrier of 0.78 eV to convert the *C<sub>3</sub>H<sub>6</sub> intermediate into *C<sub>3</sub>H<sub>7</sub>. However, for the Fe<sub>5</sub>C<sub>2</sub>/Co site, the rate-determining step of the whole process was changed to the step of *C<sub>3</sub>H<sub>7</sub> to *C<sub>3</sub>H<sub>8</sub>. The lower rate-determining energy barrier (0.43 eV) of the Fe<sub>5</sub>C<sub>2</sub>/Co site indicated a higher propylene hydrogenation activity compared to the Fe<sub>5</sub>C<sub>2</sub> site (Fig. ##FIG##3##4f##)<sup>##REF##36689604##62##</sup>. In addition, C<sub>3</sub>H<sub>6</sub>-pulse transient hydrogenation experiments performed on the spent GO-Fe/K-Co, GO/K-Fe-Co, and GO/K-10Fe-20Co catalysts were applied to realistically examine the propylene secondary hydrogenation capacities on different active sites. As calculated by DFT, with the same amount of C<sub>3</sub>H<sub>6</sub> being pulsed into the reactor, GO-Fe/K-Co exhibited a higher C<sub>3</sub>H<sub>8</sub> signal compared to GO/K-Fe-Co, which demonstrated a stronger propylene secondary hydrogenation capacity. Furthermore, GO/K-10Fe-20Co showed the highest C<sub>3</sub>H<sub>8</sub> signal, and this result further revealed that the external Fe/Co sites were the main active sites for the propylene hydrogenation reaction to obtain propane (Fig. ##FIG##3##4g##). We didn’t find pulse peaks attributed to the methane signal in the three spent catalysts (Supplementary Fig. ##SUPPL##0##34##), illustrating that no hydrocracking reaction occurred.</p>\",\n \"<p id=\\\"Par29\\\">Consequently, a reaction path for selective CO<sub>2</sub> hydrogenation over the GO-Fe/K-Co catalyst was proposed in Fig. ##FIG##3##4a## by analyzing the reaction results and the mechanism characterizations. Initially, CO<sub>2</sub> was converted into CO via the RWGS reaction at the internal Fe active sites, followed by the FTS process to produce light olefins. The diffusion effect could easily transport light olefins to the CoFe active sites on the external surface of the graphene fences, where the CoFe sites had a relatively high cobalt content. Due to their higher hydrogen adsorption capacities, the olefins were hydrogenated to the light alkanes, producing the high selectivity of LPG. Notably, the adsorption strengths of the propylene and 1-butene adsorbed on the metallic Co surface in Model 2 were stronger than those absorbed on Co<sub>2</sub>C in Model 3 (Fig. ##FIG##3##4d##), verifying a higher olefin hydrogenation efficiency of the metallic Co over Co<sub>2</sub>C in this unique CO<sub>2</sub> hydrogenation reaction system. Meanwhile, due to the inactivity of Co in the RWGS reaction, cobalt loaded on the outer surface would consume a large amount of CO produced by the internal Fe active sites, resulting in an ultra-low CO selectivity (2.2%). However, the high H<sub>2</sub> concentration resulting from the lack of RWGS reaction compelled CO<sub>2</sub> hydrogenation directly to the by-products methane and ethane at the Fe<sub>5</sub>C<sub>2</sub>/Co sites on the outer surface of the graphene fences, as demonstrated by the catalytic performance of GO/K-10Fe-20Co (Fig. ##FIG##2##3a##)<sup>##UREF##26##39##</sup>.</p>\",\n \"<p id=\\\"Par30\\\">In order to clearly explore the synergistic effect of the dual active sites in the reaction process, we performed DFT calculations for the chain growth and the olefin hydrogenation reactions from ethylene to butane over the Fe<sub>5</sub>C<sub>2</sub> and Fe<sub>5</sub>C<sub>2</sub>-Co sites (Fig. ##FIG##4##5##, Supplementary Figs. ##SUPPL##0##35##–##SUPPL##0##37##). At the Fe<sub>5</sub>C<sub>2</sub> site, alkenes are more likely to undergo C–C coupling reactions to achieve carbon chain growth than secondary hydrogenation reactions due to their lower free energy barriers. Therefore, more long-chain alkenes would be obtained. For the Fe<sub>5</sub>C<sub>2</sub>-Co site, the hydrogenation of alkenes to alkanes is easier than the chain growth reaction, so there would be more ethane than propane and butane produced. However, once the propylene and butene products diffused from the Fe<sub>5</sub>C<sub>2</sub> sites to the Fe<sub>5</sub>C<sub>2</sub>-Co sites, due to the low energy barriers of the propylene and butene hydrogenation reactions (0.52 and 0.65 eV), propylene and butene would be easily hydrogenated to propane and butane, resulting in a high selectivity of LPG products.</p>\",\n \"<p id=\\\"Par31\\\">These calculation results also revealed the difficulty of producing LPG from CO<sub>2</sub> hydrogenation via a Fischer-Tropsch pathway, that is, the contradiction between the carbon chain growth and the olefin secondary hydrogenation. For the active sites with weak hydrogen adsorption capacity, such as Fe<sub>5</sub>C<sub>2</sub>, it is difficult for alkenes to be hydrogenated to alkanes, leading to low alkane selectivity. Whereas for the sites with strong hydrogen adsorption capacity, such as Fe<sub>5</sub>C<sub>2</sub>-Co, on the other hand, it is also difficult to achieve carbon growth, and excessive methane and ethane products reduce the LPG selectivity (Figs. ##FIG##3##4c## and ##FIG##4##5##). In this situation, the proposed graphene-fence-separated dual active sites could simultaneously meet the demands of carbon chain growth and olefin hydrogenation, thus overcoming this difficulty.</p>\",\n \"<p id=\\\"Par32\\\">As summarized above, as a catalyst for producing olefins, GO/K-Fe-Co had a surface with a significant number of iron carbide active sites combined with a small number of metallic cobalt sites. The intimate contact (Fig. ##FIG##0##1f##) and electron transfers between Fe and Co make their hydrogen adsorption capacity tend to be uniform. Meanwhile, due to the lack of separation effects, the higher surface Fe/Co value (Supplementary Table ##SUPPL##0##3##) also reduced their hydrogen adsorption capacity<sup>##UREF##48##63##–##UREF##50##65##</sup>. Obviously, GO/K-Fe-Co did not exhibit a strong hydrogen chemisorption peak (Fig. ##FIG##3##4e##) in the condition of such a high Fe/Co ratio on the surface (Supplementary Table ##SUPPL##0##3##). Furthermore, as observed in the TEM images (Fig. ##FIG##0##1##f), K was uniformly distributed on the nanoparticles of Fe and Co due to the well dispersion. Potassium has been proven to restrain H<sub>2</sub> chemisorption and increase olefin selectivity<sup>##UREF##51##66##–##UREF##53##68##</sup>. Thus, without the assistance of graphene fences, the formed light olefins were easily diffused into the gas flow and carried out due to the weak olefin secondary hydrogenation capacities (Fig. ##FIG##3##4e##), and exhibited a higher light olefin selectivity (50.1%) (Fig. ##FIG##2##3b##).</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par33\">In summary, graphene-fence-regulated Fe-Co bimetallic catalysts with homogeneous active sites or scattered spatial dual active sites were successfully prepared and employed in the CO<sub>2</sub> hydrogenation reaction for the selective production of light olefins or LPG without any post-treatments. The GO/K-Fe-Co catalyst, with its uniform distribution and smaller particle sizes, reached a C<sub>2</sub><sup>=</sup><bold>–</bold>C<sub>4</sub><sup>=</sup> selectivity as high as 50.1% at a CO<sub>2</sub> conversion of 55.4%. While the graphene-fence -separated GO-Fe/K-Co catalyst displayed a 43.6% selectivity for LPG (propane and butane) and an ultra-low CO selectivity of 2.2% at a 46% CO<sub>2</sub> conversion without the help of any zeolites. This reaction result corresponded to the highest STY (151.0 g kg<sub>cat</sub><sup>−1</sup> h<sup>−1</sup>) of LPG ever reported. Characterization and theoretical calculation results demonstrated that the dual active sites (iron carbides and metallic cobalt) performed their tailor-made respective functions, simultaneously satisfying the requirements of suitable carbon chain growth and olefin secondary hydrogenation, and selectively improving the LPG selectivity. Furthermore, the graphene fences prevented the metal particles from agglomerating, thus enhancing the catalytic stability. We expect that this sophisticated method of exploiting the specific structure of graphene to fabricate catalysts with multiple active sites will inspire other important catalytic reactions. Finally, this work not only provides a multiple-active-site catalyst with a unique spatial distribution for selective CO<sub>2</sub> hydrogenation but also provides a fundamental understanding of the role of graphene fences in selective hydrogenation. It can be broadened to other supported catalysts and offers valuable guidance for the rational design of powerful reaction environments through engineering the spatial distributions of different active sites.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par33\\\">In summary, graphene-fence-regulated Fe-Co bimetallic catalysts with homogeneous active sites or scattered spatial dual active sites were successfully prepared and employed in the CO<sub>2</sub> hydrogenation reaction for the selective production of light olefins or LPG without any post-treatments. The GO/K-Fe-Co catalyst, with its uniform distribution and smaller particle sizes, reached a C<sub>2</sub><sup>=</sup><bold>–</bold>C<sub>4</sub><sup>=</sup> selectivity as high as 50.1% at a CO<sub>2</sub> conversion of 55.4%. While the graphene-fence -separated GO-Fe/K-Co catalyst displayed a 43.6% selectivity for LPG (propane and butane) and an ultra-low CO selectivity of 2.2% at a 46% CO<sub>2</sub> conversion without the help of any zeolites. This reaction result corresponded to the highest STY (151.0 g kg<sub>cat</sub><sup>−1</sup> h<sup>−1</sup>) of LPG ever reported. Characterization and theoretical calculation results demonstrated that the dual active sites (iron carbides and metallic cobalt) performed their tailor-made respective functions, simultaneously satisfying the requirements of suitable carbon chain growth and olefin secondary hydrogenation, and selectively improving the LPG selectivity. Furthermore, the graphene fences prevented the metal particles from agglomerating, thus enhancing the catalytic stability. We expect that this sophisticated method of exploiting the specific structure of graphene to fabricate catalysts with multiple active sites will inspire other important catalytic reactions. Finally, this work not only provides a multiple-active-site catalyst with a unique spatial distribution for selective CO<sub>2</sub> hydrogenation but also provides a fundamental understanding of the role of graphene fences in selective hydrogenation. It can be broadened to other supported catalysts and offers valuable guidance for the rational design of powerful reaction environments through engineering the spatial distributions of different active sites.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Tuning CO<sub>2</sub> hydrogenation product distribution to obtain high-selectivity target products is of great significance. However, due to the imprecise regulation of chain propagation and hydrogenation reactions, the oriented synthesis of a single product is challenging. Herein, we report an approach to controlling multiple sites with graphene fence engineering that enables direct conversion of CO<sub>2</sub>/H<sub>2</sub> mixtures into different types of hydrocarbons. Fe-Co active sites on the graphene fence surface present 50.1% light olefin selectivity, while the spatial Fe-Co nanoparticles separated by graphene fences achieve liquefied petroleum gas of 43.6%. With the assistance of graphene fences, iron carbides and metallic cobalt can efficiently regulate C-C coupling and olefin secondary hydrogenation reactions to achieve product-selective switching between light olefins and liquefied petroleum gas. Furthermore, it also creates a precedent for CO<sub>2</sub> direct hydrogenation to liquefied petroleum gas via a Fischer-Tropsch pathway with the highest space-time yields compared to other reported composite catalysts.</p>","<p id=\"Par2\">Product-selective switching of CO<sub>2</sub> hydrogenation is a huge challenge. Here, the authors report an approach to manipulating Fe-Co sites with three-dimensional graphene fences that achieves integrated synthesis of different products.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Tuning CO<sub>2</sub> hydrogenation product distribution to obtain high-selectivity target products is of great significance. However, due to the imprecise regulation of chain propagation and hydrogenation reactions, the oriented synthesis of a single product is challenging. Herein, we report an approach to controlling multiple sites with graphene fence engineering that enables direct conversion of CO<sub>2</sub>/H<sub>2</sub> mixtures into different types of hydrocarbons. Fe-Co active sites on the graphene fence surface present 50.1% light olefin selectivity, while the spatial Fe-Co nanoparticles separated by graphene fences achieve liquefied petroleum gas of 43.6%. With the assistance of graphene fences, iron carbides and metallic cobalt can efficiently regulate C-C coupling and olefin secondary hydrogenation reactions to achieve product-selective switching between light olefins and liquefied petroleum gas. Furthermore, it also creates a precedent for CO<sub>2</sub> direct hydrogenation to liquefied petroleum gas via a Fischer-Tropsch pathway with the highest space-time yields compared to other reported composite catalysts.</p>\",\n \"<p id=\\\"Par2\\\">Product-selective switching of CO<sub>2</sub> hydrogenation is a huge challenge. Here, the authors report an approach to manipulating Fe-Co sites with three-dimensional graphene fences that achieves integrated synthesis of different products.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary information</title>","<p>\n\n\n</p>","<title>Source data</title>","<p>\n\n</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>\\n\\n\\n</p>\",\n \"<title>Source data</title>\",\n \"<p>\\n\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary information</title>","<p>The online version contains supplementary material available at 10.1038/s41467-024-44763-9.</p>","<title>Acknowledgements</title>","<p>This work was supported by National Natural Science Foundation of China (22102001) (L.G.), NEDO (New energy and industrial Technology Development Organization) (JPNP16002) (N.T.), JST SPRING (JPMJSP2145) of Japan (J.M.L.), Liaoning province unveils science and technology project (2021JH1/10400101) (B.L.), and the Grant-in-Aid from Japan Society for the Promotion of Science (JSPS) (22H01864, 23H05404) (N.T.). We thank Bowei Meng and Hengyang Liu for synthesizing the catalysts and doing characterization during the revision process.</p>","<title>Author contributions</title>","<p>J.M.L. and J.L. completed the catalyst tests and analyzed the data. L.G. wrote the paper with input from all the authors. W.W. and C.W. synthesized the catalysts. W.G. and X.G. did the XRD analysis. Y.H. did the catalyst morphology characterizations. G.Y. and S.Y. analyzed the characterization results. B.L. and N.T. revised the paper. All the authors contributed to the discussions on the results.</p>","<title>Peer review</title>","<title>Peer review information</title>","<p id=\"Par53\"><italic>Nature Communications</italic> thanks Mingyue Ding, Tiancun Xiao and the other, anonymous, reviewer for their contribution to the peer review of this work. A peer review file is available.</p>","<title>Data availability</title>","<p>The source data generated in this study are provided in the Source Data file. <xref ref-type=\"sec\" rid=\"Sec20\">Source data</xref> are provided with this paper.</p>","<title>Competing interests</title>","<p id=\"Par54\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41467-024-44763-9.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>This work was supported by National Natural Science Foundation of China (22102001) (L.G.), NEDO (New energy and industrial Technology Development Organization) (JPNP16002) (N.T.), JST SPRING (JPMJSP2145) of Japan (J.M.L.), Liaoning province unveils science and technology project (2021JH1/10400101) (B.L.), and the Grant-in-Aid from Japan Society for the Promotion of Science (JSPS) (22H01864, 23H05404) (N.T.). We thank Bowei Meng and Hengyang Liu for synthesizing the catalysts and doing characterization during the revision process.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>J.M.L. and J.L. completed the catalyst tests and analyzed the data. L.G. wrote the paper with input from all the authors. W.W. and C.W. synthesized the catalysts. W.G. and X.G. did the XRD analysis. Y.H. did the catalyst morphology characterizations. G.Y. and S.Y. analyzed the characterization results. B.L. and N.T. revised the paper. All the authors contributed to the discussions on the results.</p>\",\n \"<title>Peer review</title>\",\n \"<title>Peer review information</title>\",\n \"<p id=\\\"Par53\\\"><italic>Nature Communications</italic> thanks Mingyue Ding, Tiancun Xiao and the other, anonymous, reviewer for their contribution to the peer review of this work. A peer review file is available.</p>\",\n \"<title>Data availability</title>\",\n \"<p>The source data generated in this study are provided in the Source Data file. <xref ref-type=\\\"sec\\\" rid=\\\"Sec20\\\">Source data</xref> are provided with this paper.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par54\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>The structure and morphology investigation of graphene-supported Fe-Co catalysts.</title><p><bold>a</bold> XRD patterns of fresh catalysts. <bold>b</bold> XRD patterns of GO and rGO. <bold>c</bold> SEM images of GO and rGO; the bars stand for 40 μm. <bold>d</bold> FTIR spectra of fresh catalysts. <bold>e</bold> Raman spectra of fresh catalysts. <bold>f</bold> TEM mapping images of GO-Fe/K-Co and GO/K-Fe-Co catalysts; the bars in the images stand for 100 nm. The yellow circles represent the overlapping distribution areas, while the light blue circles represent the separation areas. <bold>g</bold> Schematic diagram of the preparation processes, spatial structures, and species distributions of GO/K-Fe-Co and GO-Fe/K-Co. Fe species, red balls; Co species, light blue balls; K species, purple balls. <bold>h</bold> SEM images of GO/K-Fe-Co and GO-Fe/K-Co catalysts. The light blue and yellow circles represent the metal sites separated by the graphene fences.</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Phase compositions of the spent graphene-supported Fe-Co bimetallic catalysts.</title><p><bold>a</bold> XRD patterns. <bold>b</bold> Fe 2<italic>p</italic> XPS spectra. <bold>c</bold>\n<sup>57</sup>Fe Mössbauer spectra. <bold>d</bold> Co K-edge XANES spectra with fitting curves.</p></caption></fig>","<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Catalytic performances and stabilities.</title><p><bold>a</bold>–<bold>d</bold> Catalytic performances of the catalysts; Reaction conditions: 320 °C, 3.0 MPa, TOS = 8 h, W/F = 4.5 g h mol<sup>–1</sup>, H<sub>2</sub>/CO<sub>2</sub>/Ar = 68: 27: 5. <bold>e</bold>–<bold>f</bold> Stabilities of GO-Fe/K-Co (<bold>e</bold>) and GO/K-Fe-Co (<bold>f</bold>). <bold>g</bold> Schematic diagrams of the graphene fences regulated Fe-Co active sites controlling product type. Fe species, red balls; Co species, light blue balls; K species, purple balls. Characters in varied colors represent varied active sites in the frame.</p></caption></fig>","<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>The exploration of the reaction mechanism and path.</title><p><bold>a</bold> Reaction path diagram. Fe species, red balls; Co species, light blue balls; K species, purple balls. <bold>b</bold> DFT calculation models of Fe<sub>5</sub>C<sub>2</sub>, Fe<sub>5</sub>C<sub>2</sub>/Co, and Fe<sub>5</sub>C<sub>2</sub>/Co<sub>2</sub>C. Substrate colors: Fe, pink; C, brown; Co, blue. <bold>c</bold> H<sub>2</sub> adsorption models and adsorption energies over Fe<sub>5</sub>C<sub>2</sub> and Fe<sub>5</sub>C<sub>2</sub>/Co models. Substrate colors: Fe, pink; C, brown; Co, blue; H, white. <bold>d</bold> Propylene and 1-Butene adsorption models and adsorption energies over Fe<sub>5</sub>C<sub>2</sub>/Co and Fe<sub>5</sub>C<sub>2</sub>/Co<sub>2</sub>C models. <bold>e</bold> H<sub>2</sub>-TPD profile (<bold>i</bold>) and three-dimensional H<sub>2</sub>-TPD diagram (<bold>ii</bold>). <bold>f</bold> DFT calculated proposed catalytic reaction pathway for propylene hydrogenation over Fe<sub>5</sub>C<sub>2</sub> site and Fe<sub>5</sub>C<sub>2</sub>-Co site in the conditions of 320 °C. The reaction free energies (eV) are shown in the inset. The shaded parts represented the energy barriers of the speed-determining step. <bold>g</bold> C<sub>3</sub>H<sub>6</sub>-pulse transient hydrogenation profiles of the spent catalysts (C<sub>3</sub>H<sub>6</sub> signal and C<sub>3</sub>H<sub>8</sub> signal).</p></caption></fig>","<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>The designed Fischer-Tropsch pathway for CO<sub>2</sub> hydrogenation to LPG.</title><p><bold>a</bold> Synergistic effect of dual active sites for producing LPG from CO<sub>2</sub> hydrogenation. The numbers represent the free energy barriers (eV). <bold>b</bold>–<bold>c</bold> DFT calculations of chain growth and olefin hydrogenation reactions from ethylene to propane over the Fe<sub>5</sub>C<sub>2</sub> site (<bold>b</bold>) and the Fe<sub>5</sub>C<sub>2</sub>-Co site (<bold>c</bold>). Red and blue balls inside represent Fe and Co species, respectively.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><title>The structure and morphology investigation of graphene-supported Fe-Co catalysts.</title><p><bold>a</bold> XRD patterns of fresh catalysts. <bold>b</bold> XRD patterns of GO and rGO. <bold>c</bold> SEM images of GO and rGO; the bars stand for 40 μm. <bold>d</bold> FTIR spectra of fresh catalysts. <bold>e</bold> Raman spectra of fresh catalysts. <bold>f</bold> TEM mapping images of GO-Fe/K-Co and GO/K-Fe-Co catalysts; the bars in the images stand for 100 nm. The yellow circles represent the overlapping distribution areas, while the light blue circles represent the separation areas. <bold>g</bold> Schematic diagram of the preparation processes, spatial structures, and species distributions of GO/K-Fe-Co and GO-Fe/K-Co. Fe species, red balls; Co species, light blue balls; K species, purple balls. <bold>h</bold> SEM images of GO/K-Fe-Co and GO-Fe/K-Co catalysts. The light blue and yellow circles represent the metal sites separated by the graphene fences.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><title>Phase compositions of the spent graphene-supported Fe-Co bimetallic catalysts.</title><p><bold>a</bold> XRD patterns. <bold>b</bold> Fe 2<italic>p</italic> XPS spectra. <bold>c</bold>\\n<sup>57</sup>Fe Mössbauer spectra. <bold>d</bold> Co K-edge XANES spectra with fitting curves.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Fig. 3</label><caption><title>Catalytic performances and stabilities.</title><p><bold>a</bold>–<bold>d</bold> Catalytic performances of the catalysts; Reaction conditions: 320 °C, 3.0 MPa, TOS = 8 h, W/F = 4.5 g h mol<sup>–1</sup>, H<sub>2</sub>/CO<sub>2</sub>/Ar = 68: 27: 5. <bold>e</bold>–<bold>f</bold> Stabilities of GO-Fe/K-Co (<bold>e</bold>) and GO/K-Fe-Co (<bold>f</bold>). <bold>g</bold> Schematic diagrams of the graphene fences regulated Fe-Co active sites controlling product type. Fe species, red balls; Co species, light blue balls; K species, purple balls. Characters in varied colors represent varied active sites in the frame.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Fig. 4</label><caption><title>The exploration of the reaction mechanism and path.</title><p><bold>a</bold> Reaction path diagram. Fe species, red balls; Co species, light blue balls; K species, purple balls. <bold>b</bold> DFT calculation models of Fe<sub>5</sub>C<sub>2</sub>, Fe<sub>5</sub>C<sub>2</sub>/Co, and Fe<sub>5</sub>C<sub>2</sub>/Co<sub>2</sub>C. Substrate colors: Fe, pink; C, brown; Co, blue. <bold>c</bold> H<sub>2</sub> adsorption models and adsorption energies over Fe<sub>5</sub>C<sub>2</sub> and Fe<sub>5</sub>C<sub>2</sub>/Co models. Substrate colors: Fe, pink; C, brown; Co, blue; H, white. <bold>d</bold> Propylene and 1-Butene adsorption models and adsorption energies over Fe<sub>5</sub>C<sub>2</sub>/Co and Fe<sub>5</sub>C<sub>2</sub>/Co<sub>2</sub>C models. <bold>e</bold> H<sub>2</sub>-TPD profile (<bold>i</bold>) and three-dimensional H<sub>2</sub>-TPD diagram (<bold>ii</bold>). <bold>f</bold> DFT calculated proposed catalytic reaction pathway for propylene hydrogenation over Fe<sub>5</sub>C<sub>2</sub> site and Fe<sub>5</sub>C<sub>2</sub>-Co site in the conditions of 320 °C. The reaction free energies (eV) are shown in the inset. The shaded parts represented the energy barriers of the speed-determining step. <bold>g</bold> C<sub>3</sub>H<sub>6</sub>-pulse transient hydrogenation profiles of the spent catalysts (C<sub>3</sub>H<sub>6</sub> signal and C<sub>3</sub>H<sub>8</sub> signal).</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Fig. 5</label><caption><title>The designed Fischer-Tropsch pathway for CO<sub>2</sub> hydrogenation to LPG.</title><p><bold>a</bold> Synergistic effect of dual active sites for producing LPG from CO<sub>2</sub> hydrogenation. The numbers represent the free energy barriers (eV). <bold>b</bold>–<bold>c</bold> DFT calculations of chain growth and olefin hydrogenation reactions from ethylene to propane over the Fe<sub>5</sub>C<sub>2</sub> site (<bold>b</bold>) and the Fe<sub>5</sub>C<sub>2</sub>-Co site (<bold>c</bold>). Red and blue balls inside represent Fe and Co species, respectively.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":["<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>"],"string":"[\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM1\\\"></supplementary-material>\",\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM2\\\"></supplementary-material>\",\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM3\\\"></supplementary-material>\"\n]","isConversational":false},"footnote":{"kind":"list like","value":["<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Jiaming Liang, Jiangtao Liu.</p></fn></fn-group>"],"string":"[\n \"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Jiaming Liang, Jiangtao Liu.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41467_2024_44763_Fig1_HTML\" id=\"d32e555\"/>","<graphic xlink:href=\"41467_2024_44763_Fig2_HTML\" id=\"d32e869\"/>","<graphic xlink:href=\"41467_2024_44763_Fig3_HTML\" id=\"d32e947\"/>","<graphic xlink:href=\"41467_2024_44763_Fig4_HTML\" id=\"d32e1356\"/>","<graphic xlink:href=\"41467_2024_44763_Fig5_HTML\" id=\"d32e1685\"/>"],"string":"[\n \"<graphic xlink:href=\\\"41467_2024_44763_Fig1_HTML\\\" id=\\\"d32e555\\\"/>\",\n \"<graphic xlink:href=\\\"41467_2024_44763_Fig2_HTML\\\" id=\\\"d32e869\\\"/>\",\n \"<graphic xlink:href=\\\"41467_2024_44763_Fig3_HTML\\\" id=\\\"d32e947\\\"/>\",\n \"<graphic xlink:href=\\\"41467_2024_44763_Fig4_HTML\\\" id=\\\"d32e1356\\\"/>\",\n \"<graphic xlink:href=\\\"41467_2024_44763_Fig5_HTML\\\" id=\\\"d32e1685\\\"/>\"\n]","isConversational":false},"media":{"kind":"list like","value":["<media xlink:href=\"41467_2024_44763_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>","<media xlink:href=\"41467_2024_44763_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>","<media xlink:href=\"41467_2024_44763_MOESM3_ESM.zip\"><caption><p>Source data</p></caption></media>"],"string":"[\n \"<media xlink:href=\\\"41467_2024_44763_MOESM1_ESM.pdf\\\"><caption><p>Supplementary Information</p></caption></media>\",\n \"<media xlink:href=\\\"41467_2024_44763_MOESM2_ESM.pdf\\\"><caption><p>Peer Review File</p></caption></media>\",\n \"<media xlink:href=\\\"41467_2024_44763_MOESM3_ESM.zip\\\"><caption><p>Source data</p></caption></media>\"\n]","isConversational":false},"unknown_pub":{"kind":"string","value":"[{\"label\": [\"2.\"], \"surname\": [\"Liang\"], \"given-names\": [\"J\"], \"article-title\": [\"Direct conversion of CO\"], \"sub\": [\"2\"], \"source\": [\"Ind. Eng. Chem. Res.\"], \"year\": [\"2022\"], \"volume\": [\"61\"], \"fpage\": [\"10336\"], \"lpage\": [\"10346\"], \"pub-id\": [\"10.1021/acs.iecr.1c05061\"]}, {\"label\": [\"5.\"], \"surname\": [\"Sachs\"], \"given-names\": [\"JD\"], \"article-title\": [\"Six transformations to achieve the sustainable development goals\"], \"source\": [\"Nat. Sustain.\"], \"year\": [\"2019\"], \"volume\": [\"2\"], \"fpage\": [\"805\"], \"lpage\": [\"814\"], \"pub-id\": [\"10.1038/s41893-019-0352-9\"]}, {\"label\": [\"7.\"], \"surname\": [\"Dong\"], \"given-names\": [\"X\"], \"article-title\": [\"CO\"], \"sub\": [\"2\", \"2\"], \"source\": [\"Appl. Catal. B\"], \"year\": [\"2016\"], \"volume\": [\"191\"], \"fpage\": [\"8\"], \"lpage\": [\"17\"], \"pub-id\": [\"10.1016/j.apcatb.2016.03.014\"]}, {\"label\": [\"8.\"], \"surname\": [\"Frusteri\"], \"given-names\": [\"F\"], \"article-title\": [\"Stepwise tuning of metal-oxide and acid sites of CuZnZr-MFI hybrid catalysts for the direct DME synthesis by CO\"], \"sub\": [\"2\"], \"source\": [\"Appl. Catal. B.\"], \"year\": [\"2015\"], \"volume\": [\"176\"], \"fpage\": [\"522\"], \"lpage\": [\"531\"], \"pub-id\": [\"10.1016/j.apcatb.2015.04.032\"]}, {\"label\": [\"10.\"], \"surname\": [\"Federsel\", \"Jackstell\", \"Beller\"], \"given-names\": [\"C\", \"R\", \"M\"], \"article-title\": [\"State-of-the-art catalysts for hydrogenation of carbon dioxide\"], \"source\": [\"Angew. Chem. Int. 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Commun.\"], \"year\": [\"2016\"], \"volume\": [\"52\"], \"fpage\": [\"7352\"], \"lpage\": [\"7355\"], \"pub-id\": [\"10.1039/C6CC01965J\"]}, {\"label\": [\"13.\"], \"surname\": [\"Zhou\"], \"given-names\": [\"C\"], \"article-title\": [\"Highly active ZnO-ZrO\"], \"sub\": [\"2\"], \"source\": [\"ACS Catal.\"], \"year\": [\"2020\"], \"volume\": [\"10\"], \"fpage\": [\"302\"], \"lpage\": [\"310\"], \"pub-id\": [\"10.1021/acscatal.9b04309\"]}, {\"label\": [\"14.\"], \"surname\": [\"Gao\"], \"given-names\": [\"W\"], \"article-title\": [\"Capsule-like zeolite catalyst fabricated by solvent-free strategy for para-Xylene formation from CO\"], \"sub\": [\"2\"], \"source\": [\"Appl. Catal. 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Today\"], \"year\": [\"2015\"], \"volume\": [\"251\"], \"fpage\": [\"34\"], \"lpage\": [\"40\"], \"pub-id\": [\"10.1016/j.cattod.2015.01.011\"]}, {\"label\": [\"33.\"], \"surname\": [\"Li\"], \"given-names\": [\"W\"], \"article-title\": [\"The anti-sintering catalysts: Fe-Co-Zr polymetallic fibers for CO\"], \"sub\": [\"2\", \"2\", \"4\"], \"sup\": [\"=\", \"=\"], \"bold\": [\"\\u2013\"], \"source\": [\"J. CO2 Util.\"], \"year\": [\"2018\"], \"volume\": [\"23\"], \"fpage\": [\"219\"], \"lpage\": [\"225\"], \"pub-id\": [\"10.1016/j.jcou.2017.07.005\"]}, {\"label\": [\"34.\"], \"surname\": [\"Sandupatla\", \"Banerjee\", \"Deo\"], \"given-names\": [\"A\", \"A\", \"G\"], \"article-title\": [\"Optimizing CO\"], \"sub\": [\"2\"], \"source\": [\"Appl. Surf. 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Unfortunately, deployed resistance (R) genes frequently become ineffective due to genetic changes in the pathogens. Breeding for resistance is costly and introduction of resistance genes may reduce the rate of yield increase<sup>##UREF##0##1##</sup>. Thus, breeding varieties with durable resistance without compromising yield potential is a major challenge in crop improvement.</p>","<p id=\"Par4\">Plants and pathogens have formed various interactions during the long-term coevolution. Many host plants exhibit resistance during the entire life cycle often referred to as seedling or all-stage resistance (ASR)<sup>##REF##17635216##2##</sup>. ASR genes typically encode nucleotide-binding leucine-rich repeat (NLR) receptors that recognize specific pathogen avirulence (Avr) proteins, providing race-specific resistance. Thus, such genes are generally nondurable when deployed singlely<sup>##REF##31154077##3##</sup>. Interestingly, paired NLRs, such as PigmS/PigmR, have been shown to confer durable resistance against rice blast<sup>##REF##28154240##4##</sup>. In other cases, resistance is restricted to particular developmental stages, the best known of which is adult plant resistance (APR)<sup>##REF##20565663##5##</sup>. Some APR genes encode non-NLR receptors and confer race non-specific and durable resistance<sup>##UREF##1##6##</sup>. The examples include <italic>Yr36</italic> which is a cytoplasmic protein kinase gene<sup>##REF##19228999##7##</sup>, <italic>Lr34</italic> is an ABC transporter gene and <italic>Lr67</italic> is a hexose transporter gene<sup>##REF##19229000##8##,##REF##26551671##9##</sup>. Nevertheless, certain well-documented APR genes of wheat, such as <italic>Lr12</italic>, <italic>Lr37</italic> and <italic>Yr49</italic>, still confer race-specific resistance<sup>##REF##25505474##10##</sup>. Notably, rice adult-plant race-specific genes <italic>Xa3</italic> and <italic>Xa4</italic> for resistance to bacterial blight demonstrated durability<sup>##REF##30011120##11##</sup>. Additionally, host plants may also express tissue-specific resistance<sup>##UREF##2##12##,##UREF##3##13##</sup>. For instance, some wheat genotypes with either ASR or APR to rust may develop susceptible symptoms on spikes. Consequently, despite numerous NLR genes and atypical resistance genes, such as receptor-like kinases (RLKs), tandem kinase proteins (TKPs), like receptor-like kinases (WAKs) and transcription factors (TFs), have been isolated from plants in the past decades, our understanding of the molecular mechanisms underlying divergent resistance among host plants remains limited.</p>","<p id=\"Par5\">NLR encoding locus that contains a single R gene with one or more alleles encoding different resistance specificities is common in plant kingdom. For examples, wheat powdery mildew (Pm) resistance gene <italic>Pm2</italic> includes eight functional alleles<sup>##REF##33629439##14##</sup>, and <italic>Pm3</italic> includes 17 functional alleles identified to date<sup>##REF##19470492##15##</sup>. Recent studies have expanded our understanding of the avirulence (AVR) effectors from the pathogen recognized by allelic series, which were sequence unrelated proteins<sup>##UREF##4##16##,##REF##26452600##17##</sup>. Thus, as an alternative to the pyramiding of different R genes, different allelic variants can also be combined for achieving more durable and broad-spectrum resistance<sup>##REF##20804457##18##</sup>. However, in some cases, pyramiding different R genes or alleles can cause mutual suppression of their functions. A few reports have described the genetic basis of disease resistance suppression in wheat with the examples including suppression of stem rust resistance by a subunit of the mediator complex gene<sup>##REF##32111840##19##</sup>, and direct interactions between homologous NLR receptors encoded by alleles of <italic>Pm3</italic> and <italic>Pm8</italic> to suppress powdery mildew resistance<sup>##REF##24942051##20##,##REF##24942074##21##</sup>. In contrast, our knowledge regarding the suppression of unrelated NLR immune receptors is limited.</p>","<p id=\"Par6\">Powdery mildew, caused by <italic>Blumeria graminis</italic> f. sp. <italic>tritici</italic> (<italic>Bgt</italic>), is a common disease that limits wheat production in temperate regions. To enhance host resistance, numerous disease resistance genes have been transferred into bread wheat from its wild relatives. Remarkably, five powdery mildew resistance genes, <italic>Pm21</italic>, <italic>Pm55</italic>, <italic>Pm62</italic>, <italic>Pm67</italic> and <italic>Pm5V</italic> have been introgressed from different <italic>Dasypyrum villosum</italic> (L.) (2<italic>n</italic> = 2<italic>x</italic> = 14, VV) accessions into wheat through Robertsonian translocations (RobTs)<sup>##REF##29567451##22##–##REF##36038638##24##</sup>. Among these, wheat-<italic>D. villosum</italic> T5DL·5 V#4 S and T5AL·5 V#4 S translocation lines, carrying the gene <italic>Pm55</italic> from <italic>D. villosum</italic> accession 91C43, showed developmental-stage and tissue-specific resistance to wheat powdery mildew<sup>##UREF##2##12##</sup>. However, the T5DL·5 V#5 S translocation line, carrying the gene <italic>Pm5V</italic> from <italic>D. villosum</italic> accession 01I140, exhibited broad effectiveness against <italic>Bgt</italic> races at all stages<sup>##REF##36038638##24##</sup>. Therefore, the different 5VS-introgression lines present an opportunity to isolate and pyramid genes that confer distinct resistance in wheat. Additionally, previous research has shown that the transfer of <italic>Pm55</italic> and <italic>Pm5V</italic> into diverse wheat genetic backgrounds did not negatively affect yield-related traits<sup>##REF##36038638##24##,##UREF##5##25##</sup>. Consequently, these genes have been utilized in the wheat breeding program in China to develop elite resistance lines<sup>##UREF##6##26##</sup>.</p>","<p id=\"Par7\">In this work, we aim to elucidate the genetic and molecular basis underlying the distinct types of resistance against powdery mildew as observed in different 5VS-translocation lines. Our findings reveal that <italic>Pm55</italic> and <italic>Pm5V</italic> are allelic on chromosome arm 5VS; however, they have divergent interactions with a linked inhibitor gene, <italic>SuPm55</italic>, which causes the distinct resistance of different 5VS-translocation lines. Notably, <italic>Pm55</italic> and <italic>SuPm55</italic> are unrelated NLR proteins and knockout of <italic>SuPm55</italic> significantly reduces the plant fitness. Accordingly, our results reveal the complex interactions of different NLR immune receptors in disease resistance, and provide insights into the suppression of resistance in wheat. Combining the T5AL·5 V#4 S and T5DL·5 V#5 S translocations offers a promising strategy for breeding durable resistance to wheat powdery mildew.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par3\\\">Host resistance is the most effective way to reduce yield loss caused by crop diseases. Unfortunately, deployed resistance (R) genes frequently become ineffective due to genetic changes in the pathogens. Breeding for resistance is costly and introduction of resistance genes may reduce the rate of yield increase<sup>##UREF##0##1##</sup>. Thus, breeding varieties with durable resistance without compromising yield potential is a major challenge in crop improvement.</p>\",\n \"<p id=\\\"Par4\\\">Plants and pathogens have formed various interactions during the long-term coevolution. Many host plants exhibit resistance during the entire life cycle often referred to as seedling or all-stage resistance (ASR)<sup>##REF##17635216##2##</sup>. ASR genes typically encode nucleotide-binding leucine-rich repeat (NLR) receptors that recognize specific pathogen avirulence (Avr) proteins, providing race-specific resistance. Thus, such genes are generally nondurable when deployed singlely<sup>##REF##31154077##3##</sup>. Interestingly, paired NLRs, such as PigmS/PigmR, have been shown to confer durable resistance against rice blast<sup>##REF##28154240##4##</sup>. In other cases, resistance is restricted to particular developmental stages, the best known of which is adult plant resistance (APR)<sup>##REF##20565663##5##</sup>. Some APR genes encode non-NLR receptors and confer race non-specific and durable resistance<sup>##UREF##1##6##</sup>. The examples include <italic>Yr36</italic> which is a cytoplasmic protein kinase gene<sup>##REF##19228999##7##</sup>, <italic>Lr34</italic> is an ABC transporter gene and <italic>Lr67</italic> is a hexose transporter gene<sup>##REF##19229000##8##,##REF##26551671##9##</sup>. Nevertheless, certain well-documented APR genes of wheat, such as <italic>Lr12</italic>, <italic>Lr37</italic> and <italic>Yr49</italic>, still confer race-specific resistance<sup>##REF##25505474##10##</sup>. Notably, rice adult-plant race-specific genes <italic>Xa3</italic> and <italic>Xa4</italic> for resistance to bacterial blight demonstrated durability<sup>##REF##30011120##11##</sup>. Additionally, host plants may also express tissue-specific resistance<sup>##UREF##2##12##,##UREF##3##13##</sup>. For instance, some wheat genotypes with either ASR or APR to rust may develop susceptible symptoms on spikes. Consequently, despite numerous NLR genes and atypical resistance genes, such as receptor-like kinases (RLKs), tandem kinase proteins (TKPs), like receptor-like kinases (WAKs) and transcription factors (TFs), have been isolated from plants in the past decades, our understanding of the molecular mechanisms underlying divergent resistance among host plants remains limited.</p>\",\n \"<p id=\\\"Par5\\\">NLR encoding locus that contains a single R gene with one or more alleles encoding different resistance specificities is common in plant kingdom. For examples, wheat powdery mildew (Pm) resistance gene <italic>Pm2</italic> includes eight functional alleles<sup>##REF##33629439##14##</sup>, and <italic>Pm3</italic> includes 17 functional alleles identified to date<sup>##REF##19470492##15##</sup>. Recent studies have expanded our understanding of the avirulence (AVR) effectors from the pathogen recognized by allelic series, which were sequence unrelated proteins<sup>##UREF##4##16##,##REF##26452600##17##</sup>. Thus, as an alternative to the pyramiding of different R genes, different allelic variants can also be combined for achieving more durable and broad-spectrum resistance<sup>##REF##20804457##18##</sup>. However, in some cases, pyramiding different R genes or alleles can cause mutual suppression of their functions. A few reports have described the genetic basis of disease resistance suppression in wheat with the examples including suppression of stem rust resistance by a subunit of the mediator complex gene<sup>##REF##32111840##19##</sup>, and direct interactions between homologous NLR receptors encoded by alleles of <italic>Pm3</italic> and <italic>Pm8</italic> to suppress powdery mildew resistance<sup>##REF##24942051##20##,##REF##24942074##21##</sup>. In contrast, our knowledge regarding the suppression of unrelated NLR immune receptors is limited.</p>\",\n \"<p id=\\\"Par6\\\">Powdery mildew, caused by <italic>Blumeria graminis</italic> f. sp. <italic>tritici</italic> (<italic>Bgt</italic>), is a common disease that limits wheat production in temperate regions. To enhance host resistance, numerous disease resistance genes have been transferred into bread wheat from its wild relatives. Remarkably, five powdery mildew resistance genes, <italic>Pm21</italic>, <italic>Pm55</italic>, <italic>Pm62</italic>, <italic>Pm67</italic> and <italic>Pm5V</italic> have been introgressed from different <italic>Dasypyrum villosum</italic> (L.) (2<italic>n</italic> = 2<italic>x</italic> = 14, VV) accessions into wheat through Robertsonian translocations (RobTs)<sup>##REF##29567451##22##–##REF##36038638##24##</sup>. Among these, wheat-<italic>D. villosum</italic> T5DL·5 V#4 S and T5AL·5 V#4 S translocation lines, carrying the gene <italic>Pm55</italic> from <italic>D. villosum</italic> accession 91C43, showed developmental-stage and tissue-specific resistance to wheat powdery mildew<sup>##UREF##2##12##</sup>. However, the T5DL·5 V#5 S translocation line, carrying the gene <italic>Pm5V</italic> from <italic>D. villosum</italic> accession 01I140, exhibited broad effectiveness against <italic>Bgt</italic> races at all stages<sup>##REF##36038638##24##</sup>. Therefore, the different 5VS-introgression lines present an opportunity to isolate and pyramid genes that confer distinct resistance in wheat. Additionally, previous research has shown that the transfer of <italic>Pm55</italic> and <italic>Pm5V</italic> into diverse wheat genetic backgrounds did not negatively affect yield-related traits<sup>##REF##36038638##24##,##UREF##5##25##</sup>. Consequently, these genes have been utilized in the wheat breeding program in China to develop elite resistance lines<sup>##UREF##6##26##</sup>.</p>\",\n \"<p id=\\\"Par7\\\">In this work, we aim to elucidate the genetic and molecular basis underlying the distinct types of resistance against powdery mildew as observed in different 5VS-translocation lines. Our findings reveal that <italic>Pm55</italic> and <italic>Pm5V</italic> are allelic on chromosome arm 5VS; however, they have divergent interactions with a linked inhibitor gene, <italic>SuPm55</italic>, which causes the distinct resistance of different 5VS-translocation lines. Notably, <italic>Pm55</italic> and <italic>SuPm55</italic> are unrelated NLR proteins and knockout of <italic>SuPm55</italic> significantly reduces the plant fitness. Accordingly, our results reveal the complex interactions of different NLR immune receptors in disease resistance, and provide insights into the suppression of resistance in wheat. Combining the T5AL·5 V#4 S and T5DL·5 V#5 S translocations offers a promising strategy for breeding durable resistance to wheat powdery mildew.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Methods</title>","<title>Plant materials and field trials</title>","<p id=\"Par32\"><italic>Dasypyrum villosum</italic> accessions and two panels of wheat-<italic>D. villosum</italic> introgression lines listed in Supplementary Table ##SUPPL##0##1## are maintained at the Cytogenetics Institute, Nanjing Agricultural University (CINAU). The powdery mildew susceptible wheat cv. Fielder was used for transgenic experiments. Yield trials of NAU0686 and NAU2021 were performed in randomized plot design with five replications. Each plot consisted of seven rows, measuring 3.5 m in length and 2.0 m in width. Fifty plants located in the middle of the internal rows of each materials were randomly selected for the analysis of yield-related traits, including plant height, thousand-kernel weight, spikes per plant, seeds spike and grain yield per plot.</p>","<title><italic>Bgt</italic> infection, staining and microscopy</title>","<p id=\"Par33\">Wheat <italic>Bgt</italic> isolates E09, E26 and E31 were provided by Prof. Yilin Zhou, Institute of Plant Protection, Chinese Academy of Agricultural Science, Beijing. Isolate E09 is virulent on <italic>Pm1a</italic>, <italic>3a</italic>, <italic>3b</italic>, <italic>3c</italic>, <italic>3</italic> <italic>f</italic>, <italic>3e</italic>, <italic>5a</italic>, <italic>6</italic>, <italic>7</italic>, <italic>8</italic>, <italic>19</italic>, <italic>25</italic>, <italic>34</italic> and <italic>35</italic>. Isolate E26 is virulent on <italic>Pm1a</italic>, <italic>3a</italic>, <italic>3c</italic>, <italic>3d</italic>, <italic>3</italic> <italic>f</italic>, <italic>3e</italic>, <italic>5a</italic>, <italic>6</italic>, <italic>7</italic>, <italic>8</italic>, <italic>19</italic> and <italic>25</italic>, whereas, E31 is virulent on <italic>Pm1a</italic>, <italic>2a</italic>, <italic>3a</italic>, <italic>3b</italic>, <italic>3c</italic>, <italic>3d</italic>, <italic>3</italic> <italic>f</italic>, <italic>3e</italic>, <italic>4a</italic>, <italic>4b</italic>, <italic>5a</italic>, <italic>6</italic>, <italic>7</italic>, <italic>8</italic>, <italic>19</italic>, <italic>24</italic>, <italic>25</italic>, <italic>30</italic> and <italic>34</italic>. Additionally, the other 18 <italic>Bgt</italic> isolates collected in China were maintained at the Hubei Academy of Agricultural Science, at Wuhan. Powdery mildew responses on wheat seedlings were carried out in a greenhouse at 20–22 °C under a 12 h light/12 h darkness photoperiod. The <italic>Bgt</italic> isolates were maintained and increased on seedlings of wheat cv. Chancellor. Wheat lines were inoculated at the two-leaf stage and infection types (IT) were recorded on a 0–4 infection type (IT) scale at 7 days post inoculation when susceptible NAU0686 control plants were heavily diseased<sup>##UREF##3##13##</sup>. Adult-plant tests on lines grown in a field nursery were performed using the <italic>Bgt</italic> isolates E09. Susceptible cv. NAU0686 planted on both sides of each test row served as inoculum spreader and control. Reactions on leaves and sheaths were recorded at development stages of stem elongation, and heading on a 0–9 response scale<sup>##UREF##2##12##</sup>.</p>","<p id=\"Par34\">To evaluate the differences in mycelial development, leaf segments were prepared using an endogenous peroxidase-dependent in situ histochemical staining procedure<sup>##UREF##3##13##</sup>. In order to detect leaf cell death, primary leaves from TF5V-1and NAU1908 plants were incubated by 0.5% (w/v) trypan blue staining at 7 days after <italic>Bgt</italic> isolate E09 inoculation (dpi)<sup>##REF##29281751##38##</sup>. To visualize hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) accumulation, seedling leaves were stained at 2 dpi with 3,3’-diaminobenzidine (DAB) solution (1 mg/mL, pH 5.8) for 12 h, and then bleached in absolute ethanol<sup>##REF##32015344##39##</sup>. The treated leaves were observed under an Olympus BX60 microscope (Olympus, Tokyo, Japan).</p>","<title>Genome assembly of the <italic>PM55</italic> and <italic>SuPM55</italic> loci</title>","<p id=\"Par35\">To identify candidate genes in the genetic interval, we isolated and sequenced the translocated chromosome T5DL·5 V#4 S from line TF5V-1. Briefly, liquid suspensions of intact mitotic chromosomes were prepared from line TF5V-1. The chromosomes in suspension were fluorescently labeled by FISHIS using oligonucleotide 5’-FITC-GAA7-FITC-3’ (Integrated DNA Technologies, Inc., Iowa, USA) and counterstained by DAPI (4´,6-diamidino 2-phenylindole). Next, bivariate flow karyotyping and chromosome sorting was done on a FACSAria II SORP flow cytometer and sorter (Becton Dickinson Immunocytometry Systems, San José, USA)<sup>##UREF##8##40##,##REF##18565235##41##</sup>. The chromosome content of the flow-sorted fractions was determined by FISH on ~2000 chromosomes flow-sorted onto microscope slides using probes for pSc119.2, pTa71 and Afa family repetitive DNA sequences. Subsequently, DNA from flow-sorted chromosomes was purified, and the sheared DNA was used to prepare sequencing libraries using NEBNext Ultra™ II DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, USA). The libraries were sequenced on an Illumina NovaSeq 6000 to produce 2 × 250 bp paired-end reads. The size of the T5DL·5 V#4 S de novo assembly was 496.7 Mb with an average scaffold N50 size of 18.3 Kb. The total number of scaffolds was 57,258.</p>","<p id=\"Par36\">Moreover, a bacterial artificial chromosome (BAC) library was constructed using genomic DNA from <italic>D. villosum</italic> 01I140 (V#5). The nuclei of <italic>D. villosum</italic> accession 01I140 were isolated from approximately 30 g of etiolated young leaf tissue. High-molecular-weight (HMW) DNA was released from nuclei by proteinase K in lysis buffer (0.1 mg/mL proteinase K dissolved in 0.5 M EDTA, pH 9.1) at 50 °C for 48 h. The lysis buffer was changed after 24 h. Plugs (usually containing 5 to 6 μg undigested HMW DNA) were partially digested with <italic>BamH I</italic> or <italic>Hind III</italic><sup>##REF##25464379##42##</sup>. The BAC library comprised 351,520 clones and represented ~6.0-fold coverage of the <italic>D. villosum</italic> genome ( ~ 4.05 Gb). BAC clones were extracted from each of primary pools using a Qiagen Large-construct Kit (Germany). For each selected BAC clone, at least 300 x illumina paired-end short reads and 50x PacBio continuous long reads (CLR) were generated from the Illumina NovaSeq platform and PacBio Sequel platform, respectively. Library preparation and sequencing were performed at Novogene Co., Ltd (Beijing, China). Complete sequences for BAC clones were finally assembled from the short- and long- reads using an assembly workflow<sup>##UREF##9##43##</sup>. Finally, putative genes were annotated with the TriAnnot pipeline (<ext-link ext-link-type=\"uri\" xlink:href=\"https://urgi.versailles.inra.fr/triannot/?pipeline\">https://urgi.versailles.inra.fr/triannot/?pipeline</ext-link>).</p>","<title>Primer design and PCR amplification</title>","<p id=\"Par37\">DNA sequences from 5VS genome were used as templates for the development of molecular markers (Supplementary Table ##SUPPL##0##10##). All primers were designed using the Primer blast tool (<italic>Triticeae</italic> Multi-omics Center/ViroBlast Home Page and DNAMAN V6). PCR amplifications were performed in 10 µL reaction mixture containing 5 μL 2 × Phanta® Master Mix (Vazyme, Nanjing, China), 0.2 μL of each primer, and 1 μL of genomic DNA. DNA amplification was performed at 95 °C for 3 min, followed by 33–35 cycles at 95 °C for 10 s, 55–60 °C (depending on the annealing temperature of primer pairs) for 30 s, and 72 °C for 30 s/Kb, with a final extension at 72 °C for 5 min. PCR products were separated in 1% agarose gels.</p>","<title>Genomic DNA and RNA isolation and transcript analysis</title>","<p id=\"Par38\">Genomic DNA for molecular detection and gene cloning was extracted from seedling or adult-plant leaves of mutants, transgenic plants and parents by CTAB method<sup>##REF##17406474##44##</sup>. Total RNA from the <italic>Bgt</italic>-inoculated leaves were extracted at 72 hpi for full-length transcript sequencing and cDNA analysis. Reverse transcription was performed using a HiScript III 1st Strand cDNA Synthesis Kit ( + gDNA wiper) (Vazyme, Nanjing, China). A cDNA library was constructed using SMARTer PCR cDNA Synthesis Kit (Tiangen, Nanjing, China), and sequenced using an Illumina platform at Beijing Biomark Biotechnology Co. Ltd. Raw data of 64.38 GB and 84.75 GB were obtained for NAU1908 and TF5V-1, respectively. To obtain a cyclic consensus sequence (CCS) the raw data were processed using pbccs 6.4.0 software provided by the PacBio Sequel platform. Isoseq3 3.8.2 software was used to remove chimeras and polyA to get full-length transcripts.</p>","<title>Gene expression analysis by qRT-PCR</title>","<p id=\"Par39\">Total RNA was isolated using TriPure Isolation Reagent (Roche, Mannheim, Germany). First-strand cDNA was synthesized using 2 μg of total RNA by HiScript III reverse transcriptase (Vazyme Biotech, Nanjing, China) following the manufacturer’s instructions. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed using SYBR Green (Vazyme Biotech, Nanjing, China) with a LightCycler® 480 instrument (Roche, Mannheim, Germany)<sup>##REF##32877006##45##</sup>. The ACTIN gene was used as an internal control, and the 2<sup>(-ΔΔCT)</sup> method was used to calculate relative gene expression<sup>##REF##11846609##46##</sup>. Three biological replicates and four technical replicates for each sample were performed.</p>","<title>BSMV-induced gene silencing (BSMV-VIGS)</title>","<p id=\"Par40\">BSMV-VIGS was used to investigate candidate gene function in NAU1908, TF5V-1 and R5VS-15. A 211 bp fragment in the <italic>CNL1</italic> CC domain was inserted into the BSMV vector to form a recombinant virus BSMV: CNL1 was used to silence <italic>CNL1</italic> in TF5V-1. Fragments of 229 bp and 197 bp in the <italic>CNL2#5</italic> NB-ARC domain were separately inserted into the BSMV vector for silencing in NAU1908, and a 266 bp fragment in the <italic>CNL2#4</italic> NB-ARC domain for silencing in R5VS-15. A 265 bp fragment in the <italic>Pm2-5V#4</italic> LRR domain was use in silencing R5VS-15. The recombinant virus BSMV: PDS was used as a positive control and BSMV: γ was used as a negative control<sup>##REF##22031834##47##</sup>. When the positive PDS control gene (phytoene dehydrogenase) was silenced and showed photobleaching symptoms, the fourth leaves of inoculated plants were taken for in vitro identification and determination of qRT-PCR silencing efficiency. Isolated leaves were cultured on 6-BA medium for 7 days, and disease development was assessed by hyphal development. At least 10 plants of each genotype were challenged by each BSMV vector, and the experiments were repeated three times.</p>","<title>EMS-induced mutants</title>","<p id=\"Par41\">Approximately 2000 seeds of NAU1908 and 3000 seeds of TF5V-1 were treated with 1.0% ethyl methanesulfonate (EMS). Treated seeds were then sown in the field to generate M<sub>1</sub> plants, and 678 independent M<sub>1</sub>-generation plants of NAU1908 and 1042 M<sub>1</sub> plants of TF5V-1 were obtained, respectively. To screen susceptible mutants, about 50 seeds of each M<sub>1</sub> plant were evaluated for the response to <italic>Bgt</italic> isolate E09. Susceptible M<sub>2</sub> plants were further advanced to the M<sub>3</sub> generation and their progeny tested. The full-length genomic sequences of <italic>CNL2</italic> alleles were amplified from the susceptible mutants of TF5V-1 and NAU1908.</p>","<title>Transformation <italic>CNL2</italic> alleles</title>","<p id=\"Par42\">The 8391, and 8149 bp genome sequences encompassing the native promoters and terminators of <italic>CNL2#5</italic> and <italic>CNL2#4</italic>, respectively, were cloned into the LGY-OE3 binary vector. The genomic fragments were inserted into the <italic>Hind</italic> III<italic>–Bam</italic> HI restriction endonuclease sites of the digested pLGY-OE3 with an In-Fusion HD Cloning Kit (Clontech Laboratories, Mountain View, CA, USA). The constructs were introduced into the powdery mildew-susceptible bread wheat cv. Fielder by <italic>Agrobacterium tumefaciens</italic>-mediated transformation<sup>##REF##25300841##48##,##UREF##10##49##</sup>. Both T<sub>0</sub> and T<sub>1</sub> plants were tested for the presence of the transgene by PCR amplification using the <italic>CNL2#4</italic>- and <italic>CNL2#5</italic>-specific markers, respectively. Wheat cv. Fielder was used as the negative control. The powdery mildew responses of T<sub>0</sub> transgenic plants and sibling controls were examined at the adult-plant stage, and T<sub>1</sub> and T<sub>2</sub> plants were test at the seedling stage as described above<sup>##REF##30477421##50##</sup>.</p>","<title>CRISPR-Cas9 editing of <italic>CNL1</italic></title>","<p id=\"Par43\">For editing <italic>CNL1</italic> with CRISPR-Cas9, a sgRNA targeting the CC domain of <italic>CNL1</italic> was designed. The Cas9-sgRNA expression vectors were constructed using CRISPRdirect (<ext-link ext-link-type=\"uri\" xlink:href=\"http://crispr.dbcls.jp/\">http://crispr.dbcls.jp/</ext-link>) and introduced into the <italic>Agrobacterium strain</italic> EHA105. <italic>Agrobacterium</italic>-mediated transformation was performed on immature embryos of TF5V-1<sup>##REF##34143557##51##</sup>. Positive transgenic seedlings were selected with hygromycin (100 mg/L), and genomic DNA was extracted for PCR detection of the Cas9 gene fragment, 35 S promoter and hygromycin B phosphotransferase gene. PCR markers specific to <italic>CNL1</italic> were used to identify the mutants in the CC domain. Homologous mutants were obtained by sequencing in T<sub>1</sub> and T<sub>2</sub> generations.</p>","<title>Yeast two-hybrid (Y2H) assays</title>","<p id=\"Par44\">The MATCHMAKER GAL4 Two-Hybrid System 3 (Clontech) was used to examine the interaction between proteins. Appropriate amounts of bacterial solution were spread on SD/-Leu-Trp (SD/-L-T) media and incubated inverted at 30 °C for 3 days. The monoclonal antibody was picked up from SD/Leu-Trp-His-Ade (SD/-L-T-H-A) medium and resuspended in water, then diluted to a 1, 10<sup>−1</sup>, 10<sup>−2</sup>, 10<sup>−3</sup> gradient, spotted on SD/-L-T-H-A medium, cultured at 30 °C and observed after one week<sup>##REF##28348572##52##</sup>.</p>","<title>Bimolecular fluorescence complementation (BiFC) assays</title>","<p id=\"Par45\">The recombinant vectors Pm55-CC-nYFP and SuPm55-CC-cYFP were constructed using the CC domain sequences of <italic>Pm55</italic> and <italic>SuPm55</italic>, and the <italic>Agrobacterium</italic> GV3101 (Tiangen, Nanjing) containing the recombinant plasmid was inoculated in 20 mL LB liquid medium<sup>##REF##15469499##53##</sup>. The cells were cultured at 28 °Cuntil the logarithmic phase of <italic>Agrobacterium</italic> growth, and were collected by centrifugation for 10 min at 5000 × g and room temperature. <italic>Agrobacterium</italic> was suspended by leaching solution (10 mM MgCl<sub>2</sub>, 10 mM MES, 150 μM AS, pH = 5.6) to OD600 = 0.8–1.0, and then incubated for 3 h. Equal volumes of mixed bacterial suspension was injected into <italic>N. benthamiana</italic> leaves, and after 48 h, the fluorescence signal of YFP was observed by laser confocal microscope and photographed (Leica SP8, Germany).</p>","<title>Cell death assay in <italic>N. benthamiana</italic> leaves</title>","<p id=\"Par46\"><italic>Agrobacterium</italic> strain GV3101 carrying relevant plasmids was suspended in buffer to OD = 0.5, it was then injected into <italic>N. benthamiana</italic> leaves, followed by the observation of cell necrosis for 24–72 h. After the appearance of the cell death, the leaves were stained with 0.4% TPN and subsequently decolorized with ethanol: acetic acid (3: 1, V/V) until the background became invisible. Finally, the leaves were photographed for recording<sup>##REF##22685408##54##</sup>.</p>","<title>Luciferase complementation (Luc) imaging assay</title>","<p id=\"Par47\">To investigate interactions between the CC domains of <italic>Pm55</italic> and <italic>SuPm55</italic>, a luciferase complementation assay was performed. In this assay, bacterial solution of cLUC-<italic>Pm55</italic>CC/nLUC-<italic>SuPm55</italic>CC, cLUC/nLUC-<italic>SuPm55</italic>CC, cLUC-<italic>Pm55</italic>CC/nLUC, and a mixture containing empty vectors cLUC and nLUC were injected into four different regions of the <italic>N. benthamiana</italic> leaves<sup>##REF##35763387##55##</sup>. The signals were detected 48 h after infiltration, and 10 leaves were analyzed. NightShade LB985 (Berthold Technologies, Germany) for fluorescence detection.</p>","<title>Co-immunoprecipitation assay</title>","<p id=\"Par48\">Co-immunoprecipitation (Co-IP) experiments were performed following the Pierce HA Tag IP/Co-IP Kit instruction (Thermo). The transient expression of <italic>N. benthamiana</italic> leaves was followed by Co-immunoprecipitation assay. The fusion protein carrier carrying the expression Flag- or HA- tag is transferred into <italic>Agrobacterium</italic> strain GV3101, and the <italic>Agrobacterium</italic> cells containing Flag-Pm55CC and HA-SuPm55CC are mixed and infiltrated into the leaves of <italic>N. benthamiana</italic> at OD600 = 0.6 for full expression of the protein. Cracking bait-target cells and separation and purification of protein complexes, bait compound boiled and degeneration in SDS, then use 10% SDS-PAGE separation precipitation, with detection of HA or Flag antibodies (1:500, Abcam, Shanghai, China, No. AB 9110, AB 205606).</p>","<title>Cloning and sequencing of <italic>Pm55</italic> homologs</title>","<p id=\"Par49\">The primer pairs used for cloning full-length genomic sequences of <italic>Pm55</italic> homologs are listed in Supplementary Table ##SUPPL##0##10##. Amplified fragments were ligated to a pToPo-Blunt (Aidlab, Nanjing, China) for sequencing at TongYong Co. (Nanjing, China). Putative domains of the cloned gene were analyzed using BLAST (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/blast/\">http://www.ncbi.nlm.nih.gov/blast/</ext-link>). Protein prediction and multiple sequence alignment analysis were performed by the software of SMART (<ext-link ext-link-type=\"uri\" xlink:href=\"http://smart.embl-heidelberg.de/\">http://smart.embl-heidelberg.de/</ext-link>) and DNAMAN 7.0 software (Lynnon Biosoft, USA), respectively. R gene protein sequences with an N-terminal coiled-coil domain (CNL class) from the NCBI database were aligned using MUSCLE and a phylogenetic tree was constructed using the UPGMA (unweighted pair group method with arithmetic mean) program in MEGA 6.0 software<sup>##REF##21988835##56##</sup>. Evolutionary distances were determined by the Neighbor-Joining method with Poisson correction, and the units were used to show the number of amino acid substitutions per site.</p>","<title>Statistical analysis</title>","<p id=\"Par50\">The mean values and standard errors of the treatments were determined by Microsoft Excel. <italic>T</italic> or ANOVA test was performed with SPSS 26.0 software (SPSS, Inc., Chicago, IL) to determine the significance of differences. Significant differences between two treatments were determined with a probability (<italic>P</italic>) value.</p>","<title>Reporting summary</title>","<p id=\"Par51\">Further information on research design is available in the ##SUPPL##5##Nature Portfolio Reporting Summary## linked to this article.</p>"],"string":"[\n \"<title>Methods</title>\",\n \"<title>Plant materials and field trials</title>\",\n \"<p id=\\\"Par32\\\"><italic>Dasypyrum villosum</italic> accessions and two panels of wheat-<italic>D. villosum</italic> introgression lines listed in Supplementary Table ##SUPPL##0##1## are maintained at the Cytogenetics Institute, Nanjing Agricultural University (CINAU). The powdery mildew susceptible wheat cv. Fielder was used for transgenic experiments. Yield trials of NAU0686 and NAU2021 were performed in randomized plot design with five replications. Each plot consisted of seven rows, measuring 3.5 m in length and 2.0 m in width. Fifty plants located in the middle of the internal rows of each materials were randomly selected for the analysis of yield-related traits, including plant height, thousand-kernel weight, spikes per plant, seeds spike and grain yield per plot.</p>\",\n \"<title><italic>Bgt</italic> infection, staining and microscopy</title>\",\n \"<p id=\\\"Par33\\\">Wheat <italic>Bgt</italic> isolates E09, E26 and E31 were provided by Prof. Yilin Zhou, Institute of Plant Protection, Chinese Academy of Agricultural Science, Beijing. Isolate E09 is virulent on <italic>Pm1a</italic>, <italic>3a</italic>, <italic>3b</italic>, <italic>3c</italic>, <italic>3</italic> <italic>f</italic>, <italic>3e</italic>, <italic>5a</italic>, <italic>6</italic>, <italic>7</italic>, <italic>8</italic>, <italic>19</italic>, <italic>25</italic>, <italic>34</italic> and <italic>35</italic>. Isolate E26 is virulent on <italic>Pm1a</italic>, <italic>3a</italic>, <italic>3c</italic>, <italic>3d</italic>, <italic>3</italic> <italic>f</italic>, <italic>3e</italic>, <italic>5a</italic>, <italic>6</italic>, <italic>7</italic>, <italic>8</italic>, <italic>19</italic> and <italic>25</italic>, whereas, E31 is virulent on <italic>Pm1a</italic>, <italic>2a</italic>, <italic>3a</italic>, <italic>3b</italic>, <italic>3c</italic>, <italic>3d</italic>, <italic>3</italic> <italic>f</italic>, <italic>3e</italic>, <italic>4a</italic>, <italic>4b</italic>, <italic>5a</italic>, <italic>6</italic>, <italic>7</italic>, <italic>8</italic>, <italic>19</italic>, <italic>24</italic>, <italic>25</italic>, <italic>30</italic> and <italic>34</italic>. Additionally, the other 18 <italic>Bgt</italic> isolates collected in China were maintained at the Hubei Academy of Agricultural Science, at Wuhan. Powdery mildew responses on wheat seedlings were carried out in a greenhouse at 20–22 °C under a 12 h light/12 h darkness photoperiod. The <italic>Bgt</italic> isolates were maintained and increased on seedlings of wheat cv. Chancellor. Wheat lines were inoculated at the two-leaf stage and infection types (IT) were recorded on a 0–4 infection type (IT) scale at 7 days post inoculation when susceptible NAU0686 control plants were heavily diseased<sup>##UREF##3##13##</sup>. Adult-plant tests on lines grown in a field nursery were performed using the <italic>Bgt</italic> isolates E09. Susceptible cv. NAU0686 planted on both sides of each test row served as inoculum spreader and control. Reactions on leaves and sheaths were recorded at development stages of stem elongation, and heading on a 0–9 response scale<sup>##UREF##2##12##</sup>.</p>\",\n \"<p id=\\\"Par34\\\">To evaluate the differences in mycelial development, leaf segments were prepared using an endogenous peroxidase-dependent in situ histochemical staining procedure<sup>##UREF##3##13##</sup>. In order to detect leaf cell death, primary leaves from TF5V-1and NAU1908 plants were incubated by 0.5% (w/v) trypan blue staining at 7 days after <italic>Bgt</italic> isolate E09 inoculation (dpi)<sup>##REF##29281751##38##</sup>. To visualize hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) accumulation, seedling leaves were stained at 2 dpi with 3,3’-diaminobenzidine (DAB) solution (1 mg/mL, pH 5.8) for 12 h, and then bleached in absolute ethanol<sup>##REF##32015344##39##</sup>. The treated leaves were observed under an Olympus BX60 microscope (Olympus, Tokyo, Japan).</p>\",\n \"<title>Genome assembly of the <italic>PM55</italic> and <italic>SuPM55</italic> loci</title>\",\n \"<p id=\\\"Par35\\\">To identify candidate genes in the genetic interval, we isolated and sequenced the translocated chromosome T5DL·5 V#4 S from line TF5V-1. Briefly, liquid suspensions of intact mitotic chromosomes were prepared from line TF5V-1. The chromosomes in suspension were fluorescently labeled by FISHIS using oligonucleotide 5’-FITC-GAA7-FITC-3’ (Integrated DNA Technologies, Inc., Iowa, USA) and counterstained by DAPI (4´,6-diamidino 2-phenylindole). Next, bivariate flow karyotyping and chromosome sorting was done on a FACSAria II SORP flow cytometer and sorter (Becton Dickinson Immunocytometry Systems, San José, USA)<sup>##UREF##8##40##,##REF##18565235##41##</sup>. The chromosome content of the flow-sorted fractions was determined by FISH on ~2000 chromosomes flow-sorted onto microscope slides using probes for pSc119.2, pTa71 and Afa family repetitive DNA sequences. Subsequently, DNA from flow-sorted chromosomes was purified, and the sheared DNA was used to prepare sequencing libraries using NEBNext Ultra™ II DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, USA). The libraries were sequenced on an Illumina NovaSeq 6000 to produce 2 × 250 bp paired-end reads. The size of the T5DL·5 V#4 S de novo assembly was 496.7 Mb with an average scaffold N50 size of 18.3 Kb. The total number of scaffolds was 57,258.</p>\",\n \"<p id=\\\"Par36\\\">Moreover, a bacterial artificial chromosome (BAC) library was constructed using genomic DNA from <italic>D. villosum</italic> 01I140 (V#5). The nuclei of <italic>D. villosum</italic> accession 01I140 were isolated from approximately 30 g of etiolated young leaf tissue. High-molecular-weight (HMW) DNA was released from nuclei by proteinase K in lysis buffer (0.1 mg/mL proteinase K dissolved in 0.5 M EDTA, pH 9.1) at 50 °C for 48 h. The lysis buffer was changed after 24 h. Plugs (usually containing 5 to 6 μg undigested HMW DNA) were partially digested with <italic>BamH I</italic> or <italic>Hind III</italic><sup>##REF##25464379##42##</sup>. The BAC library comprised 351,520 clones and represented ~6.0-fold coverage of the <italic>D. villosum</italic> genome ( ~ 4.05 Gb). BAC clones were extracted from each of primary pools using a Qiagen Large-construct Kit (Germany). For each selected BAC clone, at least 300 x illumina paired-end short reads and 50x PacBio continuous long reads (CLR) were generated from the Illumina NovaSeq platform and PacBio Sequel platform, respectively. Library preparation and sequencing were performed at Novogene Co., Ltd (Beijing, China). Complete sequences for BAC clones were finally assembled from the short- and long- reads using an assembly workflow<sup>##UREF##9##43##</sup>. Finally, putative genes were annotated with the TriAnnot pipeline (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://urgi.versailles.inra.fr/triannot/?pipeline\\\">https://urgi.versailles.inra.fr/triannot/?pipeline</ext-link>).</p>\",\n \"<title>Primer design and PCR amplification</title>\",\n \"<p id=\\\"Par37\\\">DNA sequences from 5VS genome were used as templates for the development of molecular markers (Supplementary Table ##SUPPL##0##10##). All primers were designed using the Primer blast tool (<italic>Triticeae</italic> Multi-omics Center/ViroBlast Home Page and DNAMAN V6). PCR amplifications were performed in 10 µL reaction mixture containing 5 μL 2 × Phanta® Master Mix (Vazyme, Nanjing, China), 0.2 μL of each primer, and 1 μL of genomic DNA. DNA amplification was performed at 95 °C for 3 min, followed by 33–35 cycles at 95 °C for 10 s, 55–60 °C (depending on the annealing temperature of primer pairs) for 30 s, and 72 °C for 30 s/Kb, with a final extension at 72 °C for 5 min. PCR products were separated in 1% agarose gels.</p>\",\n \"<title>Genomic DNA and RNA isolation and transcript analysis</title>\",\n \"<p id=\\\"Par38\\\">Genomic DNA for molecular detection and gene cloning was extracted from seedling or adult-plant leaves of mutants, transgenic plants and parents by CTAB method<sup>##REF##17406474##44##</sup>. Total RNA from the <italic>Bgt</italic>-inoculated leaves were extracted at 72 hpi for full-length transcript sequencing and cDNA analysis. Reverse transcription was performed using a HiScript III 1st Strand cDNA Synthesis Kit ( + gDNA wiper) (Vazyme, Nanjing, China). A cDNA library was constructed using SMARTer PCR cDNA Synthesis Kit (Tiangen, Nanjing, China), and sequenced using an Illumina platform at Beijing Biomark Biotechnology Co. Ltd. Raw data of 64.38 GB and 84.75 GB were obtained for NAU1908 and TF5V-1, respectively. To obtain a cyclic consensus sequence (CCS) the raw data were processed using pbccs 6.4.0 software provided by the PacBio Sequel platform. Isoseq3 3.8.2 software was used to remove chimeras and polyA to get full-length transcripts.</p>\",\n \"<title>Gene expression analysis by qRT-PCR</title>\",\n \"<p id=\\\"Par39\\\">Total RNA was isolated using TriPure Isolation Reagent (Roche, Mannheim, Germany). First-strand cDNA was synthesized using 2 μg of total RNA by HiScript III reverse transcriptase (Vazyme Biotech, Nanjing, China) following the manufacturer’s instructions. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed using SYBR Green (Vazyme Biotech, Nanjing, China) with a LightCycler® 480 instrument (Roche, Mannheim, Germany)<sup>##REF##32877006##45##</sup>. The ACTIN gene was used as an internal control, and the 2<sup>(-ΔΔCT)</sup> method was used to calculate relative gene expression<sup>##REF##11846609##46##</sup>. Three biological replicates and four technical replicates for each sample were performed.</p>\",\n \"<title>BSMV-induced gene silencing (BSMV-VIGS)</title>\",\n \"<p id=\\\"Par40\\\">BSMV-VIGS was used to investigate candidate gene function in NAU1908, TF5V-1 and R5VS-15. A 211 bp fragment in the <italic>CNL1</italic> CC domain was inserted into the BSMV vector to form a recombinant virus BSMV: CNL1 was used to silence <italic>CNL1</italic> in TF5V-1. Fragments of 229 bp and 197 bp in the <italic>CNL2#5</italic> NB-ARC domain were separately inserted into the BSMV vector for silencing in NAU1908, and a 266 bp fragment in the <italic>CNL2#4</italic> NB-ARC domain for silencing in R5VS-15. A 265 bp fragment in the <italic>Pm2-5V#4</italic> LRR domain was use in silencing R5VS-15. The recombinant virus BSMV: PDS was used as a positive control and BSMV: γ was used as a negative control<sup>##REF##22031834##47##</sup>. When the positive PDS control gene (phytoene dehydrogenase) was silenced and showed photobleaching symptoms, the fourth leaves of inoculated plants were taken for in vitro identification and determination of qRT-PCR silencing efficiency. Isolated leaves were cultured on 6-BA medium for 7 days, and disease development was assessed by hyphal development. At least 10 plants of each genotype were challenged by each BSMV vector, and the experiments were repeated three times.</p>\",\n \"<title>EMS-induced mutants</title>\",\n \"<p id=\\\"Par41\\\">Approximately 2000 seeds of NAU1908 and 3000 seeds of TF5V-1 were treated with 1.0% ethyl methanesulfonate (EMS). Treated seeds were then sown in the field to generate M<sub>1</sub> plants, and 678 independent M<sub>1</sub>-generation plants of NAU1908 and 1042 M<sub>1</sub> plants of TF5V-1 were obtained, respectively. To screen susceptible mutants, about 50 seeds of each M<sub>1</sub> plant were evaluated for the response to <italic>Bgt</italic> isolate E09. Susceptible M<sub>2</sub> plants were further advanced to the M<sub>3</sub> generation and their progeny tested. The full-length genomic sequences of <italic>CNL2</italic> alleles were amplified from the susceptible mutants of TF5V-1 and NAU1908.</p>\",\n \"<title>Transformation <italic>CNL2</italic> alleles</title>\",\n \"<p id=\\\"Par42\\\">The 8391, and 8149 bp genome sequences encompassing the native promoters and terminators of <italic>CNL2#5</italic> and <italic>CNL2#4</italic>, respectively, were cloned into the LGY-OE3 binary vector. The genomic fragments were inserted into the <italic>Hind</italic> III<italic>–Bam</italic> HI restriction endonuclease sites of the digested pLGY-OE3 with an In-Fusion HD Cloning Kit (Clontech Laboratories, Mountain View, CA, USA). The constructs were introduced into the powdery mildew-susceptible bread wheat cv. Fielder by <italic>Agrobacterium tumefaciens</italic>-mediated transformation<sup>##REF##25300841##48##,##UREF##10##49##</sup>. Both T<sub>0</sub> and T<sub>1</sub> plants were tested for the presence of the transgene by PCR amplification using the <italic>CNL2#4</italic>- and <italic>CNL2#5</italic>-specific markers, respectively. Wheat cv. Fielder was used as the negative control. The powdery mildew responses of T<sub>0</sub> transgenic plants and sibling controls were examined at the adult-plant stage, and T<sub>1</sub> and T<sub>2</sub> plants were test at the seedling stage as described above<sup>##REF##30477421##50##</sup>.</p>\",\n \"<title>CRISPR-Cas9 editing of <italic>CNL1</italic></title>\",\n \"<p id=\\\"Par43\\\">For editing <italic>CNL1</italic> with CRISPR-Cas9, a sgRNA targeting the CC domain of <italic>CNL1</italic> was designed. The Cas9-sgRNA expression vectors were constructed using CRISPRdirect (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"http://crispr.dbcls.jp/\\\">http://crispr.dbcls.jp/</ext-link>) and introduced into the <italic>Agrobacterium strain</italic> EHA105. <italic>Agrobacterium</italic>-mediated transformation was performed on immature embryos of TF5V-1<sup>##REF##34143557##51##</sup>. Positive transgenic seedlings were selected with hygromycin (100 mg/L), and genomic DNA was extracted for PCR detection of the Cas9 gene fragment, 35 S promoter and hygromycin B phosphotransferase gene. PCR markers specific to <italic>CNL1</italic> were used to identify the mutants in the CC domain. Homologous mutants were obtained by sequencing in T<sub>1</sub> and T<sub>2</sub> generations.</p>\",\n \"<title>Yeast two-hybrid (Y2H) assays</title>\",\n \"<p id=\\\"Par44\\\">The MATCHMAKER GAL4 Two-Hybrid System 3 (Clontech) was used to examine the interaction between proteins. Appropriate amounts of bacterial solution were spread on SD/-Leu-Trp (SD/-L-T) media and incubated inverted at 30 °C for 3 days. The monoclonal antibody was picked up from SD/Leu-Trp-His-Ade (SD/-L-T-H-A) medium and resuspended in water, then diluted to a 1, 10<sup>−1</sup>, 10<sup>−2</sup>, 10<sup>−3</sup> gradient, spotted on SD/-L-T-H-A medium, cultured at 30 °C and observed after one week<sup>##REF##28348572##52##</sup>.</p>\",\n \"<title>Bimolecular fluorescence complementation (BiFC) assays</title>\",\n \"<p id=\\\"Par45\\\">The recombinant vectors Pm55-CC-nYFP and SuPm55-CC-cYFP were constructed using the CC domain sequences of <italic>Pm55</italic> and <italic>SuPm55</italic>, and the <italic>Agrobacterium</italic> GV3101 (Tiangen, Nanjing) containing the recombinant plasmid was inoculated in 20 mL LB liquid medium<sup>##REF##15469499##53##</sup>. The cells were cultured at 28 °Cuntil the logarithmic phase of <italic>Agrobacterium</italic> growth, and were collected by centrifugation for 10 min at 5000 × g and room temperature. <italic>Agrobacterium</italic> was suspended by leaching solution (10 mM MgCl<sub>2</sub>, 10 mM MES, 150 μM AS, pH = 5.6) to OD600 = 0.8–1.0, and then incubated for 3 h. Equal volumes of mixed bacterial suspension was injected into <italic>N. benthamiana</italic> leaves, and after 48 h, the fluorescence signal of YFP was observed by laser confocal microscope and photographed (Leica SP8, Germany).</p>\",\n \"<title>Cell death assay in <italic>N. benthamiana</italic> leaves</title>\",\n \"<p id=\\\"Par46\\\"><italic>Agrobacterium</italic> strain GV3101 carrying relevant plasmids was suspended in buffer to OD = 0.5, it was then injected into <italic>N. benthamiana</italic> leaves, followed by the observation of cell necrosis for 24–72 h. After the appearance of the cell death, the leaves were stained with 0.4% TPN and subsequently decolorized with ethanol: acetic acid (3: 1, V/V) until the background became invisible. Finally, the leaves were photographed for recording<sup>##REF##22685408##54##</sup>.</p>\",\n \"<title>Luciferase complementation (Luc) imaging assay</title>\",\n \"<p id=\\\"Par47\\\">To investigate interactions between the CC domains of <italic>Pm55</italic> and <italic>SuPm55</italic>, a luciferase complementation assay was performed. In this assay, bacterial solution of cLUC-<italic>Pm55</italic>CC/nLUC-<italic>SuPm55</italic>CC, cLUC/nLUC-<italic>SuPm55</italic>CC, cLUC-<italic>Pm55</italic>CC/nLUC, and a mixture containing empty vectors cLUC and nLUC were injected into four different regions of the <italic>N. benthamiana</italic> leaves<sup>##REF##35763387##55##</sup>. The signals were detected 48 h after infiltration, and 10 leaves were analyzed. NightShade LB985 (Berthold Technologies, Germany) for fluorescence detection.</p>\",\n \"<title>Co-immunoprecipitation assay</title>\",\n \"<p id=\\\"Par48\\\">Co-immunoprecipitation (Co-IP) experiments were performed following the Pierce HA Tag IP/Co-IP Kit instruction (Thermo). The transient expression of <italic>N. benthamiana</italic> leaves was followed by Co-immunoprecipitation assay. The fusion protein carrier carrying the expression Flag- or HA- tag is transferred into <italic>Agrobacterium</italic> strain GV3101, and the <italic>Agrobacterium</italic> cells containing Flag-Pm55CC and HA-SuPm55CC are mixed and infiltrated into the leaves of <italic>N. benthamiana</italic> at OD600 = 0.6 for full expression of the protein. Cracking bait-target cells and separation and purification of protein complexes, bait compound boiled and degeneration in SDS, then use 10% SDS-PAGE separation precipitation, with detection of HA or Flag antibodies (1:500, Abcam, Shanghai, China, No. AB 9110, AB 205606).</p>\",\n \"<title>Cloning and sequencing of <italic>Pm55</italic> homologs</title>\",\n \"<p id=\\\"Par49\\\">The primer pairs used for cloning full-length genomic sequences of <italic>Pm55</italic> homologs are listed in Supplementary Table ##SUPPL##0##10##. Amplified fragments were ligated to a pToPo-Blunt (Aidlab, Nanjing, China) for sequencing at TongYong Co. (Nanjing, China). Putative domains of the cloned gene were analyzed using BLAST (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"http://www.ncbi.nlm.nih.gov/blast/\\\">http://www.ncbi.nlm.nih.gov/blast/</ext-link>). Protein prediction and multiple sequence alignment analysis were performed by the software of SMART (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"http://smart.embl-heidelberg.de/\\\">http://smart.embl-heidelberg.de/</ext-link>) and DNAMAN 7.0 software (Lynnon Biosoft, USA), respectively. R gene protein sequences with an N-terminal coiled-coil domain (CNL class) from the NCBI database were aligned using MUSCLE and a phylogenetic tree was constructed using the UPGMA (unweighted pair group method with arithmetic mean) program in MEGA 6.0 software<sup>##REF##21988835##56##</sup>. Evolutionary distances were determined by the Neighbor-Joining method with Poisson correction, and the units were used to show the number of amino acid substitutions per site.</p>\",\n \"<title>Statistical analysis</title>\",\n \"<p id=\\\"Par50\\\">The mean values and standard errors of the treatments were determined by Microsoft Excel. <italic>T</italic> or ANOVA test was performed with SPSS 26.0 software (SPSS, Inc., Chicago, IL) to determine the significance of differences. Significant differences between two treatments were determined with a probability (<italic>P</italic>) value.</p>\",\n \"<title>Reporting summary</title>\",\n \"<p id=\\\"Par51\\\">Further information on research design is available in the ##SUPPL##5##Nature Portfolio Reporting Summary## linked to this article.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<title>High-resolution mapping reveals an inhibitor linked with <italic>Pm55</italic></title>","<p id=\"Par8\">To accurately evaluate the response of the 5VS-translocation lines to powdery mildew, the translocated chromosomes T5AL·5 V#4 S, T5DL·5 V#4 S and T5DL·5 V#5 S were transferred into a highly susceptible cv. NAU0686 genetic background, respectively. Subsequently, stable translocation lines were developed in the BC<sub>5</sub>F<sub>6</sub> progeny (Supplementary Table ##SUPPL##0##1##). Upon inoculation of these lines with <italic>Bgt</italic> isolate E09, we observed that both T5AL·5 V#4 S translocation line NAU185 and T5DL·5 V#4 S line TF5V-1, carrying <italic>Pm55</italic> displayed susceptible at 3-leaf stage (IT 4), but exhibited high resistance after the 5-leaf stage (IT 1) with susceptible leaf sheaths only at adult-plant stage (Fig. ##FIG##0##1a, b##). While, T5DL·5 V#5 S translocation line NAU1908, carrying <italic>Pm5V</italic> consistently demonstrated high resistance to powdery mildew across all stages and tissues (IT 0;). Trypan blue and DAB staining showed large number of spores produced in TF5V-1 seedling leaf (3-leaf) after inoculation with E09 (Supplementary Fig. ##SUPPL##0##1a, c##), but very mild cell death and robust accumulation of H<sub>2</sub>O<sub>2</sub> in NAU1908 seedling leaf (Supplementary Fig. ##SUPPL##0##1b, d##). Collectively, chromosome arms 5 V#4 S and 5 V#5 S conferred distinct forms against powdery mildew in wheat.</p>","<p id=\"Par9\">To map <italic>Pm55</italic> and <italic>Pm5V</italic>, a genetic mapping population containing 5425 F<sub>2</sub> individuals was previously constructed by crossing two T5DL·5VS translocation lines, TF5V-1 and NAU1908<sup>##REF##36038638##24##</sup>. <italic>Pm5V</italic> in NAU1908 was fine mapped to an approximately 0.9 Mb region referencing the genome sequence of wheat cv. Chinese Spring 5DS using forty-six crossovers on chromosome arm 5VS<sup>##REF##36038638##24##</sup>. However, it was unclear whether <italic>Pm55</italic> and <italic>Pm5V</italic> are allelic. To test their allelism, we further inoculated the F<sub>3</sub> and F<sub>4</sub> homozygous recombinant lines with <italic>Bgt</italic> isolates E09, E26 and E31 at seedling stage, and E09 at adult plant stage. As well as, sixty-three InDel markers and six EST-STS markers were used to screen these lines (Supplementary Fig. ##SUPPL##0##2##, Supplementary Data ##SUPPL##3##1##). Among these forty-six lines, we identified twenty recombinant lines between makers <italic>SCA29236</italic> and <italic>SCA12100</italic> (Type I) with the same phenotype as NAU1908, showing all-stage resistance to the three <italic>Bgt</italic> isolates. <italic>Pm5V</italic> locus was further mapped within the interval flanked by InDel markers <italic>SCA93008</italic> and <italic>SCA2324</italic>, corresponding to ~140 kb on the sequenced <italic>D. villosum</italic> 91C43<sup>DH</sup> genome<sup>##REF##36587241##27##</sup>. Additionally, we observed another set of twenty recombinant lines between makers <italic>SCA10690</italic> and <italic>SCA25706</italic> (typeII) with phenotypes identical to parent TF5V-1, showing susceptibility to all three <italic>Bgt</italic> isolates at the seedling stage but resistance to E09 at the adult plant stage, with lower susceptible sheaths. This suggested that <italic>Pm55</italic> is mapped within the interval flanked by InDel markers <italic>SCA11392</italic> and <italic>SCA23759</italic>. However, the remaining six recombinants between makers <italic>SCA11392</italic> and <italic>SCA93008</italic> (R5VS-13 to R5VS-18, type III) had contrasting phenotypes. These lines exhibited resistance to E09 and E26 but susceptibility to E31 at the seedling stage, and resistance to E09 at adult plant stage without susceptible leaf sheaths, which was distinct from the parental phenotypes of TF5V-1 and NAU1908. Based on the genotypes of type II and type III lines, one possibility is another resistance locus on 5V#5 S, flanked by InDel markers <italic>SCA11392</italic> and <italic>SCA77919</italic>, could provide the seedling resistance to E09 and E26 in type III recombination lines, or that another possibility is an inhibitor in the <italic>Pm55</italic> interval suppressed <italic>Pm55-</italic>mediated resistance to E09 and E26 in type II recombination lines. Notably, none of the recombinants was susceptible to E09 at the adult plant stage, suggesting that <italic>Pm55</italic> and <italic>Pm5V</italic> could indeed be allelic or close linkage.</p>","<p id=\"Par10\">To fine map the resistance or suppression locus linked with <italic>Pm55</italic>/<italic>Pm5V</italic> on 5VS, a cross was made between TF5V-1 and the Type III recombination line R5VS-15 (Supplementary Fig. ##SUPPL##0##3##). All F<sub>1</sub> seedlings were susceptible to E09 but showed adult-plant resistance with lower sheaths susceptibility, similar to TF5V-1. The F<sub>2</sub> population segregated into 128 resistant and 394 susceptible seedlings (Supplementary Table ##SUPPL##0##2##). However, all the F<sub>2</sub> plants showed adult-plant resistance, but the seedling susceptible plants of them had lower sheaths susceptibility. These results indicated that there was not a resistance gene on 5V#5 S, but a dominant inhibitor linked with <italic>Pm55</italic> on 5 V#4 S, suppressing <italic>Pm55-</italic>mediated resistance on seedlings and adult-plant leaf sheaths. Further genotyping of 3882 F<sub>2</sub> individuals identified six crossovers between markers <italic>SCA11392</italic> and <italic>SCA77919</italic>. Combination with five newly developed markers, the suppression interval was narrowed down to a ~ 100 kb region flanked by InDel markers <italic>SCA4218</italic> and <italic>SCA39816</italic> by comparison with the <italic>D. villosum</italic> 91C43<sup>DH</sup> genome (Supplementary Fig. ##SUPPL##0##4##, Fig. ##FIG##0##1c## and Supplementary Data ##SUPPL##4##2##). In summary, a single gene <italic>Pm5V</italic> in NAU1908 conferred resistance to powdery mildew at all stages and tissues, whereas <italic>Pm55</italic> together with a linked inhibitor in TF5V-1 conferred the developmental-stage and tissue-specific resistance.</p>","<title>Dissection of the candidates for both intervals</title>","<p id=\"Par11\">Diploid <italic>D. villosum</italic> is an outcrossing species that may produce progenies with complex haplotypes in genome locus of interest. Thus, DNA sequences of chromosome arms 5V#4 S and 5 V#5 S are necessary to isolate the haplotypes in the aimed loci on them. Previously, we flow-sorted and sequenced chromosome arm 5 V#5 S, resulting in 173.4 Mb sequence with an average scaffold N50 size of 22.4 kb<sup>##REF##36038638##24##</sup>. Building on this, we further isolated T5DL·5 V#4 S translocated chromosome from TF5V-1 by flow sorting and then sequenced it using Illumina short read technology (Supplementary Fig. ##SUPPL##0##5a, b##). The size of the 5V#4 S de novo assembly was 171.8 Mb with an average scaffold N50 size of 18.3 kb, including 1262 high-confidence protein-coding genes annotated using protein homology-based prediction methods.</p>","<p id=\"Par12\">To isolate inhibitor gene, the inhibitor physical region between markers <italic>SCA4218</italic> and <italic>SCA39816</italic> on 5 VS of 91C43<sup>DH</sup> was first subjected to collinearity analysis with Chinese Spring 5AS, 5BS and 5DS, revealing that the annotated genes in this region across different genomes were highly conserved (Supplementary Fig. ##SUPPL##0##5c##). Subsequently, we aligned the homologies of annotated genes between 5V#4 S and 5 V#5 S scaffolds and the inhibitor physical region on different genomes. We identified two 5 V#5 S scaffolds, Scaffold4218 (20,202 bp) and Scaffold3806 (39,439 bp), and two 5 V#4 S scaffolds, Scaffold15749 (54,335 bp) and Scaffold38916 (46,667 bp), containing homologs of the genes located in the inhibitor region of 5AS, 5BS and 5DS, which generated a contiguous 100,963 bp sequence between markers <italic>SCA4218</italic> and <italic>SCA39816</italic> (Supplementary Fig. ##SUPPL##0##5d##). This combination sequence included six annotated genes. Among them, an intact coiled-coil nucleotide-binding leucine-rich repeat (CNL) gene, <italic>CNL1</italic> (G4), along with gene G3 were absent in the inhibitor region on 5 VS of 91C43<sup>DH</sup> (Fig. ##FIG##0##1d##, Supplementary Fig. ##SUPPL##0##5c##). Full-length transcriptome sequencing of TF5V-1 seedling leaves inoculating with <italic>Bgt</italic> isolate E09 revealed that <italic>CNL1</italic> was the only intact transcribed gene of the six annotated genes, and therefore could be the candidate of a functional gene. The full genomic sequence of <italic>CNL1</italic> in 5V#4 S was further confirmed by PCR, which spanned 2651 base pairs (bp) from ATG start codon to TGA stop codon with six exons and five introns, and generating a predicted 565 amino-acid protein (Fig. ##FIG##0##1f##).</p>","<p id=\"Par13\">However, the 5 V#4 S and 5 V#5 S scaffolds containing the homologs of annotated genes on the genomes of CS 5AS, 5BS and 5DS between <italic>Pm55</italic>/<italic>Pm5V</italic> linked markers <italic>SCA93008</italic> and <italic>SCA2324</italic> did not cover the <italic>Pm55</italic>/<italic>Pm5V</italic> interval. Thus, we constructed a bacterial artificial chromosome (BAC) library using DNA from <italic>D. villosum</italic> 01I140 (the donor of 5V#5 S) for long-read sequencing. Screening the BAC library with markers <italic>SCA93008</italic> and <italic>SCA2324</italic> enabled us to identify and sequence ten BACs. Out of them, BAC5 and BAC8 overlapped the resistance region forming a contiguous 134,990 bp sequence that contained seven annotated genes, including three CNL paralogs <italic>CNL2</italic>, <italic>CNL3</italic> and <italic>CNL4</italic> (Fig. ##FIG##0##1e##). <italic>CNL2</italic> and <italic>CNL4</italic> were intact genes, but <italic>CNL3</italic> was a pseudogene due to a disrupted open reading frame (ORF). <italic>CNL2</italic> and <italic>CNL3</italic> were highly similar (94.8% identity) whereas the similarity of <italic>CNL2</italic> and <italic>CNL4</italic> was lower (81.8% identity). Gene expression analysis revealed that only <italic>CNL2</italic> was induced expression in seedlings of NAU1908 after infection with <italic>Bgt</italic> isolate E09 (Supplementary Fig. ##SUPPL##0##6a##). Moreover, <italic>Pm2-5V</italic>, the <italic>Pm2</italic> ortholog on 5 VS was also used as the control to verify the expression of the <italic>CNL2</italic> alleles induced by <italic>Bgt</italic> infection. Results showed that <italic>CNL2#4</italic> allele transcript levels in Type III recombination line R5VS-15 were approximately eight-fold higher at 72 hpi with isolate E09 (Supplementary Fig. ##SUPPL##0##6b##), and <italic>CNL2#5</italic> transcript levels in NAU1908 were approximately three-fold higher at 24 hpi (Supplementary Fig. ##SUPPL##0##6c##). By contrast, the transcript level of <italic>Pm2</italic> orthologs, <italic>Pm2-5V#4</italic> and <italic>Pm2-5V#5</italic> did not change significantly during 0–72 hpi. Accordingly, <italic>CNL2</italic> alleles were prioritized as the functional gene candidates.</p>","<p id=\"Par14\">The full genomic sequence of <italic>CNL2</italic> allele in NAU1908 (designed as <italic>CNL2#5</italic>) amplified by PCR was identical to those of the BAC sequences spanning 3887 bp from the translation initiation (ATG) to the stop (TAA) codons, including two exons and one intron and generated a predicted 1265 amino-acid protein (Fig. ##FIG##0##1g##). We identified a 5 V#4 S scaffold, Scaffold23759 (23,842 bp), which contained the full length of the <italic>CNL2</italic> allele (Fig. ##FIG##0##1e##). Utilizing this sequence, we amplified the full length of the <italic>CNL2</italic> allele in TF5V-1 (designed as <italic>CNL2#4</italic>), which was 3896 bp from the translation initiation (ATG) to the stop (TGA) codon, generating a predicted 1267 amino-acid protein. <italic>CNL2#4</italic> and <italic>CNL2#5</italic> shared 95.2% identity in gDNA sequence and 91.5% identity in amino acids at the protein level (Supplementary Fig. ##SUPPL##0##7##).</p>","<title>Validation of the <italic>CNL1</italic> function</title>","<p id=\"Par15\">We used virus-induced gene silencing (VIGS) to knock down <italic>CNL1</italic> in TF5V-1 to verify its function. A construct targeting the CC domain of <italic>CNL1</italic> in TF5V-1 resulted in resistant leaves at seedling stage (Fig. ##FIG##1##2a##). A comparison of mRNA expression by qPCR in TF5V-1 leaves infected with BSMV: <italic>CNL1</italic> and wild-type BSMV: γ virus showed a significant decrease in expression levels of <italic>CNL1</italic> transcripts (Fig. ##FIG##1##2b##). These results indicated that <italic>CNL1</italic> suppressed the seedling resistance of TF5V-1.</p>","<p id=\"Par16\">CRISPR/Cas9-mediated genome editing technology was then employed to knock out <italic>CNL1</italic> in TF5V-1. We used guide RNAs (gRNAs) targeting the conserved regions in CC domain of <italic>CNL1</italic> (Supplementary Fig. ##SUPPL##0##8a##), and obtained four mutant lines with 4-bp (Del5VS-1), 5-bp (Del5VS-2), 6-bp (Del5VS-3) and 27-bp (Del5VS-4) deletions in the target region as putative knockouts of the <italic>CNL1</italic> gene (Fig. ##FIG##1##2c##). The inoculation of <italic>Bgt</italic> isolate E09 showed that the amounts of fungal growth and spores on the seedling leaves of four homozygous mutants significant reduced compared with TF5V-1 (Fig. ##FIG##1##2d,e##, Supplementary Fig. ##SUPPL##0##8b to f##). Homozygous T<sub>2</sub> progeny from a Del5VS-4 heterozygous T<sub>1</sub> plant were all ASR to isolate E09 without lower leaf sheath susceptibility (Fig. ##FIG##1##2f, g## Supplementary Fig. ##SUPPL##0##9a, b## and Supplementary Table ##SUPPL##0##3##), confirming the role of <italic>CNL1</italic> as the <italic>Pm55</italic> suppression gene, hereafter designated as <italic>SuPm55</italic>.</p>","<p id=\"Par17\">Additionally, we found that <italic>SuPm55</italic> knockout plants of Del5VS-1 to Del5VS-4 created by CRISPR/Cas9 had significantly lower number of tillers compared with TF5V-1 in a powdery mildew-free field (Supplementary Fig. ##SUPPL##0##10a##), resulting in lower spike number per plant (Supplementary Fig. ##SUPPL##0##10c##), as well as lower grain yield per plant (Supplementary Fig. ##SUPPL##0##10f##). While, there were no significant differences in plant height (Supplementary Fig. ##SUPPL##0##10b##), thousand-grain weight (Supplementary Fig. ##SUPPL##0##10d##) and seeds per spike (Supplementary Fig. ##SUPPL##0##10e##). Similar results were come out in T<sub>2</sub> population derived from a Del5VS-4 heterozygous T<sub>1</sub> plant, which showed the tillers and spikes reduced in plants with 27 bp deletion of <italic>SuPm55</italic> compared with plants without a deletion (Supplementary Fig. ##SUPPL##0##10g, h##, Supplementary Fig. ##SUPPL##0##11##). These results indicated that the inactivation of <italic>SuPm55</italic> could have negative effect on plant fitness.</p>","<title>Validation of the <italic>CNL2</italic> alleles function</title>","<p id=\"Par18\">To test <italic>CNL2</italic> alleles function by VIGS, we designed silencing constructs based on two specific targets in the NB-ARC domain in <italic>CNL2#5</italic>, and one target in the NB-ARC domain of <italic>CNL2#4</italic> (Supplementary Fig. ##SUPPL##0##12a, b##). Addition of the <italic>CNL2</italic> silencing constructs in infected NAU1908 (<italic>Pm5V</italic>) and Type III recombinant line R5VS-15 (<italic>Pm55</italic>) led to 50 to 80% reductions in transcript levels and abundant development of powdery mildew, whereas empty vector-inoculated plants remained resistant (Fig. ##FIG##2##3a–d##, Supplementary Fig. ##SUPPL##0##12c to f##). These results demonstrated that the <italic>CNL2</italic> alleles could be functional resistance genes in both NAU1908 and R5VS-15.</p>","<p id=\"Par19\">To further validate the function of <italic>CNL2</italic> alleles, the two full-length <italic>CNL2</italic>-derived genomic sequences with native promoters were transformed separately into susceptible wheat cv. Fielder by <italic>Agrobacterium</italic>-mediated transformation to determine whether the cloned <italic>CNL2</italic> alleles were sufficient to confer resistance (Fig. ##FIG##2##3e##). Subsequently, T<sub>0</sub> individuals were identified by PCR analysis using <italic>CNL2</italic>-specific markers. A total of eleven and 15 positive T<sub>0</sub> transgenic plants with <italic>CNL2#5</italic> and <italic>CNL2#4</italic>, respectively, were obtained. They displayed various levels of transgene transcription (Supplementary Figs. ##SUPPL##0##13a##, b and ##SUPPL##0##14 a, b##) and some of them were resistant to <italic>Bgt</italic> isolate E09 (Fig. ##FIG##2##3f,g##). T<sub>1</sub> progeny from six positive T<sub>0</sub> plants segregated 3 R: 1 S or in more complex ratios, indicative of one or more <italic>CNL2</italic> insertions (Supplementary Table ##SUPPL##0##4##). As expected, all resistant T<sub>1</sub> seedlings co-segregated with the presence of the <italic>CNL2</italic>-specific marker (Supplementary Figs. ##SUPPL##0##13c## and ##SUPPL##0##14c##). Homozygous T<sub>2</sub> seedlings inoculated by the other 18 <italic>Bgt</italic> isolates showed that <italic>CNL2#5</italic> transgenic line CNL2#5T<sub>2</sub>-9 was resistant to all isolates similar as NAU1908. While, <italic>CNL2#4</italic> transgenic line CNL2#4T<sub>2</sub>-3 was resistant to 15 isolates among them, which was similar as R5VS-15. By contrast, the non-transgenic cv. Fielder was susceptible to all races (Supplementary Table ##SUPPL##0##5##). These results demonstrated that <italic>CNL2#4</italic> and <italic>CNL2#5</italic> conferred broad-spectrum <italic>Bgt</italic> resistance but had distinct response spectra in wheat.</p>","<p id=\"Par20\">To determine whether the <italic>CNL2</italic> alleles were required for <italic>Pm55</italic> and <italic>Pm5V</italic> resistance, we generated EMS-mutagenized populations of NAU1908 and TF5V-1. M<sub>1</sub> plants were inoculated with <italic>Bgt</italic> isolate E09. Three susceptible mutants of NAU1908 were obtained and F<sub>1</sub> plants from their intercrosses were also susceptible (Fig. ##FIG##2##3h##). Sequence alignments revealed a premature stop codon in the highly conserved segment of the LRR domain of <italic>CNL2#5</italic> in line M18 (L798Stop), and missense mutations in the conserved LRR domain in lines M477 (C1065Y) and M512 (P1089L) (Fig. ##FIG##2##3j##). In addition, ten susceptible mutants of TF5V-1 were identified in adult-plant tests (Fig. ##FIG##2##3i##). Sequencing of the <italic>CNL2#4</italic> in these mutants revealed SNPs causing amino acid substitutions in each mutant (Fig. ##FIG##2##3j##, Supplementary Table ##SUPPL##0##6##). The loss of function mutations occurring in <italic>CNL2#4</italic> and <italic>CNL2#5</italic> alleles provided additional evidence that they respectively were <italic>Pm55</italic> and <italic>Pm5V</italic>. Taken together, <italic>Pm55</italic> and <italic>Pm5V</italic> were confirmed to be functional alleles, hereafter renamed as <italic>Pm55a</italic> and <italic>Pm55b</italic>, respectively.</p>","<title>Evolution and allelic variations of <italic>SuPm55</italic> and <italic>Pm55</italic></title>","<p id=\"Par21\">To determine the evolutionary relationships of <italic>SuPm55</italic> and <italic>Pm55</italic> to other known R genes, we compared their protein sequences with a panel of 33 cloned NLR proteins in wheat (Supplementary Fig. ##SUPPL##0##15##). Their phylogenetic analysis revealed that <italic>Pm55a</italic> was most closely related to <italic>Pm2</italic> (78.1% identity), and <italic>SuPm55</italic> was most closely related to <italic>Yr10</italic> (35.8% identity). Moreover, <italic>Pm55a</italic> and <italic>SuPm55</italic> represented non-homologous CNL genes, as their DNA sequences shared very low identity (28.8%). Comparative genomic analysis revealed that <italic>SuPm55</italic> homologs were absent on homoeologous group 5 chromosomes of wheat relatives rye and barley, as well as on A and D genomes of wheat, with only one copy present on the B genome. A 2875 bp deletion led to the absence of <italic>SuPm55</italic> alleles on 5VS genomes of NAU1908 and sequenced <italic>D. villosum</italic> 91C43<sup>DH</sup>. Additionally, <italic>SuPm55</italic> alleles were all absent in the other five <italic>D. villosum</italic> accessions (Supplementary Table ##SUPPL##0##1##) when detection by molecular marker. Based on this, we presume that <italic>SuPm55</italic> could be rare in <italic>D. villosum</italic>.</p>","<p id=\"Par22\">In comparing the genomes of <italic>Pm55</italic> locus with that of hexaploid wheat, we revealed low sequence conservation, indicating that <italic>Pm55</italic> locus diverged during the evolution (Supplementary Fig. ##SUPPL##0##16a##). The orthologs of <italic>CNL2</italic>, <italic>CNL3</italic> and <italic>CNL4</italic> were all absent in wheat chromosome arm 5AS and barley 5HS, indicating that the origin of <italic>Pm55</italic> predates the speciation of <italic>D. villosum</italic> and barley, probably 14.9 million years ago<sup>##REF##36587241##27##</sup>. Only one homolog of <italic>Pm55</italic> was present in each of chromosome arms 5BS and 5DS of wheat, and 5RS of rye. These homologs should be orthologous to <italic>CNL2</italic>, as <italic>CNL3</italic> was a non-complete gene, and an 84 bp sequence of <italic>CNL4</italic> was missing in all homologs in the subgenome chromosome arms (Supplementary Fig. ##SUPPL##0##16b##). <italic>Pm55</italic> orthologs in wheat and its relatives had 82 to 91% identity, significantly lower than that among <italic>Pm2</italic> orthologs ( > 93% identity) (Supplementary Table ##SUPPL##0##7##), suggesting considerable changes after divergence of <italic>D. villosum</italic> and related species. The full-length sequences of <italic>Pm55</italic> alleles isolated from the other five <italic>D. villosum</italic> accessions (named haplotypes <italic>Pm55_h1</italic> – <italic>Pm55_h5</italic>) differed (Supplementary Table ##SUPPL##0##8##), sharing sequence identity of >93% but lower than that of the <italic>Pm2</italic> series ( > 97%)<sup>##REF##27935041##28##</sup>. A phylogenetic analysis clustered all predicted proteins to a single branch, consisting of sub-clusters of PM2 and PM55 homologs. All PM2 homologs clustered together phylogenetically, as did all PM55 haplotypes (Supplementary Fig. ##SUPPL##0##17##). Nonsynonymous (Ka) and synonymous (Ks) nucleotide substitution rates among PM55 full-length proteins were determined and a relatively lower Ka/Ks ratio ( < 1.0) indicated purifying selection (Supplementary Table ##SUPPL##0##9##).</p>","<title><italic>SuPm55</italic> interacts with <italic>Pm55</italic> alleles at protein level but does not suppress <italic>Pm55b</italic>-mediated resistance</title>","<p id=\"Par23\">The expression analysis indicated that <italic>SuPm55</italic> was highly expressed in non-infected TF5V-1 seedlings, but was significantly lower after the 3-leaf stage (Fig. ##FIG##3##4a##). Additionally, <italic>SuPm55</italic> was highly expressed in TF5V-1 leaf sheaths, with negligible expression in other organs at the adult stage (Fig. ##FIG##3##4b##). These observations suggested that <italic>SuPm55</italic> expression was developmentally regulated. We also detected the significant reduction of <italic>SuPm55</italic> expression in Del5VS-4 seedlings using the qPCR primer that was located in the 27 bp deletion region (Supplementary Fig. ##SUPPL##0##8a##, Fig. ##FIG##3##4c##). However, the transcript levels of <italic>Pm55a</italic> in seedlings and adult-leaf sheaths were unaltered in TF5V-1 and Del5VS-4 plants (Fig. ##FIG##3##4d##), which indicated that <italic>SuPm55</italic> could not suppress <italic>Pm55a</italic> transcription. A probably of functional interference between <italic>Pm55a</italic> and <italic>SuPm55</italic> occurs at protein level.</p>","<p id=\"Par24\">We then conducted several assays to investigate the interaction between PM55 and SuPM55. Through Y2H assays, we found that PM55a and SuPM55 interacted through their CC domains, but not NB-ARC or LRR domains (Fig. ##FIG##3##4e##, Supplementary Fig. ##SUPPL##0##18a##). This interaction was further confirmed by co-immunoprecipitation (coIP), bimolecular fluorescence complementation (BiFC) and luciferase complementation (Luc) (Fig. ##FIG##3##4f–h##). Moreover, cell death assays demonstrated that expression of the <italic>Pm55a</italic> CC domain alone in <italic>Nicotiana benthamiana</italic> leaves induced hypersensitive response cell death in the injection region, whereas the longer CC-NBS or CC-NBS-LRR did not induce cell death responses (Supplementary Fig. ##SUPPL##0##19a##), suggesting that the full CC domain of <italic>Pm55a</italic> is active in cell-death signaling. However, the CC domain of <italic>SuPm55</italic> suppressed hypersensitive response cell death induced by <italic>Pm55a</italic> in tobacco leaves (Fig. ##FIG##3##4i##), indicating that <italic>SuPm55</italic> CC domain is essential to inhibit <italic>Pm55a</italic>-triggered cell death. Interestingly, expression of the <italic>Pm55b</italic> CC domain alone in tobacco leaves also induced hypersensitive response cell death (Supplementary Fig. ##SUPPL##0##19b##), and the CC domains of <italic>Pm55b</italic> and <italic>SuPm55</italic> interacted in Y2H assays (Supplementary Fig. ##SUPPL##0##18b##). However, the CC domain of <italic>SuPm55</italic> did not inhibit <italic>Pm55b</italic>-triggered cell death in tobacco leaves (Supplementary Fig. ##SUPPL##0##19b##), probably due to an amino acid change in the CC domain of <italic>Pm55b</italic> (Supplementary Fig. ##SUPPL##0##7##). Furthermore, type I recombinant lines (R5VS-27 to R5VS-31) that contained <italic>SuPm55</italic> and <italic>Pm55b</italic> showed no suppression in tests with <italic>Bgt</italic> isolates E09, E26 and E31 (Supplementary Fig. ##SUPPL##0##3##), as well as the R5VS-27 line showed similar response spectra as NAU1908 when tested with other 18 <italic>Bgt</italic> isolates (Supplementary Table ##SUPPL##0##5##). These findings collectively demonstrated that <italic>SuPm55</italic> did not suppress <italic>Pm55b</italic>-mediated resistance.</p>","<title>Pyramiding <italic>SuPm55</italic>/<italic>Pm55a</italic> and <italic>Pm55b</italic> in wheat shows no suppression or yield penalty</title>","<p id=\"Par25\">We crossed the T5AL·5V#4 S translocation line NAU185 (<italic>SuPm55</italic>/<italic>Pm55a</italic>) with the T5DL·5 V#5 S translocation line NAU1908 (<italic>Pm55b</italic>) in order to combine the distinct resistance conferred by 5 V#4 S and 5 V#5 S. In the F<sub>2</sub> progeny, the homozygous multi-translocations line NAU2021 was identified using GISH/FISH (Supplementary Fig.20a, Fig. ##FIG##4##5a,b##). Detailed characterization indicated that NAU2021 has the similar development stages as the background parent NAU0686 (Fig. ##FIG##4##5c##), but displayed a reduction in plant height of approximately 5.0 cm compared to NAU0686 under field conditions without powdery mildew (Fig. ##FIG##4##5d, e##). However, NAU2021 did not show a significant reduction in yield-related trials, such as seeds per spike and thousand-grain weight (Fig. ##FIG##4##5f, g##), and instead showed a slight increase in spikes per plant and plot grain yield compared to NAU0686 (Fig. ##FIG##4##5h, i##).</p>","<p id=\"Par26\">To determine if the suppression of resistance could occur between allelic <italic>Pm55a</italic> and <italic>Pm55b</italic>, we initially tested NAU2021 seedlings with <italic>Bgt</italic> isolates E09, E26 and E31. The results demonstrated that while <italic>Pm55a</italic> was susceptible to E31 (IT 4), NAU2021 seedlings did not compromise resistance to this isolate in <italic>Pm55b</italic> (Fig. ##FIG##4##5j–l##). Further, we compared the resistance spectrum of R5VS-15, NAU1908 and NAU2021 tested with the 18 <italic>Bgt</italic> isolates (Supplementary Table ##SUPPL##0##5##). The <italic>Pm55a</italic> line R5VS-15 showed susceptibility to three <italic>Bgt</italic> isolates 48-28 (IT 3), Nj-16 (IT 3) and E21-4 (IT 3), out of the 18 <italic>Bgt</italic> isolates. Whereas, <italic>Pm55b</italic> line NAU1908 exhibited medium resistance to isolates 48-18 (IT 2), 48-28 (IT 2) and Nj-16 (IT 2), and high resistance to the remaining isolates. By contrast, NAU2021 displayed additive resistance, as it showed medium resistance to isolates 48-28 (IT 2) and Nj-16 (IT 2), and immunity to the remaining 16 isolates. In addition, NAU2021 also showed immunity to powdery mildew at the adult plant stage without a susceptible leaf sheath (Fig. ##FIG##4##5c##). Overall, the combination of T5AL·5V#4 S and T5DL·5 V#5 S translocated chromosomes did not lead to mutual allele suppression of resistance or yield penalty, thus providing a strategy to pyramid distinct resistance for durable and broad-spectrum resistance to powdery mildew in wheat (Supplementary Fig. ##SUPPL##0##20b##).</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>High-resolution mapping reveals an inhibitor linked with <italic>Pm55</italic></title>\",\n \"<p id=\\\"Par8\\\">To accurately evaluate the response of the 5VS-translocation lines to powdery mildew, the translocated chromosomes T5AL·5 V#4 S, T5DL·5 V#4 S and T5DL·5 V#5 S were transferred into a highly susceptible cv. NAU0686 genetic background, respectively. Subsequently, stable translocation lines were developed in the BC<sub>5</sub>F<sub>6</sub> progeny (Supplementary Table ##SUPPL##0##1##). Upon inoculation of these lines with <italic>Bgt</italic> isolate E09, we observed that both T5AL·5 V#4 S translocation line NAU185 and T5DL·5 V#4 S line TF5V-1, carrying <italic>Pm55</italic> displayed susceptible at 3-leaf stage (IT 4), but exhibited high resistance after the 5-leaf stage (IT 1) with susceptible leaf sheaths only at adult-plant stage (Fig. ##FIG##0##1a, b##). While, T5DL·5 V#5 S translocation line NAU1908, carrying <italic>Pm5V</italic> consistently demonstrated high resistance to powdery mildew across all stages and tissues (IT 0;). Trypan blue and DAB staining showed large number of spores produced in TF5V-1 seedling leaf (3-leaf) after inoculation with E09 (Supplementary Fig. ##SUPPL##0##1a, c##), but very mild cell death and robust accumulation of H<sub>2</sub>O<sub>2</sub> in NAU1908 seedling leaf (Supplementary Fig. ##SUPPL##0##1b, d##). Collectively, chromosome arms 5 V#4 S and 5 V#5 S conferred distinct forms against powdery mildew in wheat.</p>\",\n \"<p id=\\\"Par9\\\">To map <italic>Pm55</italic> and <italic>Pm5V</italic>, a genetic mapping population containing 5425 F<sub>2</sub> individuals was previously constructed by crossing two T5DL·5VS translocation lines, TF5V-1 and NAU1908<sup>##REF##36038638##24##</sup>. <italic>Pm5V</italic> in NAU1908 was fine mapped to an approximately 0.9 Mb region referencing the genome sequence of wheat cv. Chinese Spring 5DS using forty-six crossovers on chromosome arm 5VS<sup>##REF##36038638##24##</sup>. However, it was unclear whether <italic>Pm55</italic> and <italic>Pm5V</italic> are allelic. To test their allelism, we further inoculated the F<sub>3</sub> and F<sub>4</sub> homozygous recombinant lines with <italic>Bgt</italic> isolates E09, E26 and E31 at seedling stage, and E09 at adult plant stage. As well as, sixty-three InDel markers and six EST-STS markers were used to screen these lines (Supplementary Fig. ##SUPPL##0##2##, Supplementary Data ##SUPPL##3##1##). Among these forty-six lines, we identified twenty recombinant lines between makers <italic>SCA29236</italic> and <italic>SCA12100</italic> (Type I) with the same phenotype as NAU1908, showing all-stage resistance to the three <italic>Bgt</italic> isolates. <italic>Pm5V</italic> locus was further mapped within the interval flanked by InDel markers <italic>SCA93008</italic> and <italic>SCA2324</italic>, corresponding to ~140 kb on the sequenced <italic>D. villosum</italic> 91C43<sup>DH</sup> genome<sup>##REF##36587241##27##</sup>. Additionally, we observed another set of twenty recombinant lines between makers <italic>SCA10690</italic> and <italic>SCA25706</italic> (typeII) with phenotypes identical to parent TF5V-1, showing susceptibility to all three <italic>Bgt</italic> isolates at the seedling stage but resistance to E09 at the adult plant stage, with lower susceptible sheaths. This suggested that <italic>Pm55</italic> is mapped within the interval flanked by InDel markers <italic>SCA11392</italic> and <italic>SCA23759</italic>. However, the remaining six recombinants between makers <italic>SCA11392</italic> and <italic>SCA93008</italic> (R5VS-13 to R5VS-18, type III) had contrasting phenotypes. These lines exhibited resistance to E09 and E26 but susceptibility to E31 at the seedling stage, and resistance to E09 at adult plant stage without susceptible leaf sheaths, which was distinct from the parental phenotypes of TF5V-1 and NAU1908. Based on the genotypes of type II and type III lines, one possibility is another resistance locus on 5V#5 S, flanked by InDel markers <italic>SCA11392</italic> and <italic>SCA77919</italic>, could provide the seedling resistance to E09 and E26 in type III recombination lines, or that another possibility is an inhibitor in the <italic>Pm55</italic> interval suppressed <italic>Pm55-</italic>mediated resistance to E09 and E26 in type II recombination lines. Notably, none of the recombinants was susceptible to E09 at the adult plant stage, suggesting that <italic>Pm55</italic> and <italic>Pm5V</italic> could indeed be allelic or close linkage.</p>\",\n \"<p id=\\\"Par10\\\">To fine map the resistance or suppression locus linked with <italic>Pm55</italic>/<italic>Pm5V</italic> on 5VS, a cross was made between TF5V-1 and the Type III recombination line R5VS-15 (Supplementary Fig. ##SUPPL##0##3##). All F<sub>1</sub> seedlings were susceptible to E09 but showed adult-plant resistance with lower sheaths susceptibility, similar to TF5V-1. The F<sub>2</sub> population segregated into 128 resistant and 394 susceptible seedlings (Supplementary Table ##SUPPL##0##2##). However, all the F<sub>2</sub> plants showed adult-plant resistance, but the seedling susceptible plants of them had lower sheaths susceptibility. These results indicated that there was not a resistance gene on 5V#5 S, but a dominant inhibitor linked with <italic>Pm55</italic> on 5 V#4 S, suppressing <italic>Pm55-</italic>mediated resistance on seedlings and adult-plant leaf sheaths. Further genotyping of 3882 F<sub>2</sub> individuals identified six crossovers between markers <italic>SCA11392</italic> and <italic>SCA77919</italic>. Combination with five newly developed markers, the suppression interval was narrowed down to a ~ 100 kb region flanked by InDel markers <italic>SCA4218</italic> and <italic>SCA39816</italic> by comparison with the <italic>D. villosum</italic> 91C43<sup>DH</sup> genome (Supplementary Fig. ##SUPPL##0##4##, Fig. ##FIG##0##1c## and Supplementary Data ##SUPPL##4##2##). In summary, a single gene <italic>Pm5V</italic> in NAU1908 conferred resistance to powdery mildew at all stages and tissues, whereas <italic>Pm55</italic> together with a linked inhibitor in TF5V-1 conferred the developmental-stage and tissue-specific resistance.</p>\",\n \"<title>Dissection of the candidates for both intervals</title>\",\n \"<p id=\\\"Par11\\\">Diploid <italic>D. villosum</italic> is an outcrossing species that may produce progenies with complex haplotypes in genome locus of interest. Thus, DNA sequences of chromosome arms 5V#4 S and 5 V#5 S are necessary to isolate the haplotypes in the aimed loci on them. Previously, we flow-sorted and sequenced chromosome arm 5 V#5 S, resulting in 173.4 Mb sequence with an average scaffold N50 size of 22.4 kb<sup>##REF##36038638##24##</sup>. Building on this, we further isolated T5DL·5 V#4 S translocated chromosome from TF5V-1 by flow sorting and then sequenced it using Illumina short read technology (Supplementary Fig. ##SUPPL##0##5a, b##). The size of the 5V#4 S de novo assembly was 171.8 Mb with an average scaffold N50 size of 18.3 kb, including 1262 high-confidence protein-coding genes annotated using protein homology-based prediction methods.</p>\",\n \"<p id=\\\"Par12\\\">To isolate inhibitor gene, the inhibitor physical region between markers <italic>SCA4218</italic> and <italic>SCA39816</italic> on 5 VS of 91C43<sup>DH</sup> was first subjected to collinearity analysis with Chinese Spring 5AS, 5BS and 5DS, revealing that the annotated genes in this region across different genomes were highly conserved (Supplementary Fig. ##SUPPL##0##5c##). Subsequently, we aligned the homologies of annotated genes between 5V#4 S and 5 V#5 S scaffolds and the inhibitor physical region on different genomes. We identified two 5 V#5 S scaffolds, Scaffold4218 (20,202 bp) and Scaffold3806 (39,439 bp), and two 5 V#4 S scaffolds, Scaffold15749 (54,335 bp) and Scaffold38916 (46,667 bp), containing homologs of the genes located in the inhibitor region of 5AS, 5BS and 5DS, which generated a contiguous 100,963 bp sequence between markers <italic>SCA4218</italic> and <italic>SCA39816</italic> (Supplementary Fig. ##SUPPL##0##5d##). This combination sequence included six annotated genes. Among them, an intact coiled-coil nucleotide-binding leucine-rich repeat (CNL) gene, <italic>CNL1</italic> (G4), along with gene G3 were absent in the inhibitor region on 5 VS of 91C43<sup>DH</sup> (Fig. ##FIG##0##1d##, Supplementary Fig. ##SUPPL##0##5c##). Full-length transcriptome sequencing of TF5V-1 seedling leaves inoculating with <italic>Bgt</italic> isolate E09 revealed that <italic>CNL1</italic> was the only intact transcribed gene of the six annotated genes, and therefore could be the candidate of a functional gene. The full genomic sequence of <italic>CNL1</italic> in 5V#4 S was further confirmed by PCR, which spanned 2651 base pairs (bp) from ATG start codon to TGA stop codon with six exons and five introns, and generating a predicted 565 amino-acid protein (Fig. ##FIG##0##1f##).</p>\",\n \"<p id=\\\"Par13\\\">However, the 5 V#4 S and 5 V#5 S scaffolds containing the homologs of annotated genes on the genomes of CS 5AS, 5BS and 5DS between <italic>Pm55</italic>/<italic>Pm5V</italic> linked markers <italic>SCA93008</italic> and <italic>SCA2324</italic> did not cover the <italic>Pm55</italic>/<italic>Pm5V</italic> interval. Thus, we constructed a bacterial artificial chromosome (BAC) library using DNA from <italic>D. villosum</italic> 01I140 (the donor of 5V#5 S) for long-read sequencing. Screening the BAC library with markers <italic>SCA93008</italic> and <italic>SCA2324</italic> enabled us to identify and sequence ten BACs. Out of them, BAC5 and BAC8 overlapped the resistance region forming a contiguous 134,990 bp sequence that contained seven annotated genes, including three CNL paralogs <italic>CNL2</italic>, <italic>CNL3</italic> and <italic>CNL4</italic> (Fig. ##FIG##0##1e##). <italic>CNL2</italic> and <italic>CNL4</italic> were intact genes, but <italic>CNL3</italic> was a pseudogene due to a disrupted open reading frame (ORF). <italic>CNL2</italic> and <italic>CNL3</italic> were highly similar (94.8% identity) whereas the similarity of <italic>CNL2</italic> and <italic>CNL4</italic> was lower (81.8% identity). Gene expression analysis revealed that only <italic>CNL2</italic> was induced expression in seedlings of NAU1908 after infection with <italic>Bgt</italic> isolate E09 (Supplementary Fig. ##SUPPL##0##6a##). Moreover, <italic>Pm2-5V</italic>, the <italic>Pm2</italic> ortholog on 5 VS was also used as the control to verify the expression of the <italic>CNL2</italic> alleles induced by <italic>Bgt</italic> infection. Results showed that <italic>CNL2#4</italic> allele transcript levels in Type III recombination line R5VS-15 were approximately eight-fold higher at 72 hpi with isolate E09 (Supplementary Fig. ##SUPPL##0##6b##), and <italic>CNL2#5</italic> transcript levels in NAU1908 were approximately three-fold higher at 24 hpi (Supplementary Fig. ##SUPPL##0##6c##). By contrast, the transcript level of <italic>Pm2</italic> orthologs, <italic>Pm2-5V#4</italic> and <italic>Pm2-5V#5</italic> did not change significantly during 0–72 hpi. Accordingly, <italic>CNL2</italic> alleles were prioritized as the functional gene candidates.</p>\",\n \"<p id=\\\"Par14\\\">The full genomic sequence of <italic>CNL2</italic> allele in NAU1908 (designed as <italic>CNL2#5</italic>) amplified by PCR was identical to those of the BAC sequences spanning 3887 bp from the translation initiation (ATG) to the stop (TAA) codons, including two exons and one intron and generated a predicted 1265 amino-acid protein (Fig. ##FIG##0##1g##). We identified a 5 V#4 S scaffold, Scaffold23759 (23,842 bp), which contained the full length of the <italic>CNL2</italic> allele (Fig. ##FIG##0##1e##). Utilizing this sequence, we amplified the full length of the <italic>CNL2</italic> allele in TF5V-1 (designed as <italic>CNL2#4</italic>), which was 3896 bp from the translation initiation (ATG) to the stop (TGA) codon, generating a predicted 1267 amino-acid protein. <italic>CNL2#4</italic> and <italic>CNL2#5</italic> shared 95.2% identity in gDNA sequence and 91.5% identity in amino acids at the protein level (Supplementary Fig. ##SUPPL##0##7##).</p>\",\n \"<title>Validation of the <italic>CNL1</italic> function</title>\",\n \"<p id=\\\"Par15\\\">We used virus-induced gene silencing (VIGS) to knock down <italic>CNL1</italic> in TF5V-1 to verify its function. A construct targeting the CC domain of <italic>CNL1</italic> in TF5V-1 resulted in resistant leaves at seedling stage (Fig. ##FIG##1##2a##). A comparison of mRNA expression by qPCR in TF5V-1 leaves infected with BSMV: <italic>CNL1</italic> and wild-type BSMV: γ virus showed a significant decrease in expression levels of <italic>CNL1</italic> transcripts (Fig. ##FIG##1##2b##). These results indicated that <italic>CNL1</italic> suppressed the seedling resistance of TF5V-1.</p>\",\n \"<p id=\\\"Par16\\\">CRISPR/Cas9-mediated genome editing technology was then employed to knock out <italic>CNL1</italic> in TF5V-1. We used guide RNAs (gRNAs) targeting the conserved regions in CC domain of <italic>CNL1</italic> (Supplementary Fig. ##SUPPL##0##8a##), and obtained four mutant lines with 4-bp (Del5VS-1), 5-bp (Del5VS-2), 6-bp (Del5VS-3) and 27-bp (Del5VS-4) deletions in the target region as putative knockouts of the <italic>CNL1</italic> gene (Fig. ##FIG##1##2c##). The inoculation of <italic>Bgt</italic> isolate E09 showed that the amounts of fungal growth and spores on the seedling leaves of four homozygous mutants significant reduced compared with TF5V-1 (Fig. ##FIG##1##2d,e##, Supplementary Fig. ##SUPPL##0##8b to f##). Homozygous T<sub>2</sub> progeny from a Del5VS-4 heterozygous T<sub>1</sub> plant were all ASR to isolate E09 without lower leaf sheath susceptibility (Fig. ##FIG##1##2f, g## Supplementary Fig. ##SUPPL##0##9a, b## and Supplementary Table ##SUPPL##0##3##), confirming the role of <italic>CNL1</italic> as the <italic>Pm55</italic> suppression gene, hereafter designated as <italic>SuPm55</italic>.</p>\",\n \"<p id=\\\"Par17\\\">Additionally, we found that <italic>SuPm55</italic> knockout plants of Del5VS-1 to Del5VS-4 created by CRISPR/Cas9 had significantly lower number of tillers compared with TF5V-1 in a powdery mildew-free field (Supplementary Fig. ##SUPPL##0##10a##), resulting in lower spike number per plant (Supplementary Fig. ##SUPPL##0##10c##), as well as lower grain yield per plant (Supplementary Fig. ##SUPPL##0##10f##). While, there were no significant differences in plant height (Supplementary Fig. ##SUPPL##0##10b##), thousand-grain weight (Supplementary Fig. ##SUPPL##0##10d##) and seeds per spike (Supplementary Fig. ##SUPPL##0##10e##). Similar results were come out in T<sub>2</sub> population derived from a Del5VS-4 heterozygous T<sub>1</sub> plant, which showed the tillers and spikes reduced in plants with 27 bp deletion of <italic>SuPm55</italic> compared with plants without a deletion (Supplementary Fig. ##SUPPL##0##10g, h##, Supplementary Fig. ##SUPPL##0##11##). These results indicated that the inactivation of <italic>SuPm55</italic> could have negative effect on plant fitness.</p>\",\n \"<title>Validation of the <italic>CNL2</italic> alleles function</title>\",\n \"<p id=\\\"Par18\\\">To test <italic>CNL2</italic> alleles function by VIGS, we designed silencing constructs based on two specific targets in the NB-ARC domain in <italic>CNL2#5</italic>, and one target in the NB-ARC domain of <italic>CNL2#4</italic> (Supplementary Fig. ##SUPPL##0##12a, b##). Addition of the <italic>CNL2</italic> silencing constructs in infected NAU1908 (<italic>Pm5V</italic>) and Type III recombinant line R5VS-15 (<italic>Pm55</italic>) led to 50 to 80% reductions in transcript levels and abundant development of powdery mildew, whereas empty vector-inoculated plants remained resistant (Fig. ##FIG##2##3a–d##, Supplementary Fig. ##SUPPL##0##12c to f##). These results demonstrated that the <italic>CNL2</italic> alleles could be functional resistance genes in both NAU1908 and R5VS-15.</p>\",\n \"<p id=\\\"Par19\\\">To further validate the function of <italic>CNL2</italic> alleles, the two full-length <italic>CNL2</italic>-derived genomic sequences with native promoters were transformed separately into susceptible wheat cv. Fielder by <italic>Agrobacterium</italic>-mediated transformation to determine whether the cloned <italic>CNL2</italic> alleles were sufficient to confer resistance (Fig. ##FIG##2##3e##). Subsequently, T<sub>0</sub> individuals were identified by PCR analysis using <italic>CNL2</italic>-specific markers. A total of eleven and 15 positive T<sub>0</sub> transgenic plants with <italic>CNL2#5</italic> and <italic>CNL2#4</italic>, respectively, were obtained. They displayed various levels of transgene transcription (Supplementary Figs. ##SUPPL##0##13a##, b and ##SUPPL##0##14 a, b##) and some of them were resistant to <italic>Bgt</italic> isolate E09 (Fig. ##FIG##2##3f,g##). T<sub>1</sub> progeny from six positive T<sub>0</sub> plants segregated 3 R: 1 S or in more complex ratios, indicative of one or more <italic>CNL2</italic> insertions (Supplementary Table ##SUPPL##0##4##). As expected, all resistant T<sub>1</sub> seedlings co-segregated with the presence of the <italic>CNL2</italic>-specific marker (Supplementary Figs. ##SUPPL##0##13c## and ##SUPPL##0##14c##). Homozygous T<sub>2</sub> seedlings inoculated by the other 18 <italic>Bgt</italic> isolates showed that <italic>CNL2#5</italic> transgenic line CNL2#5T<sub>2</sub>-9 was resistant to all isolates similar as NAU1908. While, <italic>CNL2#4</italic> transgenic line CNL2#4T<sub>2</sub>-3 was resistant to 15 isolates among them, which was similar as R5VS-15. By contrast, the non-transgenic cv. Fielder was susceptible to all races (Supplementary Table ##SUPPL##0##5##). These results demonstrated that <italic>CNL2#4</italic> and <italic>CNL2#5</italic> conferred broad-spectrum <italic>Bgt</italic> resistance but had distinct response spectra in wheat.</p>\",\n \"<p id=\\\"Par20\\\">To determine whether the <italic>CNL2</italic> alleles were required for <italic>Pm55</italic> and <italic>Pm5V</italic> resistance, we generated EMS-mutagenized populations of NAU1908 and TF5V-1. M<sub>1</sub> plants were inoculated with <italic>Bgt</italic> isolate E09. Three susceptible mutants of NAU1908 were obtained and F<sub>1</sub> plants from their intercrosses were also susceptible (Fig. ##FIG##2##3h##). Sequence alignments revealed a premature stop codon in the highly conserved segment of the LRR domain of <italic>CNL2#5</italic> in line M18 (L798Stop), and missense mutations in the conserved LRR domain in lines M477 (C1065Y) and M512 (P1089L) (Fig. ##FIG##2##3j##). In addition, ten susceptible mutants of TF5V-1 were identified in adult-plant tests (Fig. ##FIG##2##3i##). Sequencing of the <italic>CNL2#4</italic> in these mutants revealed SNPs causing amino acid substitutions in each mutant (Fig. ##FIG##2##3j##, Supplementary Table ##SUPPL##0##6##). The loss of function mutations occurring in <italic>CNL2#4</italic> and <italic>CNL2#5</italic> alleles provided additional evidence that they respectively were <italic>Pm55</italic> and <italic>Pm5V</italic>. Taken together, <italic>Pm55</italic> and <italic>Pm5V</italic> were confirmed to be functional alleles, hereafter renamed as <italic>Pm55a</italic> and <italic>Pm55b</italic>, respectively.</p>\",\n \"<title>Evolution and allelic variations of <italic>SuPm55</italic> and <italic>Pm55</italic></title>\",\n \"<p id=\\\"Par21\\\">To determine the evolutionary relationships of <italic>SuPm55</italic> and <italic>Pm55</italic> to other known R genes, we compared their protein sequences with a panel of 33 cloned NLR proteins in wheat (Supplementary Fig. ##SUPPL##0##15##). Their phylogenetic analysis revealed that <italic>Pm55a</italic> was most closely related to <italic>Pm2</italic> (78.1% identity), and <italic>SuPm55</italic> was most closely related to <italic>Yr10</italic> (35.8% identity). Moreover, <italic>Pm55a</italic> and <italic>SuPm55</italic> represented non-homologous CNL genes, as their DNA sequences shared very low identity (28.8%). Comparative genomic analysis revealed that <italic>SuPm55</italic> homologs were absent on homoeologous group 5 chromosomes of wheat relatives rye and barley, as well as on A and D genomes of wheat, with only one copy present on the B genome. A 2875 bp deletion led to the absence of <italic>SuPm55</italic> alleles on 5VS genomes of NAU1908 and sequenced <italic>D. villosum</italic> 91C43<sup>DH</sup>. Additionally, <italic>SuPm55</italic> alleles were all absent in the other five <italic>D. villosum</italic> accessions (Supplementary Table ##SUPPL##0##1##) when detection by molecular marker. Based on this, we presume that <italic>SuPm55</italic> could be rare in <italic>D. villosum</italic>.</p>\",\n \"<p id=\\\"Par22\\\">In comparing the genomes of <italic>Pm55</italic> locus with that of hexaploid wheat, we revealed low sequence conservation, indicating that <italic>Pm55</italic> locus diverged during the evolution (Supplementary Fig. ##SUPPL##0##16a##). The orthologs of <italic>CNL2</italic>, <italic>CNL3</italic> and <italic>CNL4</italic> were all absent in wheat chromosome arm 5AS and barley 5HS, indicating that the origin of <italic>Pm55</italic> predates the speciation of <italic>D. villosum</italic> and barley, probably 14.9 million years ago<sup>##REF##36587241##27##</sup>. Only one homolog of <italic>Pm55</italic> was present in each of chromosome arms 5BS and 5DS of wheat, and 5RS of rye. These homologs should be orthologous to <italic>CNL2</italic>, as <italic>CNL3</italic> was a non-complete gene, and an 84 bp sequence of <italic>CNL4</italic> was missing in all homologs in the subgenome chromosome arms (Supplementary Fig. ##SUPPL##0##16b##). <italic>Pm55</italic> orthologs in wheat and its relatives had 82 to 91% identity, significantly lower than that among <italic>Pm2</italic> orthologs ( > 93% identity) (Supplementary Table ##SUPPL##0##7##), suggesting considerable changes after divergence of <italic>D. villosum</italic> and related species. The full-length sequences of <italic>Pm55</italic> alleles isolated from the other five <italic>D. villosum</italic> accessions (named haplotypes <italic>Pm55_h1</italic> – <italic>Pm55_h5</italic>) differed (Supplementary Table ##SUPPL##0##8##), sharing sequence identity of >93% but lower than that of the <italic>Pm2</italic> series ( > 97%)<sup>##REF##27935041##28##</sup>. A phylogenetic analysis clustered all predicted proteins to a single branch, consisting of sub-clusters of PM2 and PM55 homologs. All PM2 homologs clustered together phylogenetically, as did all PM55 haplotypes (Supplementary Fig. ##SUPPL##0##17##). Nonsynonymous (Ka) and synonymous (Ks) nucleotide substitution rates among PM55 full-length proteins were determined and a relatively lower Ka/Ks ratio ( < 1.0) indicated purifying selection (Supplementary Table ##SUPPL##0##9##).</p>\",\n \"<title><italic>SuPm55</italic> interacts with <italic>Pm55</italic> alleles at protein level but does not suppress <italic>Pm55b</italic>-mediated resistance</title>\",\n \"<p id=\\\"Par23\\\">The expression analysis indicated that <italic>SuPm55</italic> was highly expressed in non-infected TF5V-1 seedlings, but was significantly lower after the 3-leaf stage (Fig. ##FIG##3##4a##). Additionally, <italic>SuPm55</italic> was highly expressed in TF5V-1 leaf sheaths, with negligible expression in other organs at the adult stage (Fig. ##FIG##3##4b##). These observations suggested that <italic>SuPm55</italic> expression was developmentally regulated. We also detected the significant reduction of <italic>SuPm55</italic> expression in Del5VS-4 seedlings using the qPCR primer that was located in the 27 bp deletion region (Supplementary Fig. ##SUPPL##0##8a##, Fig. ##FIG##3##4c##). However, the transcript levels of <italic>Pm55a</italic> in seedlings and adult-leaf sheaths were unaltered in TF5V-1 and Del5VS-4 plants (Fig. ##FIG##3##4d##), which indicated that <italic>SuPm55</italic> could not suppress <italic>Pm55a</italic> transcription. A probably of functional interference between <italic>Pm55a</italic> and <italic>SuPm55</italic> occurs at protein level.</p>\",\n \"<p id=\\\"Par24\\\">We then conducted several assays to investigate the interaction between PM55 and SuPM55. Through Y2H assays, we found that PM55a and SuPM55 interacted through their CC domains, but not NB-ARC or LRR domains (Fig. ##FIG##3##4e##, Supplementary Fig. ##SUPPL##0##18a##). This interaction was further confirmed by co-immunoprecipitation (coIP), bimolecular fluorescence complementation (BiFC) and luciferase complementation (Luc) (Fig. ##FIG##3##4f–h##). Moreover, cell death assays demonstrated that expression of the <italic>Pm55a</italic> CC domain alone in <italic>Nicotiana benthamiana</italic> leaves induced hypersensitive response cell death in the injection region, whereas the longer CC-NBS or CC-NBS-LRR did not induce cell death responses (Supplementary Fig. ##SUPPL##0##19a##), suggesting that the full CC domain of <italic>Pm55a</italic> is active in cell-death signaling. However, the CC domain of <italic>SuPm55</italic> suppressed hypersensitive response cell death induced by <italic>Pm55a</italic> in tobacco leaves (Fig. ##FIG##3##4i##), indicating that <italic>SuPm55</italic> CC domain is essential to inhibit <italic>Pm55a</italic>-triggered cell death. Interestingly, expression of the <italic>Pm55b</italic> CC domain alone in tobacco leaves also induced hypersensitive response cell death (Supplementary Fig. ##SUPPL##0##19b##), and the CC domains of <italic>Pm55b</italic> and <italic>SuPm55</italic> interacted in Y2H assays (Supplementary Fig. ##SUPPL##0##18b##). However, the CC domain of <italic>SuPm55</italic> did not inhibit <italic>Pm55b</italic>-triggered cell death in tobacco leaves (Supplementary Fig. ##SUPPL##0##19b##), probably due to an amino acid change in the CC domain of <italic>Pm55b</italic> (Supplementary Fig. ##SUPPL##0##7##). Furthermore, type I recombinant lines (R5VS-27 to R5VS-31) that contained <italic>SuPm55</italic> and <italic>Pm55b</italic> showed no suppression in tests with <italic>Bgt</italic> isolates E09, E26 and E31 (Supplementary Fig. ##SUPPL##0##3##), as well as the R5VS-27 line showed similar response spectra as NAU1908 when tested with other 18 <italic>Bgt</italic> isolates (Supplementary Table ##SUPPL##0##5##). These findings collectively demonstrated that <italic>SuPm55</italic> did not suppress <italic>Pm55b</italic>-mediated resistance.</p>\",\n \"<title>Pyramiding <italic>SuPm55</italic>/<italic>Pm55a</italic> and <italic>Pm55b</italic> in wheat shows no suppression or yield penalty</title>\",\n \"<p id=\\\"Par25\\\">We crossed the T5AL·5V#4 S translocation line NAU185 (<italic>SuPm55</italic>/<italic>Pm55a</italic>) with the T5DL·5 V#5 S translocation line NAU1908 (<italic>Pm55b</italic>) in order to combine the distinct resistance conferred by 5 V#4 S and 5 V#5 S. In the F<sub>2</sub> progeny, the homozygous multi-translocations line NAU2021 was identified using GISH/FISH (Supplementary Fig.20a, Fig. ##FIG##4##5a,b##). Detailed characterization indicated that NAU2021 has the similar development stages as the background parent NAU0686 (Fig. ##FIG##4##5c##), but displayed a reduction in plant height of approximately 5.0 cm compared to NAU0686 under field conditions without powdery mildew (Fig. ##FIG##4##5d, e##). However, NAU2021 did not show a significant reduction in yield-related trials, such as seeds per spike and thousand-grain weight (Fig. ##FIG##4##5f, g##), and instead showed a slight increase in spikes per plant and plot grain yield compared to NAU0686 (Fig. ##FIG##4##5h, i##).</p>\",\n \"<p id=\\\"Par26\\\">To determine if the suppression of resistance could occur between allelic <italic>Pm55a</italic> and <italic>Pm55b</italic>, we initially tested NAU2021 seedlings with <italic>Bgt</italic> isolates E09, E26 and E31. The results demonstrated that while <italic>Pm55a</italic> was susceptible to E31 (IT 4), NAU2021 seedlings did not compromise resistance to this isolate in <italic>Pm55b</italic> (Fig. ##FIG##4##5j–l##). Further, we compared the resistance spectrum of R5VS-15, NAU1908 and NAU2021 tested with the 18 <italic>Bgt</italic> isolates (Supplementary Table ##SUPPL##0##5##). The <italic>Pm55a</italic> line R5VS-15 showed susceptibility to three <italic>Bgt</italic> isolates 48-28 (IT 3), Nj-16 (IT 3) and E21-4 (IT 3), out of the 18 <italic>Bgt</italic> isolates. Whereas, <italic>Pm55b</italic> line NAU1908 exhibited medium resistance to isolates 48-18 (IT 2), 48-28 (IT 2) and Nj-16 (IT 2), and high resistance to the remaining isolates. By contrast, NAU2021 displayed additive resistance, as it showed medium resistance to isolates 48-28 (IT 2) and Nj-16 (IT 2), and immunity to the remaining 16 isolates. In addition, NAU2021 also showed immunity to powdery mildew at the adult plant stage without a susceptible leaf sheath (Fig. ##FIG##4##5c##). Overall, the combination of T5AL·5V#4 S and T5DL·5 V#5 S translocated chromosomes did not lead to mutual allele suppression of resistance or yield penalty, thus providing a strategy to pyramid distinct resistance for durable and broad-spectrum resistance to powdery mildew in wheat (Supplementary Fig. ##SUPPL##0##20b##).</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par27\">Alien introgressions have greatly enriched the otherwise limited gene pool in bread wheat. However, the successful map-based cloning of introgressed resistance genes has often been hampered by insufficient recombination in the alien chromatin regions<sup>##REF##27909815##29##</sup>. Here, we employ T5DL·5 VS derivatives of different <italic>D. villosum</italic> accessions to develop the recombinants of chromosome arm 5VS. The identification of homologous recombinants of chromosome arm 5VS contributes to the evaluation of the complex responses to powdery mildew and ultimately confirms the resistance and suppression loci on 5VS. Importantly, our mapped-based cloning efforts were facilitated using chromosome flow sorting and sequencing 5V#4 S and 5 V#5 S DNA. The sequences obtained not only helped to develop polymorphism InDel markers for genetic mapping, but also facilitated in aligning their annotated genes with the reference genomes within the interested genome locus. The alignment of the annotated genes in the <italic>SuPm55</italic> interval with 5AS, 5BS, and 5DS of Chinese Spring revealed high collinearity, providing valuable insights into genetic variability and potential candidate genes for further analysis. Through the assembly of two 5 V#4 S scaffolds combined with two 5 V#5 S scaffolds, we obtained a contiguous sequence covering the <italic>SuPm55</italic> interval based on the high identity sequences that they shared. This led to the identification of six annotated genes as candidates for <italic>SuPm55</italic>. Notably, <italic>SuPm55</italic> (<italic>CNL1</italic>) is rare in <italic>D. villosum</italic>, and is absent in both 5 VS of 91C43<sup>DH</sup> and 5V#5 S, suggesting that the 5 V#4 S flow-sorted sequence plays a key role in isolating <italic>SuPm55</italic>. While 5 VS short sequences did not cover the <italic>Pm55</italic> interval, the scaffolds containing upstream and downstream sequences of <italic>Pm55</italic> alleles are still valuable for functional analysis. Therefore, a combination of the reference genome and the flow-sorted target genome sequences represents a valid approach for cloning alien genes in wheat, especially for wild outcrossing species that usually results in complex haplotypes within the interested genetic locus.</p>","<p id=\"Par28\">The successful cloning of alien genes <italic>Pm55</italic>a and <italic>Pm55b</italic> adds to the repertoire of more than 10 NLR genes conferring powdery mildew resistance in wheat. Notably, <italic>Pm55</italic> is not orthologous to <italic>Pm2</italic> on chromosome arm 5 VS of <italic>D. villosum</italic>, which is distinct from other alien genes such as <italic>Pm8</italic>, an ortholog of wheat <italic>Pm3</italic><sup>##REF##24124925##30##</sup>, and <italic>Pm21</italic>, an ortholog of wheat <italic>Pm12</italic><sup>##UREF##7##31##</sup>. While the <italic>Pm2</italic> gene has been widely utilized in wheat breeding, it is currently losing its effectiveness against prevalent <italic>Bgt</italic> isolates in the main wheat production regions in China<sup>##REF##36018491##32##</sup>. However, its ancestral orthologs, genetic mapping and expression analysis of <italic>Pm2-5V#4</italic> in TF5V-1 and <italic>Pm2-5V#5</italic> in NAU1908 revealed that they do not confer resistance. In contrast to highly conserved <italic>Pm2</italic> homologs in wheat and related genomes, the <italic>Pm55</italic> homologs exhibit significant sequence diversity and rapid evolution. The variation in amino acid composition between the PM55a and PM55b proteins is mainly located in the LRR domain, leading to a distinct resistance spectrum against <italic>Bgt</italic> isolates. Both <italic>Pm55a</italic> and <italic>Pm55b</italic> demonstrate broad-spectrum resistance powdery mildew based on infection assays with 21 <italic>Bgt</italic> isolates, highlighting their potential as resistance genes in wheat breeding.</p>","<p id=\"Par29\">Previous studies demonstrated that the CC domain heterodimerization of NLR proteins can abrogate disease resistance, as the homodimerization of NLR receptors via CC domains plays a role in R-mediated immunity<sup>##REF##33205883##33##</sup>. For example, <italic>PigmS</italic> competitively attenuates paralogous <italic>PigmR</italic> homodimerization through heterodimers in their CC domains to suppress the blast resistance in rice<sup>##REF##28154240##4##</sup>. However, in case of homologous NLRs suppression among <italic>Pm3</italic> alleles, the N-terminal part of the LRR domain was identified as the major determinant of suppression<sup>##REF##24942051##20##</sup>. In addition, <italic>SuSr-D1</italic>, a subunit of the mediator complex gene, has been shown to suppress stem rust resistance in wheat through regulating the expression of resistance genes<sup>##REF##32111840##19##</sup>. Here, despite <italic>Pm55</italic> and <italic>SuPm55</italic> encode unrelated NLR proteins, the <italic>SuPm55</italic> CC domain can interact to suppress hypersensitive response cell death induced by <italic>Pm55a</italic>, but it does not inhibit <italic>Pm55b</italic>-triggered cell death in tobacco leaves. This suggests that the inhibition of triggered cell death may play a crucial role in the suppression of <italic>Pm55a</italic>-mediated resistance. Thus, the interactions of <italic>Pm55</italic> alleles with <italic>SuPm55</italic> might follow a different mechanism than that observed in interactions among homologous NLR immune receptors.</p>","<p id=\"Par30\">Plant NLR genes function as singletons, in pairs, or in networks in mediating resistance against pathogens<sup>##REF##31154077##3##</sup>. Paired NLRs have been shown to regulate NLR-mediated resistance by balancing defense and fitness<sup>##REF##30011120##11##</sup>. For examples, the rice NLR pairs <italic>RGA4</italic>/<italic>RGA5</italic> confer resistance to <italic>Magnaporthe oryzae</italic><sup>##REF##25024433##34##</sup>, and <italic>PigmS</italic>/<italic>PigmR</italic> confer rice blast resistance<sup>##REF##28154240##4##</sup>. In these cases, one of the NLRs triggers cell death but is constitutively expressed, while the other NLR represses its partner to prevent autoimmunity. Our results demonstrate that <italic>Pm55a</italic> is induced expression by <italic>Bgt</italic> and functions as singleton in transgenic plants, whereas the expression of antagonistic gene <italic>SuPm55</italic> is developmentally regulated by an unclear mechanism. In this case, introduction of <italic>Pm55a</italic> alone does not lead to autoimmunity in wheat, suggesting that the interaction between <italic>Pm55a</italic> and <italic>SuPm55</italic> differs from the paired NLRs. Interestingly, the inactivation of <italic>SuPm55</italic> significantly reduces grain yield, indicating that the expression of <italic>SuPm55</italic> in both seedlings and adult-leaf sheaths contributes to enhancing plant fitness. These results highlight that certain singleton NLRs should not be considered as individual entities, but rather as coevolutionary components with their inhibitors when utilized in breeding programs aimed at developing high-yield, efficient disease-resistance crops.</p>","<p id=\"Par31\">To breed crops with effective and durable resistance, it is highly desirable to combine different forms of resistance<sup>##REF##25505474##10##,##REF##29382771##35##,##REF##33398152##36##</sup>. However, resistance alleles cannot be combined through classical crossbreeding. This limitation can only be overcome through genetic engineering or the use of F<sub>1</sub> hybrids<sup>##REF##29302719##37##</sup>. In our study, we achieved the combination of distinct powdery mildew resistance conferred by <italic>Pm55</italic> alleles through T5AL·5V#4 S translocation with <italic>SuPm55</italic>/<italic>Pm55a</italic> and T5DL·5 V#5 S translocation with <italic>Pm55b</italic>. The T5AL·5 V#4 S and T5DL·5 V#5 S translocations line NAU2021, developed by the combination of classical crossbreeding, showed no mutual allele suppression or yield penalty in the field. Moreover, NAU2021 exhibited additional effective resistance when pyramiding the two translocated chromosomes in wheat. Therefore, the multi-translocations line NAU2021 represents a valuable resource for wheat improvement and provides a practical basis for pyramiding allelic series via crossbreeding to achieve a durable resistance.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par27\\\">Alien introgressions have greatly enriched the otherwise limited gene pool in bread wheat. However, the successful map-based cloning of introgressed resistance genes has often been hampered by insufficient recombination in the alien chromatin regions<sup>##REF##27909815##29##</sup>. Here, we employ T5DL·5 VS derivatives of different <italic>D. villosum</italic> accessions to develop the recombinants of chromosome arm 5VS. The identification of homologous recombinants of chromosome arm 5VS contributes to the evaluation of the complex responses to powdery mildew and ultimately confirms the resistance and suppression loci on 5VS. Importantly, our mapped-based cloning efforts were facilitated using chromosome flow sorting and sequencing 5V#4 S and 5 V#5 S DNA. The sequences obtained not only helped to develop polymorphism InDel markers for genetic mapping, but also facilitated in aligning their annotated genes with the reference genomes within the interested genome locus. The alignment of the annotated genes in the <italic>SuPm55</italic> interval with 5AS, 5BS, and 5DS of Chinese Spring revealed high collinearity, providing valuable insights into genetic variability and potential candidate genes for further analysis. Through the assembly of two 5 V#4 S scaffolds combined with two 5 V#5 S scaffolds, we obtained a contiguous sequence covering the <italic>SuPm55</italic> interval based on the high identity sequences that they shared. This led to the identification of six annotated genes as candidates for <italic>SuPm55</italic>. Notably, <italic>SuPm55</italic> (<italic>CNL1</italic>) is rare in <italic>D. villosum</italic>, and is absent in both 5 VS of 91C43<sup>DH</sup> and 5V#5 S, suggesting that the 5 V#4 S flow-sorted sequence plays a key role in isolating <italic>SuPm55</italic>. While 5 VS short sequences did not cover the <italic>Pm55</italic> interval, the scaffolds containing upstream and downstream sequences of <italic>Pm55</italic> alleles are still valuable for functional analysis. Therefore, a combination of the reference genome and the flow-sorted target genome sequences represents a valid approach for cloning alien genes in wheat, especially for wild outcrossing species that usually results in complex haplotypes within the interested genetic locus.</p>\",\n \"<p id=\\\"Par28\\\">The successful cloning of alien genes <italic>Pm55</italic>a and <italic>Pm55b</italic> adds to the repertoire of more than 10 NLR genes conferring powdery mildew resistance in wheat. Notably, <italic>Pm55</italic> is not orthologous to <italic>Pm2</italic> on chromosome arm 5 VS of <italic>D. villosum</italic>, which is distinct from other alien genes such as <italic>Pm8</italic>, an ortholog of wheat <italic>Pm3</italic><sup>##REF##24124925##30##</sup>, and <italic>Pm21</italic>, an ortholog of wheat <italic>Pm12</italic><sup>##UREF##7##31##</sup>. While the <italic>Pm2</italic> gene has been widely utilized in wheat breeding, it is currently losing its effectiveness against prevalent <italic>Bgt</italic> isolates in the main wheat production regions in China<sup>##REF##36018491##32##</sup>. However, its ancestral orthologs, genetic mapping and expression analysis of <italic>Pm2-5V#4</italic> in TF5V-1 and <italic>Pm2-5V#5</italic> in NAU1908 revealed that they do not confer resistance. In contrast to highly conserved <italic>Pm2</italic> homologs in wheat and related genomes, the <italic>Pm55</italic> homologs exhibit significant sequence diversity and rapid evolution. The variation in amino acid composition between the PM55a and PM55b proteins is mainly located in the LRR domain, leading to a distinct resistance spectrum against <italic>Bgt</italic> isolates. Both <italic>Pm55a</italic> and <italic>Pm55b</italic> demonstrate broad-spectrum resistance powdery mildew based on infection assays with 21 <italic>Bgt</italic> isolates, highlighting their potential as resistance genes in wheat breeding.</p>\",\n \"<p id=\\\"Par29\\\">Previous studies demonstrated that the CC domain heterodimerization of NLR proteins can abrogate disease resistance, as the homodimerization of NLR receptors via CC domains plays a role in R-mediated immunity<sup>##REF##33205883##33##</sup>. For example, <italic>PigmS</italic> competitively attenuates paralogous <italic>PigmR</italic> homodimerization through heterodimers in their CC domains to suppress the blast resistance in rice<sup>##REF##28154240##4##</sup>. However, in case of homologous NLRs suppression among <italic>Pm3</italic> alleles, the N-terminal part of the LRR domain was identified as the major determinant of suppression<sup>##REF##24942051##20##</sup>. In addition, <italic>SuSr-D1</italic>, a subunit of the mediator complex gene, has been shown to suppress stem rust resistance in wheat through regulating the expression of resistance genes<sup>##REF##32111840##19##</sup>. Here, despite <italic>Pm55</italic> and <italic>SuPm55</italic> encode unrelated NLR proteins, the <italic>SuPm55</italic> CC domain can interact to suppress hypersensitive response cell death induced by <italic>Pm55a</italic>, but it does not inhibit <italic>Pm55b</italic>-triggered cell death in tobacco leaves. This suggests that the inhibition of triggered cell death may play a crucial role in the suppression of <italic>Pm55a</italic>-mediated resistance. Thus, the interactions of <italic>Pm55</italic> alleles with <italic>SuPm55</italic> might follow a different mechanism than that observed in interactions among homologous NLR immune receptors.</p>\",\n \"<p id=\\\"Par30\\\">Plant NLR genes function as singletons, in pairs, or in networks in mediating resistance against pathogens<sup>##REF##31154077##3##</sup>. Paired NLRs have been shown to regulate NLR-mediated resistance by balancing defense and fitness<sup>##REF##30011120##11##</sup>. For examples, the rice NLR pairs <italic>RGA4</italic>/<italic>RGA5</italic> confer resistance to <italic>Magnaporthe oryzae</italic><sup>##REF##25024433##34##</sup>, and <italic>PigmS</italic>/<italic>PigmR</italic> confer rice blast resistance<sup>##REF##28154240##4##</sup>. In these cases, one of the NLRs triggers cell death but is constitutively expressed, while the other NLR represses its partner to prevent autoimmunity. Our results demonstrate that <italic>Pm55a</italic> is induced expression by <italic>Bgt</italic> and functions as singleton in transgenic plants, whereas the expression of antagonistic gene <italic>SuPm55</italic> is developmentally regulated by an unclear mechanism. In this case, introduction of <italic>Pm55a</italic> alone does not lead to autoimmunity in wheat, suggesting that the interaction between <italic>Pm55a</italic> and <italic>SuPm55</italic> differs from the paired NLRs. Interestingly, the inactivation of <italic>SuPm55</italic> significantly reduces grain yield, indicating that the expression of <italic>SuPm55</italic> in both seedlings and adult-leaf sheaths contributes to enhancing plant fitness. These results highlight that certain singleton NLRs should not be considered as individual entities, but rather as coevolutionary components with their inhibitors when utilized in breeding programs aimed at developing high-yield, efficient disease-resistance crops.</p>\",\n \"<p id=\\\"Par31\\\">To breed crops with effective and durable resistance, it is highly desirable to combine different forms of resistance<sup>##REF##25505474##10##,##REF##29382771##35##,##REF##33398152##36##</sup>. However, resistance alleles cannot be combined through classical crossbreeding. This limitation can only be overcome through genetic engineering or the use of F<sub>1</sub> hybrids<sup>##REF##29302719##37##</sup>. In our study, we achieved the combination of distinct powdery mildew resistance conferred by <italic>Pm55</italic> alleles through T5AL·5V#4 S translocation with <italic>SuPm55</italic>/<italic>Pm55a</italic> and T5DL·5 V#5 S translocation with <italic>Pm55b</italic>. The T5AL·5 V#4 S and T5DL·5 V#5 S translocations line NAU2021, developed by the combination of classical crossbreeding, showed no mutual allele suppression or yield penalty in the field. Moreover, NAU2021 exhibited additional effective resistance when pyramiding the two translocated chromosomes in wheat. Therefore, the multi-translocations line NAU2021 represents a valuable resource for wheat improvement and provides a practical basis for pyramiding allelic series via crossbreeding to achieve a durable resistance.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Powdery mildew poses a significant threat to wheat crops worldwide, emphasizing the need for durable disease control strategies. The wheat-<italic>Dasypyrum villosum</italic> T5AL·5 V#4 S and T5DL·5 V#4 S translocation lines carrying powdery mildew resistant gene <italic>Pm55</italic> shows developmental-stage and tissue-specific resistance, whereas T5DL·5 V#5 S line carrying <italic>Pm5V</italic> confers resistance at all stages. Here, we clone <italic>Pm55</italic> and <italic>Pm5V</italic>, and reveal that they are allelic and renamed as <italic>Pm55a</italic> and <italic>Pm55b</italic>, respectively. The two <italic>Pm55</italic> alleles encode coiled-coil, nucleotide-binding site-leucine-rich repeat (CNL) proteins, conferring broad-spectrum resistance to powdery mildew. However, they interact differently with a linked inhibitor gene, <italic>SuPm55</italic> to cause different resistance to wheat powdery mildew. Notably, <italic>Pm55</italic> and <italic>SuPm55</italic> encode unrelated CNL proteins, and the inactivation of <italic>SuPm55</italic> significantly reduces plant fitness. Combining <italic>SuPm55</italic>/<italic>Pm55a</italic> and <italic>Pm55b</italic> in wheat does not result in allele suppression or yield penalty. Our results provide not only insights into the suppression of resistance in wheat, but also a strategy for breeding durable resistance.</p>","<p id=\"Par2\">Powdery mildew threatens worldwide wheat production. Here, the authors report the cloning of two powdery mildew resistant <italic>Pm55</italic> alleles and show that they exhibit distinct interactions with the inhibitor SuPm55 to cause different resistance.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Powdery mildew poses a significant threat to wheat crops worldwide, emphasizing the need for durable disease control strategies. The wheat-<italic>Dasypyrum villosum</italic> T5AL·5 V#4 S and T5DL·5 V#4 S translocation lines carrying powdery mildew resistant gene <italic>Pm55</italic> shows developmental-stage and tissue-specific resistance, whereas T5DL·5 V#5 S line carrying <italic>Pm5V</italic> confers resistance at all stages. Here, we clone <italic>Pm55</italic> and <italic>Pm5V</italic>, and reveal that they are allelic and renamed as <italic>Pm55a</italic> and <italic>Pm55b</italic>, respectively. The two <italic>Pm55</italic> alleles encode coiled-coil, nucleotide-binding site-leucine-rich repeat (CNL) proteins, conferring broad-spectrum resistance to powdery mildew. However, they interact differently with a linked inhibitor gene, <italic>SuPm55</italic> to cause different resistance to wheat powdery mildew. Notably, <italic>Pm55</italic> and <italic>SuPm55</italic> encode unrelated CNL proteins, and the inactivation of <italic>SuPm55</italic> significantly reduces plant fitness. Combining <italic>SuPm55</italic>/<italic>Pm55a</italic> and <italic>Pm55b</italic> in wheat does not result in allele suppression or yield penalty. Our results provide not only insights into the suppression of resistance in wheat, but also a strategy for breeding durable resistance.</p>\",\n \"<p id=\\\"Par2\\\">Powdery mildew threatens worldwide wheat production. Here, the authors report the cloning of two powdery mildew resistant <italic>Pm55</italic> alleles and show that they exhibit distinct interactions with the inhibitor SuPm55 to cause different resistance.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary information</title>","<p>\n\n\n\n\n\n\n</p>","<title>Source data</title>","<p>\n\n</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>\\n\\n\\n\\n\\n\\n\\n</p>\",\n \"<title>Source data</title>\",\n \"<p>\\n\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary information</title>","<p>The online version contains supplementary material available at 10.1038/s41467-024-44796-0.</p>","<title>Acknowledgements</title>","<p>We thank Prof. Robert McIntosh, University of Sydney, for reviewing the manuscript. We also thank Zdeňka Dubská, Romana Šperková and Jitka Weiserová for preparation of chromosome samples for flow cytometry. This work was supported by the National Natural Science Foundation of China (32272062, 31971938); The Special Fund for Independent Innovation of Agricultural Science and Technology in Jiangsu (No. CX (19)1001); and the “JBGS” Project of Seed Industry Revitalization in Jiangsu Province (JBGS (2021) 013). IM was supported from a Marie Curie Fellowship grant award ‘AEGILWHEAT’ (H2020-MSCA-IF-2016-746253) and from the Hungarian National Research, Development and Innovation Office (K135057), KH, MS and JD were supported from the ERDF project “Plants as a Tool for Sustainable Global Ddevelopment” (No. CZ.02.1.01/0.0/0.0/16_019/0000827). Computational resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures.</p>","<title>Author contributions</title>","<p>R.Z. designed the study. C.L., J.D., H.C., S.G., Y.J., X.M., T.Z., B.F., I.M., K.H., M.S., L.X., L.K., J.D., G.L. and J.W. performed the research. R.Z., C.L. and J.D. analyzed the data. R.Z., P.C. and A.C. wrote the paper.</p>","<title>Peer review</title>","<title>Peer review information</title>","<p id=\"Par52\">: <italic>Nature Communications</italic> thanks Beat Keller, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.</p>","<title>Data availability</title>","<p>Data supporting the findings of this work are available within the paper and Supplementary Information files. The plant materials and datasets generated and analyzed during the present study are available from the corresponding authors upon request. Detailed genomic sequences of <italic>Pm55</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928403\">OQ928403</ext-link>), <italic>Pm5V</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/ON109832\">ON109832</ext-link>), <italic>SuPm5V</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928410\">OQ928410</ext-link>), <italic>Pm2-5V#4</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928409\">OQ928409</ext-link>), <italic>Pm2-5V#5</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/OM646566\">OM646566</ext-link>), <italic>Pm55_h1</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928404\">OQ928404</ext-link>), <italic>Pm55_h2</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928405\">OQ928405</ext-link>) <italic>Pm55_h3</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928406\">OQ928406</ext-link>), <italic>Pm55_h4</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928407\">OQ928407</ext-link>), and <italic>Pm55_h5</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928408\">OQ928408</ext-link>) were deposited in NCBI Genbank. The following public databases were used in this study: <italic>D. villosum</italic> 91C43<sup>DH</sup> genome [<ext-link ext-link-type=\"uri\" xlink:href=\"https://bigd.big.ac.cn/\">https://bigd.big.ac.cn/</ext-link>], IWGSC RefSeq v2.1 [<ext-link ext-link-type=\"uri\" xlink:href=\"https://wheat-urgi.versailles.inra.fr/Seq\">https://wheat-urgi.versailles.inra.fr/Seq</ext-link>], and Triticeae genomes [<ext-link ext-link-type=\"uri\" xlink:href=\"http://wheatomics.sdau.edu.cn/\">http://wheatomics.sdau.edu.cn/</ext-link>]. All primers used in this study are listed in Supplementary Table ##SUPPL##0##10##. <xref ref-type=\"sec\" rid=\"Sec31\">Source data</xref> are provided with this paper.</p>","<title>Competing interests</title>","<p id=\"Par53\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41467-024-44796-0.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>We thank Prof. Robert McIntosh, University of Sydney, for reviewing the manuscript. We also thank Zdeňka Dubská, Romana Šperková and Jitka Weiserová for preparation of chromosome samples for flow cytometry. This work was supported by the National Natural Science Foundation of China (32272062, 31971938); The Special Fund for Independent Innovation of Agricultural Science and Technology in Jiangsu (No. CX (19)1001); and the “JBGS” Project of Seed Industry Revitalization in Jiangsu Province (JBGS (2021) 013). IM was supported from a Marie Curie Fellowship grant award ‘AEGILWHEAT’ (H2020-MSCA-IF-2016-746253) and from the Hungarian National Research, Development and Innovation Office (K135057), KH, MS and JD were supported from the ERDF project “Plants as a Tool for Sustainable Global Ddevelopment” (No. CZ.02.1.01/0.0/0.0/16_019/0000827). Computational resources were supplied by the project “e-Infrastruktura CZ” (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>R.Z. designed the study. C.L., J.D., H.C., S.G., Y.J., X.M., T.Z., B.F., I.M., K.H., M.S., L.X., L.K., J.D., G.L. and J.W. performed the research. R.Z., C.L. and J.D. analyzed the data. R.Z., P.C. and A.C. wrote the paper.</p>\",\n \"<title>Peer review</title>\",\n \"<title>Peer review information</title>\",\n \"<p id=\\\"Par52\\\">: <italic>Nature Communications</italic> thanks Beat Keller, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.</p>\",\n \"<title>Data availability</title>\",\n \"<p>Data supporting the findings of this work are available within the paper and Supplementary Information files. The plant materials and datasets generated and analyzed during the present study are available from the corresponding authors upon request. Detailed genomic sequences of <italic>Pm55</italic> (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928403\\\">OQ928403</ext-link>), <italic>Pm5V</italic> (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.ncbi.nlm.nih.gov/nuccore/ON109832\\\">ON109832</ext-link>), <italic>SuPm5V</italic> (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928410\\\">OQ928410</ext-link>), <italic>Pm2-5V#4</italic> (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928409\\\">OQ928409</ext-link>), <italic>Pm2-5V#5</italic> (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.ncbi.nlm.nih.gov/nuccore/OM646566\\\">OM646566</ext-link>), <italic>Pm55_h1</italic> (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928404\\\">OQ928404</ext-link>), <italic>Pm55_h2</italic> (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928405\\\">OQ928405</ext-link>) <italic>Pm55_h3</italic> (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928406\\\">OQ928406</ext-link>), <italic>Pm55_h4</italic> (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928407\\\">OQ928407</ext-link>), and <italic>Pm55_h5</italic> (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.ncbi.nlm.nih.gov/nuccore/OQ928408\\\">OQ928408</ext-link>) were deposited in NCBI Genbank. The following public databases were used in this study: <italic>D. villosum</italic> 91C43<sup>DH</sup> genome [<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://bigd.big.ac.cn/\\\">https://bigd.big.ac.cn/</ext-link>], IWGSC RefSeq v2.1 [<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://wheat-urgi.versailles.inra.fr/Seq\\\">https://wheat-urgi.versailles.inra.fr/Seq</ext-link>], and Triticeae genomes [<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"http://wheatomics.sdau.edu.cn/\\\">http://wheatomics.sdau.edu.cn/</ext-link>]. All primers used in this study are listed in Supplementary Table ##SUPPL##0##10##. <xref ref-type=\\\"sec\\\" rid=\\\"Sec31\\\">Source data</xref> are provided with this paper.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par53\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Map-based cloning of the candidates in both inhibitor and <italic>Pm55/Pm5V</italic> intervals on the chromosome arm 5VS of <italic>D. villosum</italic>.</title><p><bold>a</bold> Seedling responses of three 5VS-introgression lines inoculated with <italic>Bgt</italic> isolate E09. At least ten plants of each lines were observed. The translocated chromosomes in each lines were identified by GISH/FISH, among them, <italic>D. villosum</italic> gDNA labeled with fuorescein-12-dUTP (green) as probe was used for GISH, and FISH probe was Oligo-pAs1 labeled with TAM (red) to produce the red signals on the chromosomes of wheat D genome. Scale bar, 5.0 mm. <bold>b</bold> Response to powdery mildew of 5VS-introgression lines at adult-plant stage. <bold>c</bold> Fine mapping <italic>Pm55/Pm5V</italic> and inhibitor intervals on chromosome arm 5VS. <bold>d</bold> Physical maps with four overlapping 5VS scaffolds, annotating six genes in the inhibitor interval. <bold>e</bold> Physical maps with two overlapping BACs, annotating seven genes in the <italic>Pm55/Pm5V</italic> interval. <bold>f</bold> Gene structure of <italic>CNL1</italic> in TF5V-1. <bold>g</bold> Gene structure of <italic>CNL2</italic> in NAU1908. Black rectangles, black lines and gray rectangles indicate exons, introns and UTRs respectively.</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>The function validation of <italic>CNL1</italic> in TF5V-1.</title><p><bold>a</bold> Target of <italic>CNL1</italic> CC domain silenced by VIGS in TF5V-1. <italic>Bgt</italic> is not visible on four independent BSMV: CNL1-infected leaves. Scale bars, 5.0 mm. <bold>b</bold> The expression of <italic>CNL1</italic> in BSMV: CNL1-infected leaves determined by qRT-PCR and compared to that in BSMV: γ leaves. Values are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 4 biologically independent experiments, *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01; ****<italic>P</italic> < 0.0001). <bold>c</bold> Four mutations in the CC domain of <italic>CNL1</italic> in TF5V-1 created by CRISPR/Cas9 editing. <bold>d</bold> Responses of CRISPR/Cas9-mediated mutant seedlings to <italic>Bgt</italic> isolate E09. Ten plants of each lines were recorded. Scale bars, 5.0 mm. <bold>e</bold> The micro-colony index decreas<bold>e</bold>d significantly in <italic>CNL1</italic> mutation seedling leaves. Value is the mean ± SD (two-sided <italic>t</italic>-test, ****<italic>P</italic> < 0.0001). <italic>n</italic> = 20 in TF5V-1, <italic>n</italic> = 18 in Del5VS-1, <italic>n</italic> = 17 in Del5VS-2, <italic>n</italic> = 17 in Del5VS-3, and <italic>n</italic> = 18 in Del5VS-4. <bold>f</bold> The mutant Del5VS-4 showed no susceptibility on the lower leaf sheaths. <bold>g</bold> The plants with 27 bp deletion in the CC domain of <italic>CNL1</italic> showed resistance to powdery mildew on the leaf sheaths in a T<sub>2</sub> population, whereas plants with no deletion were leaf sheaths susceptibility. Source data are provided as a Source Data file.</p></caption></fig>","<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>The function validation of alleles <italic>CNL2#4</italic> and <italic>CNL2#5</italic>.</title><p><bold>a</bold> Targets of CNL2#5 in NAU1908 silenced by VIGS exhibiting susceptibility on seedling leaves. Scale bar, 5.0 mm. <bold>b</bold> Reduced expression levels of <italic>CNL2#5</italic> in silenced leaves. Values are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 4 biologically independent experiments, ****<italic>P</italic> < 0.0001), BSMV: γ leaves were used as the control; <bold>c</bold> Target of <italic>CNL2#4</italic> in R5VS-15 silenced by VIGS exhibiting susceptible responses on seedling leaves. Scale bar, 5.0 mm. <bold>d</bold> Reduced expression levels of <italic>CNL2#4</italic> in silenced leaves. BSMV: CNL2#4-1 and BSMV: CNL2#4-2 are two independent injection experiments. Values are the mean ± SD of qRT-PCR (two-sided <italic>t</italic>-test, <italic>n</italic> = 4 biologically independent experiments, ****<italic>P</italic> < 0.0001), BSMV: γ leaves were used as the control. <bold>e</bold> Structures of ProCNL2#5: CNL2#5 and ProCNL2#4: CNL2#4 used for transgenic assays. The ProCNL2#5: CNL2#5 construct contained the entire <italic>CNL2#5</italic> gene, 3011 bp promoter, and 1493 bp terminal region. The ProCNL2#4: CNL2#4 construct contained the entire <italic>CNL2#4</italic> gene, 2737 bp promoter, and 1515 bp terminal region. <bold>f</bold> Responses of Fielder T<sub>0</sub> transgenic plants of ProCNL2#5: CNL2#5 to <italic>Bgt</italic> isolate E09. <bold>g</bold> Responses of Fielder T<sub>0</sub> transgenic plants of ProCNL2#4: CNL2#4. <bold>h</bold> Responses of <italic>CNL2#5</italic> EMS mutants and their F<sub>1</sub> plants. At least 10 plants of each lines were observed. Scale bar, 5.0 mm. <bold>i</bold> Responses of ten <italic>CNL2#4</italic> EMS mutants. At least 10 plants of each lines were observed. Scale bar, 5.0 mm. <bold>j</bold> EMS mutants carrying single nonsense or missense mutations in the <italic>CNL2#4</italic> and <italic>CNL2#5</italic> sequences. Black lines indicate the positions of the mutations. Source data are provided as a Source Data file.</p></caption></fig>","<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>The CC domains of <italic>Pm55</italic> and <italic>SuPm55</italic> interacted at protein level.</title><p><bold>a</bold> Expression levels of <italic>SuPm55</italic> in one-leaf to five-leaf seedlings. The values of qRT-PCR are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 3 biologically independent experiments, ***<italic>P</italic> < 0.001, **<italic>P</italic> < 0.01, *<italic>P</italic> < 0.05, N.S., no significant). The one-leaf was used as control. <bold>b</bold> Expression levels of <italic>SuPm55</italic> in adult-plant tissues. The values of qRT-PCR are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 3 biologically independent experiments, ****<italic>P</italic> < 0.0001, **<italic>P</italic> < 0.01, *<italic>P</italic> < 0.05). <bold>c</bold> Expression levels of <italic>SuPm55</italic> in leaf sheaths of TF5V-1 and Del5VS-4. The values of qRT-PCR are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 3 biologically independent experiments, ****<italic>P</italic> < 0.0001). <bold>d</bold> Expression levels of <italic>Pm55a</italic> in TF5V-1 and Del5VS-4 at seedling and adult-plant stages. The values of qRT-PCR are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 3 biologically independent experiments, N.S., no significant). <bold>e</bold> Interactions between the CC domains of <italic>Pm55a</italic> and <italic>SuPm55</italic> in Y2H assays. AD, activation domain; BD, binding domain. SD/-L-T, SD media lacking Leu and Trp; SD/-L/-T/-H/-A, SD medium lacking Leu, Trp, His and Ade. Numbers in column headings indicate dilution series of yeast cells expressing AD and BD fusions. Interaction between the CC domains of <italic>Pm55a</italic> and <italic>SuPm55</italic> detected by Co-immunoprecipitation (coIP) (<bold>f</bold>), BiFC (<bold>g</bold>), Scale bars, 20 µm, and Luc (<bold>h</bold>) assays. <bold>i</bold> The CC domain of <italic>SuPm55</italic> inhibited <italic>Pm55a</italic>-triggered cell death when their CC domains were both injected in <italic>N. benthamiana</italic> leaves. Source data are provided as a Source Data file.</p></caption></fig>","<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Multiple 5 VS-translocations line NAU2021 shows no mutual allele suppression or yield penalty.</title><p><bold>a</bold> Karyotypes of NAU2021 root-tip cell chromosomes. Arrows and triangles respectively indicate the translocated chromosomes T5AL·5V#4 S and T5DL·5 V#5 S. Bar, 10 μm. <bold>b</bold> Metaphase I of meiosis in NAU2021 pollen mother cell showing the 21 bivalents. Arrows indicate the translocated chromosomes pair forming two ring bivalents. Bar, 10 μm. <bold>c</bold>, <bold>d</bold> NAU2021 showing similar development stages as background parent NAU0686. Comparing the agronomic traits between NAU0686 and NAU2021 in plant height (<italic>n</italic> = 50) (<bold>e</bold>), seeds per spike (<italic>n</italic> = 50) (<bold>f</bold>), TGW (<italic>n</italic> = 50) (<bold>g</bold>), spikes per plant (<italic>n</italic> = 50) (<bold>h</bold>) and plot grain yield (<italic>n</italic> = 5) (<bold>i</bold>). NAU2021 showing no yield penalty comparing with NAU0686, but significant reduction in plant height, and a little increase in spikes per plant and plot grain yield. Values are the mean ± SD (two-sided <italic>t</italic>-test, ****<italic>P</italic> < 0.0001, ***<italic>P</italic> < 0.001, N.S., no significant). The responses of NAU2021 seedlings tested with <italic>Bgt</italic> isolates E09 (<bold>j</bold>), E26 (<bold>k</bold>) and E31 (<bold>l</bold>). Ten plants of each lines were recorded the response to each <italic>Bgt</italic> isolates. Bars, 5.0 mm. Source data are provided as a Source Data file.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><title>Map-based cloning of the candidates in both inhibitor and <italic>Pm55/Pm5V</italic> intervals on the chromosome arm 5VS of <italic>D. villosum</italic>.</title><p><bold>a</bold> Seedling responses of three 5VS-introgression lines inoculated with <italic>Bgt</italic> isolate E09. At least ten plants of each lines were observed. The translocated chromosomes in each lines were identified by GISH/FISH, among them, <italic>D. villosum</italic> gDNA labeled with fuorescein-12-dUTP (green) as probe was used for GISH, and FISH probe was Oligo-pAs1 labeled with TAM (red) to produce the red signals on the chromosomes of wheat D genome. Scale bar, 5.0 mm. <bold>b</bold> Response to powdery mildew of 5VS-introgression lines at adult-plant stage. <bold>c</bold> Fine mapping <italic>Pm55/Pm5V</italic> and inhibitor intervals on chromosome arm 5VS. <bold>d</bold> Physical maps with four overlapping 5VS scaffolds, annotating six genes in the inhibitor interval. <bold>e</bold> Physical maps with two overlapping BACs, annotating seven genes in the <italic>Pm55/Pm5V</italic> interval. <bold>f</bold> Gene structure of <italic>CNL1</italic> in TF5V-1. <bold>g</bold> Gene structure of <italic>CNL2</italic> in NAU1908. Black rectangles, black lines and gray rectangles indicate exons, introns and UTRs respectively.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><title>The function validation of <italic>CNL1</italic> in TF5V-1.</title><p><bold>a</bold> Target of <italic>CNL1</italic> CC domain silenced by VIGS in TF5V-1. <italic>Bgt</italic> is not visible on four independent BSMV: CNL1-infected leaves. Scale bars, 5.0 mm. <bold>b</bold> The expression of <italic>CNL1</italic> in BSMV: CNL1-infected leaves determined by qRT-PCR and compared to that in BSMV: γ leaves. Values are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 4 biologically independent experiments, *<italic>P</italic> < 0.05, **<italic>P</italic> < 0.01; ****<italic>P</italic> < 0.0001). <bold>c</bold> Four mutations in the CC domain of <italic>CNL1</italic> in TF5V-1 created by CRISPR/Cas9 editing. <bold>d</bold> Responses of CRISPR/Cas9-mediated mutant seedlings to <italic>Bgt</italic> isolate E09. Ten plants of each lines were recorded. Scale bars, 5.0 mm. <bold>e</bold> The micro-colony index decreas<bold>e</bold>d significantly in <italic>CNL1</italic> mutation seedling leaves. Value is the mean ± SD (two-sided <italic>t</italic>-test, ****<italic>P</italic> < 0.0001). <italic>n</italic> = 20 in TF5V-1, <italic>n</italic> = 18 in Del5VS-1, <italic>n</italic> = 17 in Del5VS-2, <italic>n</italic> = 17 in Del5VS-3, and <italic>n</italic> = 18 in Del5VS-4. <bold>f</bold> The mutant Del5VS-4 showed no susceptibility on the lower leaf sheaths. <bold>g</bold> The plants with 27 bp deletion in the CC domain of <italic>CNL1</italic> showed resistance to powdery mildew on the leaf sheaths in a T<sub>2</sub> population, whereas plants with no deletion were leaf sheaths susceptibility. Source data are provided as a Source Data file.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Fig. 3</label><caption><title>The function validation of alleles <italic>CNL2#4</italic> and <italic>CNL2#5</italic>.</title><p><bold>a</bold> Targets of CNL2#5 in NAU1908 silenced by VIGS exhibiting susceptibility on seedling leaves. Scale bar, 5.0 mm. <bold>b</bold> Reduced expression levels of <italic>CNL2#5</italic> in silenced leaves. Values are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 4 biologically independent experiments, ****<italic>P</italic> < 0.0001), BSMV: γ leaves were used as the control; <bold>c</bold> Target of <italic>CNL2#4</italic> in R5VS-15 silenced by VIGS exhibiting susceptible responses on seedling leaves. Scale bar, 5.0 mm. <bold>d</bold> Reduced expression levels of <italic>CNL2#4</italic> in silenced leaves. BSMV: CNL2#4-1 and BSMV: CNL2#4-2 are two independent injection experiments. Values are the mean ± SD of qRT-PCR (two-sided <italic>t</italic>-test, <italic>n</italic> = 4 biologically independent experiments, ****<italic>P</italic> < 0.0001), BSMV: γ leaves were used as the control. <bold>e</bold> Structures of ProCNL2#5: CNL2#5 and ProCNL2#4: CNL2#4 used for transgenic assays. The ProCNL2#5: CNL2#5 construct contained the entire <italic>CNL2#5</italic> gene, 3011 bp promoter, and 1493 bp terminal region. The ProCNL2#4: CNL2#4 construct contained the entire <italic>CNL2#4</italic> gene, 2737 bp promoter, and 1515 bp terminal region. <bold>f</bold> Responses of Fielder T<sub>0</sub> transgenic plants of ProCNL2#5: CNL2#5 to <italic>Bgt</italic> isolate E09. <bold>g</bold> Responses of Fielder T<sub>0</sub> transgenic plants of ProCNL2#4: CNL2#4. <bold>h</bold> Responses of <italic>CNL2#5</italic> EMS mutants and their F<sub>1</sub> plants. At least 10 plants of each lines were observed. Scale bar, 5.0 mm. <bold>i</bold> Responses of ten <italic>CNL2#4</italic> EMS mutants. At least 10 plants of each lines were observed. Scale bar, 5.0 mm. <bold>j</bold> EMS mutants carrying single nonsense or missense mutations in the <italic>CNL2#4</italic> and <italic>CNL2#5</italic> sequences. Black lines indicate the positions of the mutations. Source data are provided as a Source Data file.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Fig. 4</label><caption><title>The CC domains of <italic>Pm55</italic> and <italic>SuPm55</italic> interacted at protein level.</title><p><bold>a</bold> Expression levels of <italic>SuPm55</italic> in one-leaf to five-leaf seedlings. The values of qRT-PCR are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 3 biologically independent experiments, ***<italic>P</italic> < 0.001, **<italic>P</italic> < 0.01, *<italic>P</italic> < 0.05, N.S., no significant). The one-leaf was used as control. <bold>b</bold> Expression levels of <italic>SuPm55</italic> in adult-plant tissues. The values of qRT-PCR are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 3 biologically independent experiments, ****<italic>P</italic> < 0.0001, **<italic>P</italic> < 0.01, *<italic>P</italic> < 0.05). <bold>c</bold> Expression levels of <italic>SuPm55</italic> in leaf sheaths of TF5V-1 and Del5VS-4. The values of qRT-PCR are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 3 biologically independent experiments, ****<italic>P</italic> < 0.0001). <bold>d</bold> Expression levels of <italic>Pm55a</italic> in TF5V-1 and Del5VS-4 at seedling and adult-plant stages. The values of qRT-PCR are the mean ± SD (two-sided <italic>t</italic>-test, <italic>n</italic> = 3 biologically independent experiments, N.S., no significant). <bold>e</bold> Interactions between the CC domains of <italic>Pm55a</italic> and <italic>SuPm55</italic> in Y2H assays. AD, activation domain; BD, binding domain. SD/-L-T, SD media lacking Leu and Trp; SD/-L/-T/-H/-A, SD medium lacking Leu, Trp, His and Ade. Numbers in column headings indicate dilution series of yeast cells expressing AD and BD fusions. Interaction between the CC domains of <italic>Pm55a</italic> and <italic>SuPm55</italic> detected by Co-immunoprecipitation (coIP) (<bold>f</bold>), BiFC (<bold>g</bold>), Scale bars, 20 µm, and Luc (<bold>h</bold>) assays. <bold>i</bold> The CC domain of <italic>SuPm55</italic> inhibited <italic>Pm55a</italic>-triggered cell death when their CC domains were both injected in <italic>N. benthamiana</italic> leaves. Source data are provided as a Source Data file.</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Fig. 5</label><caption><title>Multiple 5 VS-translocations line NAU2021 shows no mutual allele suppression or yield penalty.</title><p><bold>a</bold> Karyotypes of NAU2021 root-tip cell chromosomes. Arrows and triangles respectively indicate the translocated chromosomes T5AL·5V#4 S and T5DL·5 V#5 S. Bar, 10 μm. <bold>b</bold> Metaphase I of meiosis in NAU2021 pollen mother cell showing the 21 bivalents. Arrows indicate the translocated chromosomes pair forming two ring bivalents. Bar, 10 μm. <bold>c</bold>, <bold>d</bold> NAU2021 showing similar development stages as background parent NAU0686. Comparing the agronomic traits between NAU0686 and NAU2021 in plant height (<italic>n</italic> = 50) (<bold>e</bold>), seeds per spike (<italic>n</italic> = 50) (<bold>f</bold>), TGW (<italic>n</italic> = 50) (<bold>g</bold>), spikes per plant (<italic>n</italic> = 50) (<bold>h</bold>) and plot grain yield (<italic>n</italic> = 5) (<bold>i</bold>). NAU2021 showing no yield penalty comparing with NAU0686, but significant reduction in plant height, and a little increase in spikes per plant and plot grain yield. Values are the mean ± SD (two-sided <italic>t</italic>-test, ****<italic>P</italic> < 0.0001, ***<italic>P</italic> < 0.001, N.S., no significant). The responses of NAU2021 seedlings tested with <italic>Bgt</italic> isolates E09 (<bold>j</bold>), E26 (<bold>k</bold>) and E31 (<bold>l</bold>). Ten plants of each lines were recorded the response to each <italic>Bgt</italic> isolates. Bars, 5.0 mm. Source data are provided as a Source Data file.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":["<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>","<supplementary-material content-type=\"local-data\" 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\"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM7\\\"></supplementary-material>\"\n]","isConversational":false},"footnote":{"kind":"list like","value":["<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Chuntian Lu, Jie Du.</p></fn></fn-group>"],"string":"[\n \"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Chuntian Lu, Jie Du.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41467_2024_44796_Fig1_HTML\" id=\"d32e750\"/>","<graphic xlink:href=\"41467_2024_44796_Fig2_HTML\" id=\"d32e1263\"/>","<graphic xlink:href=\"41467_2024_44796_Fig3_HTML\" id=\"d32e1496\"/>","<graphic 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xlink:href=\\\"41467_2024_44796_MOESM5_ESM.xlsx\\\"><caption><p>Supplementary Data 2</p></caption></media>\",\n \"<media xlink:href=\\\"41467_2024_44796_MOESM6_ESM.pdf\\\"><caption><p>Reporting Summary</p></caption></media>\",\n \"<media xlink:href=\\\"41467_2024_44796_MOESM7_ESM.xlsx\\\"><caption><p>Source Data</p></caption></media>\"\n]","isConversational":false},"unknown_pub":{"kind":"string","value":"[{\"label\": [\"1.\"], \"surname\": [\"Tian\", \"Traw\", \"Chen\", \"Kreitman\", \"Bergelson\"], \"given-names\": [\"D\", \"MB\", \"JQ\", \"M\", \"J\"], \"article-title\": [\"Fitness costs of R-gene-mediated resistance in \"], \"italic\": [\"Arabidopsis thaliana\"], \"source\": [\"Nature\"], \"year\": [\"2013\"], \"volume\": [\"423\"], \"fpage\": [\"74\"], \"lpage\": [\"77\"], \"pub-id\": [\"10.1038/nature01588\"]}, {\"label\": [\"6.\"], \"surname\": [\"Johnson\"], \"given-names\": [\"R\"], \"article-title\": [\"A critical analysis of durable resistance\"], \"source\": [\"Annu. Rev. Phytopathol.\"], \"year\": [\"1984\"], \"volume\": [\"22\"], \"fpage\": [\"309\"], \"lpage\": [\"330\"], \"pub-id\": [\"10.1146/annurev.py.22.090184.001521\"]}, {\"label\": [\"12.\"], \"surname\": [\"Zhang\"], \"given-names\": [\"RQ\"], \"italic\": [\"Pm55\", \"Dasypyrum villosum\"], \"source\": [\"Theor. Appl. Genet.\"], \"year\": [\"2016\"], \"volume\": [\"129\"], \"fpage\": [\"975\"], \"lpage\": [\"1984\"], \"pub-id\": [\"10.1007/s00122-016-2753-8\"]}, {\"label\": [\"13.\"], \"surname\": [\"Zhang\"], \"given-names\": [\"RQ\"], \"italic\": [\"Pm67\", \"Dasypyrum villosum\", \"Triticum aestivum\"], \"source\": [\"Crop J.\"], \"year\": [\"2021\"], \"volume\": [\"9\"], \"fpage\": [\"882\"], \"lpage\": [\"888\"], \"pub-id\": [\"10.1016/j.cj.2020.09.012\"]}, {\"label\": [\"16.\"], \"surname\": [\"Lu\"], \"given-names\": [\"X\"], \"article-title\": [\"Allelic barley MLA immune receptors recognize sequence unrelated avirulence effectors of the powdery mildew pathogen\"], \"source\": [\"Proc. Natl Acad. Sci. USA\"], \"year\": [\"2016\"], \"volume\": [\"113\"], \"fpage\": [\"6486\"], \"lpage\": [\"6495\"], \"pub-id\": [\"10.1073/pnas.1612947113\"]}, {\"label\": [\"25.\"], \"surname\": [\"Zhang\"], \"given-names\": [\"RQ\"], \"article-title\": [\"The genetic effect of wheat-\"], \"italic\": [\"D. villosum\"], \"source\": [\"Sci. Agric. Sin.\"], \"year\": [\"2015\"], \"volume\": [\"48\"], \"fpage\": [\"1041\"], \"lpage\": [\"1051\"]}, {\"label\": [\"26.\"], \"surname\": [\"Fu\"], \"given-names\": [\"BS\"], \"article-title\": [\"Development of molecular markers specific to 5VS chromosome arm of \"], \"italic\": [\"Dasypyrum villosum\"], \"source\": [\"J. Plant Gene. Res.\"], \"year\": [\"2022\"], \"volume\": [\"23\"], \"fpage\": [\"195\"], \"lpage\": [\"208\"]}, {\"label\": [\"31.\"], \"surname\": [\"Zhu\"], \"given-names\": [\"SY\"], \"article-title\": [\"Orthologous genes \"], \"italic\": [\"Pm12\", \"Pm21\"], \"source\": [\"Plant Commun.\"], \"year\": [\"2022\"], \"volume\": [\"9\"], \"fpage\": [\"100472\"]}, {\"label\": [\"40.\"], \"surname\": [\"Said\"], \"given-names\": [\"M\"], \"article-title\": [\"Dissecting the complex genome of crested wheatgrass by chromosome flow sorting\"], \"source\": [\"Plant Genome\"], \"year\": [\"2019\"], \"volume\": [\"12\"], \"fpage\": [\"180096\"], \"pub-id\": [\"10.3835/plantgenome2018.12.0096\"]}, {\"label\": [\"43.\"], \"mixed-citation\": [\"Zeng, Q. D. et al. Novel high-accuracy genome assembly method utilizing a high-throughput workflow. Preprint 10.1101/2020.11.26.400507 (2020).\"]}, {\"label\": [\"49.\"], \"surname\": [\"Li\"], \"given-names\": [\"JH\"], \"article-title\": [\"Dissecting conserved cis-regulatory modules of \"], \"italic\": [\"Glu-1\"], \"source\": [\"Crop J.\"], \"year\": [\"2019\"], \"volume\": [\"7\"], \"fpage\": [\"8\"], \"lpage\": [\"18\"], \"pub-id\": [\"10.1016/j.cj.2018.08.003\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":56,"string":"56"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:57"},"citation":{"kind":"string","value":"Nat Commun. 2024 Jan 13; 15:503"},"package_file":{"kind":"string","value":"oa_package/a3/7b/PMC10787760.tar.gz"}}},{"rowIdx":91,"cells":{"accession_id":{"kind":"string","value":"PMC10787761"},"pmid":{"kind":"string","value":"38218959"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par2\">Accumulation of evidence regarding molecular interactions in biological processes has paved the way for the construction of various biological networks, including signaling, Protein-Protein Interaction (PPI), metabolic, and gene regulatory networks, among others. These networks have found various applications, ranging from visualizing omics data<sup>##REF##14597658##1##,##REF##17947979##2##</sup> to enriching gene sets using topology<sup>##REF##33719329##3##</sup>, identifying functional modules<sup>##UREF##0##4##</sup>, conducting causal analyses<sup>##UREF##1##5##,##REF##24336805##6##</sup>, and developing computational models to understand the effects of network perturbations on cellular states<sup>##REF##25833842##7##</sup>. Moreover, recent efforts have been directed towards associating changes in biological networks with diseases, leading to the emergence of “disease maps“<sup>##REF##35473910##8##–##REF##29900005##11##</sup>. Undoubtedly, the comprehensiveness and accuracy of biological networks form the fundamental keys for their successful application in network-based research.</p>","<p id=\"Par3\">A number of popular knowledge bases, such as KEGG<sup>##REF##10592173##12##,##REF##36300620##13##</sup>, Reactome<sup>##REF##31691815##14##</sup>, and BioCyc<sup>##REF##15642094##15##</sup>, hold valuable information on molecular interactions in biological processes. To represent the complex relationships between biological molecules, several languages have been developed, such as KGML, BioPAX<sup>##REF##20829833##16##</sup>, GPML<sup>##UREF##2##17##</sup>, and SBML<sup>##UREF##3##18##</sup>. However, converting this information into a comprehensive topological network has been a challenging endeavor, especially when dealing with different types of networks, such as signaling and metabolic networks. These networks often utilize distinct definitions for nodes and edges, leading to confusion and potential misinterpretations.</p>","<p id=\"Par4\">For instance, in signaling networks, an edge starting from node A and ending in node B, i.e., “node A activates node B”, typically implies that A is an enzyme, while B is the substrate and product of a post-transcriptional modification (PTM) reaction, resulting in the retention of the same names for both the substrate and product. In contrast, metabolic networks involve substantial changes in substrates, leading to the generation of products with new names. Therefore, in a metabolic network, an edge starting from A and ending in B, i.e., “node A generates node B”, refers to A being the substrate, and B being the product in this reaction, which significantly differs from the definitions in signaling networks. Without unifying the definitions of the nodes and edges, direct integration of signaling and metabolic networks may introduce confusion and misguidance.</p>","<p id=\"Par5\">Various tools have been developed to read and parse these languages, with the ability to convert the information into the Simple Interaction Format (SIF)<sup>##REF##14597658##1##,##REF##17947979##2##</sup>. SIF is a semi-structured format, in which each line specifies a source node, a character string describing the type of the edge(s), and one or more target nodes. However, the conversions often work better for signaling networks than for metabolic networks, as multiple substrates in metabolic reactions can lead to the ambiguity of multiple participants. Consequently, information regarding “who participates which reaction” can be lost during the conversion process.</p>","<p id=\"Par6\">To address these challenges, knowledge bases often visualize networks with edges pointing to edges, such as KEGG, Reactome, and Wikipathways<sup>##REF##33211851##19##</sup>. Although this visualization is user-friendly, it is not suitable to work with common network analysis algorithms and tools. With mounting evidence suggesting the importance of crosstalk between signaling and metabolic networks, there is an urgent need to integrate these networks into a global integrative network, termed “GIN”. Efforts have been made, but mainly focus on the visualization<sup>##UREF##4##20##</sup> or leveraging information from PPI network<sup>##REF##31386654##21##</sup>, leaving the signaling and metabolic networks topologically disconnected.</p>","<p id=\"Par7\">In this context, we propose a visualization layout called “meta-pathway” to fundamentally unify the topological structure of signaling and metabolic networks. To convert conventional pathways into meta-pathways, we introduce an intermediate node for each reaction in the pathways to represent a conceptual “intermediate” state of molecules in biochemical reactions. In most of biochemical reactions with multiple substrates or at least one enzyme, the substrate(s) and the enzyme need to get close enough to each other for the reactions to proceed, which forms the intermediate state. This intermediate state of the molecules is temporary, and will quickly be converted into products. Therefore, the intermediate nodes which come from the intermediate state of the molecules capture the relationships between molecules in real world, and enables both signaling and metabolic reactions to be considered as chemical reactions, facilitating storage in SIF-like format. By converting the pathways into meta-pathways and merging them, we have successfully built GINs for 7077 species based on KEGG<sup>##REF##36533264##22##</sup>.</p>","<p id=\"Par8\">In addition to KEGG, multiple biological knowledge bases offer valuable molecular interaction data across various aspects. In this study, we have converted molecular interaction data from ten different knowledge bases into the SIF format with intermediate nodes (referred to as SIFI). Subsequently, we conducted a thorough analysis of the consensus among these interactions before integrating the GINs into a single, comprehensive network, namely GIN for human version 2.0 (GINv2.0). Our results demonstrate that this version of GIN is currently one of the most comprehensive human databases of molecular interactions, allowing for straightforward visualization and interpretation of the crosstalk between signaling and metabolic networks, exemplified through a detailed examination of the glycolysis process and the related regulative proteins.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">Accumulation of evidence regarding molecular interactions in biological processes has paved the way for the construction of various biological networks, including signaling, Protein-Protein Interaction (PPI), metabolic, and gene regulatory networks, among others. These networks have found various applications, ranging from visualizing omics data<sup>##REF##14597658##1##,##REF##17947979##2##</sup> to enriching gene sets using topology<sup>##REF##33719329##3##</sup>, identifying functional modules<sup>##UREF##0##4##</sup>, conducting causal analyses<sup>##UREF##1##5##,##REF##24336805##6##</sup>, and developing computational models to understand the effects of network perturbations on cellular states<sup>##REF##25833842##7##</sup>. Moreover, recent efforts have been directed towards associating changes in biological networks with diseases, leading to the emergence of “disease maps“<sup>##REF##35473910##8##–##REF##29900005##11##</sup>. Undoubtedly, the comprehensiveness and accuracy of biological networks form the fundamental keys for their successful application in network-based research.</p>\",\n \"<p id=\\\"Par3\\\">A number of popular knowledge bases, such as KEGG<sup>##REF##10592173##12##,##REF##36300620##13##</sup>, Reactome<sup>##REF##31691815##14##</sup>, and BioCyc<sup>##REF##15642094##15##</sup>, hold valuable information on molecular interactions in biological processes. To represent the complex relationships between biological molecules, several languages have been developed, such as KGML, BioPAX<sup>##REF##20829833##16##</sup>, GPML<sup>##UREF##2##17##</sup>, and SBML<sup>##UREF##3##18##</sup>. However, converting this information into a comprehensive topological network has been a challenging endeavor, especially when dealing with different types of networks, such as signaling and metabolic networks. These networks often utilize distinct definitions for nodes and edges, leading to confusion and potential misinterpretations.</p>\",\n \"<p id=\\\"Par4\\\">For instance, in signaling networks, an edge starting from node A and ending in node B, i.e., “node A activates node B”, typically implies that A is an enzyme, while B is the substrate and product of a post-transcriptional modification (PTM) reaction, resulting in the retention of the same names for both the substrate and product. In contrast, metabolic networks involve substantial changes in substrates, leading to the generation of products with new names. Therefore, in a metabolic network, an edge starting from A and ending in B, i.e., “node A generates node B”, refers to A being the substrate, and B being the product in this reaction, which significantly differs from the definitions in signaling networks. Without unifying the definitions of the nodes and edges, direct integration of signaling and metabolic networks may introduce confusion and misguidance.</p>\",\n \"<p id=\\\"Par5\\\">Various tools have been developed to read and parse these languages, with the ability to convert the information into the Simple Interaction Format (SIF)<sup>##REF##14597658##1##,##REF##17947979##2##</sup>. SIF is a semi-structured format, in which each line specifies a source node, a character string describing the type of the edge(s), and one or more target nodes. However, the conversions often work better for signaling networks than for metabolic networks, as multiple substrates in metabolic reactions can lead to the ambiguity of multiple participants. Consequently, information regarding “who participates which reaction” can be lost during the conversion process.</p>\",\n \"<p id=\\\"Par6\\\">To address these challenges, knowledge bases often visualize networks with edges pointing to edges, such as KEGG, Reactome, and Wikipathways<sup>##REF##33211851##19##</sup>. Although this visualization is user-friendly, it is not suitable to work with common network analysis algorithms and tools. With mounting evidence suggesting the importance of crosstalk between signaling and metabolic networks, there is an urgent need to integrate these networks into a global integrative network, termed “GIN”. Efforts have been made, but mainly focus on the visualization<sup>##UREF##4##20##</sup> or leveraging information from PPI network<sup>##REF##31386654##21##</sup>, leaving the signaling and metabolic networks topologically disconnected.</p>\",\n \"<p id=\\\"Par7\\\">In this context, we propose a visualization layout called “meta-pathway” to fundamentally unify the topological structure of signaling and metabolic networks. To convert conventional pathways into meta-pathways, we introduce an intermediate node for each reaction in the pathways to represent a conceptual “intermediate” state of molecules in biochemical reactions. In most of biochemical reactions with multiple substrates or at least one enzyme, the substrate(s) and the enzyme need to get close enough to each other for the reactions to proceed, which forms the intermediate state. This intermediate state of the molecules is temporary, and will quickly be converted into products. Therefore, the intermediate nodes which come from the intermediate state of the molecules capture the relationships between molecules in real world, and enables both signaling and metabolic reactions to be considered as chemical reactions, facilitating storage in SIF-like format. By converting the pathways into meta-pathways and merging them, we have successfully built GINs for 7077 species based on KEGG<sup>##REF##36533264##22##</sup>.</p>\",\n \"<p id=\\\"Par8\\\">In addition to KEGG, multiple biological knowledge bases offer valuable molecular interaction data across various aspects. In this study, we have converted molecular interaction data from ten different knowledge bases into the SIF format with intermediate nodes (referred to as SIFI). Subsequently, we conducted a thorough analysis of the consensus among these interactions before integrating the GINs into a single, comprehensive network, namely GIN for human version 2.0 (GINv2.0). Our results demonstrate that this version of GIN is currently one of the most comprehensive human databases of molecular interactions, allowing for straightforward visualization and interpretation of the crosstalk between signaling and metabolic networks, exemplified through a detailed examination of the glycolysis process and the related regulative proteins.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Methods</title>","<title>Construction of the Global Integrative Network from ten databases</title>","<p id=\"Par30\">The owl files of BioPAX level 3 prepared by PathwayCommons were downloaded from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.pathwaycommons.org/archives/PC2/v12/\">https://www.pathwaycommons.org/archives/PC2/v12/</ext-link>. Specifically, we selected DrugBank, HumanCyc, INOH, KEGG, NetPath, PANTHER, PID, PhosphoSitePlus (PSP), and Recon X from PathwayCommons, which contain sufficient number of biochemical reactions extracted from the owl files. The BioPAX level 3 owl file of Reactome was acquired through <ext-link ext-link-type=\"uri\" xlink:href=\"https://reactome.org/download-data\">https://reactome.org/download-data</ext-link>. The owl files were parsed by function “readBiopax” from R package rBiopaxParser<sup>##REF##23274212##57##</sup>, which generated a dataframe for each of the databases.</p>","<p id=\"Par31\">We built a R package to extract the reactions from the dataframe and convert them into SIFI format. Specifically, we first extracted the reactions from classes of TransportWithBiochemicalReaction, Transport, BiochemicalReaction, ComplexAssembly, Degradation, and Conversion. Then we extracted the information of the enzymes from the classes of Catalysis, Control, and Modulation, and linked the enzymes with the reactions. These information was finally organized into one temporary table.</p>","<p id=\"Par32\">Subsequently, we created a component matching table designed to capture the relationships between proteins and complexes. We did not use the conventional name of the complexes; instead, we adopted a distinct approach wherein the complexes were systematically deconstructed into constituent proteins through recursive processes. Then the name of the complexes were given by concatenation of all the names of the components in alphabetical order, separated by underscores (“_”) .</p>","<p id=\"Par33\">Next, we replaced the complex IDs in the reaction table with the name generated from the component names, and convert the reactions into SIFI format. The intermediate nodes were introduced during this conversion step. The names of the intermediate nodes were the concatenation of all the substrates and enzymes, separated by semicolon (“;”). Note that the result of this step still used the local ID system for each owl file specifically which cannot be shared with other owl files.</p>","<p id=\"Par34\">Since the owl file of each database provides mapping relations between local IDs and commonly used (external) IDs, we replaced the local IDs with the external IDs suggested by each database. However, each databases has its own preference on the use of the ID sources, thus we had to uniform the sources of the IDs to ensure that the same gene/chemical got the same ID in different databases. Uniprot IDs were converted to gene symbols by R package biomaRt<sup>##REF##19617889##58##</sup>. For metabolite IDs, We constructed a mapping table using R package metaboliteIDmapping<sup>##UREF##6##59##</sup>, and used the strategy in Supplementary Fig. ##SUPPL##0##1## to uniform the IDs with a preference of the sources. A tutorial for the conversion of KEGG’s owl file to SIFI format can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/BIGchix/SIFItools\">https://github.com/BIGchix/SIFItools</ext-link>.</p>","<p id=\"Par35\">Finally, we concatenated all the SIFI files into one single file, and removed the redundant edges. The edges containing gene IDs of other species were removed.</p>","<title>Network analysis of human GINv2.0</title>","<p id=\"Par36\">The intersection results of genes, metabolites and edges were visualized by R package UpSetR<sup>##REF##28645171##60##</sup>. The total network and the network of glycolysis were visualized by Cytoscape<sup>##REF##14597658##1##,##REF##17947979##2##</sup>. The community detection was performed by python package leidenalg<sup>##REF##30914743##34##</sup>, to efficiently work with large directed graph using the Leiden algorithm<sup>##REF##30914743##34##</sup>.</p>","<title>Reporting summary</title>","<p id=\"Par37\">Further information on research design is available in the ##SUPPL##1##Nature Research Reporting Summary## linked to this article.</p>"],"string":"[\n \"<title>Methods</title>\",\n \"<title>Construction of the Global Integrative Network from ten databases</title>\",\n \"<p id=\\\"Par30\\\">The owl files of BioPAX level 3 prepared by PathwayCommons were downloaded from <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.pathwaycommons.org/archives/PC2/v12/\\\">https://www.pathwaycommons.org/archives/PC2/v12/</ext-link>. Specifically, we selected DrugBank, HumanCyc, INOH, KEGG, NetPath, PANTHER, PID, PhosphoSitePlus (PSP), and Recon X from PathwayCommons, which contain sufficient number of biochemical reactions extracted from the owl files. The BioPAX level 3 owl file of Reactome was acquired through <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://reactome.org/download-data\\\">https://reactome.org/download-data</ext-link>. The owl files were parsed by function “readBiopax” from R package rBiopaxParser<sup>##REF##23274212##57##</sup>, which generated a dataframe for each of the databases.</p>\",\n \"<p id=\\\"Par31\\\">We built a R package to extract the reactions from the dataframe and convert them into SIFI format. Specifically, we first extracted the reactions from classes of TransportWithBiochemicalReaction, Transport, BiochemicalReaction, ComplexAssembly, Degradation, and Conversion. Then we extracted the information of the enzymes from the classes of Catalysis, Control, and Modulation, and linked the enzymes with the reactions. These information was finally organized into one temporary table.</p>\",\n \"<p id=\\\"Par32\\\">Subsequently, we created a component matching table designed to capture the relationships between proteins and complexes. We did not use the conventional name of the complexes; instead, we adopted a distinct approach wherein the complexes were systematically deconstructed into constituent proteins through recursive processes. Then the name of the complexes were given by concatenation of all the names of the components in alphabetical order, separated by underscores (“_”) .</p>\",\n \"<p id=\\\"Par33\\\">Next, we replaced the complex IDs in the reaction table with the name generated from the component names, and convert the reactions into SIFI format. The intermediate nodes were introduced during this conversion step. The names of the intermediate nodes were the concatenation of all the substrates and enzymes, separated by semicolon (“;”). Note that the result of this step still used the local ID system for each owl file specifically which cannot be shared with other owl files.</p>\",\n \"<p id=\\\"Par34\\\">Since the owl file of each database provides mapping relations between local IDs and commonly used (external) IDs, we replaced the local IDs with the external IDs suggested by each database. However, each databases has its own preference on the use of the ID sources, thus we had to uniform the sources of the IDs to ensure that the same gene/chemical got the same ID in different databases. Uniprot IDs were converted to gene symbols by R package biomaRt<sup>##REF##19617889##58##</sup>. For metabolite IDs, We constructed a mapping table using R package metaboliteIDmapping<sup>##UREF##6##59##</sup>, and used the strategy in Supplementary Fig. ##SUPPL##0##1## to uniform the IDs with a preference of the sources. A tutorial for the conversion of KEGG’s owl file to SIFI format can be found at <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://github.com/BIGchix/SIFItools\\\">https://github.com/BIGchix/SIFItools</ext-link>.</p>\",\n \"<p id=\\\"Par35\\\">Finally, we concatenated all the SIFI files into one single file, and removed the redundant edges. The edges containing gene IDs of other species were removed.</p>\",\n \"<title>Network analysis of human GINv2.0</title>\",\n \"<p id=\\\"Par36\\\">The intersection results of genes, metabolites and edges were visualized by R package UpSetR<sup>##REF##28645171##60##</sup>. The total network and the network of glycolysis were visualized by Cytoscape<sup>##REF##14597658##1##,##REF##17947979##2##</sup>. The community detection was performed by python package leidenalg<sup>##REF##30914743##34##</sup>, to efficiently work with large directed graph using the Leiden algorithm<sup>##REF##30914743##34##</sup>.</p>\",\n \"<title>Reporting summary</title>\",\n \"<p id=\\\"Par37\\\">Further information on research design is available in the ##SUPPL##1##Nature Research Reporting Summary## linked to this article.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<title>Conversion of BioPAX to SIFI</title>","<p id=\"Par9\">In our efforts to tackle the challenges of different knowledge base languages, we developed a R package named “SIFItools” to efficiently convert BioPAX level 3 owl files from various databases into SIFI format. OWL (Web Ontology Language) format is a powerful and expressive ontology language that allows users to define rich and complex relationships between entities. In the context of Biological Pathway Exchange (BioPAX) language, OWL is used to represent biological pathways and their components, such as molecules, interactions, and cellular processes, in a semantically meaningful way. With SIFItools, we firstly extracted biochemical reactions from the owl files of nine databases prepared by PathwayCommons<sup>##REF##31647099##23##</sup>, including HumanCyc, DrugBank<sup>##REF##29126136##24##</sup>, INOH<sup>##REF##22120663##25##</sup>, KEGG, NetPath<sup>##REF##20067622##26##</sup>, PANTHER<sup>##REF##34717010##27##</sup>, PhosphoSitePlus (PSP)<sup>##REF##25514926##28##</sup>, PID<sup>##REF##18832364##29##</sup>, and Recon X<sup>##REF##17267599##30##</sup>, as well as the owl file of Reactome from its official webpage (not from PathwayCommons). This facilitated the analysis of molecular interactions across multiple databases and laid the groundwork for building a comprehensive network for human cells (Fig. ##FIG##0##1a, b##). Then each of the reactions was converted into the structure of meta-pathway, introducing an intermediate node (Fig. ##FIG##0##1c##). After standardization of the 71 ID formats into seven, we analyzed the overlapping genes, chemicals and edges, then integrated all ten databases into one global integrative network, which we refer as GINv2.0 (Fig. ##FIG##0##1d##).</p>","<p id=\"Par10\">Notably, although SIFItools automated much of the curation process, manual curation was still necessary due to the diverse naming conventions and special characters used in different databases. In the process of manual curation, the most complicated task involved the conversion of internal IDs from each database’s owl file to corresponding external gene or chemical IDs. This complexity arose from the fact that a single gene or chemical could have different internal IDs across various databases, each linked to one or more distinct external IDs. To overcome this challenge, we developed a two-step approach. Firstly, we constructed an ID mapping table using internal “XRef” links, enabling us to convert the internal IDs to external IDs from 71 different sources. Subsequently, we aggregated the external IDs from diverse sources into gene symbols and unified chemical ID types (UC_IDs), which includes CID<sup>##REF##36305812##31##</sup>, SID<sup>##REF##36305812##31##</sup>, CAS registry number, KEGG, HMDB<sup>##REF##34986597##32##</sup>, and ChEBI<sup>##REF##26467479##33##</sup> (Supplementary Fig. ##SUPPL##0##1##). This method ensured consistency and standardization across the databases, facilitating seamless integration of the data in our subsequent analyses.</p>","<title>Consensus analysis of the databases</title>","<p id=\"Par11\">Conversion of BioPAX level3 into SIFI format generated networks varied in the number of edges and nodes, ranging from 873 nodes (NetPath) to 5614 nodes (Reactome) (Fig. ##FIG##1##2a##), and from 2444 edges (NetPath) to 29898 edges (Reactome) (Supplementary Fig. ##SUPPL##0##2##). Notably, the ratio between the quantity of genes and the number of chemicals exhibited variations across the databases. These variations accurately mirrored the distinct scopes of molecular interactions inherent to each individual database. For example, the SIFI format of NetPath and PSP exclusively contained human genes, while Recon X exclusively included chemical IDs (Fig. ##FIG##1##2a##). This distinction highlights the significance of our integrative approach in capturing a comprehensive picture of human molecular interactions.</p>","<p id=\"Par12\">Next, we conducted an analysis of the overlapping gene symbols (Fig. ##FIG##1##2b##), UC_IDs (Fig. ##FIG##1##2c##), and edges (Fig. ##FIG##1##2d##) among the ten databases. For clarity, Recon X was excluded from Fig. ##FIG##1##2b## due to its exclusive focus on chemicals. Similarly, NetPath and PSP were excluded from Fig. ##FIG##1##2c##. Our analysis revealed that the overlap of gene symbols was notably larger than the overlap of chemical IDs. For instance, in the case of Reactome, the number of unique gene symbols accounted for only 11.2% of its total gene symbols (445 out of 3981), whereas the number of unique chemical IDs represented 76.1% of its total chemical IDs (1243 out of 1633). Furthermore, we found that the overlap of interactions between databases was limited, with over 96.8% (110202 out of 113876) of the interactions being unique to each database for the majority of cases. This observation underscores the distinctiveness and database-specific nature of the interactions. The limited overlap of interactions highlights the importance of our integrative approach in leveraging data from multiple sources to build a comprehensive and interconnected network.</p>","<title>Integration of the ten databases</title>","<p id=\"Par13\">We merged the SIFI files from all ten databases to construct the raw global integrative network of human. Redundant edges were removed before importing the network into Cytoscape for visualization (Fig. ##FIG##2##3a##). The final GINv2.0 for human comprises 39,548 nodes and 113,876 edges, encompassing 6330 genes, 3579 chemical IDs, 3957 complexes, and 25,682 intermediate nodes. To facilitate further analysis, we utilized the Python package leidenalg<sup>##REF##30914743##34##</sup> to cluster the network into distinct sub-networks. In Fig. ##FIG##2##3a##, we presented the top 20 sub-networks with the largest number of nodes. These sub-networks exhibit diverse compositions of genes, chemicals, and intermediates. Notably, most sub-networks are a mix of genes and chemicals; however, some sub-networks, such as clusters 3, 5, 6, 8, 11, 13, 15, 17, 18, and 19, are predominantly gene-driven, while others, like clusters 4 and 16, are primarily chemical-centric (Fig. ##FIG##2##3b##). This observation underscores the complex interplay between signaling networks and metabolic pathways, contributing to the complexity of the network.</p>","<p id=\"Par14\">Additionally, we calculated the topological network metrics, presented in Table ##TAB##0##1##. Notably, the node with the highest degree was water, followed by ATP and ADP. These findings indicate that water, ATP, and ADP are central participants in biological processes within human cells, aligning well with established knowledge in the field. To gain deeper insights into specific sub-networks, we conducted a focused examination of cluster 16. We identified several nodes with high degrees, including HIF1A, KDM1A, Succinic acid, Acetyl-CoA, Formaldehyde, CO2, NADH, and NAD+ (Fig. ##FIG##2##3c##).</p>","<p id=\"Par15\">To investigate the composition of the database sources of each cluster, we calculated the percentage of the edges contributed by different databases to each cluster (Fig. ##FIG##2##3d##). Our analysis showed that ReconX’s data (only consists of chemicals) mainly presents in cluster 1 and cluster 10. For cluster 1, there are three major sources, ReconX, INOH, and HumanCyc. In cluster 10, the major sources are ReconX, Reactome, and HumanCyc. Similar results can be observed for PSP and NetPath. These evidences suggest that the databases focusing on only genes or chemicals are well mixed with other databases. On the other hand, KEGG and Reactome contribute the majority of edges of cluster 4 and cluster 16 respectively, which are chemical-centric, and cluster 15 which is gene-centric by Reactome. This suggests that these two comprehensive databases, KEGG and Reactome, who both cover signaling and metabolic pathways, may have distinct scopes of signaling and metabolic reactions.</p>","<title>Regulation of glycolysis by signaling proteins</title>","<p id=\"Par16\">To demonstrate the practical application of GINv2.0 in analyzing signaling and metabolic networks, we extracted nodes representing the metabolites, intermediates, and protein enzymes involved in glycolysis, along with the proteins that regulate these enzymes. Subsequently, we visualized the network in Cytoscape (Fig. ##FIG##3##4##). Glycolysis is a fundamental cellular metabolic process that converts glucose to pyruvate, generating ATP and NADH. The GINv2.0 visualization of glycolysis clearly illustrates how enzymes are linked to metabolites through intermediate nodes. Moreover, each intermediate node represents a specific reaction, effectively circumventing ambiguity arising from multiple isozymes catalyzing the same reaction.</p>","<p id=\"Par17\">Subsequently, we focused on the incoming nodes of the enzymes involved in glycolysis, which provided insights into the proteins regulating this crucial metabolic pathway. Our analysis revealed that, out of the ten steps comprising glycolysis, seven steps were regulated by various kinases, including SRC<sup>##REF##28054552##35##,##REF##15592455##36##</sup>, ULK1<sup>##REF##27153534##37##,##UREF##5##38##</sup>, AKT1<sup>##REF##16964243##39##</sup>, AKT2<sup>##REF##21979951##40##</sup>, PRKAA1<sup>##REF##26626483##41##</sup>, PRKCD<sup>##REF##27129213##42##</sup>, PAK1<sup>##REF##27251275##43##</sup>, MAPK1<sup>##REF##29229835##44##</sup>, MAPK8<sup>##REF##26258887##45##</sup>, GSK3B<sup>##REF##30297868##46##</sup>, PIM2<sup>##REF##24142698##47##</sup>, EGFR<sup>##REF##27251275##43##</sup>, and CDK6<sup>##REF##27485204##48##</sup>. These findings highlight the complicated control mechanisms governing glycolysis, ensuring its harmonious coordination with the activation and inhibition of other cellular pathways, ultimately balancing energy production.</p>","<p id=\"Par18\">Notably, we found that ULK1 is a prominent positive regulator of HK1<sup>##REF##27153534##37##</sup>, PFKM<sup>##REF##27153534##37##</sup>, and ENO1<sup>##REF##27153534##37##,##UREF##5##38##</sup>, making it a pivotal protein in governing glycolysis based on the number of controlling enzymes. While SRC is known for its broad involvement in various cellular processes, ULK1 is well known for its essential role in initiating autophagy<sup>##REF##21205641##49##</sup>. Building on this intriguing clue, we deeply explored the relationship between these kinases and autophagy regulation. Remarkably, seven out of the thirteen identified proteins were found to exhibit direct or indirect regulatory effects on autophagy. Protein Kinase AMP-Activated Catalytic Subunit Alpha 1 (PRKAA1), the catalytic subunit of AMPK, plays a crucial role in autophagy initiation under glucose deprivation by directly phosphorylating ULK1<sup>##REF##26626483##41##</sup>. Additionally, AKT suppresses tuberous sclerosis complex proteins 1/2 (TSC1/2) through phosphorylation, leading to mTORC1 activation and subsequent autophagy inhibition<sup>##REF##12172553##50##</sup>. Reports have shown that GSK3B promotes ULK1 acetylation by mediating KAT5/TIP60 phosphorylation during starvation<sup>##REF##30704899##51##</sup>. Furthermore, MAPK8 activates autophagy by mediating BCL2 phosphorylation, facilitating the dissociation of BCL2 from BECN1<sup>##REF##18570871##52##</sup>. Finally, emerging evidence suggests that PIM2 is capable of phosphorylating HK2, thereby promoting autophagy under glucose deprivation<sup>##REF##29985480##53##</sup>.</p>","<p id=\"Par19\">Collectively, these findings indicate a synergistic regulation of glycolysis and autophagy, particularly under glucose-starved conditions, enriching the understanding of cellular adaptation to varying nutrient availability. In summary, our comprehensive network analysis empowers researchers with fresh perspectives on the cross-talk between metabolic and cellular regulatory networks, paving the way for deeper investigations into the underlying molecular complexities.</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>Conversion of BioPAX to SIFI</title>\",\n \"<p id=\\\"Par9\\\">In our efforts to tackle the challenges of different knowledge base languages, we developed a R package named “SIFItools” to efficiently convert BioPAX level 3 owl files from various databases into SIFI format. OWL (Web Ontology Language) format is a powerful and expressive ontology language that allows users to define rich and complex relationships between entities. In the context of Biological Pathway Exchange (BioPAX) language, OWL is used to represent biological pathways and their components, such as molecules, interactions, and cellular processes, in a semantically meaningful way. With SIFItools, we firstly extracted biochemical reactions from the owl files of nine databases prepared by PathwayCommons<sup>##REF##31647099##23##</sup>, including HumanCyc, DrugBank<sup>##REF##29126136##24##</sup>, INOH<sup>##REF##22120663##25##</sup>, KEGG, NetPath<sup>##REF##20067622##26##</sup>, PANTHER<sup>##REF##34717010##27##</sup>, PhosphoSitePlus (PSP)<sup>##REF##25514926##28##</sup>, PID<sup>##REF##18832364##29##</sup>, and Recon X<sup>##REF##17267599##30##</sup>, as well as the owl file of Reactome from its official webpage (not from PathwayCommons). This facilitated the analysis of molecular interactions across multiple databases and laid the groundwork for building a comprehensive network for human cells (Fig. ##FIG##0##1a, b##). Then each of the reactions was converted into the structure of meta-pathway, introducing an intermediate node (Fig. ##FIG##0##1c##). After standardization of the 71 ID formats into seven, we analyzed the overlapping genes, chemicals and edges, then integrated all ten databases into one global integrative network, which we refer as GINv2.0 (Fig. ##FIG##0##1d##).</p>\",\n \"<p id=\\\"Par10\\\">Notably, although SIFItools automated much of the curation process, manual curation was still necessary due to the diverse naming conventions and special characters used in different databases. In the process of manual curation, the most complicated task involved the conversion of internal IDs from each database’s owl file to corresponding external gene or chemical IDs. This complexity arose from the fact that a single gene or chemical could have different internal IDs across various databases, each linked to one or more distinct external IDs. To overcome this challenge, we developed a two-step approach. Firstly, we constructed an ID mapping table using internal “XRef” links, enabling us to convert the internal IDs to external IDs from 71 different sources. Subsequently, we aggregated the external IDs from diverse sources into gene symbols and unified chemical ID types (UC_IDs), which includes CID<sup>##REF##36305812##31##</sup>, SID<sup>##REF##36305812##31##</sup>, CAS registry number, KEGG, HMDB<sup>##REF##34986597##32##</sup>, and ChEBI<sup>##REF##26467479##33##</sup> (Supplementary Fig. ##SUPPL##0##1##). This method ensured consistency and standardization across the databases, facilitating seamless integration of the data in our subsequent analyses.</p>\",\n \"<title>Consensus analysis of the databases</title>\",\n \"<p id=\\\"Par11\\\">Conversion of BioPAX level3 into SIFI format generated networks varied in the number of edges and nodes, ranging from 873 nodes (NetPath) to 5614 nodes (Reactome) (Fig. ##FIG##1##2a##), and from 2444 edges (NetPath) to 29898 edges (Reactome) (Supplementary Fig. ##SUPPL##0##2##). Notably, the ratio between the quantity of genes and the number of chemicals exhibited variations across the databases. These variations accurately mirrored the distinct scopes of molecular interactions inherent to each individual database. For example, the SIFI format of NetPath and PSP exclusively contained human genes, while Recon X exclusively included chemical IDs (Fig. ##FIG##1##2a##). This distinction highlights the significance of our integrative approach in capturing a comprehensive picture of human molecular interactions.</p>\",\n \"<p id=\\\"Par12\\\">Next, we conducted an analysis of the overlapping gene symbols (Fig. ##FIG##1##2b##), UC_IDs (Fig. ##FIG##1##2c##), and edges (Fig. ##FIG##1##2d##) among the ten databases. For clarity, Recon X was excluded from Fig. ##FIG##1##2b## due to its exclusive focus on chemicals. Similarly, NetPath and PSP were excluded from Fig. ##FIG##1##2c##. Our analysis revealed that the overlap of gene symbols was notably larger than the overlap of chemical IDs. For instance, in the case of Reactome, the number of unique gene symbols accounted for only 11.2% of its total gene symbols (445 out of 3981), whereas the number of unique chemical IDs represented 76.1% of its total chemical IDs (1243 out of 1633). Furthermore, we found that the overlap of interactions between databases was limited, with over 96.8% (110202 out of 113876) of the interactions being unique to each database for the majority of cases. This observation underscores the distinctiveness and database-specific nature of the interactions. The limited overlap of interactions highlights the importance of our integrative approach in leveraging data from multiple sources to build a comprehensive and interconnected network.</p>\",\n \"<title>Integration of the ten databases</title>\",\n \"<p id=\\\"Par13\\\">We merged the SIFI files from all ten databases to construct the raw global integrative network of human. Redundant edges were removed before importing the network into Cytoscape for visualization (Fig. ##FIG##2##3a##). The final GINv2.0 for human comprises 39,548 nodes and 113,876 edges, encompassing 6330 genes, 3579 chemical IDs, 3957 complexes, and 25,682 intermediate nodes. To facilitate further analysis, we utilized the Python package leidenalg<sup>##REF##30914743##34##</sup> to cluster the network into distinct sub-networks. In Fig. ##FIG##2##3a##, we presented the top 20 sub-networks with the largest number of nodes. These sub-networks exhibit diverse compositions of genes, chemicals, and intermediates. Notably, most sub-networks are a mix of genes and chemicals; however, some sub-networks, such as clusters 3, 5, 6, 8, 11, 13, 15, 17, 18, and 19, are predominantly gene-driven, while others, like clusters 4 and 16, are primarily chemical-centric (Fig. ##FIG##2##3b##). This observation underscores the complex interplay between signaling networks and metabolic pathways, contributing to the complexity of the network.</p>\",\n \"<p id=\\\"Par14\\\">Additionally, we calculated the topological network metrics, presented in Table ##TAB##0##1##. Notably, the node with the highest degree was water, followed by ATP and ADP. These findings indicate that water, ATP, and ADP are central participants in biological processes within human cells, aligning well with established knowledge in the field. To gain deeper insights into specific sub-networks, we conducted a focused examination of cluster 16. We identified several nodes with high degrees, including HIF1A, KDM1A, Succinic acid, Acetyl-CoA, Formaldehyde, CO2, NADH, and NAD+ (Fig. ##FIG##2##3c##).</p>\",\n \"<p id=\\\"Par15\\\">To investigate the composition of the database sources of each cluster, we calculated the percentage of the edges contributed by different databases to each cluster (Fig. ##FIG##2##3d##). Our analysis showed that ReconX’s data (only consists of chemicals) mainly presents in cluster 1 and cluster 10. For cluster 1, there are three major sources, ReconX, INOH, and HumanCyc. In cluster 10, the major sources are ReconX, Reactome, and HumanCyc. Similar results can be observed for PSP and NetPath. These evidences suggest that the databases focusing on only genes or chemicals are well mixed with other databases. On the other hand, KEGG and Reactome contribute the majority of edges of cluster 4 and cluster 16 respectively, which are chemical-centric, and cluster 15 which is gene-centric by Reactome. This suggests that these two comprehensive databases, KEGG and Reactome, who both cover signaling and metabolic pathways, may have distinct scopes of signaling and metabolic reactions.</p>\",\n \"<title>Regulation of glycolysis by signaling proteins</title>\",\n \"<p id=\\\"Par16\\\">To demonstrate the practical application of GINv2.0 in analyzing signaling and metabolic networks, we extracted nodes representing the metabolites, intermediates, and protein enzymes involved in glycolysis, along with the proteins that regulate these enzymes. Subsequently, we visualized the network in Cytoscape (Fig. ##FIG##3##4##). Glycolysis is a fundamental cellular metabolic process that converts glucose to pyruvate, generating ATP and NADH. The GINv2.0 visualization of glycolysis clearly illustrates how enzymes are linked to metabolites through intermediate nodes. Moreover, each intermediate node represents a specific reaction, effectively circumventing ambiguity arising from multiple isozymes catalyzing the same reaction.</p>\",\n \"<p id=\\\"Par17\\\">Subsequently, we focused on the incoming nodes of the enzymes involved in glycolysis, which provided insights into the proteins regulating this crucial metabolic pathway. Our analysis revealed that, out of the ten steps comprising glycolysis, seven steps were regulated by various kinases, including SRC<sup>##REF##28054552##35##,##REF##15592455##36##</sup>, ULK1<sup>##REF##27153534##37##,##UREF##5##38##</sup>, AKT1<sup>##REF##16964243##39##</sup>, AKT2<sup>##REF##21979951##40##</sup>, PRKAA1<sup>##REF##26626483##41##</sup>, PRKCD<sup>##REF##27129213##42##</sup>, PAK1<sup>##REF##27251275##43##</sup>, MAPK1<sup>##REF##29229835##44##</sup>, MAPK8<sup>##REF##26258887##45##</sup>, GSK3B<sup>##REF##30297868##46##</sup>, PIM2<sup>##REF##24142698##47##</sup>, EGFR<sup>##REF##27251275##43##</sup>, and CDK6<sup>##REF##27485204##48##</sup>. These findings highlight the complicated control mechanisms governing glycolysis, ensuring its harmonious coordination with the activation and inhibition of other cellular pathways, ultimately balancing energy production.</p>\",\n \"<p id=\\\"Par18\\\">Notably, we found that ULK1 is a prominent positive regulator of HK1<sup>##REF##27153534##37##</sup>, PFKM<sup>##REF##27153534##37##</sup>, and ENO1<sup>##REF##27153534##37##,##UREF##5##38##</sup>, making it a pivotal protein in governing glycolysis based on the number of controlling enzymes. While SRC is known for its broad involvement in various cellular processes, ULK1 is well known for its essential role in initiating autophagy<sup>##REF##21205641##49##</sup>. Building on this intriguing clue, we deeply explored the relationship between these kinases and autophagy regulation. Remarkably, seven out of the thirteen identified proteins were found to exhibit direct or indirect regulatory effects on autophagy. Protein Kinase AMP-Activated Catalytic Subunit Alpha 1 (PRKAA1), the catalytic subunit of AMPK, plays a crucial role in autophagy initiation under glucose deprivation by directly phosphorylating ULK1<sup>##REF##26626483##41##</sup>. Additionally, AKT suppresses tuberous sclerosis complex proteins 1/2 (TSC1/2) through phosphorylation, leading to mTORC1 activation and subsequent autophagy inhibition<sup>##REF##12172553##50##</sup>. Reports have shown that GSK3B promotes ULK1 acetylation by mediating KAT5/TIP60 phosphorylation during starvation<sup>##REF##30704899##51##</sup>. Furthermore, MAPK8 activates autophagy by mediating BCL2 phosphorylation, facilitating the dissociation of BCL2 from BECN1<sup>##REF##18570871##52##</sup>. Finally, emerging evidence suggests that PIM2 is capable of phosphorylating HK2, thereby promoting autophagy under glucose deprivation<sup>##REF##29985480##53##</sup>.</p>\",\n \"<p id=\\\"Par19\\\">Collectively, these findings indicate a synergistic regulation of glycolysis and autophagy, particularly under glucose-starved conditions, enriching the understanding of cellular adaptation to varying nutrient availability. In summary, our comprehensive network analysis empowers researchers with fresh perspectives on the cross-talk between metabolic and cellular regulatory networks, paving the way for deeper investigations into the underlying molecular complexities.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par20\">In this work, we compiled a much more comprehensive GIN for human compared with the previous version. The previous GIN<sup>##REF##36533264##22##</sup> for human was built only upon KEGG, which includes 5145 genes and 1501 metabolites. In the present work, we compiled a new GIN for human from ten different databases, which involved 6330 genes and 3579 metabolites, with 23.0% and 138.4% increase, respectively. The new GIN for human is much more useful than the previous one, as the integration of various databases greatly enhances the comprehensiveness of the network. This is exemplified by the demonstration of the orchestrated regulation of autophagy and glucose metabolism under stress, which leveraged information from multiple databases.</p>","<p id=\"Par21\">We also offer a new tool for the conversion of BioPax level3 files into SIFI format. In our previous work, we built the GIN by a pipeline of perl scripts specifically written to parse KGML files. Since the use of KGML format is currently limited to KEGG, our previous pipeline lacks the ability to process the files of other databases. In our present work, we construct a R package (SIFItools) which can convert BioPax level3 files into SIFI format with minimum manual curations required. Because many biological databases share their data in BioPax level3 format, our new package, SIFItools is more convenient and have much more potential applications when building GIN from databases.</p>","<p id=\"Par22\">Also in this work, we compared the overlapping information between the 10 databases. We were not able to conduct such analysis in our previous work since we only converted KEGG database into GIN. In our present work, we compared the molecular interactions of the 10 databases and surprisingly found that the overlaps between the databases were rare. Although this could partly due to the different focus of the databases, there is still a great proportion of unmatched ids, especially for metabolites. This could lead to confusing results when applying over-representation analysis (ORA) of pathways, as pointed out by another work<sup>##REF##34492007##54##</sup>.</p>","<p id=\"Par23\">The knowledge bases of pathways serve as repositories for capturing molecular interactions in both physiological and pathological contexts. While each database emphasizes distinct molecular interactions, synthesizing the collective insights from various sources can outline the comprehensive scope of these knowledge bases. However, the exploration of consensus among diverse knowledge bases has been limited, in part due to the varying data formats used by each database. Despite PathwayCommons’ efforts to standardize data formats, the inherent features of XML format have posed challenges for direct cross-database comparisons. For example, in BioPAX level3, key information about a given reaction may be dispersed across properties such as “left”, “right”, “product”, “controlled”, “controller”, or “cofactor.” This distribution necessitates the extraction of reaction details from multiple attributes to facilitate comparison, thereby complicating and impeding the efficiency of the process. The introduction of meta-pathways and SIFI format has alleviated this predicament by structuring reaction information into a SIF-like three-column configuration. This transformation enables rapid comparisons between reactions, streamlining the comparative analysis.</p>","<p id=\"Par24\">We noticed that there are overlaps between the concepts of meta-pathway, SIFI format and GIN. To clarify the definitions of the three concepts: (1) Meta-pathway is the way of displaying pathways using intermediates to connect the substrates and products. (2) The GIN (Global Integrative Network) is a network combining the molecular interactions from all pathways. (3) SIFI (Simple Interaction Format with Intermediates) is a format we use to store the molecular interactions of meta-pathways and GIN. The differences between the three concepts are: meta-pathway is the component of GIN, while they can be both stored in SIFI format.</p>","<p id=\"Par25\">The consensus analysis of GINs generated from different databases highlighted significant diversity across the databases, particularly concerning the edges and nodes related to metabolites. This observed diversity could potentially rise from variations in the specific focus of each database or disparities in naming conventions. Such variations raise valid concerns regarding the reliability of metabolite enrichment analysis, aligning with findings from a recent investigation into the ORA of pathways leveraging metabolomics data. Notably, the authors of this study revealed significant disparities in ORA results when employing distinct databases, such as KEGG, Reactome and BioCyc<sup>##REF##34492007##54##</sup>, which may partially due to the inconsistency we found in our consensus analysis.</p>","<p id=\"Par26\">The credibility of the edges is also important for network analysis, since questionable edges will create misleading path when conducting path-related network analysis, as evidenced in our previous work<sup>##REF##36533264##22##</sup>. In the comparative analysis of different databases, repeated edges may be more credible since it has been repeatedly validated by different databases. In fact, one of our original goals to compare the edges from different databases was to score the edges based on the number of repeats. However, with the analysis of the databases, we found that a large number of the non-redundant edges are the results of the variations of the scopes of databases. For example, in Fig. ##FIG##3##4##, the edges extracted from the PSP database are not found in any other databases, but all of these edges have credible sources of publications. This means that a large proportion of non-redundant edges may be credible. Based on this consideration, we excluded the analysis of the credibility of edges in our current work.</p>","<p id=\"Par27\">By analyzing GINv2.0, we found that the number of intermediate nodes was substantially larger than the combined count of both genes and metabolites. Since each intermediate node represents a distinct biochemical reaction, the number of genes/metabolites involved in a pathway, which is often used in conventional enrichment analysis such as GO, may not truly reflect the number of reactions associated with the pathway. For instance, consider a scenario where five genes are shared between the input gene set and a pathway gene set. While ORA and GSEA<sup>##REF##16199517##55##,##REF##12808457##56##</sup> might not distinguish whether these five genes participate in one single reaction or five distinct ones, the possibilities of significant associations between the input and pathway gene sets are distinct, judging by instinct. Thus, the intermediate nodes are likely a hidden layer reside between the genes/metabolites and pathways, which has not been investigated for enrichment analysis. The construction of GINs is therefore, a starting point for building the relations between genes/metabolites, intermediate states, and pathways, and further promote the improvement of gene set/pathway analysis.</p>","<p id=\"Par28\">The illustration of the glycolysis process and the regulative proteins underscores the benefits of the integration of multiple knowledge bases. Notably, we found that the core nodes and edges of the glycolysis process was primarily derived from KEGG, Reactome, HumanCyc, and INOH, while the regulatory interplays between kinases and glycolytic enzymes were from PSP. Individual GINs of any single databases were not able to provide such comprehensive view of molecular interactions. This demonstrates the necessity of database integration to forge a comprehensive and unified network.</p>","<p id=\"Par29\">In the current version of GIN (v2.0), the intermediate nodes are built for metabolic reactions and PTM reactions, but not for PPI. The reason for excluding PPI is that the GIN we built is a directed graph, but PPI networks are undirected, therefore, current PPI data does not fit for GIN we built. However, we are working on the solution to generate appropriate intermediate nodes for the complexes with multiple protein participants in PPI. With the flexibility of the meta-pathway’s structure, other types of data regarding molecular interactions in cells, including the relations of transcription factors (TFs) and their targets, miRNAs and their targets, will soon be incorporated in GINs as well.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par20\\\">In this work, we compiled a much more comprehensive GIN for human compared with the previous version. The previous GIN<sup>##REF##36533264##22##</sup> for human was built only upon KEGG, which includes 5145 genes and 1501 metabolites. In the present work, we compiled a new GIN for human from ten different databases, which involved 6330 genes and 3579 metabolites, with 23.0% and 138.4% increase, respectively. The new GIN for human is much more useful than the previous one, as the integration of various databases greatly enhances the comprehensiveness of the network. This is exemplified by the demonstration of the orchestrated regulation of autophagy and glucose metabolism under stress, which leveraged information from multiple databases.</p>\",\n \"<p id=\\\"Par21\\\">We also offer a new tool for the conversion of BioPax level3 files into SIFI format. In our previous work, we built the GIN by a pipeline of perl scripts specifically written to parse KGML files. Since the use of KGML format is currently limited to KEGG, our previous pipeline lacks the ability to process the files of other databases. In our present work, we construct a R package (SIFItools) which can convert BioPax level3 files into SIFI format with minimum manual curations required. Because many biological databases share their data in BioPax level3 format, our new package, SIFItools is more convenient and have much more potential applications when building GIN from databases.</p>\",\n \"<p id=\\\"Par22\\\">Also in this work, we compared the overlapping information between the 10 databases. We were not able to conduct such analysis in our previous work since we only converted KEGG database into GIN. In our present work, we compared the molecular interactions of the 10 databases and surprisingly found that the overlaps between the databases were rare. Although this could partly due to the different focus of the databases, there is still a great proportion of unmatched ids, especially for metabolites. This could lead to confusing results when applying over-representation analysis (ORA) of pathways, as pointed out by another work<sup>##REF##34492007##54##</sup>.</p>\",\n \"<p id=\\\"Par23\\\">The knowledge bases of pathways serve as repositories for capturing molecular interactions in both physiological and pathological contexts. While each database emphasizes distinct molecular interactions, synthesizing the collective insights from various sources can outline the comprehensive scope of these knowledge bases. However, the exploration of consensus among diverse knowledge bases has been limited, in part due to the varying data formats used by each database. Despite PathwayCommons’ efforts to standardize data formats, the inherent features of XML format have posed challenges for direct cross-database comparisons. For example, in BioPAX level3, key information about a given reaction may be dispersed across properties such as “left”, “right”, “product”, “controlled”, “controller”, or “cofactor.” This distribution necessitates the extraction of reaction details from multiple attributes to facilitate comparison, thereby complicating and impeding the efficiency of the process. The introduction of meta-pathways and SIFI format has alleviated this predicament by structuring reaction information into a SIF-like three-column configuration. This transformation enables rapid comparisons between reactions, streamlining the comparative analysis.</p>\",\n \"<p id=\\\"Par24\\\">We noticed that there are overlaps between the concepts of meta-pathway, SIFI format and GIN. To clarify the definitions of the three concepts: (1) Meta-pathway is the way of displaying pathways using intermediates to connect the substrates and products. (2) The GIN (Global Integrative Network) is a network combining the molecular interactions from all pathways. (3) SIFI (Simple Interaction Format with Intermediates) is a format we use to store the molecular interactions of meta-pathways and GIN. The differences between the three concepts are: meta-pathway is the component of GIN, while they can be both stored in SIFI format.</p>\",\n \"<p id=\\\"Par25\\\">The consensus analysis of GINs generated from different databases highlighted significant diversity across the databases, particularly concerning the edges and nodes related to metabolites. This observed diversity could potentially rise from variations in the specific focus of each database or disparities in naming conventions. Such variations raise valid concerns regarding the reliability of metabolite enrichment analysis, aligning with findings from a recent investigation into the ORA of pathways leveraging metabolomics data. Notably, the authors of this study revealed significant disparities in ORA results when employing distinct databases, such as KEGG, Reactome and BioCyc<sup>##REF##34492007##54##</sup>, which may partially due to the inconsistency we found in our consensus analysis.</p>\",\n \"<p id=\\\"Par26\\\">The credibility of the edges is also important for network analysis, since questionable edges will create misleading path when conducting path-related network analysis, as evidenced in our previous work<sup>##REF##36533264##22##</sup>. In the comparative analysis of different databases, repeated edges may be more credible since it has been repeatedly validated by different databases. In fact, one of our original goals to compare the edges from different databases was to score the edges based on the number of repeats. However, with the analysis of the databases, we found that a large number of the non-redundant edges are the results of the variations of the scopes of databases. For example, in Fig. ##FIG##3##4##, the edges extracted from the PSP database are not found in any other databases, but all of these edges have credible sources of publications. This means that a large proportion of non-redundant edges may be credible. Based on this consideration, we excluded the analysis of the credibility of edges in our current work.</p>\",\n \"<p id=\\\"Par27\\\">By analyzing GINv2.0, we found that the number of intermediate nodes was substantially larger than the combined count of both genes and metabolites. Since each intermediate node represents a distinct biochemical reaction, the number of genes/metabolites involved in a pathway, which is often used in conventional enrichment analysis such as GO, may not truly reflect the number of reactions associated with the pathway. For instance, consider a scenario where five genes are shared between the input gene set and a pathway gene set. While ORA and GSEA<sup>##REF##16199517##55##,##REF##12808457##56##</sup> might not distinguish whether these five genes participate in one single reaction or five distinct ones, the possibilities of significant associations between the input and pathway gene sets are distinct, judging by instinct. Thus, the intermediate nodes are likely a hidden layer reside between the genes/metabolites and pathways, which has not been investigated for enrichment analysis. The construction of GINs is therefore, a starting point for building the relations between genes/metabolites, intermediate states, and pathways, and further promote the improvement of gene set/pathway analysis.</p>\",\n \"<p id=\\\"Par28\\\">The illustration of the glycolysis process and the regulative proteins underscores the benefits of the integration of multiple knowledge bases. Notably, we found that the core nodes and edges of the glycolysis process was primarily derived from KEGG, Reactome, HumanCyc, and INOH, while the regulatory interplays between kinases and glycolytic enzymes were from PSP. Individual GINs of any single databases were not able to provide such comprehensive view of molecular interactions. This demonstrates the necessity of database integration to forge a comprehensive and unified network.</p>\",\n \"<p id=\\\"Par29\\\">In the current version of GIN (v2.0), the intermediate nodes are built for metabolic reactions and PTM reactions, but not for PPI. The reason for excluding PPI is that the GIN we built is a directed graph, but PPI networks are undirected, therefore, current PPI data does not fit for GIN we built. However, we are working on the solution to generate appropriate intermediate nodes for the complexes with multiple protein participants in PPI. With the flexibility of the meta-pathway’s structure, other types of data regarding molecular interactions in cells, including the relations of transcription factors (TFs) and their targets, miRNAs and their targets, will soon be incorporated in GINs as well.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a “meta-pathway” structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/BIGchix/GINv2.0\">https://github.com/BIGchix/GINv2.0</ext-link>.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both, integrating these networks into a unified topological network has proven to be challenging. The intricacy of molecular interactions and the diverse formats employed to store and display them contribute to the complexity of this task. In a prior study, we addressed this challenge by introducing a “meta-pathway” structure that integrated the advantages of the Simple Interaction Format (SIF) while accommodating reaction information. Nevertheless, the earlier Global Integrative Network (GIN) was limited to reliance on KEGG alone. Here, we present GIN version 2.0, which incorporates human molecular interaction data from ten distinct knowledge bases, including KEGG, Reactome, and HumanCyc, among others. We standardized the data structure, gene IDs, and chemical IDs, and conducted a comprehensive analysis of the consistency among the ten knowledge bases before combining all unified interactions into GINv2.0. Utilizing GINv2.0, we investigated the glycolysis process and its regulatory proteins, revealing coordinated regulations on glycolysis and autophagy, particularly under glucose starvation. The expanded scope and enhanced capabilities of GINv2.0 provide a valuable resource for comprehensive systems-level analyses in the field of biological research. GINv2.0 can be accessed at: <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://github.com/BIGchix/GINv2.0\\\">https://github.com/BIGchix/GINv2.0</ext-link>.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary information</title>","<p>\n\n\n</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>\\n\\n\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary information</title>","<p>The online version contains supplementary material available at 10.1038/s41540-024-00330-y.</p>","<title>Acknowledgements</title>","<p>We thank Yan Yan for her insightful discussion on SIFItools, and Siyi Su for his help on the glycolysis illustration.</p>","<title>Author contributions</title>","<p>Xu.C. and Xiao.C. conceived the study; S.Y. developed SIFItools; Xiao.C., Y.Z., L.L., Z.G. and X.L. collected data and performed conversion; Xu.C., S.Y. and Y.Z. analyzed the data; Y.Z. performed network visualization; Xu.C. wrote the manuscript; All authors helped in the writing of the manuscript. All authors approved the final version of the manuscript.</p>","<title>Data availability</title>","<p>We used the BioPAX level3 owl files prepared by PathwayCommons<sup>##REF##31647099##23##</sup> which can be accessed here (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.pathwaycommons.org/archives/PC2/v12/\">https://www.pathwaycommons.org/archives/PC2/v12/</ext-link>). The BioPAX level 3 owl file of Reactome was acquired through <ext-link ext-link-type=\"uri\" xlink:href=\"https://reactome.org/download-data\">https://reactome.org/download-data</ext-link>. The GINv2.0 generated in this work can be freely accessed from github: <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/BIGchix/GINv2.0\">https://github.com/BIGchix/GINv2.0</ext-link>.</p>","<title>Code availability</title>","<p>The R package “SIFItools” can be freely accessed and installed from github: <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/BIGchix/SIFItools\">https://github.com/BIGchix/SIFItools</ext-link>.</p>","<title>Competing interests</title>","<p id=\"Par38\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41540-024-00330-y.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>We thank Yan Yan for her insightful discussion on SIFItools, and Siyi Su for his help on the glycolysis illustration.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>Xu.C. and Xiao.C. conceived the study; S.Y. developed SIFItools; Xiao.C., Y.Z., L.L., Z.G. and X.L. collected data and performed conversion; Xu.C., S.Y. and Y.Z. analyzed the data; Y.Z. performed network visualization; Xu.C. wrote the manuscript; All authors helped in the writing of the manuscript. All authors approved the final version of the manuscript.</p>\",\n \"<title>Data availability</title>\",\n \"<p>We used the BioPAX level3 owl files prepared by PathwayCommons<sup>##REF##31647099##23##</sup> which can be accessed here (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.pathwaycommons.org/archives/PC2/v12/\\\">https://www.pathwaycommons.org/archives/PC2/v12/</ext-link>). The BioPAX level 3 owl file of Reactome was acquired through <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://reactome.org/download-data\\\">https://reactome.org/download-data</ext-link>. The GINv2.0 generated in this work can be freely accessed from github: <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://github.com/BIGchix/GINv2.0\\\">https://github.com/BIGchix/GINv2.0</ext-link>.</p>\",\n \"<title>Code availability</title>\",\n \"<p>The R package “SIFItools” can be freely accessed and installed from github: <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://github.com/BIGchix/SIFItools\\\">https://github.com/BIGchix/SIFItools</ext-link>.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par38\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Construction of global integrative network for human.</title><p>The workflow consists of (<bold>a</bold>, <bold>b</bold>), the extraction of reactions from the BioPAX level3 (owl) files of 10 databases, (<bold>c</bold>) the conversion of the data into meta-pathways, and (<bold>d</bold>) the analysis of overlaps and the integration of the databases. The upper part of (<bold>b</bold>) represents a metabolic reaction and the lower part represents a signaling reaction.</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>The overlap analysis of the databases.</title><p><bold>a</bold> The number of human genes and chemical IDs in the ten databases. <bold>b</bold>–<bold>d</bold> Upset plots showing the overlap of the genes, chemicals and edges between the 10 databases. Each column in the matrix at the lower part of the plot shows the sources of the set whose number is displayed as a bar in the upper part of the plot.</p></caption></fig>","<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>GINv2.0 constructed from 10 databases.</title><p><bold>a</bold> Visualization of GINv2.0 by Cytoscape. The largest 20 sub-networks are shown in colors. The small and fragmented interactions which do not connect to the major network are shown on the right bottom. <bold>b</bold> The node composition of the top 20 sub-networks. n_itmd, number of intermediate nodes. <bold>c</bold> Zoomed visualization of cluster16. <bold>d</bold> Dot-plot showing the contribution of different databases to the edges of the clusters. The size of the circle represents the percentage of the edges. The color represents the log10 transformed counts of the overlapping edges.</p></caption></fig>","<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Illustration of glycolysis and its regulative proteins.</title><p>The regulative protein kinases which are associated with autophagy were clustered in a light-yellow box.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><title>Construction of global integrative network for human.</title><p>The workflow consists of (<bold>a</bold>, <bold>b</bold>), the extraction of reactions from the BioPAX level3 (owl) files of 10 databases, (<bold>c</bold>) the conversion of the data into meta-pathways, and (<bold>d</bold>) the analysis of overlaps and the integration of the databases. The upper part of (<bold>b</bold>) represents a metabolic reaction and the lower part represents a signaling reaction.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><title>The overlap analysis of the databases.</title><p><bold>a</bold> The number of human genes and chemical IDs in the ten databases. <bold>b</bold>–<bold>d</bold> Upset plots showing the overlap of the genes, chemicals and edges between the 10 databases. Each column in the matrix at the lower part of the plot shows the sources of the set whose number is displayed as a bar in the upper part of the plot.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Fig. 3</label><caption><title>GINv2.0 constructed from 10 databases.</title><p><bold>a</bold> Visualization of GINv2.0 by Cytoscape. The largest 20 sub-networks are shown in colors. The small and fragmented interactions which do not connect to the major network are shown on the right bottom. <bold>b</bold> The node composition of the top 20 sub-networks. n_itmd, number of intermediate nodes. <bold>c</bold> Zoomed visualization of cluster16. <bold>d</bold> Dot-plot showing the contribution of different databases to the edges of the clusters. The size of the circle represents the percentage of the edges. The color represents the log10 transformed counts of the overlapping edges.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Fig. 4</label><caption><title>Illustration of glycolysis and its regulative proteins.</title><p>The regulative protein kinases which are associated with autophagy were clustered in a light-yellow box.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":["<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Top 20 nodes with highest degrees.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Id</th><th>Name</th><th>Degree</th></tr></thead><tbody><tr><td>CID962</td><td>Water</td><td>3179</td></tr><tr><td>CID1038</td><td>H+</td><td>2925</td></tr><tr><td>CID6022</td><td>Adenosine-5’-Diphosphate</td><td>1933</td></tr><tr><td>CID5957</td><td>Adenosine-5’-Triphosphate</td><td>1498</td></tr><tr><td>SID8148096</td><td>ATP(4-)</td><td>655</td></tr><tr><td>CID977</td><td>Oxygen</td><td>524</td></tr><tr><td>CID87642</td><td>Coenzyme A</td><td>499</td></tr><tr><td>CID1004</td><td>Phosphoric Acid</td><td>490</td></tr><tr><td>CID5884</td><td>NADPH</td><td>486</td></tr><tr><td>SID85646635</td><td>ADP(3-)</td><td>397</td></tr><tr><td>CID1061</td><td>Phosphate Ion</td><td>383</td></tr><tr><td>\n<italic>SRC</italic></td><td><italic>SRC Proto-Oncogene, Non-Receptor Tyrosine Kinase</italic></td><td><bold>379</bold></td></tr><tr><td>SID99319226</td><td>NAD(1-)</td><td>345</td></tr><tr><td>SID111978360</td><td>Nucleoside Triphosphate(4-)</td><td>344</td></tr><tr><td>\n<italic>PRKACA</italic></td><td><italic>Protein Kinase CAMP-Activated Catalytic Subunit Alpha</italic></td><td><bold>343</bold></td></tr><tr><td>CID923</td><td>Sodium Ion</td><td>342</td></tr><tr><td>CID4995</td><td>Diphosphate(2-)</td><td>336</td></tr><tr><td>CID21604869</td><td>beta-NADH</td><td>325</td></tr><tr><td>\n<italic>AKT1</italic></td><td><italic>AKT Serine/Threonine Kinase 1</italic></td><td><bold>324</bold></td></tr><tr><td>CID280</td><td>Carbon Dioxide</td><td>299</td></tr></tbody></table></table-wrap>"],"string":"[\n \"<table-wrap id=\\\"Tab1\\\"><label>Table 1</label><caption><p>Top 20 nodes with highest degrees.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th>Id</th><th>Name</th><th>Degree</th></tr></thead><tbody><tr><td>CID962</td><td>Water</td><td>3179</td></tr><tr><td>CID1038</td><td>H+</td><td>2925</td></tr><tr><td>CID6022</td><td>Adenosine-5’-Diphosphate</td><td>1933</td></tr><tr><td>CID5957</td><td>Adenosine-5’-Triphosphate</td><td>1498</td></tr><tr><td>SID8148096</td><td>ATP(4-)</td><td>655</td></tr><tr><td>CID977</td><td>Oxygen</td><td>524</td></tr><tr><td>CID87642</td><td>Coenzyme A</td><td>499</td></tr><tr><td>CID1004</td><td>Phosphoric Acid</td><td>490</td></tr><tr><td>CID5884</td><td>NADPH</td><td>486</td></tr><tr><td>SID85646635</td><td>ADP(3-)</td><td>397</td></tr><tr><td>CID1061</td><td>Phosphate Ion</td><td>383</td></tr><tr><td>\\n<italic>SRC</italic></td><td><italic>SRC Proto-Oncogene, Non-Receptor Tyrosine Kinase</italic></td><td><bold>379</bold></td></tr><tr><td>SID99319226</td><td>NAD(1-)</td><td>345</td></tr><tr><td>SID111978360</td><td>Nucleoside Triphosphate(4-)</td><td>344</td></tr><tr><td>\\n<italic>PRKACA</italic></td><td><italic>Protein Kinase CAMP-Activated Catalytic Subunit Alpha</italic></td><td><bold>343</bold></td></tr><tr><td>CID923</td><td>Sodium Ion</td><td>342</td></tr><tr><td>CID4995</td><td>Diphosphate(2-)</td><td>336</td></tr><tr><td>CID21604869</td><td>beta-NADH</td><td>325</td></tr><tr><td>\\n<italic>AKT1</italic></td><td><italic>AKT Serine/Threonine Kinase 1</italic></td><td><bold>324</bold></td></tr><tr><td>CID280</td><td>Carbon Dioxide</td><td>299</td></tr></tbody></table></table-wrap>\"\n]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":["<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>","<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>"],"string":"[\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM1\\\"></supplementary-material>\",\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM2\\\"></supplementary-material>\"\n]","isConversational":false},"footnote":{"kind":"list like","value":["<table-wrap-foot><p>The IDs in bold are protein kinases.</p></table-wrap-foot>","<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Xiao Chang, Shen Yan.</p></fn></fn-group>"],"string":"[\n \"<table-wrap-foot><p>The IDs in bold are protein kinases.</p></table-wrap-foot>\",\n \"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Xiao Chang, Shen Yan.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41540_2024_330_Fig1_HTML\" id=\"d32e410\"/>","<graphic xlink:href=\"41540_2024_330_Fig2_HTML\" id=\"d32e461\"/>","<graphic xlink:href=\"41540_2024_330_Fig3_HTML\" id=\"d32e515\"/>","<graphic xlink:href=\"41540_2024_330_Fig4_HTML\" id=\"d32e725\"/>"],"string":"[\n \"<graphic xlink:href=\\\"41540_2024_330_Fig1_HTML\\\" id=\\\"d32e410\\\"/>\",\n \"<graphic xlink:href=\\\"41540_2024_330_Fig2_HTML\\\" id=\\\"d32e461\\\"/>\",\n \"<graphic xlink:href=\\\"41540_2024_330_Fig3_HTML\\\" id=\\\"d32e515\\\"/>\",\n \"<graphic xlink:href=\\\"41540_2024_330_Fig4_HTML\\\" id=\\\"d32e725\\\"/>\"\n]","isConversational":false},"media":{"kind":"list like","value":["<media xlink:href=\"41540_2024_330_MOESM1_ESM.pdf\"><caption><p>Supplementary Figures</p></caption></media>","<media xlink:href=\"41540_2024_330_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>"],"string":"[\n \"<media xlink:href=\\\"41540_2024_330_MOESM1_ESM.pdf\\\"><caption><p>Supplementary Figures</p></caption></media>\",\n \"<media xlink:href=\\\"41540_2024_330_MOESM2_ESM.pdf\\\"><caption><p>Reporting Summary</p></caption></media>\"\n]","isConversational":false},"unknown_pub":{"kind":"string","value":"[{\"label\": [\"4.\"], \"surname\": [\"Loers\", \"Vermeirssen\"], \"given-names\": [\"JU\", \"V\"], \"article-title\": [\"SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks\"], \"source\": [\"BMC Bioinforma.\"], \"year\": [\"2022\"], \"volume\": [\"23\"], \"fpage\": [\"363\"], \"lpage\": [\"363\"], \"pub-id\": [\"10.1186/s12859-022-04908-3\"]}, {\"label\": [\"5.\"], \"surname\": [\"Catlett\"], \"given-names\": [\"NL\"], \"article-title\": [\"Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data\"], \"source\": [\"BMC Bioinforma.\"], \"year\": [\"2013\"], \"volume\": [\"14\"], \"fpage\": [\"340\"], \"pub-id\": [\"10.1186/1471-2105-14-340\"]}, {\"label\": [\"17.\"], \"surname\": [\"van Iersel\"], \"given-names\": [\"MP\"], \"article-title\": [\"Presenting and exploring biological pathways with PathVisio\"], \"source\": [\"BMC Bioinforma.\"], \"year\": [\"2008\"], \"volume\": [\"9\"], \"fpage\": [\"399\"], \"pub-id\": [\"10.1186/1471-2105-9-399\"]}, {\"label\": [\"18.\"], \"mixed-citation\": [\"Hucka, M. in \"], \"italic\": [\"Encyclopedia of Systems Biology\"]}, {\"label\": [\"20.\"], \"surname\": [\"Sompairac\"], \"given-names\": [\"N\"], \"article-title\": [\"Metabolic and signalling network maps integration: application to cross-talk 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Proteom.\"], \"year\": [\"2012\"], \"volume\": [\"75\"], \"fpage\": [\"1343\"], \"lpage\": [\"1356\"], \"pub-id\": [\"10.1016/j.jprot.2011.11.004\"]}, {\"label\": [\"59.\"], \"mixed-citation\": [\"Canzler, S. metaboliteIDmapping: mapping of metabolite IDs from different sources. \"], \"italic\": [\"R package version 0.99.10\"], \"ext-link\": [\"https://github.com/yigbt/metaboliteIDmapping\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":60,"string":"60"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:57"},"citation":{"kind":"string","value":"NPJ Syst Biol Appl. 2024 Jan 13; 10:4"},"package_file":{"kind":"string","value":"oa_package/88/16/PMC10787761.tar.gz"}}},{"rowIdx":92,"cells":{"accession_id":{"kind":"string","value":"PMC10787762"},"pmid":{"kind":"string","value":"38218942"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par10\">The ‘use of opportunistic modes of nutrient acquisition’ was recently described as a hallmark of cancer cells [##REF##26771115##1##], living in a nutrient-poor microenvironment. Cancer cells must adapt their metabolism to support biomass production, ATP generation and maintain a redox state. Disrupting these processes can interfere with both tumor growth and proliferation [##REF##32049045##2##]. Amino acids, like other biomacromolecules, play an important role in rapidly proliferating cancer cells, as carbon and nitrogen donors to get rid of the nutrition limitation [##REF##18712276##3##]. Thus, amino acid metabolism has been extensively studied following glucose metabolism in tumor.</p>","<p id=\"Par11\">While the definition of essential amino acids (EAAs) and nonessential amino acids (NEAAs) is appropriate for normal cells, the classification does not apply to cancer cells [##REF##33340176##4##]. Altered amino acids metabolism is common in tumors, and non-essential amino acids usually become essential in tumors. Thus, targeting certain kind of amino acids has the potential to control specific tumors. Targeting asparagine metabolism enzyme such as asparaginase have the potential to treat leukemia, which is currently in clinical use. Furthermore, targeting molecules in amino acid metabolic signaling pathway also has the potential to treat tumors [##REF##33511116##5##]. For example, targeting mammalian target of rapamycin (mTOR) can control the growth of various tumors including breast cancer, kidney cancer, neuroendocrine cancer and so on. Other key molecules including MYC, and KRAS in amino acid metabolic signaling pathway are also burgeoning approaches for tumor biotherapy.</p>","<p id=\"Par12\">In addition to altered nutrient and signal pathway, solid tumors are known to recruit immune cells in the stroma and create favorable conditions for their growth and survival [##REF##32675345##6##], which is known as tumor microenvironment (TME). Cells in TME could not only resist immune surveillance and drug therapy, but also provide amino acids to tumors to meet their growth needs. Thus, restricting amino acids in TME is an effective way to limit tumor growth [##REF##33795176##7##]. Besides, amino acids also play an important role in epigenetics like DNA methylation and histone modification [##REF##26427799##8##]. Improving our understanding of its role in tumor progression and immune evasion could provide novel ideas for metabolic cancer therapy.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par10\\\">The ‘use of opportunistic modes of nutrient acquisition’ was recently described as a hallmark of cancer cells [##REF##26771115##1##], living in a nutrient-poor microenvironment. Cancer cells must adapt their metabolism to support biomass production, ATP generation and maintain a redox state. Disrupting these processes can interfere with both tumor growth and proliferation [##REF##32049045##2##]. Amino acids, like other biomacromolecules, play an important role in rapidly proliferating cancer cells, as carbon and nitrogen donors to get rid of the nutrition limitation [##REF##18712276##3##]. Thus, amino acid metabolism has been extensively studied following glucose metabolism in tumor.</p>\",\n \"<p id=\\\"Par11\\\">While the definition of essential amino acids (EAAs) and nonessential amino acids (NEAAs) is appropriate for normal cells, the classification does not apply to cancer cells [##REF##33340176##4##]. Altered amino acids metabolism is common in tumors, and non-essential amino acids usually become essential in tumors. Thus, targeting certain kind of amino acids has the potential to control specific tumors. Targeting asparagine metabolism enzyme such as asparaginase have the potential to treat leukemia, which is currently in clinical use. Furthermore, targeting molecules in amino acid metabolic signaling pathway also has the potential to treat tumors [##REF##33511116##5##]. For example, targeting mammalian target of rapamycin (mTOR) can control the growth of various tumors including breast cancer, kidney cancer, neuroendocrine cancer and so on. Other key molecules including MYC, and KRAS in amino acid metabolic signaling pathway are also burgeoning approaches for tumor biotherapy.</p>\",\n \"<p id=\\\"Par12\\\">In addition to altered nutrient and signal pathway, solid tumors are known to recruit immune cells in the stroma and create favorable conditions for their growth and survival [##REF##32675345##6##], which is known as tumor microenvironment (TME). Cells in TME could not only resist immune surveillance and drug therapy, but also provide amino acids to tumors to meet their growth needs. Thus, restricting amino acids in TME is an effective way to limit tumor growth [##REF##33795176##7##]. Besides, amino acids also play an important role in epigenetics like DNA methylation and histone modification [##REF##26427799##8##]. Improving our understanding of its role in tumor progression and immune evasion could provide novel ideas for metabolic cancer therapy.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":[],"string":"[]","isConversational":false},"results":{"kind":"list like","value":[],"string":"[]","isConversational":false},"discussion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Amino acid metabolism plays important roles in tumor biology and tumor therapy. Accumulating evidence has shown that amino acids contribute to tumorigenesis and tumor immunity by acting as nutrients, signaling molecules, and could also regulate gene transcription and epigenetic modification. Therefore, targeting amino acid metabolism will provide new ideas for tumor treatment and become an important therapeutic approach after surgery, radiotherapy, and chemotherapy. In this review, we systematically summarize the recent progress of amino acid metabolism in malignancy and their interaction with signal pathways as well as their effect on tumor microenvironment and epigenetic modification. Collectively, we also highlight the potential therapeutic application and future expectation.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Amino acid metabolism plays important roles in tumor biology and tumor therapy. Accumulating evidence has shown that amino acids contribute to tumorigenesis and tumor immunity by acting as nutrients, signaling molecules, and could also regulate gene transcription and epigenetic modification. Therefore, targeting amino acid metabolism will provide new ideas for tumor treatment and become an important therapeutic approach after surgery, radiotherapy, and chemotherapy. In this review, we systematically summarize the recent progress of amino acid metabolism in malignancy and their interaction with signal pathways as well as their effect on tumor microenvironment and epigenetic modification. Collectively, we also highlight the potential therapeutic application and future expectation.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Facts</title>","<p id=\"Par2\">\n<list list-type=\"bullet\"><list-item><p id=\"Par3\">Altered amino acid metabolism in tumors challenges the traditional classification of essential and nonessential amino acids.</p></list-item><list-item><p id=\"Par4\">Amino acids have emerged as pivotal regulators in tumors, participated in a myriad of bidirectional interactions including signal pathways, tumor microenvironment, and epigenetic modifications.</p></list-item><list-item><p id=\"Par5\">Clinical trials align with the idea that limiting amino acid intake may improve cancer prognoses.</p></list-item></list>\n</p>","<title>Open Questions</title>","<p id=\"Par6\">\n<list list-type=\"bullet\"><list-item><p id=\"Par7\">Among the several effects that are simultaneously regulated by certain amino acid, is there a chief effect that determines the progression or repression of tumor?</p></list-item><list-item><p id=\"Par8\">What are the optimal strategies and urgent challenges for the clinical translation of amino acids-based therapies in the near future?</p></list-item><list-item><p id=\"Par9\">Is the altered amino acids metabolism, described in different tumors, causally linked to their tumor etiology and pathogenesis?</p></list-item></list>\n</p>","<title>Reprogrammed amino acid metabolism in cancer</title>","<p id=\"Par13\">A number of cancers have been found auxotrophic for NEAAs [##REF##31819187##9##]. It may be that the demand for proliferate is too large and exceed the supply, or the related enzymes are mutated, or metabolic pathways are dysregulated. These amino acids are named conditional EAAs. Considering the swift proliferation of tumors within an environment deprived of nutrients, the composition of amino acids frequently displays instability. This fluctuation in amino acids can significantly impact overall cellular metabolism, ultimately culminating in cell proliferation or death [##REF##31980738##10##]. Therefore, the 20 standard proteinogenic amino acids, including conditional EAAs (glutamine, arginine), EAAs (branched-chain amino acids, tryptophan), and non-essential amino acids (asparagine, aspartate) play flexible roles in protein synthesis or energy supply activities in tumor.</p>","<title>Glutamine metabolism</title>","<p id=\"Par14\">Glutamine (Gln) is a conditional EAA, which is not essential for normal cells but becomes crucial for tumor cells due to their heightened demand. It is the most abundant amino acid found in plasma, and the most rapidly consumed amino acid in tumor cells [##REF##27988359##11##]. As an EAA in tumors, glutamine participates in rapid biosynthetic reactions in tumor. Tumor cells utilize glutamine avidly, known as glutamine addiction [##REF##19033189##12##]. Thus, it always functions as the rate-limiting molecule of the cell reproductive cycle. Once deprived of glutamine, cancer cells usually arrest in S-phase [##REF##26921316##13##]. Additionally, glutamine also plays a crucial role in maintaining redox homeostasis, replenishing the tricarboxylic acid (TCA) cycle, and participating in signal transduction processes within tumors [##REF##28938091##14##].</p>","<p id=\"Par15\">ASCT2(SLC1A5) is the main glutamine transporter in tumor (Fig. ##FIG##0##1##). It is regulated by multiple tumor associated transcription factors including Rb/E2F [##REF##23353822##15##], androgen receptor 3 [##REF##24052624##16##] and ATF4 [##REF##25142020##17##]. ASCT2 is highly expressed in tumor tissue, and its expression level is negatively correlated with patients’ prognosis. As ASCT2 transports glutamine for tumor consumption, inhibiting ASCT2 induces apoptosis and exhibits anti-cancer activity in acute myeloid leukemia [##REF##24014241##18##], gastric cancer [##REF##29100325##19##], prostate cancer [##REF##25693838##20##], and triple-negative breast cancer [##REF##26455325##21##]. In addition, tumor cells are capable of synthesize glutamine by themselves from glutamate (Glu) and ammonia. Glutamine synthetase (GS) is highly expressed in cancer cells to support their rapid proliferation. Moreover, GS can also promote cell proliferation independently of its catalytic function, only by interacting with nuclear pore protein [##REF##35145325##22##]. Therefore, tumor cells acquire a substantial amount of glutamine through both intrinsically synthesis and extrinsically uptake, emphasizing its critical role in tumor reprogrammed metabolism.</p>","<p id=\"Par16\">Glutaminolysis is a process catalyzed by glutaminase 1(GLS1) or GLS2 to produce glutamate [##REF##33194749##23##]. GLS1 and GLS2 are isozymes that play opposite roles in tumor development [##REF##31652551##24##]. GLS1 has oncogenic properties, while GLS2 has been described as a tumor suppressor. Numerous studies have reported a higher expression of GLS1 and lower expression of GLS2 in various tumor types, including liver cancer and colorectal cancer [##REF##35895807##25##, ##REF##30674873##26##]. GLS1 is regulated by the oncogenes MYC [##REF##19219026##27##], Rho GTPases [##REF##20832749##28##] and Notch [##REF##26422827##29##]. In colorectal cancer cells, GLS1 is essential to tumor growth, invasion, and metastatic colonization. Mechanically, under hypoxia TME, HIF-1 activates the expression of the GLS1 to promote tumor migration, invasion, and metastatic colonization [##REF##30674873##26##]. Besides, GLS1 plays a crucial role in boosting the production of GSH and NADH, contributing to oxidative balance maintain to promote tumor proliferation [##REF##11906817##30##]. Conversely, GLS2 expression is transcriptionally upregulated by tumor suppressor and stress-related proteins, including p53, p63, and p73. Although GLS2 plays a role in the production of GSH, it is worth noticing that GLS2 modestly regulates the ratio of GSH/GSSG, which is essential to oxidative balance. Different from GLS1, GLS2 catalyzes glutamate metabolism to promote α-ketoglutarate (α-KG) production, participating in TCA cycle and thereby facilitating the production of lipid ROS. The accumulation of ROS leads to mitochondrial membrane hyperpolarization and thereby inducing ferroptosis [##REF##35895807##25##]. In addition to glutaminolysis, glutamine can be metabolized into intermediates like carbamoyl phosphate (CP) and phosphoribosyl amine (PRA) for the synthesis of purines and pyrimidines, which are essential components for DNA synthesis and repair during rapidly tumor proliferation [##REF##31167399##31##, ##REF##31365859##32##].</p>","<p id=\"Par17\">Glutamate, the metabolite of glutaminolysis, provides important resources for energy and biomacromolecule synthesis in tumor (Fig. ##FIG##0##1##). In tumors under glucose-limited condition, glutamate acts as a substitute for glucose, producing the intermediate α-KG to facilitate the TCA cycle. However, the provision of α-KG alone is inadequate for sustaining the TCA cycle. This insufficiency arises from the limited availability of acetyl-CoA, a rate-limiting molecule in TCA cycle. Studies have found that in glutamine addictive tumor, mitochondrial phosphoenolpyruvate carboxykinase (PCK2) expression was elevated, facilitating phosphoenolpyruvate (PEP) production from oxaloacetate generated in the TCA cycle [##REF##26474064##33##]. Thus, glutamine-derived PEP acts as a substitute for glucose-derived PEP, offering a valuable source to acetyl-CoA production that replenishes the TCA cycle [##REF##33511116##5##]. Glutamate and α-KG also participate in the transaminate to other non-essential amino acids. In addition, α-KG serves as both substrate and cofactor for DNA dioxygenase enzymes involved in DNA demethylation [##REF##31467060##34##] (Detailed later in amino acid metabolism and epigenetic modification).</p>","<title>Arginine metabolism</title>","<p id=\"Par18\">Arginine (Arg) is also identified as a conditional EAA in tumor [##REF##33511116##5##]. It can be synthesized de novo under the catalysis of argininosuccinate synthase 1 (ASS1) and argininosuccinate lyase (ASL) from aspartate and citrulline in urea cycle (Fig. ##FIG##0##1##). However, ASS1, the rate-limiting enzyme in urea cycle, is usually downregulated in cancer and its downregulation has been reported to be associated with advanced tumor stage [##REF##32409304##35##]. ASS1 downregulation redirects aspartate from urea production towards pyrimidine biosynthesis, facilitating the high demand of rapid tumor proliferation. This phenomenon is commonly known as urea cycle dysregulation [##REF##30100185##36##]. Since tumors underexpress urea cycle related enzymes like ASS1 and therefore downregulate arginine synthesis, exogenous arginine supply is critical for tumors survival and proliferation. Arginine can be obtained through cationic amino acid transporter (CAT) family transporters, including CAT-1, CAT-2 and CAT-3. CATs are always upregulated in many human cancers [##REF##32899180##37##]. Thus, in arginine-dependent cancer cells, CATs knockdown can decrease the viability of cancer cells and induce apoptosis [##REF##21308737##38##].</p>","<p id=\"Par19\">Arginine can be hydrolyzed by arginases (cytoplasmic, ARG1; mitochondrial, ARG2) and both arginases are upregulated in cancer cells to ensure the production of polyamines. ARG1 is upregulated in a wider range of tumors compared to ARG2. ARGs convert arginine to urea and ornithine. In tumors, ornithine is metabolized by upregulated ornithine decarboxylase (ODC) into polyamines including putrescine, spermidine, and spermine [##REF##30404920##39##]. Polyamines are well known for their crucial role in tumor proliferation and DNA stability [##REF##32499785##40##–##REF##34186159##42##]. They facilitate cell proliferation by increasing DNA synthesis through the activation of enzymes such as DNA polymerases, helicases, and DNA ligases. Besides, cellular protein synthesis is also positively correlated with polyamines. Moreover, natural polyamines function as free radical scavengers. Thus, the strong affinity between polyamines and DNA enables the stabilization of DNA structure, granting polyamines the capacity to protect nucleic acids from damage [##REF##9736703##43##, ##REF##9514920##44##].</p>","<p id=\"Par20\">In addition, arginine can produce NO under the catalysis of nitric oxide synthase-2 (NOS-2) in tumor and macrophage. NO affects TME and tumor proliferation by promoting angiogenesis [##REF##3131684##45##]. Besides, NO derived peroxynitrite can nitrate tyrosine residues and block tyrosine protein phosphorylation, reducing T cell proliferation and activation [##REF##10092790##46##].</p>","<title>Branched-chain amino acid (BCAA) metabolism</title>","<p id=\"Par21\">BCAAs, namely isoleucine (Ile), leucine (Leu), and valine (Val), are closely interconnected and classified as EAAs, whether in normal or tumor cells. Changes in the level of one of the BCAAs are accompanied by changes in the other two with the same direction and magnitude [##REF##32049045##2##]. As EAAs, BCAAs cannot be synthesized in human and thus corresponding transporters are critical. LAT1(SLC7A5) and LAT2(SLC7A8) serve as primary transporters for BCAAs [##REF##29695141##47##, ##REF##26101697##48##] (Fig. ##FIG##0##1##), exhibiting high expression levels in glioblastoma and clear cell renal cell carcinoma [##REF##32088728##49##, ##REF##23103253##50##]. Drugs targeting LATs (BAY-8002, JPH203, OKY034 etc.) have already been used in preclinical treatment of cancer [##REF##30115664##51##, ##REF##37047148##52##].</p>","<p id=\"Par22\">BCAAs affect protein synthesis either by transmitting the signal of cell nutritional state or acting as proteinogenic amino acids [##REF##29211698##53##]. BCAAs accumulation mainly promotes mTORC1 activition to enhance tumor development and growth [##REF##30661928##54##]. Explicitly, mTORC1 triggers a cascade of signaling pathway through phosphorylating its downstream effectors, including eukaryotic translation initiation factor 4E binding protein 1(4EBP-1), p70 ribosomal S6 kinase 1 (S6K1), and sterol regulatory element binding protein (SREBP), to regulate autophagy and synthesize lipids, nucleotides and proteins [##REF##30754640##55##] (Detailed later in signal pathways in amino acid metabolism). For another, BCAAs, especially leucine, are essential for protein synthesis as they are in great demand in new protein translation [##REF##25644265##56##].</p>","<p id=\"Par23\">BCAAs catabolism and related enzymes are closely related to tumorigenesis. Leucine, isoleucine and valine catabolism is mediated by BCAA transaminase 2 (BCAT2) to produce branched chain α-keto acid (BCKA) including α-ketoisocaproate (α-KIC), α-ketoamethylvalerate (α-KMV) and α-ketoisovalerate (α-KIV) respectively. Subsequently, BCKAs like α-KIC can undergo further metabolic conversion into acetyl-CoA, while α-KIV can be metabolized into succinyl-CoA. As for α-KMV, it can undergo further metabolic conversion into both acetyl-CoA and succinyl-CoA. These metabolites actively participate in the TCA cycle. Thus, BCAAs catabolism is critical for the development of cancers, especially pancreatic ductal adenocarcinoma [##REF##30661928##54##]. BCAAs also play a vital role in the synthesis of nucleotide through sustaining the levels of the enzyme ribonucleotide reductase regulatory subunit M2 (RRM2) [##REF##31575908##57##, ##REF##16756507##58##]. Since BCAAs are tightly related to tumors, altered BCAAs level in blood can predict the development of certain tumors in both humans and mouse [##REF##25261994##59##].</p>","<title>Tryptophan metabolism</title>","<p id=\"Par24\">Tryptophan (Trp) is also an EAA as its anabolism is absence in vivo. Tryptophan is involved in inherent malignant characteristic of tumors and can limit tumor immunity. The most important metabolic pathway for tryptophan is the kynurenine pathway. Free rather than albumin-binding tryptophan can be catalyzed by indoleamine 2,3-dioxygenase1 (IDO1) and tryptophan 2,3-dioxygenase (TDO) to produce kynurenine [##REF##33460767##60##] (Fig. ##FIG##0##1##). Along the kynurenine pathway, a series of biologically active molecules are produced to influence tumor progression. The primary metabolite kynurenine has been reported to block T cell proliferation and induce T cell death [##REF##16902152##61##]. Advanced cancers were associated with an increased kynurenine/tryptophan ratio, indicating that kynurenine level is correlated with tumor malignancy [##REF##35918736##62##]. Under the catalyze of kynurenine 3-monooxygenase(KMO) and kynureninase(KYNU), kynurenine can be further catabolized to NAD<sup>+</sup> and alanine. Kynurenine pathway is known as the de novo NAD<sup>+</sup> synthesis pathway, exhibiting potent resistance to oxidative stress and promoting cancer cell metastasis [##REF##37352864##63##]. In vivo studies have revealed that changes in tryptophan metabolism can decrease NAD<sup>+</sup> synthesis and DNA damage, thereby promoting hepatocarcinogenesis [##REF##25453901##64##]. Besides, alanine is deleterious for spheroid growth and thereby suppresses cancer progression [##REF##32788725##65##].</p>","<p id=\"Par25\">Apart from kynurenine pathway, tryptophan can also be metabolized in 5-hydroxytryptamine (5-HT) pathway and indole pathway, accounting for less than 5% of tryptophan metabolism [##REF##30760888##66##]. 5-HT is also called serotonin, which has more recently emerged as a growth factor for human tumor cells of different origins [##REF##29936294##67##]. Serotonin enhanced expression of PD-L1 on mouse and human cancer cells in vitro via serotonylation, covalent bonds formation between glutamine residues and serotonin, resulting in tumor progression [##REF##34524861##68##]. Along with the indole pathway, indole production activates the aryl hydrocarbon receptor (AhR) in tumor-associated macrophages (TAMs), and thus inhibits intratumoral CD8<sup>+</sup> T cell function [##REF##35139353##69##]. Tryptophan metabolism also plays a vital role in the TME, see below amino acid in TME for details.</p>","<p id=\"Par26\">Increased kynurenine pathway is correlated with tumor progression. In tumors such as non-small cell lung cancer (NSCLC) and esophageal squamous cell cancer, higher IDO1 and TDO expression in kynurenine pathway is associated with higher TNM stage and shorter overall survival [##REF##25683635##70##]. IDO1 expression can be either triggered as a counter regulatory response to cytokines like IL-1β and IL-6 released from tumor-infiltrating immune cells or maintained through tumor-intrinsic oncogenic signaling [##REF##29163470##71##, ##REF##30940904##72##]. Studies found that intratumoural IDO1 expression has been shown to correlate with the frequency of liver metastases in colorectal cancer [##REF##16489067##73##]. Besides, overexpression of IDO1 augmentes the motility of lung cancer cells, whereas its knockdown reduced cancer motility [##REF##28903363##74##]. TDO, an enzyme that catalyzes the same reaction as IDO1, is also linked to a poor prognosis when overexpressed [##REF##30760888##66##]. In a mouse model of lung cancer, inhibited TDO resulted in a reduction in the number of tumor nodules in the lungs [##REF##27050278##75##].</p>","<title>Asparagine and aspartate</title>","<p id=\"Par27\">Asparagine (Asn) and aspartate (Asp) are inter-convertible and are classified as non-essential amino acids, which play vital roles in tumor proliferation and metastasis. Asparagine synthase (ASNS) catalyzes aspartate to generate asparagine, while asparaginase (ASNase) catalyzes asparagine to produce aspartate (Fig. ##FIG##0##1##). Another way to produce aspartate is to use amino from glutamate and OAA from TCA cycle under the catalyze of glutamic oxaloacetic transaminase (GOT). Aspartate is the limiting metabolite for proliferation in tumors under hypoxia, which level is correlated with hypoxic markers [##REF##29941933##76##]. Besides, aspartate has poor cell permeability, which prevents its environmental acquisition. Therefore, inhibited intracellular aspartate synthesis and limited extracellular aspartate uptake represse tumors proliferation [##REF##26232224##77##]. However, asparagine can be efficiently imported into tumors. Tumors with high ASNase expression can rescue tumor suppression through conversion of asparagine into aspartate, bypassing intrinsic aspartate limitation and promoting tumor growth [##REF##29941931##78##].</p>","<p id=\"Par28\">Asparagine and aspartate metabolism plays multiple roles in tumor progression. Aspartate originally participates in urea cycle. However, in many tumors, loss ASS1 in urea cycle promotes cancer proliferation by diversion of aspartate substrate towards carbamoyl-phosphate synthase 2 (CPS2), aspartate transcarbamylase (ATC), and dihydroorotase (DHO), enzymes that catalyze the first three reactions in the pyrimidine synthesis pathway, resulting in increased tumor progression [##REF##30100185##36##]. Besides, asparagine export is accompanied by reverse transport of serine, arginine and histidine. Thus its intracellular level is critical for various amino acids uptake and therefore protein synthesis [##REF##27126896##79##]. Proteomic studies have shown that asparagine is specifically enriched in proteins associated with epithelial-mesenchymal transition (EMT) and restricting its availability hampers the level of EMT associated proteins [##REF##29414946##80##].</p>","<p id=\"Par29\">Despite regulating pyrimidine and EMT-related proteins synthesis to promote tumor progression, asparagine also regulates mesenchymal-epithelial transition (MET) to complete tumor colonization at distant metastatic sites [##REF##29719230##81##]. Mechanically, the scarcity of glutamine at distant metastatic sites, coupled with the heightened bioavailability of asparagine within these sites, triggers the activation of GS [##REF##29337136##82##]. This activation propels glutamine biosynthesis, fostering the accumulation of HIF1α and MYC, which are pivotal factors in metastatic processes. The relative abundance of asparagine and glutamine may thus have critical effects on tumor cells at metastatic sites. Besides, HIF1α and MYC are associated with increased oxidant stress and play important roles in the transition of EMT-like tumor cells to MET-like state, which is necessary for metastatic colonization. Thus, in an aggressive breast cancer model, ASNS upregulation promotes metastasis and results in the development of widespread metastases in the brain, liver, and lungs [##REF##29414946##80##]. Consistently, asparagine restriction can repress above processes and prolong the survival of patients [##REF##32499785##40##].</p>","<title>Serine/glycine and one-carbon metabolism</title>","<p id=\"Par30\">As one-carbon donors in the folate cycle, serine, glycine and their associated enzymes significantly contribute to nucleotide synthesis, methylation reaction and redox homeostasis to promote tumor progression. Serine hydroxymethyltransferase (cytoplasmic, SHMT1; mitochondrial, SHMT2) catalyzes the transfer of carbon from serine to tetrahydrofolate (THF), resulting in the formation of 5,10-methylene-THF, which is essential for nucleotide synthesis to fuel rapid tumor proliferation (Fig. ##FIG##0##1##). Large-scale genomic study of human tumors reveals that SHMT2 is essential for cancer cell survival and its knockdown severely impairs cancer cell proliferation [##REF##24755469##83##, ##REF##22628656##84##]. Methylenetetrahydrofolate dehydrogenase2 (MTHFD2), acting as one-carbon metabolism related enzyme, is upregulated and associated folate cycle with methionine cycle to promote S-adenosyl methionine (SAM) production in tumor cells [##REF##24451681##85##]. Thereby, serine/glycine metabolism contributes to methylation of genes and proteins as well as maintains redox homeostasis [##REF##26774282##86##] (Detailed later in amino acid metabolism and epigenetic modification). Additionally, post-translational modifications of those metabolic enzymes also play a regulatory role in tumor metabolism and progression. Deacetylation of SHMT2 by SIRT3 promotes its enzymatic activity, increases serine consumption and finally promotes colorectal carcinogenesis [##REF##30367038##87##]. When MTHFD2 is hyperacetylated, its enzymatic activity is inhibited and thereby NADPH level is downregulated. SIRT3 is also responsible for MTHFD2 deacetylation to maintain redox balance, which can be inhibited by cisplatin in colorectal cancer cells [##REF##32811824##88##].</p>","<p id=\"Par31\">As a non-essential amino acid, the synthesis of serine is vital to tumors. De novo serine synthesis pathway (SSP) startes from 3-phosphoglyceric acid (3PG) generated from glycolysis, catalyzed by enzymes phosphoglycerate dehydrogenase (PHGDH), phosphoserine aminotransferase (PSAT), and phosphoserine phosphatase (PSPH). As the rate-limiting enzyme, tumor cells highly express PHGDH to counteract limited serine availability [##REF##32783943##89##]. Conversely, RNF5, an E3 ubiquitin ligase, mediates PHGDH degradation and suppresses tumor progression [##REF##32783943##89##, ##REF##33446657##90##]. With enough serine, serine palmitoyltransferase (SPT) catalyzes the de novo biosynthesis of sphingolipids. However, when serine synthesis is limited, SPT will instead use alanine as a substrate to synthesize cytotoxic deoxysphingolipids and then suppress tumor [##REF##32788725##65##]. Thus, maintaining certain level of serine is necessary for tumor cells to escape from cytotoxic suppression.</p>","<p id=\"Par32\">Mitochondrial SHMT2 is the primary catalyst for glycine production from serine, thereby promoting the folate cycle. Elevated glycine level is linked to cancer progression like multiple myeloma (MM) and lymphoma [##REF##33569544##91##]. The glycine concentration in the bone marrow is elevated due to bone collagen degradation mediated by MM cell-secreted matrix metallopeptidase 13 (MMP13) [##REF##35817773##92##]. Although glycine is a non-essential amino acid, experiments have shown that limiting the supply of exogenous glycine induces tumor cells arrested in the growth phase (G1 phase). It is worth noting that glycine is required for nucleotide biosynthesis, directly supplying carbons for de novo purine biosynthesis, or donating one-carbon unit to the folate pool via the mitochondrial glycine cleavage system under the catalyze of glycine dehydrogenase (GLDC) [##REF##35508169##93##]. Besides, remodeled glycine metabolism mediated by protein arginine methyltransferase 7 (PRMT7) induces toxic death of leukemia stem cells [##REF##19033189##12##]. Mechanistically, PRMT7 loss resulted in reduced expression of glycine decarboxylase, leading to the reprogramed glycine metabolism to generate methylglyoxal, which is detrimental to leukemia stem cells.</p>","<title>Signal pathways in amino acid metabolism</title>","<p id=\"Par33\">Conventional understanding states amino acids as essential building blocks for peptide and protein synthesis. However, recent research has shed light on the profound significance of amino acids as bioactive molecules that play active roles in signaling pathways and metabolic regulation. mTOR, MYC and KRAS, which sense cellular amino acid levels and orchestrate these signals in a coordinated manner, play crucial roles in maintaining cellular metabolic homeostasis. Importantly, not only changes in amino acid levels impact signal pathways, but alterations in signaling pathways can also affect amino acid metabolism.</p>","<title>mTOR senses and regulates amino acid metabolism</title>","<p id=\"Par34\">mTOR is an atypical serine/threonine protein kinase, acting as a convergence point for anabolism and catabolism. Due to differences in structure and function, mTOR complexes are categorized as mTORC1 and mTORC2. mTORC1, which is sensitive to rapamycin inhibition, comprises mTOR, Raptor, mLST8, Tti/Tel2 and suppressive subunits PRAS40 and Deptor. The phosphorylation of PRAS40 and Deptor relieves its inhibition and activates mTORC1 [##REF##28388417##94##]. 4EBP-1, S6K1 and SREBP are downstream effectors of mTORC1, which are associated with upregulated synthesis as well as poor prognosis in cancer [##REF##24131622##95##]. mTORC1 is negatively regulated by low energy conditions, hypoxia, and DNA damage. It is also positively regulated by growth factors like insulin/insulin-like growth factor-1 (IGF-1) pathway and receptor tyrosine kinase-dependent Ras signaling. Particularly, when amino acids are abundant, the mTORC1 signaling pathway is positively regulated to transmit signals to facilitate protein synthesis. Conversely, under condition of amino acid insufficiency, the translation of proteins is inhibited to meet energy demand. Considering that cancer cells often exist in a nutrient-deficient environment, mTORC1 is consistently negatively regulated to adapt to metabolic alterations. mTORC2, which is insensitive to rapamycin, consists of mTOR, mSIN1, mLST8, Tti/Tel2 and suppressive subunit Rictor and Deptor. The balance between mTORC1 and mTORC2 orchestrates various metabolic processes, although our understanding of mTORC2 remains limited [##REF##22500797##96##]. We mainly focus on the function of mTORC1 below.</p>","<p id=\"Par35\">Amino acid sensors in cytoplasm like sestrins, SAR1B, CASTOR1/2, SAMTOR and LARS sense amino acid levels and thereby regulate mTOR signaling pathway [##REF##23361334##97##] (Fig. ##FIG##1##2a##). The Rag GTPase promotes the localization of mTORC1 to the lysosomal surface and activation. Rag GTPase is further regulated by amino acids through sensors-GATOR2-GATOR1 axis. GATOR1, a negative regulator of mTORC1, interacts with Rag, leading to the inhibition of mTORC1 activity, while GATOR2 modulates mTORC1 activity by inhibiting GATOR1. Sestrins and SAR1B are leucine sensors in cytosolic. In situations of leucine deprivation, they bind to and inhibit GATOR2, a positive regulator of mTORC1 [##REF##29198988##98##, ##REF##34290409##99##]. Leucine can bind to sestrins and SAR1B, dissociating GATOR2 from the complex to activate mTORC1. Furthermore, in cases of amino acid deficiency, the general control nonderepressible 2 (GCN2)/ATF4 pathway is activated by uncharged tRNA, leading to the upregulation of sestrins expression to inhibit mTORC1 activity. Similar to sestrins, in arginine-depleted conditions, CASTOR1/2 form either a CASTOR1 homodimer or CASTOR1/2 heterodimer to inhibit GATOR2 and subsequently inhibit mTORC1 activity [##REF##22749528##100##]. Arginine disrupts the CASTOR1-GATOR2 complex by binding to CASTOR1, and activates GATOR2 to stimulate mTORC1. SAMTOR senses the changes of intracellular methionine concentration in the form of SAM. SAM disrupts the SAMTOR-GATOR1 complex by binding directly to SAMTOR, and reduces the GTPase-activating protein (GAP) activity of GATOR1, which then activates the mTORC1 signaling pathway [##REF##29123071##101##]. With adequate amino acids, the E3 ubiquitin ligase KLHL22 acts as a positive regulator of mTORC1 by promoting the degradation of GATOR1 [##REF##29769719##102##]. Leucyl-tRNA synthetase (LARS) senses intracellular leucine and directly activates mTORC1 activation by directly interacting with Rag rather than acting with GATOR1/2 [##REF##22424946##103##]. LARS mediates the leucylation of RagA/B, which subsequently activates mTORC1 [##REF##22424946##103##]. Consistently, abovementioned amino acid sensors eventually active Rag, and mediate lysosomal translocation of mTORC1 [##REF##30425336##104##], a critical step in the activation of the mTORC1. When mTORC1 is activated at lysosomal membrane, autophagy is inhibited and tumorigenesis is promoted.</p>","<p id=\"Par36\">In addition to above cytoplasm sensors, lysosomal sensors also play a crucial role in mTORC1 activation. Ragulator complex provides a platform for lysosome to tether Rag. The Ragulator interacts with the Rag heterodimers in an amino acid- and v-ATPase-dependent fashion, which finally activates mTORC1 [##REF##22980980##105##]. SLC38A9, a lysosomal membrane protein with homology to amino acid transporters, participating in the activation of mTORC1 signaling pathway by influencing Rag [##REF##29053970##106##]. Mechanically, SLC38A9 stimulates the release of GDP from Rag A upon activation by arginine. This action propels Rag into the activated state, subsequently activating mTORC1 [##REF##30181260##107##]. Notably, SLC38A9 is crucial for the efflux of leucine, glutamine, tyrosine, and phenylalanine generated from lysosomal proteolysis. This efflux is necessary to activate mTORC1 through cytoplasmic sensors [##REF##25567906##108##]. Thus, lysosomal sensors allow for the integration of lysosomal nutrient information into the regulation of mTORC1 activity. Collectively, amino acids are not only sources for energy and protein synthesis in tumorigenesis, but also act on mTORC1 as signaling molecules.</p>","<p id=\"Par37\">Meanwhile, mTORC1 can also regulate amino acid metabolism through its downstream signaling effectors (Fig. ##FIG##1##2a##). In response to growth signals, mTORC1 activates ATF4 to stimulate enzymes in serine synthesis for folate cycle and purine biosynthesis [##REF##26912861##109##]. Besides, signaling effector ATF4 also transcriptionally regulates serine transporters SLC1A5 and therefore facilitates the uptake of serine [##REF##30976106##110##]. Astutely, ATF4 can also regulate the expression of other amino acid transporters such as CHAC1, SESN2, SLC7A11, SLC7A5, SLC7A1, and SLC3A2, and increase amino acid uptake [##REF##35316619##111##]. Upon the accumulation of glutamine, mTORC1 downregulates miR-23a and miR-23b and subsequently promotes GLS expression to accelerate glutamine catabolism. When glutamine is deficient, mTORC1 represses the transcription of GDH inhibitor SIRT4, prompting glutamine anaplerosis [##REF##23663782##112##]. mTORC1 also activates arginine catabolism by promoting ODC expression in RAS transformed cells to promote polyamine production and tumor progression. Mechanically, mTORC1 promotes the association between ODC mRNA and mRNA-binding protein, promoting ODC mRNA stabilization and expression [##REF##22070140##113##]. Besides, positively regulated mTORC1 leads to the stabilization of MYC, which in turn induces ASS1 expression by competing with HIF1α for ASS1 promoter binding sites and therefore promotes arginine expression [##REF##22461507##114##]. Collectively, mTORC1 regulates amino acid metabolism through multiple signaling effectors, including amino acid transporters, synthetic and catabolic enzymes. mTORC1 downstream signaling molecule MYC also plays extensive regulatory roles in amino acid metabolism.</p>","<title><italic>Myc</italic> drives amino acid metabolism</title>","<p id=\"Par38\"><italic>Myc</italic> is a proto-oncogene which encodes transcription factor MYC, constitutively expressed in tumor and associated with altered metabolism [##REF##25038584##115##]. MYC directly regulates key metabolic enzymes expression, resulting in altered metabolism like increased nutrient uptake, enhanced glycolysis, and elevated fatty acid and nucleotide synthesis [##REF##25341931##116##]. Amino acid metabolism, both EAAs and NEAAs are also regulated by MYC (Fig. ##FIG##1##2b##).</p>","<p id=\"Par39\">As EAAs rely on external resources, corresponding amino acid transporters are crucial and often up regulated in cancer. A positive feedback circuit called MYC-SLC7A5/SLC43A1 is critical in EAAs metabolism in tumor. SLC7A5 imports EAAs in exchange for glutamine export, while SLC43A1 facilitates the import of large neutral essential amino acids (LNEAAs) like BCAAs and tryptophan [##REF##12930836##117##, ##REF##9726963##118##]. MYC plays a pivotal role in promoting the transcription of SLC7A5/SLC43A1 and consequently the uptake of EAAs, which in turn activates mTORC1 and accelerates <italic>Myc</italic> transcription. When SLC7A5/SLC43A1 is blocked and thus amino acids uptake is decreased, the GCN2-eIF2α amino acid stress response pathway will be triggered, leading to the inhibition of MYC mRNA translation. Collectively, this circuit leads to a cascade that affects the entire amino acid metabolic process and oncogene transcription, ultimately promoting tumorigenesis [##REF##29281830##119##]. Specifically, MYC can enhance tryptophan uptake by upregulating the expression of transporters such as SLC7A5 and SLC1A5. Additionally, it can promote the conversion of tryptophan to kynurenine by inducing arylformamidase (AFMID) within the kynurenine pathway [##REF##31416966##120##, ##REF##32651356##121##]. Elevated level of kynurenine has been found to help tumors to evade immune surveillance [##REF##28751584##122##]. Additionally, <italic>Myc</italic> upregulates BCAT1, a crucial enzyme in BCAAs catabolism, and increases biosynthesis and promotes tumor development [##REF##28771772##123##].</p>","<p id=\"Par40\">In addition to above regulation of EAAs, <italic>Myc</italic> can also catalyze NEAAs metabolism, like glutamine, proline, and serine. Besides glucose, glutamine could function as major fuels in tumors. MYC prompts the expression of glutamine transporters such as SLC1A5 and SLC38A5 [##REF##28751584##122##]. In addition, <italic>Myc</italic> also plays a part in glutamine anabolism and catabolism. MYC can demethylate the promoter of GS, prompting the synthesis of glutamine in cancer cells [##REF##26603296##124##]. However, <italic>Myc</italic> also participates in glutamine catabolism through acting on miR-23a/b in some cancers. miR-23a/b can suppress the expression of GLS and then facilitate glutaminolysis in tumor cells [##REF##34390414##125##]. Collectively, MYC functions differently in different types of tumor cells, and metabolic requirements differential within specific cancer types might dictate the outcome of glutamine metabolism regulated by MYC.</p>","<p id=\"Par41\">NEAA proline can be synthesized by aldehyde dehydrogenase family 18 member A1 (ALDH18A1, P5CS) and pyrroline-5-carboxylate reductase (PYCR) from glutamine and arginine, thus MYC induced P5CS and PYCR upregulation can promote the proliferation and invasion of cancer cells [##REF##20832749##28##, ##REF##28771772##123##]. In this way, the biosynthesis of glutamine-to-proline is prompted, assisting tumor cells to alleviate ER stress and promote proline homeostasis. MYC increases miR-23b to decrease the expression of proline oxidase/proline dehydrogenase (POX/PRODH), leading to the inhibition of proline catabolism [##REF##20562915##126##]. Thus, MYC can not only promote the synthesis of proline, but also inhibit its break down [##REF##22615405##127##].</p>","<p id=\"Par42\">MYC also participates in serine metabolism. Enhanced activity of MYC activates metabolic enzymes in SSP such as PHGDH, PSAT and PSPH, resulting in enhanced serine production [##REF##28771772##123##]. MYC upregulates SHMT2, which is critical for maintaining cellular redox homeostasis. Under the catalyze of SHMT2, serine metabolized glycine directly participates in glutathione (GSH) production. In this way, MYC promotes GSH synthesis de novo, and then finally resists oxidant to promote tumor progression. MYC upregulates SHMT2, leading to increased production of one-carbon unit 5’m-THF and therefore the generation of NADPH. NADPH also plays a crucial role in maintaining the redox balance by reducing GSSG to GSH [##UREF##0##128##]. Collectively, MYC promotes serine synthesis and thereby GSH and NADPH production to resist oxidation and promote tumor growth.</p>","<title>Altered KRAS and amino acid metabolism</title>","<p id=\"Par43\">KRAS, a frequently mutated oncogenic protein in human cancers, plays a pivotal role in regulating MYC and mTOR activity through RAF-MEK-ERK and PI3K-AKT pathways, respectively [##REF##32209560##129##, ##REF##28802037##130##] (Fig. ##FIG##1##2c##). The mutation impairs its GTPase activity, leading to persistent activation of downstream signaling cascades, influencing the cellular metabolism and promoting tumor cell proliferation. Amino acid metabolism is also regulated by KRAS, and targeting the metabolic network downstream of KRAS may offer potential avenues for treating KRAS-driven tumors [##REF##24929527##131##].</p>","<p id=\"Par44\">KRAS-induced macropinocytosis maintains intracellular glutamine levels. Besides, KRAS regulates enzymes in glutamine catabolism. KRAS downregulates glutamate dehydrogenase1(GLUD1), leading to alterations in glutamine metabolism to produce NEAAs instead of participating in TCA cycle. Besides, KRAS upregulates GOT2 in the mitochondrial and GOT1 in the cytoplasm [##UREF##1##132##]. Under such circumstance, glutamine-derived aspartate is converted into OAA by GOT1 in the cytoplasm, and finally converted into pyruvate, resulting in the production of NADPH to maintain the cellular redox balance. Thus, <italic>Kras</italic>-mutated cells resist cisplatin treatment by upregulating glutamine consumption to maintain a redox state. As upregulated glutamine catabolism is essential for tumor but dispensable for normal cells, inhibiting enzymes like GOT1 in the glutamine catabolic pathway leads to increased levels of reactive oxygen species (ROS), reducing the levels of GSH and ultimately inhibiting tumor growth [##REF##21317241##133##].</p>","<title>Amino acids in tumor microenvironment</title>","<p id=\"Par45\">Tumors thrive within the intricate TME, which is complex and continuously evolving including surrounding blood vessels, immune cells, fibroblasts and the extracellular matrix (ECM) [##REF##31350295##134##]. The bidirectional interaction between tumor and TME takes various forms. Tumors assimilate essential nutrients through macropinocytosis to satisfy its vigorous metabolism. Thus, rapidly proliferating tumor cells compete for relatively scarce nutrients with fibroblasts and immune cells, shaping a commonly hypoxic, acidic, and nutrient-deprived TME. Collectively, the TME promotes tumor progression and immune evasion through nutrients deprivation. Besides, tumors secrete various bioactive molecules that profoundly influence the TME.</p>","<title>Macropinocytosis in TME takes up amino acids</title>","<p id=\"Par46\">Macropinocytosis is a type of endocytosis that involves the nonspecific uptake of extracellular nutrient molecules like proteins and amino acids [##REF##14732047##135##] (Fig. ##FIG##2##3##). Even targeted drugs block enzymes in vital biosynthesis process, cancer cells still take up necessary biological materials from TME (e.g., collagen fragments) through macropinocytosis to maintain proliferation. Macropinosomes formation is an actin-dependent process that is initiated upon stimulation of growth factors like colony stimulating factor (CSF-1), epidermal growth factor (EGF), or platelet-derived growth factor. Besides, oncogenic mutations in <italic>Kras</italic> or PI3K pathway activation can also drive micropinocytosis [##REF##28973876##136##]. Proteins ingested through macropinocytosis can be decomposed into free amino acids by cellular autophagy for new protein synthesis, or catabolized to generate ATP for energy supply [##UREF##2##137##]. Therefore, macropinocytosis enables tumor cells to survive in harsh environment by providing both materials and energy.</p>","<p id=\"Par47\">Macropinocytosis is a metabolic adaptation to nutrient stress, thus amino acids depletion drives macropinocytosis in cancers to obtain nutrients. Mechanically, a low amino acid environment inhibits the Hippo pathway and promotes membrane localization of EGFR and TGFBRII, triggering macropinocytosis [##REF##35082383##138##]. When intracellular free amino acids are abundant, mTORC1 precisely controls the utilization of extracellular protein-derived amino acids within lysosomes by repressing macropinocytosed protein catabolism [##REF##25085245##139##].</p>","<title>Amino acid metabolism and CAFs</title>","<p id=\"Par48\">TME is hypoxia and devoid of nutrients, which is unfavorable for the survival of cancer cells. Therefore, cancer cells cleverly hold fibroblasts in TME, converting them into CAFs to create an altered homeostasis suitable for tumor growth [##REF##35082383##138##]. CAFs enhance tumor invasion through their bidirectional interaction with tumor cells and highly secretory phenotype to produce ECM [##REF##10409103##140##] (Fig. ##FIG##2##3##). CAFs regulate amino acid metabolism to promote multiple processes like tumorigenesis, angiogenesis, and metastasis [##REF##30470818##141##]. CAFs functional significance in cancer makes them attractive targets for cancer treatment.</p>","<p id=\"Par49\">CAFs play a critical role in tumor progression by interacting with tumor cells and modulating tumor metabolism through amino acid transport (Fig. ##FIG##2##3##). In the stiff matrix of TME, ECM mechanotransduction re-localizes YAP/TAZ to the nucleus and activates the transcription of GLS1 and SLC1A3 in both cancer cells and CAFs [##REF##30122553##142##, ##REF##30293773##143##]. GLS1 promotes the transformation from glutamine to glutamate. However, upregulated-glutamate does not contribute to the TCA cycle in tumors, while in CAFs, glutamate is a major source of carbon for the TCA cycle and produces asparate. SLC1A3 is upregulated simultaneously in tumor cells and CAFs, but functions differently. SLC1A3 in CAFs could provide aspartate to cancer cells, while cancer cells in turn secrete glutamine-derived glutamate through SLC1A3 to CAFs. In such metabolic crosstalk between CAFs and cancer cells, CAF-derived aspartate promotes pyrimidine and protein synthesis to sustains cancer cells proliferation, while cancer cell-derived glutamate balances the redox state of CAFs to promote ECM remodeling. Drugs targeting SLC1A3 can significantly reduce tumor growth due to the close interaction and extensive metabolic remodeling between CAFs and tumors. The amino acid metabolism interaction between CAFs and cancer cells have been recently reviewed in detail [##REF##30470818##141##, ##REF##36209111##144##].</p>","<p id=\"Par50\">CAFs are known to be the most important cells for producing ECM. Collagen, rich in proline, is the main component of ECM, the degradation of which could provide materials and energy to tumor cells. Proline synthesis is sustained through the conversion of glutamate catalyzed by P5C synthase (P5CS) or arginine into pyrroline-5-carboxylate (P5C). P5C serves as the ultimate precursor for proline and, consequently, collagen synthesis. Proline synthesis is upregulated in CAFs and acts as a limiting factor in ECM production. Thus, P5CS deletion decreases collagen and therefore ECM production, which could be rescued with proline supplementation [##REF##32134147##145##].</p>","<title>Amino acid metabolism and immune cells</title>","<p id=\"Par51\">TME comprises diverse cell types, including immunosuppressive cells like myeloid-derived suppressor cells (MDSCs), TAMs, and regulatory T cells (Tregs), as well as tumor-antagonizing immune cells like natural killer cells (NKs), T lymphocytes, B lymphocytes and dendritic cells (DCs). Although the tumor-antagonizing immune cells within TME tend to target and kill the cancer cells in the early stage of tumorigenesis, cancer cells eventually escape immune surveillance through various mechanisms, including reprogramming of metabolism.</p>","<p id=\"Par52\">Tumor associated MDSCs highly express ARG1 (Fig. ##FIG##2##3##), depriving arginine in TME, leading to the lack of arginine in T cells and impairing T cell-mediated anti-tumor immunity [##REF##27745970##146##]. Besides, MDSCs also highly express NOS-2, not only decompose arginine but also produce NO to impair anti-tumor effect of T cells [##REF##31130949##147##]. MDSCs express the x<sub>c</sub><sup>-</sup> transporter to import cystine. Thus, in the presence of MDSCs, cysteine is reduced and T cell function is impaired [##REF##20028852##148##]. Apart from depleting arginine and cysteine, MDSCs also inhibit T cell function by expressing IDO. In tumor site and drainage lymph nodes, IDO overexpression in MDSCs deprived tryptophan in TME, which is necessary for T cell and NK cell proliferation. This eventually leads to T cell stagnating in G0 and NK cell apoptosis [##REF##23812098##149##]. IDO also generates kynurenine, which has been reported to block T cell proliferation and even induce T cell death as mentioned above. TAM also creates an immunosuppressive TME, which tightly links to glutamine metabolism [##REF##28117416##150##]. In nutrient-deprived tumor stroma, GS expression in TAM increased to promote glutamine synthesis. Increased GS provides metabolic condition skews macrophages toward an M2-like, pro-metastatic macrophages by providing more glutamine and α-KG [##REF##28813676##151##, ##REF##35897036##152##]. Similar to MDSC, TAM also overexpresses IDO and ARG1 to deplete tryptophan and arginine as well as produce kynurenine to suppress T cell function [##REF##25035953##153##]. It is worth noting that NOS-2 is also upregulated in TAM to reduce arginine and suppress T cell function.</p>","<p id=\"Par53\">Tumor-antagonizing immune cells function is also suppressed in the TME due to altered amino acid metabolism, thereby influencing the development of tumors. During T cell activation and differentiation, amino acids play dual roles as both energy source and substrates for protein and nucleic acid biosynthesis [##REF##32997964##154##]. Compared to naive CD8<sup>+</sup> T cells, activated CD8<sup>+</sup> T cells increases the density of SLC1A5 and SLC7A5 on the cell surface to enhance glutamine uptake. Glutaminolysis underlies asymmetric T cell division, as glutaminolysis and mTORC1 activation is necessary to maintain c-Myc asymmetry. Asymmetric c-Myc levels in daughter T cells would affect their proliferation, metabolism, and differentiation [##REF##27064903##155##]. Thus, the daughter T cell proximal to the antigen presenting cell is accumulated with glutamine transporters and adopts an effector-like fate, while the distal T cell absence of glutamine transporters assumes a memory-like fate. The differentiation of naive T cells into Th1 or Th17 cells also depends on glutaminolysis. When GLS is inhibited, the number of Th1 cells increases while the number of Th17 cells decreases [##REF##32031065##156##]. Besides, glutamine limitation also promotes Treg differentiation [##REF##26420908##157##]. IDO is activated in DCs to deplete tryptophan and produce kynurenine. As previously mentioned, kinase GCN2 is activated by elevated levels of uncharged tryptophan tRNA, triggering CD8<sup>+</sup> T cell-cycle arrest and functional anergy [##REF##15894280##158##]. Moreover, GCN2 activation fosters de novo Treg differentiation and enhances suppressor function in mature Tregs [##REF##23103127##159##]. On other hand, kynurenine overexpression not only impairs effector T cell, but also promotes the differentiation of Treg through the activation of AhR [##REF##20720200##160##]. DCs and tumor cells both express SLC38A2 to facilitate glutamine uptake, modulating anti-tumor immunity. Mechanistically, glutamine acts as an intercellular metabolic checkpoint that licenses DCs function in activating CD8<sup>+</sup> T cells [##REF##37407815##161##]. Glutamine promotes the formation of the FLCN-FNIP2 complex, consequently restricting TFEB activity. TFEB acts as a molecular switch, regulating exogenous antigen-presentation pathways through lysosome activation and thus influencing CD8<sup>+</sup> T cell activity.</p>","<p id=\"Par54\">As mentioned above, arginine metabolism regulates immune response and responsible for tumor progression [##REF##34831116##162##]. Arginine metabolic enzyme NOS-2 directs the polarization of gamma delta T cells towards a pro-tumoral phenotype, thereby inducing metastatic progression [##REF##27622078##163##]. Asparagine up regulates the LCK signaling pathway to enhance CD8<sup>+</sup> T cell activity and thereby inhibit tumor growth. This subverts the previous understanding of the cancer-promoting function of asparagine [##REF##33420490##164##]. Collectively, above findings reveal that amino acid metabolism in tumors and immune cells plays vital roles in the process of tumorigenesis and development.</p>","<title>Amino acid metabolism and epigenetic modification</title>","<p id=\"Par55\">Epigenetics refers to heritable phenotype changes that do not involve alterations in the DNA sequence [##REF##31341302##165##]. It encompasses various mechanisms, including DNA methylation, histone modifications, chromatin remodeling, and small RNA regulation. Amino acid metabolites like SAM and acetyl-CoA are essential substrates for epigenetic modification, while amino acid metabolism also requires epigentic modification of associated metabolic enzymes [##REF##26629854##166##]. This reciprocal regulatory relationship has a profound impact on tumor progression.</p>","<title>Methylation</title>","<p id=\"Par56\">SAM is synthesized from methionine and functions as the primary methyl donor in various methylation reactions, mainly DNA methylation (Fig. ##FIG##3##4##). DNA methylation is catalyzed by DNA methyltransferases, which transfer methyl group from SAM to DNA. As a methionine transporter, SLC7A5 is crucial for intracellular methionine concentration. Disruption in methionine concentration is closely associated with cancer development, as DNA hypermethylation usually suppresses tumor suppressor genes expression [##REF##26647078##167##].</p>","<p id=\"Par57\">Tumorigenesis is also affected when methylation occurs on histones [##REF##31270442##168##]. The effect of histone methylation depends on which amino acid residue is methylated and how many methyl groups are involved [##REF##31980738##10##]. Specifically, arginine methylation on histone can promote transcriptional activation, while lysine methylation on histone involves both transcriptional activation and repression [##REF##22473383##169##]. When it terms to transcriptional repression, tumors always overexpress methionine transporter SLC43A2 to outcompete T cells for methionine. Methionine deficiency in T cells leads to downregulation of H3K79me2, promoting CD8<sup>+</sup> T cell death and inhibiting Th17 polarization [##REF##32879489##170##]. When it terms to transcriptional activation, downregulation of SLC7A5 in T cells restricts methionine absorption, and alters H3K27me3 deposition at the promoters of key T cell stemness genes. These changes promote the maintenance of a ‘stem-like memory’ state and improve long-term persistence and anti-tumor efficacy [##REF##36717749##171##]. In conclusion, methionine metabolism regulates genomic architecture, chromatin dynamics and gene expression by dynamically modulating methylation on DNA and histone [##REF##29769529##172##].</p>","<p id=\"Par58\">In addition to methionine, other amino acids can also indirectly regulate the level of methylation. Threonine metabolizes into 5-mTHF while serine metabolizes into 5,10-mTHF, playing pivotal roles in cell fate by contributing to SAM synthesis and thereby methylation status. Previous studies reveal that threonine depletion decreased the levels of SAM, leading to decreased tumor growth and increased differentiation [##REF##23118012##173##]. Transaminase like glycine N-methyltransferase (GNMT) catalyzes the transfer of a methyl group from SAM to glycine to form sarcosine, leading to SAM depletion and S-Adenosyl-homocysteine (SAH) accumulation [##REF##24746817##174##]. The ratio of SAM/SAH affects methylation [##REF##10657384##175##]. Therefore, knock down of transaminase like GNMT or the dysregulation of glycine clearly affects the methylation of histone and DNA, affecting gene expression and tumorigenesis [##REF##18318442##176##, ##REF##19035462##177##]. Moreover, glycine can also be metabolized to produce 5,10-mTHF to fuel folate cycle, and thereby regulating methionine cycle.</p>","<title>Demethylation</title>","<p id=\"Par59\">Amino acid like glutamine can be catabolized to produce α-KG. Meanwhile, through α-KG-dependent dioxygenase, the methylation of histone and DNA can be removed [##REF##28450733##178##]. The family of α-KG-dependent dioxygenases includes Jumonji C-domain lysine demethylases (JmjC-KDMs), ten-eleven translocation (TET) DNA cytosine-oxidizing enzymes, and prolyl hydroxylases (PHDs) [##REF##33092942##179##].</p>","<p id=\"Par60\">Normal isocitrate dehydrogenases (cytoplasmic, IDH1; mitochondrial IDH2) catalyze isocitrate to produce α-KG in TCA cycle to fuel tumors. However, IDH is the most frequently mutated metabolic genes in human cancer. When IDH is mutated, it can further catalyze α-KG to 2-hydroxyglutaric acid (2-HG), and 2-HG can competitively inhibit α-KG-dependent dioxygenases [##REF##31263678##180##]. Thus, mutated IDH inhibits DNA and histone demethylation, leading to a hypermethylation phenotype and promoting the differentiation of cancer stem cells [##REF##31131326##181##]. In IDH mutant glioblastoma, the CpG island in promoter region of BCAT1 is usually hypermethylated. The inhibition of BCAT1 expression results in the reduction of glutamate and therefore inhibit tumor growth and invasion [##REF##23793099##182##]. Consistently, individuals diagnosed with IDH<sup>WT</sup>/TET2<sup>WT</sup> myeloid leukemia, α-KG is maintained at normal levels and therefore BCAT1 expression is promoted compared to IDH<sup>mut</sup>/TET<sup>mut</sup> myeloid leukemia. Elevated expression of BCAT1 serves as a robust indicator for poorer survival prognosis, which level has been observed significantly escalates upon disease relapse [##REF##29144447##183##].</p>","<title>Acetylation and deacetylation</title>","<p id=\"Par61\">Histone acetylation is a process in which acetyl-CoA donates acetyl groups to modify histone proteins catalyzed by histone acetyltransferases (HATs) [##REF##11055583##184##]. Both acetyl-CoA abundance and the ratio of acetyl-CoA to coenzyme A regulate histone acetylation in cancer [##REF##24998913##185##]. Histone acetylation leads to attractive force reduction between histones and DNA, promoting a more open chromatin structure that allows for increased transcriptional activity [##REF##27988359##11##, ##REF##32071378##186##]. Furthermore, acetyl-CoA derived from amino acid metabolism also plays a role in non-histone protein acetylation and regulates tumor growth.</p>","<p id=\"Par62\">Acetyl-CoA level and amino acid metabolism affects each other. BCAAs like isoleucine and leucine synthesize acetyl-CoA under the catalyze of transaminase. Other amino acids such as lysine, phenylalanine, tryptophan, and tyrosine also contribute to acetyl-CoA production by forming acetoacetyl-CoA. Similarly, alanine, serine, tryptophan, cysteine, glycine, and threonine synthesize acetyl-CoA through pyruvate formation. Leucine metabolism provides acetyl-CoA to the EP300 acetyltransferase, leading to acetylation of mTORC1 regulator Raptor. This acetylation ultimately results in mTORC1 activation and altered amino acid metabolism [##REF##30197302##187##]. Isoleucine and leucine catabolism generates acetyl-CoA within the mitochondria. Subsequently, mitochondrial acetyl-CoA must be transported to the cytoplasm and nucleus to regulate gene expression through epigenetic mechanisms [##REF##29779826##188##].</p>","<p id=\"Par63\">Deacetylation refers to the removal of acetyl groups from histones, which is typically catalyzed by histone deacetylase (HDAC). Histone deacetylation results in a more condensed form of DNA known as heterochromatin, which is linked to reduced levels of gene transcription. The sirtuin proteins are classified as the class III HDACs depending on NAD<sup>+</sup> in contrast to the zinc-dependent catalysis by Class I, II, and IV enzymes [##REF##10072350##189##]. Thus, the tryptophan metabolism and therefore NAD<sup>+</sup>/NADH ratio exhibit a close correlation with both the acetylation state and energy status, playing important roles in inhibiting gene expression and thereby influencing tumor progression.</p>","<title>Phosphorylation, succinylation, and lactylation</title>","<p id=\"Par64\">In addition to the aforementioned types of epigenetic modifications, it is noteworthy to consider other post-translational modifications (PTMs), including phosphorylation, succinylation, and lactylation. Each of these PTMs alters the charge and structure of proteins. For instance, modifications to histones affect their binding to DNA, thereby influencing chromatin status and gene expression [##REF##25220185##190##]. PTMs of enzymes influence their binding to substrates, thereby affecting various metabolic processes.</p>","<p id=\"Par65\">Serine, threonine as well as tyrosine residues have established as residues for histone phosphorylation [##REF##34050894##191##]. AMP-activated protein kinase (AMPK) is a sensor of cellular energy status and phosphorylates a variety of cellular substrates include histones [##REF##28974774##192##]. As a nutrient sensor, AMPK can be activated under a variety of stress conditions. Recent studies indicate that AMPK is activated by the amino acids like alanine [##REF##30190193##193##], aspartate [##REF##32273511##194##] and cysteine [##REF##32273511##194##] and then phosphorylates histones. Histone phosphorylation is linked to various cellular processes, including transcriptional activation, mitosis, DNA repair, and apoptosis [##REF##21941619##195##]. Phosphorylation also occurs in enzymes related to amino acid metabolism. When phosphorylation occurs in branched chain ketoacid dehydrogenase kinase, it regulates EMT genes and leads to the metastasis of colorectal cancer [##REF##32238881##196##]. Besides, phosphorylation of GLS is essential for its enzymatic activity and critically contributes to tumorigenesis [##REF##29515166##197##].</p>","<p id=\"Par66\">Succinylation is an innovative PTM where a succinyl group is added to a lysine residue. This modification is associated with the catabolism of BCAAs, as the generation of succinyl-CoA serves as an intermediate for succinylation [##REF##29888767##198##]. The transcriptional characteristics of succinylated histones resemble those observed in acetylated counterparts, thereby activating gene expression [##REF##29888767##198##, ##REF##1710452##199##]. Therefore, succinyl-CoA accumulation may enhance cancer initiation and progression by promoting a global succinylation program that favors cancer growth [##REF##33864000##200##]. Enzymes involved in amino acid metabolism can also be succinylated. Upon oxidative stress, enhanced succinylation of GLS leads to increased oligomerization and activity, thereby promoting glutaminolysis and tumor growth [##REF##33991485##201##].</p>","<p id=\"Par67\">Lactylation is also a novel epigenetic modification, in which lactic acid modifies the lysine residues [##REF##31645732##202##]. Hypoxia is linked to elevated lactate levels originating from heightened glycolysis activity, thereby enhancing intracellular histone lactylation [##REF##37646027##203##]. Histone lactylation can affect gene expression in tumors and immunological cells, thereby promoting malignancy and immunosuppression. In tumor-infiltrating myeloid cells, a potent accumulation of lactate occurs, leading to the upregulation of methyltransferase-like 3 (METTL3) through lactylation. The increased expression of METTL3 in these cells was correlated with the poor prognosis of patients [##REF##35320754##204##].</p>","<title>Targeting amino acid metabolism in tumor therapy</title>","<p id=\"Par68\">Primary strategies in cancer treatment involve targeting disparities between normal and tumor cells in gene expression and phenotype. Metabolism alteration in tumors is a noteworthy phenotype. Amino acids metabolism, being fundamental to vital biological processes, play a crucial role in tumor initiation and progression [##REF##33511116##5##]. Consequently, targeting amino acid metabolism like amino acid depletion is significant in cancer treatment approach [##REF##33795176##7##]. Strategies for targeting amino acid metabolism encompass inhibiting amino acid transporters, regulating amino acid biosynthesis and consumption as well as developing amino acids modified dietary (Table ##TAB##0##1##). Given different metabolism in different tumors, developing tailored strategy for distinct tumors becomes imperative.</p>","<title>Amino acid transporters inhibition</title>","<p id=\"Par69\">Amino acids substantial uptake occurs in tumor cells which overexpress amino acid transporters. Consequently, targeting these transporters to restrict amino acid availability is an effective strategy for inhibiting tumor growth. Drugs targeting amino acid transporters and related enzymes have transitioned from preclinical research to clinical trials and have demonstrated efficacy in some cases [##REF##28750681##205##]. Glutamine transporters usually highly expressed and associated with poor prognosis as external glutamine is essential for cancer cells to survive [##REF##25855379##206##, ##REF##29334372##207##]. This differentiates cancer cells from normal cells, providing a target for tumor therapy. Clinically, Tamoxifen and Raloxifene block glutamine uptake by inhibiting ASCT2 expression in breast cancer to suppress tumor [##REF##29562706##208##]. Likewise, pharmacological blockade of ASCT2 with V-9302 also resultes in attenuated cancer cell growth and proliferation [##REF##11401523##209##]. However, therapy targeting ASCT2 alone is not enough, as there are compensation from other transporters like cystine/glutamate antiporter(xCT) to replace ASCT2 to fuel tumors. Cationic amino acid transporters like CAT-1, CAT-2 and CAT-3 for lysine, arginine, and histidine are also dysregulated in tumors and associated with drug resistance. Specifically, CAT-1 expression exhibits a correlation with tumor grade in prostate cancer [##REF##29562706##208##]. It also plays a pivotal role in promoting growth, proliferation, and metastasis of colorectal cancer and breast cancer [##REF##30865893##210##]. Upregulated CAT-3 increases arginine uptake and thereby induces tumors to adapt glutamine deprivation [##REF##30404920##39##]. Downregulation of CATs (CAT-1, CAT-3) through lentiviral transduction with shRNAs or chemical like verapamil shuts down tumor proliferation and induces death [##REF##31824848##211##, ##REF##35377022##212##]. Conversely, loss of CAT-2 exacerbates inflammation-associated colon tumorigenesis [##REF##30202097##213##].</p>","<p id=\"Par70\">Apart from targeting these tranporters directly to prevent amino acids uptake, these tranporters can also be targeted to deliver drugs. The glutamine macromolecular analog polyglutamine (PGS) can mimic glutamine and selectively ferry siRNA through ASCT2. Therefore, siRNA delivered by PGS targets the growth and survival of certain cancer [##REF##30149232##214##]. Thus, RNAi in combination with chemotherapy can augment the anti-tumor effect. Apart from ASCT2, SLC6A14 is another amino acid transporter that demonstrates upregulation in numerous cancer types [##REF##31326539##215##]. Hence, SLC6A14 emerges as a promising target for tumor therapy, with its potential extending to enabling the selective delivery of anticancer drugs to tumor cells. Stealth liposomal systems functionalized with the aspartate-polyoxyethylene stearate conjugate (APS) are developed for transporter-mediated targeted delivery of docetaxel to SLC6A14, resulting in a significantly efficiency for delivering anticancer drugs into cells [##REF##27991616##216##].</p>","<title>Amino acid metabolism enzymatic inhibition</title>","<p id=\"Par71\">Amino acid metabolism is also reglulated by biosynthetic enzyme and catabolic enzyme. Numerous pharmacological inhibitors targeting key enzymes in amino acid metabolism undergo extensive research. Asparagine is the most successful and well-documented target in amino acid depletion therapy, especially in acute lymphoblastic leukemia [##UREF##3##217##]. Asparlas, a drug frequently used in clinical practice, is developed for the treatment of acute lymphoblastic leukemia. It acts as a long-acting asparagine-specific enzyme to deplete asparagine [##REF##35205650##218##]. Studies shown that inhibited ASNS or metformin inhibited ETC limits tumor asparagine synthesis, impairing tumor growth in multiple mouse models [##REF##33609439##219##]. <italic>Kras</italic> mutation activates the ATF4 signaling pathway through its downstream AKT and NRF2 in NSCLC. When ASNS is inhibited by AKT and the extracellular asparagine is depleted simultaneously, tumor growth can be reduced [##REF##29316436##220##]. Therefore, ASNS is also a promising therapeutic target for <italic>Kras</italic>-mutated NSCLC.</p>","<p id=\"Par72\">Arginine is another targeted amino acid in depletion therapy. As mentioned above, ASS1, a rate-limiting enzyme for synthesizing arginine, is frequently deficient in tumors and thereby arginine is depleted. Meanwhile, enzymes such as arginine deiminase (ADI) and ARG I could also exhaust extracellular arginine by transforming arginine into citrulline and ornithine, respectively. Thus, depleting arginine by ADI-PEG20 or rhArg1-PEG to downregulate arginine level in serum is essential to restrain cancer cells proliferation [##REF##25917076##221##, ##UREF##4##222##]. Since ARG is consistently overexpressed in both cancer cells and MDSC, the ARG inhibitor INCB001158 has the potential to normalize arginine levels within TME, consequently revitalizing T cell functionality [##REF##29254508##223##]. Simultaneously, the intracellular inhibition of ARG by INCB001158 prevents tumor cells from utilizing arginine for polyamine generation. In combination with immune checkpoint therapy, INCB001158 has been used in patients with advanced/metastatic solid tumors and has shown significant efficacy in a Phase II clinical trial. Additionally, previous study has proved that ASS1 is a new tumor repressor via epigenetic mechanism and downregulated in cancers such as ovarian cancer. Its downregulation has been reported to be associated with advanced tumor stage and susceptible to ADI-PEG20 treatment [##REF##33674736##224##].</p>","<p id=\"Par73\">Similarly, as mentioned above, tumors driven by <italic>Myc</italic> or <italic>Kras</italic> are highly dependent on exogenous glutamine, and pharmacological inhibitors of GLS like telaglenastat or 968 has shown excellent results in multiple tumors [##REF##33795176##7##]. Besides, tumors control related enzymes expression to exploit serine and other one-carbon biosynthetic pathway to reduce the dependence on exogenous supply. Drugs targeting the one-carbon metabolic pathway like methotrexate targeting dihydrofolate reductase (DHFR) or 5-fluorouracil targeting thymidylate synthase has been widely used clinically.</p>","<title>Amino acids modified dietary</title>","<p id=\"Par74\">Cumulating researches have demonstrated that amino acid like methionine, serine, glycine, leucine, glutamine and cysteine restriction plays roles in cancer intervention. Restricted methionine intake prevents tumor growth and metastasis such as TNBC [##REF##27579534##225##], CRC [##REF##20424491##226##], and glioma [##REF##31515518##227##]. Preclinical studies show that restriction of serine and glycine intake significantly inhibits tumor cells proliferation in intestinal cancer and lymphoma mice [##REF##28425994##228##]. In the leucine-rich diet, metabolism shifts from glycolytic metabolism to oxidative phosphorylation, resulting in a less aggressive tumor phenotype [##REF##31664147##229##]. Metabolomic analysis reveals that dietary uptake of glutamine effectively increases the concentration of glutamine in tumors and its downstream metabolite, α-KG, without increasing biosynthetic intermediates necessary for cell proliferation. The increase in intratumoural α-KG concentration drives hypomethylation of H3K4me3, thereby suppressing epigenetically-activated oncogenic pathways in melanoma [##REF##32620791##230##]. However, this effect may be melanoma specific, as glutamine would fuel tumor cell proliferation in most tumors.</p>","<p id=\"Par75\">Amino acid dietary restriction also affects tumor development by affecting immune cell function. Diet lack of sulfur-containing amino acids like methionine and cysteine downregulates the differentiation of CD4<sup>+</sup> T cells towards Tregs and promotes the migration of CD8<sup>+</sup> T cells, increasing the ratio of CD8<sup>+</sup>/Treg in tumors and thereby enhancing the immune killing function [##REF##36525473##231##]. Dietary methionine restriction blocks cGAS methylation, releasing cGAS from chromatin and promoting it into cytoplasm [##REF##37267951##232##]. Cytoplasm cGAS would sense dsDNA and activate the immune response, leading to tumor restriction. The effect of tumor immunotherapy is significantly enhanced by restricting the dose of amino acids in diet.</p>","<title>Summary</title>","<p id=\"Par76\">During the process of proliferation, tumor cells dysregulate their metabolism to obtain more energy and nutrients. Amino acids are used as fuels and raw materials in protein synthesis as well as signal molecules in energy metabolism, participating in tumor growth process. In addition, amino acid metabolism also intertwines with signaling pathways, TME and epigenetic modification. Amino acids act as pivotal signaling molecules, stimulating signal pathways like mTORC1, MYC and KRAS to drive tumor growth and proliferation. Within the TME, a dynamic interplay occurs as nutrient competition and amino acid availability dictate tumor progression. Tumor and immune cells collaborate to regulate amino acid metabolism, which is crucial for sustaining the metabolic needs of proliferating tumor cells and sculpting the immune response. Moreover, amino acid-derived metabolites like α-KG, acetyl-CoA, NAD<sup>+</sup> and SAM wield influence over epigenetic regulation. Comprehending this metabolism network provides foundation for targeted therapeutic approaches which aim to disrupt amino acid metabolism.</p>","<p id=\"Par77\">Compared with normal cells, tumor cells have more emphasis on amino acid requirement and dependence. Therefore, targeting amino acid metabolism to treat tumors is more effective and less damage. However, the field still faces many challenges. Some drugs targeting amino acid metabolism have shown promising effects in animal experiments but realize them clinically is still difficult. Insufficient amino acid depletion may merely maintain tumor cells in a state of cell quiescence, and once treatment is terminated, tumor cells will reappear. The effect of amino acid intervention on tumor development is not only related to tumor type but also depends on external factors even tumor location [##REF##31484055##233##]. Since most metabolism inhibitors are not effective as single agents, combination therapy may be a more reasonable strategy. Nevertheless, compared with other therapies, amino acid depletion therapy is safer to normal cells. It is hoped that with the wider understanding of amino acid metabolism in tumor and the advancement of metabolic analysis techniques, appropriate patients can be identified. Thus, fully understand of the metabolic flexibility in amino acid metabolism in cancer cells is of great significance, which provides further insight into metabolic dependences and liabilities that can be exploited therapeutically.</p>","<title>Supplementary information</title>","<p>\n\n</p>"],"string":"[\n \"<title>Facts</title>\",\n \"<p id=\\\"Par2\\\">\\n<list list-type=\\\"bullet\\\"><list-item><p id=\\\"Par3\\\">Altered amino acid metabolism in tumors challenges the traditional classification of essential and nonessential amino acids.</p></list-item><list-item><p id=\\\"Par4\\\">Amino acids have emerged as pivotal regulators in tumors, participated in a myriad of bidirectional interactions including signal pathways, tumor microenvironment, and epigenetic modifications.</p></list-item><list-item><p id=\\\"Par5\\\">Clinical trials align with the idea that limiting amino acid intake may improve cancer prognoses.</p></list-item></list>\\n</p>\",\n \"<title>Open Questions</title>\",\n \"<p id=\\\"Par6\\\">\\n<list list-type=\\\"bullet\\\"><list-item><p id=\\\"Par7\\\">Among the several effects that are simultaneously regulated by certain amino acid, is there a chief effect that determines the progression or repression of tumor?</p></list-item><list-item><p id=\\\"Par8\\\">What are the optimal strategies and urgent challenges for the clinical translation of amino acids-based therapies in the near future?</p></list-item><list-item><p id=\\\"Par9\\\">Is the altered amino acids metabolism, described in different tumors, causally linked to their tumor etiology and pathogenesis?</p></list-item></list>\\n</p>\",\n \"<title>Reprogrammed amino acid metabolism in cancer</title>\",\n \"<p id=\\\"Par13\\\">A number of cancers have been found auxotrophic for NEAAs [##REF##31819187##9##]. It may be that the demand for proliferate is too large and exceed the supply, or the related enzymes are mutated, or metabolic pathways are dysregulated. These amino acids are named conditional EAAs. Considering the swift proliferation of tumors within an environment deprived of nutrients, the composition of amino acids frequently displays instability. This fluctuation in amino acids can significantly impact overall cellular metabolism, ultimately culminating in cell proliferation or death [##REF##31980738##10##]. Therefore, the 20 standard proteinogenic amino acids, including conditional EAAs (glutamine, arginine), EAAs (branched-chain amino acids, tryptophan), and non-essential amino acids (asparagine, aspartate) play flexible roles in protein synthesis or energy supply activities in tumor.</p>\",\n \"<title>Glutamine metabolism</title>\",\n \"<p id=\\\"Par14\\\">Glutamine (Gln) is a conditional EAA, which is not essential for normal cells but becomes crucial for tumor cells due to their heightened demand. It is the most abundant amino acid found in plasma, and the most rapidly consumed amino acid in tumor cells [##REF##27988359##11##]. As an EAA in tumors, glutamine participates in rapid biosynthetic reactions in tumor. Tumor cells utilize glutamine avidly, known as glutamine addiction [##REF##19033189##12##]. Thus, it always functions as the rate-limiting molecule of the cell reproductive cycle. Once deprived of glutamine, cancer cells usually arrest in S-phase [##REF##26921316##13##]. Additionally, glutamine also plays a crucial role in maintaining redox homeostasis, replenishing the tricarboxylic acid (TCA) cycle, and participating in signal transduction processes within tumors [##REF##28938091##14##].</p>\",\n \"<p id=\\\"Par15\\\">ASCT2(SLC1A5) is the main glutamine transporter in tumor (Fig. ##FIG##0##1##). It is regulated by multiple tumor associated transcription factors including Rb/E2F [##REF##23353822##15##], androgen receptor 3 [##REF##24052624##16##] and ATF4 [##REF##25142020##17##]. ASCT2 is highly expressed in tumor tissue, and its expression level is negatively correlated with patients’ prognosis. As ASCT2 transports glutamine for tumor consumption, inhibiting ASCT2 induces apoptosis and exhibits anti-cancer activity in acute myeloid leukemia [##REF##24014241##18##], gastric cancer [##REF##29100325##19##], prostate cancer [##REF##25693838##20##], and triple-negative breast cancer [##REF##26455325##21##]. In addition, tumor cells are capable of synthesize glutamine by themselves from glutamate (Glu) and ammonia. Glutamine synthetase (GS) is highly expressed in cancer cells to support their rapid proliferation. Moreover, GS can also promote cell proliferation independently of its catalytic function, only by interacting with nuclear pore protein [##REF##35145325##22##]. Therefore, tumor cells acquire a substantial amount of glutamine through both intrinsically synthesis and extrinsically uptake, emphasizing its critical role in tumor reprogrammed metabolism.</p>\",\n \"<p id=\\\"Par16\\\">Glutaminolysis is a process catalyzed by glutaminase 1(GLS1) or GLS2 to produce glutamate [##REF##33194749##23##]. GLS1 and GLS2 are isozymes that play opposite roles in tumor development [##REF##31652551##24##]. GLS1 has oncogenic properties, while GLS2 has been described as a tumor suppressor. Numerous studies have reported a higher expression of GLS1 and lower expression of GLS2 in various tumor types, including liver cancer and colorectal cancer [##REF##35895807##25##, ##REF##30674873##26##]. GLS1 is regulated by the oncogenes MYC [##REF##19219026##27##], Rho GTPases [##REF##20832749##28##] and Notch [##REF##26422827##29##]. In colorectal cancer cells, GLS1 is essential to tumor growth, invasion, and metastatic colonization. Mechanically, under hypoxia TME, HIF-1 activates the expression of the GLS1 to promote tumor migration, invasion, and metastatic colonization [##REF##30674873##26##]. Besides, GLS1 plays a crucial role in boosting the production of GSH and NADH, contributing to oxidative balance maintain to promote tumor proliferation [##REF##11906817##30##]. Conversely, GLS2 expression is transcriptionally upregulated by tumor suppressor and stress-related proteins, including p53, p63, and p73. Although GLS2 plays a role in the production of GSH, it is worth noticing that GLS2 modestly regulates the ratio of GSH/GSSG, which is essential to oxidative balance. Different from GLS1, GLS2 catalyzes glutamate metabolism to promote α-ketoglutarate (α-KG) production, participating in TCA cycle and thereby facilitating the production of lipid ROS. The accumulation of ROS leads to mitochondrial membrane hyperpolarization and thereby inducing ferroptosis [##REF##35895807##25##]. In addition to glutaminolysis, glutamine can be metabolized into intermediates like carbamoyl phosphate (CP) and phosphoribosyl amine (PRA) for the synthesis of purines and pyrimidines, which are essential components for DNA synthesis and repair during rapidly tumor proliferation [##REF##31167399##31##, ##REF##31365859##32##].</p>\",\n \"<p id=\\\"Par17\\\">Glutamate, the metabolite of glutaminolysis, provides important resources for energy and biomacromolecule synthesis in tumor (Fig. ##FIG##0##1##). In tumors under glucose-limited condition, glutamate acts as a substitute for glucose, producing the intermediate α-KG to facilitate the TCA cycle. However, the provision of α-KG alone is inadequate for sustaining the TCA cycle. This insufficiency arises from the limited availability of acetyl-CoA, a rate-limiting molecule in TCA cycle. Studies have found that in glutamine addictive tumor, mitochondrial phosphoenolpyruvate carboxykinase (PCK2) expression was elevated, facilitating phosphoenolpyruvate (PEP) production from oxaloacetate generated in the TCA cycle [##REF##26474064##33##]. Thus, glutamine-derived PEP acts as a substitute for glucose-derived PEP, offering a valuable source to acetyl-CoA production that replenishes the TCA cycle [##REF##33511116##5##]. Glutamate and α-KG also participate in the transaminate to other non-essential amino acids. In addition, α-KG serves as both substrate and cofactor for DNA dioxygenase enzymes involved in DNA demethylation [##REF##31467060##34##] (Detailed later in amino acid metabolism and epigenetic modification).</p>\",\n \"<title>Arginine metabolism</title>\",\n \"<p id=\\\"Par18\\\">Arginine (Arg) is also identified as a conditional EAA in tumor [##REF##33511116##5##]. It can be synthesized de novo under the catalysis of argininosuccinate synthase 1 (ASS1) and argininosuccinate lyase (ASL) from aspartate and citrulline in urea cycle (Fig. ##FIG##0##1##). However, ASS1, the rate-limiting enzyme in urea cycle, is usually downregulated in cancer and its downregulation has been reported to be associated with advanced tumor stage [##REF##32409304##35##]. ASS1 downregulation redirects aspartate from urea production towards pyrimidine biosynthesis, facilitating the high demand of rapid tumor proliferation. This phenomenon is commonly known as urea cycle dysregulation [##REF##30100185##36##]. Since tumors underexpress urea cycle related enzymes like ASS1 and therefore downregulate arginine synthesis, exogenous arginine supply is critical for tumors survival and proliferation. Arginine can be obtained through cationic amino acid transporter (CAT) family transporters, including CAT-1, CAT-2 and CAT-3. CATs are always upregulated in many human cancers [##REF##32899180##37##]. Thus, in arginine-dependent cancer cells, CATs knockdown can decrease the viability of cancer cells and induce apoptosis [##REF##21308737##38##].</p>\",\n \"<p id=\\\"Par19\\\">Arginine can be hydrolyzed by arginases (cytoplasmic, ARG1; mitochondrial, ARG2) and both arginases are upregulated in cancer cells to ensure the production of polyamines. ARG1 is upregulated in a wider range of tumors compared to ARG2. ARGs convert arginine to urea and ornithine. In tumors, ornithine is metabolized by upregulated ornithine decarboxylase (ODC) into polyamines including putrescine, spermidine, and spermine [##REF##30404920##39##]. Polyamines are well known for their crucial role in tumor proliferation and DNA stability [##REF##32499785##40##–##REF##34186159##42##]. They facilitate cell proliferation by increasing DNA synthesis through the activation of enzymes such as DNA polymerases, helicases, and DNA ligases. Besides, cellular protein synthesis is also positively correlated with polyamines. Moreover, natural polyamines function as free radical scavengers. Thus, the strong affinity between polyamines and DNA enables the stabilization of DNA structure, granting polyamines the capacity to protect nucleic acids from damage [##REF##9736703##43##, ##REF##9514920##44##].</p>\",\n \"<p id=\\\"Par20\\\">In addition, arginine can produce NO under the catalysis of nitric oxide synthase-2 (NOS-2) in tumor and macrophage. NO affects TME and tumor proliferation by promoting angiogenesis [##REF##3131684##45##]. Besides, NO derived peroxynitrite can nitrate tyrosine residues and block tyrosine protein phosphorylation, reducing T cell proliferation and activation [##REF##10092790##46##].</p>\",\n \"<title>Branched-chain amino acid (BCAA) metabolism</title>\",\n \"<p id=\\\"Par21\\\">BCAAs, namely isoleucine (Ile), leucine (Leu), and valine (Val), are closely interconnected and classified as EAAs, whether in normal or tumor cells. Changes in the level of one of the BCAAs are accompanied by changes in the other two with the same direction and magnitude [##REF##32049045##2##]. As EAAs, BCAAs cannot be synthesized in human and thus corresponding transporters are critical. LAT1(SLC7A5) and LAT2(SLC7A8) serve as primary transporters for BCAAs [##REF##29695141##47##, ##REF##26101697##48##] (Fig. ##FIG##0##1##), exhibiting high expression levels in glioblastoma and clear cell renal cell carcinoma [##REF##32088728##49##, ##REF##23103253##50##]. Drugs targeting LATs (BAY-8002, JPH203, OKY034 etc.) have already been used in preclinical treatment of cancer [##REF##30115664##51##, ##REF##37047148##52##].</p>\",\n \"<p id=\\\"Par22\\\">BCAAs affect protein synthesis either by transmitting the signal of cell nutritional state or acting as proteinogenic amino acids [##REF##29211698##53##]. BCAAs accumulation mainly promotes mTORC1 activition to enhance tumor development and growth [##REF##30661928##54##]. Explicitly, mTORC1 triggers a cascade of signaling pathway through phosphorylating its downstream effectors, including eukaryotic translation initiation factor 4E binding protein 1(4EBP-1), p70 ribosomal S6 kinase 1 (S6K1), and sterol regulatory element binding protein (SREBP), to regulate autophagy and synthesize lipids, nucleotides and proteins [##REF##30754640##55##] (Detailed later in signal pathways in amino acid metabolism). For another, BCAAs, especially leucine, are essential for protein synthesis as they are in great demand in new protein translation [##REF##25644265##56##].</p>\",\n \"<p id=\\\"Par23\\\">BCAAs catabolism and related enzymes are closely related to tumorigenesis. Leucine, isoleucine and valine catabolism is mediated by BCAA transaminase 2 (BCAT2) to produce branched chain α-keto acid (BCKA) including α-ketoisocaproate (α-KIC), α-ketoamethylvalerate (α-KMV) and α-ketoisovalerate (α-KIV) respectively. Subsequently, BCKAs like α-KIC can undergo further metabolic conversion into acetyl-CoA, while α-KIV can be metabolized into succinyl-CoA. As for α-KMV, it can undergo further metabolic conversion into both acetyl-CoA and succinyl-CoA. These metabolites actively participate in the TCA cycle. Thus, BCAAs catabolism is critical for the development of cancers, especially pancreatic ductal adenocarcinoma [##REF##30661928##54##]. BCAAs also play a vital role in the synthesis of nucleotide through sustaining the levels of the enzyme ribonucleotide reductase regulatory subunit M2 (RRM2) [##REF##31575908##57##, ##REF##16756507##58##]. Since BCAAs are tightly related to tumors, altered BCAAs level in blood can predict the development of certain tumors in both humans and mouse [##REF##25261994##59##].</p>\",\n \"<title>Tryptophan metabolism</title>\",\n \"<p id=\\\"Par24\\\">Tryptophan (Trp) is also an EAA as its anabolism is absence in vivo. Tryptophan is involved in inherent malignant characteristic of tumors and can limit tumor immunity. The most important metabolic pathway for tryptophan is the kynurenine pathway. Free rather than albumin-binding tryptophan can be catalyzed by indoleamine 2,3-dioxygenase1 (IDO1) and tryptophan 2,3-dioxygenase (TDO) to produce kynurenine [##REF##33460767##60##] (Fig. ##FIG##0##1##). Along the kynurenine pathway, a series of biologically active molecules are produced to influence tumor progression. The primary metabolite kynurenine has been reported to block T cell proliferation and induce T cell death [##REF##16902152##61##]. Advanced cancers were associated with an increased kynurenine/tryptophan ratio, indicating that kynurenine level is correlated with tumor malignancy [##REF##35918736##62##]. Under the catalyze of kynurenine 3-monooxygenase(KMO) and kynureninase(KYNU), kynurenine can be further catabolized to NAD<sup>+</sup> and alanine. Kynurenine pathway is known as the de novo NAD<sup>+</sup> synthesis pathway, exhibiting potent resistance to oxidative stress and promoting cancer cell metastasis [##REF##37352864##63##]. In vivo studies have revealed that changes in tryptophan metabolism can decrease NAD<sup>+</sup> synthesis and DNA damage, thereby promoting hepatocarcinogenesis [##REF##25453901##64##]. Besides, alanine is deleterious for spheroid growth and thereby suppresses cancer progression [##REF##32788725##65##].</p>\",\n \"<p id=\\\"Par25\\\">Apart from kynurenine pathway, tryptophan can also be metabolized in 5-hydroxytryptamine (5-HT) pathway and indole pathway, accounting for less than 5% of tryptophan metabolism [##REF##30760888##66##]. 5-HT is also called serotonin, which has more recently emerged as a growth factor for human tumor cells of different origins [##REF##29936294##67##]. Serotonin enhanced expression of PD-L1 on mouse and human cancer cells in vitro via serotonylation, covalent bonds formation between glutamine residues and serotonin, resulting in tumor progression [##REF##34524861##68##]. Along with the indole pathway, indole production activates the aryl hydrocarbon receptor (AhR) in tumor-associated macrophages (TAMs), and thus inhibits intratumoral CD8<sup>+</sup> T cell function [##REF##35139353##69##]. Tryptophan metabolism also plays a vital role in the TME, see below amino acid in TME for details.</p>\",\n \"<p id=\\\"Par26\\\">Increased kynurenine pathway is correlated with tumor progression. In tumors such as non-small cell lung cancer (NSCLC) and esophageal squamous cell cancer, higher IDO1 and TDO expression in kynurenine pathway is associated with higher TNM stage and shorter overall survival [##REF##25683635##70##]. IDO1 expression can be either triggered as a counter regulatory response to cytokines like IL-1β and IL-6 released from tumor-infiltrating immune cells or maintained through tumor-intrinsic oncogenic signaling [##REF##29163470##71##, ##REF##30940904##72##]. Studies found that intratumoural IDO1 expression has been shown to correlate with the frequency of liver metastases in colorectal cancer [##REF##16489067##73##]. Besides, overexpression of IDO1 augmentes the motility of lung cancer cells, whereas its knockdown reduced cancer motility [##REF##28903363##74##]. TDO, an enzyme that catalyzes the same reaction as IDO1, is also linked to a poor prognosis when overexpressed [##REF##30760888##66##]. In a mouse model of lung cancer, inhibited TDO resulted in a reduction in the number of tumor nodules in the lungs [##REF##27050278##75##].</p>\",\n \"<title>Asparagine and aspartate</title>\",\n \"<p id=\\\"Par27\\\">Asparagine (Asn) and aspartate (Asp) are inter-convertible and are classified as non-essential amino acids, which play vital roles in tumor proliferation and metastasis. Asparagine synthase (ASNS) catalyzes aspartate to generate asparagine, while asparaginase (ASNase) catalyzes asparagine to produce aspartate (Fig. ##FIG##0##1##). Another way to produce aspartate is to use amino from glutamate and OAA from TCA cycle under the catalyze of glutamic oxaloacetic transaminase (GOT). Aspartate is the limiting metabolite for proliferation in tumors under hypoxia, which level is correlated with hypoxic markers [##REF##29941933##76##]. Besides, aspartate has poor cell permeability, which prevents its environmental acquisition. Therefore, inhibited intracellular aspartate synthesis and limited extracellular aspartate uptake represse tumors proliferation [##REF##26232224##77##]. However, asparagine can be efficiently imported into tumors. Tumors with high ASNase expression can rescue tumor suppression through conversion of asparagine into aspartate, bypassing intrinsic aspartate limitation and promoting tumor growth [##REF##29941931##78##].</p>\",\n \"<p id=\\\"Par28\\\">Asparagine and aspartate metabolism plays multiple roles in tumor progression. Aspartate originally participates in urea cycle. However, in many tumors, loss ASS1 in urea cycle promotes cancer proliferation by diversion of aspartate substrate towards carbamoyl-phosphate synthase 2 (CPS2), aspartate transcarbamylase (ATC), and dihydroorotase (DHO), enzymes that catalyze the first three reactions in the pyrimidine synthesis pathway, resulting in increased tumor progression [##REF##30100185##36##]. Besides, asparagine export is accompanied by reverse transport of serine, arginine and histidine. Thus its intracellular level is critical for various amino acids uptake and therefore protein synthesis [##REF##27126896##79##]. Proteomic studies have shown that asparagine is specifically enriched in proteins associated with epithelial-mesenchymal transition (EMT) and restricting its availability hampers the level of EMT associated proteins [##REF##29414946##80##].</p>\",\n \"<p id=\\\"Par29\\\">Despite regulating pyrimidine and EMT-related proteins synthesis to promote tumor progression, asparagine also regulates mesenchymal-epithelial transition (MET) to complete tumor colonization at distant metastatic sites [##REF##29719230##81##]. Mechanically, the scarcity of glutamine at distant metastatic sites, coupled with the heightened bioavailability of asparagine within these sites, triggers the activation of GS [##REF##29337136##82##]. This activation propels glutamine biosynthesis, fostering the accumulation of HIF1α and MYC, which are pivotal factors in metastatic processes. The relative abundance of asparagine and glutamine may thus have critical effects on tumor cells at metastatic sites. Besides, HIF1α and MYC are associated with increased oxidant stress and play important roles in the transition of EMT-like tumor cells to MET-like state, which is necessary for metastatic colonization. Thus, in an aggressive breast cancer model, ASNS upregulation promotes metastasis and results in the development of widespread metastases in the brain, liver, and lungs [##REF##29414946##80##]. Consistently, asparagine restriction can repress above processes and prolong the survival of patients [##REF##32499785##40##].</p>\",\n \"<title>Serine/glycine and one-carbon metabolism</title>\",\n \"<p id=\\\"Par30\\\">As one-carbon donors in the folate cycle, serine, glycine and their associated enzymes significantly contribute to nucleotide synthesis, methylation reaction and redox homeostasis to promote tumor progression. Serine hydroxymethyltransferase (cytoplasmic, SHMT1; mitochondrial, SHMT2) catalyzes the transfer of carbon from serine to tetrahydrofolate (THF), resulting in the formation of 5,10-methylene-THF, which is essential for nucleotide synthesis to fuel rapid tumor proliferation (Fig. ##FIG##0##1##). Large-scale genomic study of human tumors reveals that SHMT2 is essential for cancer cell survival and its knockdown severely impairs cancer cell proliferation [##REF##24755469##83##, ##REF##22628656##84##]. Methylenetetrahydrofolate dehydrogenase2 (MTHFD2), acting as one-carbon metabolism related enzyme, is upregulated and associated folate cycle with methionine cycle to promote S-adenosyl methionine (SAM) production in tumor cells [##REF##24451681##85##]. Thereby, serine/glycine metabolism contributes to methylation of genes and proteins as well as maintains redox homeostasis [##REF##26774282##86##] (Detailed later in amino acid metabolism and epigenetic modification). Additionally, post-translational modifications of those metabolic enzymes also play a regulatory role in tumor metabolism and progression. Deacetylation of SHMT2 by SIRT3 promotes its enzymatic activity, increases serine consumption and finally promotes colorectal carcinogenesis [##REF##30367038##87##]. When MTHFD2 is hyperacetylated, its enzymatic activity is inhibited and thereby NADPH level is downregulated. SIRT3 is also responsible for MTHFD2 deacetylation to maintain redox balance, which can be inhibited by cisplatin in colorectal cancer cells [##REF##32811824##88##].</p>\",\n \"<p id=\\\"Par31\\\">As a non-essential amino acid, the synthesis of serine is vital to tumors. De novo serine synthesis pathway (SSP) startes from 3-phosphoglyceric acid (3PG) generated from glycolysis, catalyzed by enzymes phosphoglycerate dehydrogenase (PHGDH), phosphoserine aminotransferase (PSAT), and phosphoserine phosphatase (PSPH). As the rate-limiting enzyme, tumor cells highly express PHGDH to counteract limited serine availability [##REF##32783943##89##]. Conversely, RNF5, an E3 ubiquitin ligase, mediates PHGDH degradation and suppresses tumor progression [##REF##32783943##89##, ##REF##33446657##90##]. With enough serine, serine palmitoyltransferase (SPT) catalyzes the de novo biosynthesis of sphingolipids. However, when serine synthesis is limited, SPT will instead use alanine as a substrate to synthesize cytotoxic deoxysphingolipids and then suppress tumor [##REF##32788725##65##]. Thus, maintaining certain level of serine is necessary for tumor cells to escape from cytotoxic suppression.</p>\",\n \"<p id=\\\"Par32\\\">Mitochondrial SHMT2 is the primary catalyst for glycine production from serine, thereby promoting the folate cycle. Elevated glycine level is linked to cancer progression like multiple myeloma (MM) and lymphoma [##REF##33569544##91##]. The glycine concentration in the bone marrow is elevated due to bone collagen degradation mediated by MM cell-secreted matrix metallopeptidase 13 (MMP13) [##REF##35817773##92##]. Although glycine is a non-essential amino acid, experiments have shown that limiting the supply of exogenous glycine induces tumor cells arrested in the growth phase (G1 phase). It is worth noting that glycine is required for nucleotide biosynthesis, directly supplying carbons for de novo purine biosynthesis, or donating one-carbon unit to the folate pool via the mitochondrial glycine cleavage system under the catalyze of glycine dehydrogenase (GLDC) [##REF##35508169##93##]. Besides, remodeled glycine metabolism mediated by protein arginine methyltransferase 7 (PRMT7) induces toxic death of leukemia stem cells [##REF##19033189##12##]. Mechanistically, PRMT7 loss resulted in reduced expression of glycine decarboxylase, leading to the reprogramed glycine metabolism to generate methylglyoxal, which is detrimental to leukemia stem cells.</p>\",\n \"<title>Signal pathways in amino acid metabolism</title>\",\n \"<p id=\\\"Par33\\\">Conventional understanding states amino acids as essential building blocks for peptide and protein synthesis. However, recent research has shed light on the profound significance of amino acids as bioactive molecules that play active roles in signaling pathways and metabolic regulation. mTOR, MYC and KRAS, which sense cellular amino acid levels and orchestrate these signals in a coordinated manner, play crucial roles in maintaining cellular metabolic homeostasis. Importantly, not only changes in amino acid levels impact signal pathways, but alterations in signaling pathways can also affect amino acid metabolism.</p>\",\n \"<title>mTOR senses and regulates amino acid metabolism</title>\",\n \"<p id=\\\"Par34\\\">mTOR is an atypical serine/threonine protein kinase, acting as a convergence point for anabolism and catabolism. Due to differences in structure and function, mTOR complexes are categorized as mTORC1 and mTORC2. mTORC1, which is sensitive to rapamycin inhibition, comprises mTOR, Raptor, mLST8, Tti/Tel2 and suppressive subunits PRAS40 and Deptor. The phosphorylation of PRAS40 and Deptor relieves its inhibition and activates mTORC1 [##REF##28388417##94##]. 4EBP-1, S6K1 and SREBP are downstream effectors of mTORC1, which are associated with upregulated synthesis as well as poor prognosis in cancer [##REF##24131622##95##]. mTORC1 is negatively regulated by low energy conditions, hypoxia, and DNA damage. It is also positively regulated by growth factors like insulin/insulin-like growth factor-1 (IGF-1) pathway and receptor tyrosine kinase-dependent Ras signaling. Particularly, when amino acids are abundant, the mTORC1 signaling pathway is positively regulated to transmit signals to facilitate protein synthesis. Conversely, under condition of amino acid insufficiency, the translation of proteins is inhibited to meet energy demand. Considering that cancer cells often exist in a nutrient-deficient environment, mTORC1 is consistently negatively regulated to adapt to metabolic alterations. mTORC2, which is insensitive to rapamycin, consists of mTOR, mSIN1, mLST8, Tti/Tel2 and suppressive subunit Rictor and Deptor. The balance between mTORC1 and mTORC2 orchestrates various metabolic processes, although our understanding of mTORC2 remains limited [##REF##22500797##96##]. We mainly focus on the function of mTORC1 below.</p>\",\n \"<p id=\\\"Par35\\\">Amino acid sensors in cytoplasm like sestrins, SAR1B, CASTOR1/2, SAMTOR and LARS sense amino acid levels and thereby regulate mTOR signaling pathway [##REF##23361334##97##] (Fig. ##FIG##1##2a##). The Rag GTPase promotes the localization of mTORC1 to the lysosomal surface and activation. Rag GTPase is further regulated by amino acids through sensors-GATOR2-GATOR1 axis. GATOR1, a negative regulator of mTORC1, interacts with Rag, leading to the inhibition of mTORC1 activity, while GATOR2 modulates mTORC1 activity by inhibiting GATOR1. Sestrins and SAR1B are leucine sensors in cytosolic. In situations of leucine deprivation, they bind to and inhibit GATOR2, a positive regulator of mTORC1 [##REF##29198988##98##, ##REF##34290409##99##]. Leucine can bind to sestrins and SAR1B, dissociating GATOR2 from the complex to activate mTORC1. Furthermore, in cases of amino acid deficiency, the general control nonderepressible 2 (GCN2)/ATF4 pathway is activated by uncharged tRNA, leading to the upregulation of sestrins expression to inhibit mTORC1 activity. Similar to sestrins, in arginine-depleted conditions, CASTOR1/2 form either a CASTOR1 homodimer or CASTOR1/2 heterodimer to inhibit GATOR2 and subsequently inhibit mTORC1 activity [##REF##22749528##100##]. Arginine disrupts the CASTOR1-GATOR2 complex by binding to CASTOR1, and activates GATOR2 to stimulate mTORC1. SAMTOR senses the changes of intracellular methionine concentration in the form of SAM. SAM disrupts the SAMTOR-GATOR1 complex by binding directly to SAMTOR, and reduces the GTPase-activating protein (GAP) activity of GATOR1, which then activates the mTORC1 signaling pathway [##REF##29123071##101##]. With adequate amino acids, the E3 ubiquitin ligase KLHL22 acts as a positive regulator of mTORC1 by promoting the degradation of GATOR1 [##REF##29769719##102##]. Leucyl-tRNA synthetase (LARS) senses intracellular leucine and directly activates mTORC1 activation by directly interacting with Rag rather than acting with GATOR1/2 [##REF##22424946##103##]. LARS mediates the leucylation of RagA/B, which subsequently activates mTORC1 [##REF##22424946##103##]. Consistently, abovementioned amino acid sensors eventually active Rag, and mediate lysosomal translocation of mTORC1 [##REF##30425336##104##], a critical step in the activation of the mTORC1. When mTORC1 is activated at lysosomal membrane, autophagy is inhibited and tumorigenesis is promoted.</p>\",\n \"<p id=\\\"Par36\\\">In addition to above cytoplasm sensors, lysosomal sensors also play a crucial role in mTORC1 activation. Ragulator complex provides a platform for lysosome to tether Rag. The Ragulator interacts with the Rag heterodimers in an amino acid- and v-ATPase-dependent fashion, which finally activates mTORC1 [##REF##22980980##105##]. SLC38A9, a lysosomal membrane protein with homology to amino acid transporters, participating in the activation of mTORC1 signaling pathway by influencing Rag [##REF##29053970##106##]. Mechanically, SLC38A9 stimulates the release of GDP from Rag A upon activation by arginine. This action propels Rag into the activated state, subsequently activating mTORC1 [##REF##30181260##107##]. Notably, SLC38A9 is crucial for the efflux of leucine, glutamine, tyrosine, and phenylalanine generated from lysosomal proteolysis. This efflux is necessary to activate mTORC1 through cytoplasmic sensors [##REF##25567906##108##]. Thus, lysosomal sensors allow for the integration of lysosomal nutrient information into the regulation of mTORC1 activity. Collectively, amino acids are not only sources for energy and protein synthesis in tumorigenesis, but also act on mTORC1 as signaling molecules.</p>\",\n \"<p id=\\\"Par37\\\">Meanwhile, mTORC1 can also regulate amino acid metabolism through its downstream signaling effectors (Fig. ##FIG##1##2a##). In response to growth signals, mTORC1 activates ATF4 to stimulate enzymes in serine synthesis for folate cycle and purine biosynthesis [##REF##26912861##109##]. Besides, signaling effector ATF4 also transcriptionally regulates serine transporters SLC1A5 and therefore facilitates the uptake of serine [##REF##30976106##110##]. Astutely, ATF4 can also regulate the expression of other amino acid transporters such as CHAC1, SESN2, SLC7A11, SLC7A5, SLC7A1, and SLC3A2, and increase amino acid uptake [##REF##35316619##111##]. Upon the accumulation of glutamine, mTORC1 downregulates miR-23a and miR-23b and subsequently promotes GLS expression to accelerate glutamine catabolism. When glutamine is deficient, mTORC1 represses the transcription of GDH inhibitor SIRT4, prompting glutamine anaplerosis [##REF##23663782##112##]. mTORC1 also activates arginine catabolism by promoting ODC expression in RAS transformed cells to promote polyamine production and tumor progression. Mechanically, mTORC1 promotes the association between ODC mRNA and mRNA-binding protein, promoting ODC mRNA stabilization and expression [##REF##22070140##113##]. Besides, positively regulated mTORC1 leads to the stabilization of MYC, which in turn induces ASS1 expression by competing with HIF1α for ASS1 promoter binding sites and therefore promotes arginine expression [##REF##22461507##114##]. Collectively, mTORC1 regulates amino acid metabolism through multiple signaling effectors, including amino acid transporters, synthetic and catabolic enzymes. mTORC1 downstream signaling molecule MYC also plays extensive regulatory roles in amino acid metabolism.</p>\",\n \"<title><italic>Myc</italic> drives amino acid metabolism</title>\",\n \"<p id=\\\"Par38\\\"><italic>Myc</italic> is a proto-oncogene which encodes transcription factor MYC, constitutively expressed in tumor and associated with altered metabolism [##REF##25038584##115##]. MYC directly regulates key metabolic enzymes expression, resulting in altered metabolism like increased nutrient uptake, enhanced glycolysis, and elevated fatty acid and nucleotide synthesis [##REF##25341931##116##]. Amino acid metabolism, both EAAs and NEAAs are also regulated by MYC (Fig. ##FIG##1##2b##).</p>\",\n \"<p id=\\\"Par39\\\">As EAAs rely on external resources, corresponding amino acid transporters are crucial and often up regulated in cancer. A positive feedback circuit called MYC-SLC7A5/SLC43A1 is critical in EAAs metabolism in tumor. SLC7A5 imports EAAs in exchange for glutamine export, while SLC43A1 facilitates the import of large neutral essential amino acids (LNEAAs) like BCAAs and tryptophan [##REF##12930836##117##, ##REF##9726963##118##]. MYC plays a pivotal role in promoting the transcription of SLC7A5/SLC43A1 and consequently the uptake of EAAs, which in turn activates mTORC1 and accelerates <italic>Myc</italic> transcription. When SLC7A5/SLC43A1 is blocked and thus amino acids uptake is decreased, the GCN2-eIF2α amino acid stress response pathway will be triggered, leading to the inhibition of MYC mRNA translation. Collectively, this circuit leads to a cascade that affects the entire amino acid metabolic process and oncogene transcription, ultimately promoting tumorigenesis [##REF##29281830##119##]. Specifically, MYC can enhance tryptophan uptake by upregulating the expression of transporters such as SLC7A5 and SLC1A5. Additionally, it can promote the conversion of tryptophan to kynurenine by inducing arylformamidase (AFMID) within the kynurenine pathway [##REF##31416966##120##, ##REF##32651356##121##]. Elevated level of kynurenine has been found to help tumors to evade immune surveillance [##REF##28751584##122##]. Additionally, <italic>Myc</italic> upregulates BCAT1, a crucial enzyme in BCAAs catabolism, and increases biosynthesis and promotes tumor development [##REF##28771772##123##].</p>\",\n \"<p id=\\\"Par40\\\">In addition to above regulation of EAAs, <italic>Myc</italic> can also catalyze NEAAs metabolism, like glutamine, proline, and serine. Besides glucose, glutamine could function as major fuels in tumors. MYC prompts the expression of glutamine transporters such as SLC1A5 and SLC38A5 [##REF##28751584##122##]. In addition, <italic>Myc</italic> also plays a part in glutamine anabolism and catabolism. MYC can demethylate the promoter of GS, prompting the synthesis of glutamine in cancer cells [##REF##26603296##124##]. However, <italic>Myc</italic> also participates in glutamine catabolism through acting on miR-23a/b in some cancers. miR-23a/b can suppress the expression of GLS and then facilitate glutaminolysis in tumor cells [##REF##34390414##125##]. Collectively, MYC functions differently in different types of tumor cells, and metabolic requirements differential within specific cancer types might dictate the outcome of glutamine metabolism regulated by MYC.</p>\",\n \"<p id=\\\"Par41\\\">NEAA proline can be synthesized by aldehyde dehydrogenase family 18 member A1 (ALDH18A1, P5CS) and pyrroline-5-carboxylate reductase (PYCR) from glutamine and arginine, thus MYC induced P5CS and PYCR upregulation can promote the proliferation and invasion of cancer cells [##REF##20832749##28##, ##REF##28771772##123##]. In this way, the biosynthesis of glutamine-to-proline is prompted, assisting tumor cells to alleviate ER stress and promote proline homeostasis. MYC increases miR-23b to decrease the expression of proline oxidase/proline dehydrogenase (POX/PRODH), leading to the inhibition of proline catabolism [##REF##20562915##126##]. Thus, MYC can not only promote the synthesis of proline, but also inhibit its break down [##REF##22615405##127##].</p>\",\n \"<p id=\\\"Par42\\\">MYC also participates in serine metabolism. Enhanced activity of MYC activates metabolic enzymes in SSP such as PHGDH, PSAT and PSPH, resulting in enhanced serine production [##REF##28771772##123##]. MYC upregulates SHMT2, which is critical for maintaining cellular redox homeostasis. Under the catalyze of SHMT2, serine metabolized glycine directly participates in glutathione (GSH) production. In this way, MYC promotes GSH synthesis de novo, and then finally resists oxidant to promote tumor progression. MYC upregulates SHMT2, leading to increased production of one-carbon unit 5’m-THF and therefore the generation of NADPH. NADPH also plays a crucial role in maintaining the redox balance by reducing GSSG to GSH [##UREF##0##128##]. Collectively, MYC promotes serine synthesis and thereby GSH and NADPH production to resist oxidation and promote tumor growth.</p>\",\n \"<title>Altered KRAS and amino acid metabolism</title>\",\n \"<p id=\\\"Par43\\\">KRAS, a frequently mutated oncogenic protein in human cancers, plays a pivotal role in regulating MYC and mTOR activity through RAF-MEK-ERK and PI3K-AKT pathways, respectively [##REF##32209560##129##, ##REF##28802037##130##] (Fig. ##FIG##1##2c##). The mutation impairs its GTPase activity, leading to persistent activation of downstream signaling cascades, influencing the cellular metabolism and promoting tumor cell proliferation. Amino acid metabolism is also regulated by KRAS, and targeting the metabolic network downstream of KRAS may offer potential avenues for treating KRAS-driven tumors [##REF##24929527##131##].</p>\",\n \"<p id=\\\"Par44\\\">KRAS-induced macropinocytosis maintains intracellular glutamine levels. Besides, KRAS regulates enzymes in glutamine catabolism. KRAS downregulates glutamate dehydrogenase1(GLUD1), leading to alterations in glutamine metabolism to produce NEAAs instead of participating in TCA cycle. Besides, KRAS upregulates GOT2 in the mitochondrial and GOT1 in the cytoplasm [##UREF##1##132##]. Under such circumstance, glutamine-derived aspartate is converted into OAA by GOT1 in the cytoplasm, and finally converted into pyruvate, resulting in the production of NADPH to maintain the cellular redox balance. Thus, <italic>Kras</italic>-mutated cells resist cisplatin treatment by upregulating glutamine consumption to maintain a redox state. As upregulated glutamine catabolism is essential for tumor but dispensable for normal cells, inhibiting enzymes like GOT1 in the glutamine catabolic pathway leads to increased levels of reactive oxygen species (ROS), reducing the levels of GSH and ultimately inhibiting tumor growth [##REF##21317241##133##].</p>\",\n \"<title>Amino acids in tumor microenvironment</title>\",\n \"<p id=\\\"Par45\\\">Tumors thrive within the intricate TME, which is complex and continuously evolving including surrounding blood vessels, immune cells, fibroblasts and the extracellular matrix (ECM) [##REF##31350295##134##]. The bidirectional interaction between tumor and TME takes various forms. Tumors assimilate essential nutrients through macropinocytosis to satisfy its vigorous metabolism. Thus, rapidly proliferating tumor cells compete for relatively scarce nutrients with fibroblasts and immune cells, shaping a commonly hypoxic, acidic, and nutrient-deprived TME. Collectively, the TME promotes tumor progression and immune evasion through nutrients deprivation. Besides, tumors secrete various bioactive molecules that profoundly influence the TME.</p>\",\n \"<title>Macropinocytosis in TME takes up amino acids</title>\",\n \"<p id=\\\"Par46\\\">Macropinocytosis is a type of endocytosis that involves the nonspecific uptake of extracellular nutrient molecules like proteins and amino acids [##REF##14732047##135##] (Fig. ##FIG##2##3##). Even targeted drugs block enzymes in vital biosynthesis process, cancer cells still take up necessary biological materials from TME (e.g., collagen fragments) through macropinocytosis to maintain proliferation. Macropinosomes formation is an actin-dependent process that is initiated upon stimulation of growth factors like colony stimulating factor (CSF-1), epidermal growth factor (EGF), or platelet-derived growth factor. Besides, oncogenic mutations in <italic>Kras</italic> or PI3K pathway activation can also drive micropinocytosis [##REF##28973876##136##]. Proteins ingested through macropinocytosis can be decomposed into free amino acids by cellular autophagy for new protein synthesis, or catabolized to generate ATP for energy supply [##UREF##2##137##]. Therefore, macropinocytosis enables tumor cells to survive in harsh environment by providing both materials and energy.</p>\",\n \"<p id=\\\"Par47\\\">Macropinocytosis is a metabolic adaptation to nutrient stress, thus amino acids depletion drives macropinocytosis in cancers to obtain nutrients. Mechanically, a low amino acid environment inhibits the Hippo pathway and promotes membrane localization of EGFR and TGFBRII, triggering macropinocytosis [##REF##35082383##138##]. When intracellular free amino acids are abundant, mTORC1 precisely controls the utilization of extracellular protein-derived amino acids within lysosomes by repressing macropinocytosed protein catabolism [##REF##25085245##139##].</p>\",\n \"<title>Amino acid metabolism and CAFs</title>\",\n \"<p id=\\\"Par48\\\">TME is hypoxia and devoid of nutrients, which is unfavorable for the survival of cancer cells. Therefore, cancer cells cleverly hold fibroblasts in TME, converting them into CAFs to create an altered homeostasis suitable for tumor growth [##REF##35082383##138##]. CAFs enhance tumor invasion through their bidirectional interaction with tumor cells and highly secretory phenotype to produce ECM [##REF##10409103##140##] (Fig. ##FIG##2##3##). CAFs regulate amino acid metabolism to promote multiple processes like tumorigenesis, angiogenesis, and metastasis [##REF##30470818##141##]. CAFs functional significance in cancer makes them attractive targets for cancer treatment.</p>\",\n \"<p id=\\\"Par49\\\">CAFs play a critical role in tumor progression by interacting with tumor cells and modulating tumor metabolism through amino acid transport (Fig. ##FIG##2##3##). In the stiff matrix of TME, ECM mechanotransduction re-localizes YAP/TAZ to the nucleus and activates the transcription of GLS1 and SLC1A3 in both cancer cells and CAFs [##REF##30122553##142##, ##REF##30293773##143##]. GLS1 promotes the transformation from glutamine to glutamate. However, upregulated-glutamate does not contribute to the TCA cycle in tumors, while in CAFs, glutamate is a major source of carbon for the TCA cycle and produces asparate. SLC1A3 is upregulated simultaneously in tumor cells and CAFs, but functions differently. SLC1A3 in CAFs could provide aspartate to cancer cells, while cancer cells in turn secrete glutamine-derived glutamate through SLC1A3 to CAFs. In such metabolic crosstalk between CAFs and cancer cells, CAF-derived aspartate promotes pyrimidine and protein synthesis to sustains cancer cells proliferation, while cancer cell-derived glutamate balances the redox state of CAFs to promote ECM remodeling. Drugs targeting SLC1A3 can significantly reduce tumor growth due to the close interaction and extensive metabolic remodeling between CAFs and tumors. The amino acid metabolism interaction between CAFs and cancer cells have been recently reviewed in detail [##REF##30470818##141##, ##REF##36209111##144##].</p>\",\n \"<p id=\\\"Par50\\\">CAFs are known to be the most important cells for producing ECM. Collagen, rich in proline, is the main component of ECM, the degradation of which could provide materials and energy to tumor cells. Proline synthesis is sustained through the conversion of glutamate catalyzed by P5C synthase (P5CS) or arginine into pyrroline-5-carboxylate (P5C). P5C serves as the ultimate precursor for proline and, consequently, collagen synthesis. Proline synthesis is upregulated in CAFs and acts as a limiting factor in ECM production. Thus, P5CS deletion decreases collagen and therefore ECM production, which could be rescued with proline supplementation [##REF##32134147##145##].</p>\",\n \"<title>Amino acid metabolism and immune cells</title>\",\n \"<p id=\\\"Par51\\\">TME comprises diverse cell types, including immunosuppressive cells like myeloid-derived suppressor cells (MDSCs), TAMs, and regulatory T cells (Tregs), as well as tumor-antagonizing immune cells like natural killer cells (NKs), T lymphocytes, B lymphocytes and dendritic cells (DCs). Although the tumor-antagonizing immune cells within TME tend to target and kill the cancer cells in the early stage of tumorigenesis, cancer cells eventually escape immune surveillance through various mechanisms, including reprogramming of metabolism.</p>\",\n \"<p id=\\\"Par52\\\">Tumor associated MDSCs highly express ARG1 (Fig. ##FIG##2##3##), depriving arginine in TME, leading to the lack of arginine in T cells and impairing T cell-mediated anti-tumor immunity [##REF##27745970##146##]. Besides, MDSCs also highly express NOS-2, not only decompose arginine but also produce NO to impair anti-tumor effect of T cells [##REF##31130949##147##]. MDSCs express the x<sub>c</sub><sup>-</sup> transporter to import cystine. Thus, in the presence of MDSCs, cysteine is reduced and T cell function is impaired [##REF##20028852##148##]. Apart from depleting arginine and cysteine, MDSCs also inhibit T cell function by expressing IDO. In tumor site and drainage lymph nodes, IDO overexpression in MDSCs deprived tryptophan in TME, which is necessary for T cell and NK cell proliferation. This eventually leads to T cell stagnating in G0 and NK cell apoptosis [##REF##23812098##149##]. IDO also generates kynurenine, which has been reported to block T cell proliferation and even induce T cell death as mentioned above. TAM also creates an immunosuppressive TME, which tightly links to glutamine metabolism [##REF##28117416##150##]. In nutrient-deprived tumor stroma, GS expression in TAM increased to promote glutamine synthesis. Increased GS provides metabolic condition skews macrophages toward an M2-like, pro-metastatic macrophages by providing more glutamine and α-KG [##REF##28813676##151##, ##REF##35897036##152##]. Similar to MDSC, TAM also overexpresses IDO and ARG1 to deplete tryptophan and arginine as well as produce kynurenine to suppress T cell function [##REF##25035953##153##]. It is worth noting that NOS-2 is also upregulated in TAM to reduce arginine and suppress T cell function.</p>\",\n \"<p id=\\\"Par53\\\">Tumor-antagonizing immune cells function is also suppressed in the TME due to altered amino acid metabolism, thereby influencing the development of tumors. During T cell activation and differentiation, amino acids play dual roles as both energy source and substrates for protein and nucleic acid biosynthesis [##REF##32997964##154##]. Compared to naive CD8<sup>+</sup> T cells, activated CD8<sup>+</sup> T cells increases the density of SLC1A5 and SLC7A5 on the cell surface to enhance glutamine uptake. Glutaminolysis underlies asymmetric T cell division, as glutaminolysis and mTORC1 activation is necessary to maintain c-Myc asymmetry. Asymmetric c-Myc levels in daughter T cells would affect their proliferation, metabolism, and differentiation [##REF##27064903##155##]. Thus, the daughter T cell proximal to the antigen presenting cell is accumulated with glutamine transporters and adopts an effector-like fate, while the distal T cell absence of glutamine transporters assumes a memory-like fate. The differentiation of naive T cells into Th1 or Th17 cells also depends on glutaminolysis. When GLS is inhibited, the number of Th1 cells increases while the number of Th17 cells decreases [##REF##32031065##156##]. Besides, glutamine limitation also promotes Treg differentiation [##REF##26420908##157##]. IDO is activated in DCs to deplete tryptophan and produce kynurenine. As previously mentioned, kinase GCN2 is activated by elevated levels of uncharged tryptophan tRNA, triggering CD8<sup>+</sup> T cell-cycle arrest and functional anergy [##REF##15894280##158##]. Moreover, GCN2 activation fosters de novo Treg differentiation and enhances suppressor function in mature Tregs [##REF##23103127##159##]. On other hand, kynurenine overexpression not only impairs effector T cell, but also promotes the differentiation of Treg through the activation of AhR [##REF##20720200##160##]. DCs and tumor cells both express SLC38A2 to facilitate glutamine uptake, modulating anti-tumor immunity. Mechanistically, glutamine acts as an intercellular metabolic checkpoint that licenses DCs function in activating CD8<sup>+</sup> T cells [##REF##37407815##161##]. Glutamine promotes the formation of the FLCN-FNIP2 complex, consequently restricting TFEB activity. TFEB acts as a molecular switch, regulating exogenous antigen-presentation pathways through lysosome activation and thus influencing CD8<sup>+</sup> T cell activity.</p>\",\n \"<p id=\\\"Par54\\\">As mentioned above, arginine metabolism regulates immune response and responsible for tumor progression [##REF##34831116##162##]. Arginine metabolic enzyme NOS-2 directs the polarization of gamma delta T cells towards a pro-tumoral phenotype, thereby inducing metastatic progression [##REF##27622078##163##]. Asparagine up regulates the LCK signaling pathway to enhance CD8<sup>+</sup> T cell activity and thereby inhibit tumor growth. This subverts the previous understanding of the cancer-promoting function of asparagine [##REF##33420490##164##]. Collectively, above findings reveal that amino acid metabolism in tumors and immune cells plays vital roles in the process of tumorigenesis and development.</p>\",\n \"<title>Amino acid metabolism and epigenetic modification</title>\",\n \"<p id=\\\"Par55\\\">Epigenetics refers to heritable phenotype changes that do not involve alterations in the DNA sequence [##REF##31341302##165##]. It encompasses various mechanisms, including DNA methylation, histone modifications, chromatin remodeling, and small RNA regulation. Amino acid metabolites like SAM and acetyl-CoA are essential substrates for epigenetic modification, while amino acid metabolism also requires epigentic modification of associated metabolic enzymes [##REF##26629854##166##]. This reciprocal regulatory relationship has a profound impact on tumor progression.</p>\",\n \"<title>Methylation</title>\",\n \"<p id=\\\"Par56\\\">SAM is synthesized from methionine and functions as the primary methyl donor in various methylation reactions, mainly DNA methylation (Fig. ##FIG##3##4##). DNA methylation is catalyzed by DNA methyltransferases, which transfer methyl group from SAM to DNA. As a methionine transporter, SLC7A5 is crucial for intracellular methionine concentration. Disruption in methionine concentration is closely associated with cancer development, as DNA hypermethylation usually suppresses tumor suppressor genes expression [##REF##26647078##167##].</p>\",\n \"<p id=\\\"Par57\\\">Tumorigenesis is also affected when methylation occurs on histones [##REF##31270442##168##]. The effect of histone methylation depends on which amino acid residue is methylated and how many methyl groups are involved [##REF##31980738##10##]. Specifically, arginine methylation on histone can promote transcriptional activation, while lysine methylation on histone involves both transcriptional activation and repression [##REF##22473383##169##]. When it terms to transcriptional repression, tumors always overexpress methionine transporter SLC43A2 to outcompete T cells for methionine. Methionine deficiency in T cells leads to downregulation of H3K79me2, promoting CD8<sup>+</sup> T cell death and inhibiting Th17 polarization [##REF##32879489##170##]. When it terms to transcriptional activation, downregulation of SLC7A5 in T cells restricts methionine absorption, and alters H3K27me3 deposition at the promoters of key T cell stemness genes. These changes promote the maintenance of a ‘stem-like memory’ state and improve long-term persistence and anti-tumor efficacy [##REF##36717749##171##]. In conclusion, methionine metabolism regulates genomic architecture, chromatin dynamics and gene expression by dynamically modulating methylation on DNA and histone [##REF##29769529##172##].</p>\",\n \"<p id=\\\"Par58\\\">In addition to methionine, other amino acids can also indirectly regulate the level of methylation. Threonine metabolizes into 5-mTHF while serine metabolizes into 5,10-mTHF, playing pivotal roles in cell fate by contributing to SAM synthesis and thereby methylation status. Previous studies reveal that threonine depletion decreased the levels of SAM, leading to decreased tumor growth and increased differentiation [##REF##23118012##173##]. Transaminase like glycine N-methyltransferase (GNMT) catalyzes the transfer of a methyl group from SAM to glycine to form sarcosine, leading to SAM depletion and S-Adenosyl-homocysteine (SAH) accumulation [##REF##24746817##174##]. The ratio of SAM/SAH affects methylation [##REF##10657384##175##]. Therefore, knock down of transaminase like GNMT or the dysregulation of glycine clearly affects the methylation of histone and DNA, affecting gene expression and tumorigenesis [##REF##18318442##176##, ##REF##19035462##177##]. Moreover, glycine can also be metabolized to produce 5,10-mTHF to fuel folate cycle, and thereby regulating methionine cycle.</p>\",\n \"<title>Demethylation</title>\",\n \"<p id=\\\"Par59\\\">Amino acid like glutamine can be catabolized to produce α-KG. Meanwhile, through α-KG-dependent dioxygenase, the methylation of histone and DNA can be removed [##REF##28450733##178##]. The family of α-KG-dependent dioxygenases includes Jumonji C-domain lysine demethylases (JmjC-KDMs), ten-eleven translocation (TET) DNA cytosine-oxidizing enzymes, and prolyl hydroxylases (PHDs) [##REF##33092942##179##].</p>\",\n \"<p id=\\\"Par60\\\">Normal isocitrate dehydrogenases (cytoplasmic, IDH1; mitochondrial IDH2) catalyze isocitrate to produce α-KG in TCA cycle to fuel tumors. However, IDH is the most frequently mutated metabolic genes in human cancer. When IDH is mutated, it can further catalyze α-KG to 2-hydroxyglutaric acid (2-HG), and 2-HG can competitively inhibit α-KG-dependent dioxygenases [##REF##31263678##180##]. Thus, mutated IDH inhibits DNA and histone demethylation, leading to a hypermethylation phenotype and promoting the differentiation of cancer stem cells [##REF##31131326##181##]. In IDH mutant glioblastoma, the CpG island in promoter region of BCAT1 is usually hypermethylated. The inhibition of BCAT1 expression results in the reduction of glutamate and therefore inhibit tumor growth and invasion [##REF##23793099##182##]. Consistently, individuals diagnosed with IDH<sup>WT</sup>/TET2<sup>WT</sup> myeloid leukemia, α-KG is maintained at normal levels and therefore BCAT1 expression is promoted compared to IDH<sup>mut</sup>/TET<sup>mut</sup> myeloid leukemia. Elevated expression of BCAT1 serves as a robust indicator for poorer survival prognosis, which level has been observed significantly escalates upon disease relapse [##REF##29144447##183##].</p>\",\n \"<title>Acetylation and deacetylation</title>\",\n \"<p id=\\\"Par61\\\">Histone acetylation is a process in which acetyl-CoA donates acetyl groups to modify histone proteins catalyzed by histone acetyltransferases (HATs) [##REF##11055583##184##]. Both acetyl-CoA abundance and the ratio of acetyl-CoA to coenzyme A regulate histone acetylation in cancer [##REF##24998913##185##]. Histone acetylation leads to attractive force reduction between histones and DNA, promoting a more open chromatin structure that allows for increased transcriptional activity [##REF##27988359##11##, ##REF##32071378##186##]. Furthermore, acetyl-CoA derived from amino acid metabolism also plays a role in non-histone protein acetylation and regulates tumor growth.</p>\",\n \"<p id=\\\"Par62\\\">Acetyl-CoA level and amino acid metabolism affects each other. BCAAs like isoleucine and leucine synthesize acetyl-CoA under the catalyze of transaminase. Other amino acids such as lysine, phenylalanine, tryptophan, and tyrosine also contribute to acetyl-CoA production by forming acetoacetyl-CoA. Similarly, alanine, serine, tryptophan, cysteine, glycine, and threonine synthesize acetyl-CoA through pyruvate formation. Leucine metabolism provides acetyl-CoA to the EP300 acetyltransferase, leading to acetylation of mTORC1 regulator Raptor. This acetylation ultimately results in mTORC1 activation and altered amino acid metabolism [##REF##30197302##187##]. Isoleucine and leucine catabolism generates acetyl-CoA within the mitochondria. Subsequently, mitochondrial acetyl-CoA must be transported to the cytoplasm and nucleus to regulate gene expression through epigenetic mechanisms [##REF##29779826##188##].</p>\",\n \"<p id=\\\"Par63\\\">Deacetylation refers to the removal of acetyl groups from histones, which is typically catalyzed by histone deacetylase (HDAC). Histone deacetylation results in a more condensed form of DNA known as heterochromatin, which is linked to reduced levels of gene transcription. The sirtuin proteins are classified as the class III HDACs depending on NAD<sup>+</sup> in contrast to the zinc-dependent catalysis by Class I, II, and IV enzymes [##REF##10072350##189##]. Thus, the tryptophan metabolism and therefore NAD<sup>+</sup>/NADH ratio exhibit a close correlation with both the acetylation state and energy status, playing important roles in inhibiting gene expression and thereby influencing tumor progression.</p>\",\n \"<title>Phosphorylation, succinylation, and lactylation</title>\",\n \"<p id=\\\"Par64\\\">In addition to the aforementioned types of epigenetic modifications, it is noteworthy to consider other post-translational modifications (PTMs), including phosphorylation, succinylation, and lactylation. Each of these PTMs alters the charge and structure of proteins. For instance, modifications to histones affect their binding to DNA, thereby influencing chromatin status and gene expression [##REF##25220185##190##]. PTMs of enzymes influence their binding to substrates, thereby affecting various metabolic processes.</p>\",\n \"<p id=\\\"Par65\\\">Serine, threonine as well as tyrosine residues have established as residues for histone phosphorylation [##REF##34050894##191##]. AMP-activated protein kinase (AMPK) is a sensor of cellular energy status and phosphorylates a variety of cellular substrates include histones [##REF##28974774##192##]. As a nutrient sensor, AMPK can be activated under a variety of stress conditions. Recent studies indicate that AMPK is activated by the amino acids like alanine [##REF##30190193##193##], aspartate [##REF##32273511##194##] and cysteine [##REF##32273511##194##] and then phosphorylates histones. Histone phosphorylation is linked to various cellular processes, including transcriptional activation, mitosis, DNA repair, and apoptosis [##REF##21941619##195##]. Phosphorylation also occurs in enzymes related to amino acid metabolism. When phosphorylation occurs in branched chain ketoacid dehydrogenase kinase, it regulates EMT genes and leads to the metastasis of colorectal cancer [##REF##32238881##196##]. Besides, phosphorylation of GLS is essential for its enzymatic activity and critically contributes to tumorigenesis [##REF##29515166##197##].</p>\",\n \"<p id=\\\"Par66\\\">Succinylation is an innovative PTM where a succinyl group is added to a lysine residue. This modification is associated with the catabolism of BCAAs, as the generation of succinyl-CoA serves as an intermediate for succinylation [##REF##29888767##198##]. The transcriptional characteristics of succinylated histones resemble those observed in acetylated counterparts, thereby activating gene expression [##REF##29888767##198##, ##REF##1710452##199##]. Therefore, succinyl-CoA accumulation may enhance cancer initiation and progression by promoting a global succinylation program that favors cancer growth [##REF##33864000##200##]. Enzymes involved in amino acid metabolism can also be succinylated. Upon oxidative stress, enhanced succinylation of GLS leads to increased oligomerization and activity, thereby promoting glutaminolysis and tumor growth [##REF##33991485##201##].</p>\",\n \"<p id=\\\"Par67\\\">Lactylation is also a novel epigenetic modification, in which lactic acid modifies the lysine residues [##REF##31645732##202##]. Hypoxia is linked to elevated lactate levels originating from heightened glycolysis activity, thereby enhancing intracellular histone lactylation [##REF##37646027##203##]. Histone lactylation can affect gene expression in tumors and immunological cells, thereby promoting malignancy and immunosuppression. In tumor-infiltrating myeloid cells, a potent accumulation of lactate occurs, leading to the upregulation of methyltransferase-like 3 (METTL3) through lactylation. The increased expression of METTL3 in these cells was correlated with the poor prognosis of patients [##REF##35320754##204##].</p>\",\n \"<title>Targeting amino acid metabolism in tumor therapy</title>\",\n \"<p id=\\\"Par68\\\">Primary strategies in cancer treatment involve targeting disparities between normal and tumor cells in gene expression and phenotype. Metabolism alteration in tumors is a noteworthy phenotype. Amino acids metabolism, being fundamental to vital biological processes, play a crucial role in tumor initiation and progression [##REF##33511116##5##]. Consequently, targeting amino acid metabolism like amino acid depletion is significant in cancer treatment approach [##REF##33795176##7##]. Strategies for targeting amino acid metabolism encompass inhibiting amino acid transporters, regulating amino acid biosynthesis and consumption as well as developing amino acids modified dietary (Table ##TAB##0##1##). Given different metabolism in different tumors, developing tailored strategy for distinct tumors becomes imperative.</p>\",\n \"<title>Amino acid transporters inhibition</title>\",\n \"<p id=\\\"Par69\\\">Amino acids substantial uptake occurs in tumor cells which overexpress amino acid transporters. Consequently, targeting these transporters to restrict amino acid availability is an effective strategy for inhibiting tumor growth. Drugs targeting amino acid transporters and related enzymes have transitioned from preclinical research to clinical trials and have demonstrated efficacy in some cases [##REF##28750681##205##]. Glutamine transporters usually highly expressed and associated with poor prognosis as external glutamine is essential for cancer cells to survive [##REF##25855379##206##, ##REF##29334372##207##]. This differentiates cancer cells from normal cells, providing a target for tumor therapy. Clinically, Tamoxifen and Raloxifene block glutamine uptake by inhibiting ASCT2 expression in breast cancer to suppress tumor [##REF##29562706##208##]. Likewise, pharmacological blockade of ASCT2 with V-9302 also resultes in attenuated cancer cell growth and proliferation [##REF##11401523##209##]. However, therapy targeting ASCT2 alone is not enough, as there are compensation from other transporters like cystine/glutamate antiporter(xCT) to replace ASCT2 to fuel tumors. Cationic amino acid transporters like CAT-1, CAT-2 and CAT-3 for lysine, arginine, and histidine are also dysregulated in tumors and associated with drug resistance. Specifically, CAT-1 expression exhibits a correlation with tumor grade in prostate cancer [##REF##29562706##208##]. It also plays a pivotal role in promoting growth, proliferation, and metastasis of colorectal cancer and breast cancer [##REF##30865893##210##]. Upregulated CAT-3 increases arginine uptake and thereby induces tumors to adapt glutamine deprivation [##REF##30404920##39##]. Downregulation of CATs (CAT-1, CAT-3) through lentiviral transduction with shRNAs or chemical like verapamil shuts down tumor proliferation and induces death [##REF##31824848##211##, ##REF##35377022##212##]. Conversely, loss of CAT-2 exacerbates inflammation-associated colon tumorigenesis [##REF##30202097##213##].</p>\",\n \"<p id=\\\"Par70\\\">Apart from targeting these tranporters directly to prevent amino acids uptake, these tranporters can also be targeted to deliver drugs. The glutamine macromolecular analog polyglutamine (PGS) can mimic glutamine and selectively ferry siRNA through ASCT2. Therefore, siRNA delivered by PGS targets the growth and survival of certain cancer [##REF##30149232##214##]. Thus, RNAi in combination with chemotherapy can augment the anti-tumor effect. Apart from ASCT2, SLC6A14 is another amino acid transporter that demonstrates upregulation in numerous cancer types [##REF##31326539##215##]. Hence, SLC6A14 emerges as a promising target for tumor therapy, with its potential extending to enabling the selective delivery of anticancer drugs to tumor cells. Stealth liposomal systems functionalized with the aspartate-polyoxyethylene stearate conjugate (APS) are developed for transporter-mediated targeted delivery of docetaxel to SLC6A14, resulting in a significantly efficiency for delivering anticancer drugs into cells [##REF##27991616##216##].</p>\",\n \"<title>Amino acid metabolism enzymatic inhibition</title>\",\n \"<p id=\\\"Par71\\\">Amino acid metabolism is also reglulated by biosynthetic enzyme and catabolic enzyme. Numerous pharmacological inhibitors targeting key enzymes in amino acid metabolism undergo extensive research. Asparagine is the most successful and well-documented target in amino acid depletion therapy, especially in acute lymphoblastic leukemia [##UREF##3##217##]. Asparlas, a drug frequently used in clinical practice, is developed for the treatment of acute lymphoblastic leukemia. It acts as a long-acting asparagine-specific enzyme to deplete asparagine [##REF##35205650##218##]. Studies shown that inhibited ASNS or metformin inhibited ETC limits tumor asparagine synthesis, impairing tumor growth in multiple mouse models [##REF##33609439##219##]. <italic>Kras</italic> mutation activates the ATF4 signaling pathway through its downstream AKT and NRF2 in NSCLC. When ASNS is inhibited by AKT and the extracellular asparagine is depleted simultaneously, tumor growth can be reduced [##REF##29316436##220##]. Therefore, ASNS is also a promising therapeutic target for <italic>Kras</italic>-mutated NSCLC.</p>\",\n \"<p id=\\\"Par72\\\">Arginine is another targeted amino acid in depletion therapy. As mentioned above, ASS1, a rate-limiting enzyme for synthesizing arginine, is frequently deficient in tumors and thereby arginine is depleted. Meanwhile, enzymes such as arginine deiminase (ADI) and ARG I could also exhaust extracellular arginine by transforming arginine into citrulline and ornithine, respectively. Thus, depleting arginine by ADI-PEG20 or rhArg1-PEG to downregulate arginine level in serum is essential to restrain cancer cells proliferation [##REF##25917076##221##, ##UREF##4##222##]. Since ARG is consistently overexpressed in both cancer cells and MDSC, the ARG inhibitor INCB001158 has the potential to normalize arginine levels within TME, consequently revitalizing T cell functionality [##REF##29254508##223##]. Simultaneously, the intracellular inhibition of ARG by INCB001158 prevents tumor cells from utilizing arginine for polyamine generation. In combination with immune checkpoint therapy, INCB001158 has been used in patients with advanced/metastatic solid tumors and has shown significant efficacy in a Phase II clinical trial. Additionally, previous study has proved that ASS1 is a new tumor repressor via epigenetic mechanism and downregulated in cancers such as ovarian cancer. Its downregulation has been reported to be associated with advanced tumor stage and susceptible to ADI-PEG20 treatment [##REF##33674736##224##].</p>\",\n \"<p id=\\\"Par73\\\">Similarly, as mentioned above, tumors driven by <italic>Myc</italic> or <italic>Kras</italic> are highly dependent on exogenous glutamine, and pharmacological inhibitors of GLS like telaglenastat or 968 has shown excellent results in multiple tumors [##REF##33795176##7##]. Besides, tumors control related enzymes expression to exploit serine and other one-carbon biosynthetic pathway to reduce the dependence on exogenous supply. Drugs targeting the one-carbon metabolic pathway like methotrexate targeting dihydrofolate reductase (DHFR) or 5-fluorouracil targeting thymidylate synthase has been widely used clinically.</p>\",\n \"<title>Amino acids modified dietary</title>\",\n \"<p id=\\\"Par74\\\">Cumulating researches have demonstrated that amino acid like methionine, serine, glycine, leucine, glutamine and cysteine restriction plays roles in cancer intervention. Restricted methionine intake prevents tumor growth and metastasis such as TNBC [##REF##27579534##225##], CRC [##REF##20424491##226##], and glioma [##REF##31515518##227##]. Preclinical studies show that restriction of serine and glycine intake significantly inhibits tumor cells proliferation in intestinal cancer and lymphoma mice [##REF##28425994##228##]. In the leucine-rich diet, metabolism shifts from glycolytic metabolism to oxidative phosphorylation, resulting in a less aggressive tumor phenotype [##REF##31664147##229##]. Metabolomic analysis reveals that dietary uptake of glutamine effectively increases the concentration of glutamine in tumors and its downstream metabolite, α-KG, without increasing biosynthetic intermediates necessary for cell proliferation. The increase in intratumoural α-KG concentration drives hypomethylation of H3K4me3, thereby suppressing epigenetically-activated oncogenic pathways in melanoma [##REF##32620791##230##]. However, this effect may be melanoma specific, as glutamine would fuel tumor cell proliferation in most tumors.</p>\",\n \"<p id=\\\"Par75\\\">Amino acid dietary restriction also affects tumor development by affecting immune cell function. Diet lack of sulfur-containing amino acids like methionine and cysteine downregulates the differentiation of CD4<sup>+</sup> T cells towards Tregs and promotes the migration of CD8<sup>+</sup> T cells, increasing the ratio of CD8<sup>+</sup>/Treg in tumors and thereby enhancing the immune killing function [##REF##36525473##231##]. Dietary methionine restriction blocks cGAS methylation, releasing cGAS from chromatin and promoting it into cytoplasm [##REF##37267951##232##]. Cytoplasm cGAS would sense dsDNA and activate the immune response, leading to tumor restriction. The effect of tumor immunotherapy is significantly enhanced by restricting the dose of amino acids in diet.</p>\",\n \"<title>Summary</title>\",\n \"<p id=\\\"Par76\\\">During the process of proliferation, tumor cells dysregulate their metabolism to obtain more energy and nutrients. Amino acids are used as fuels and raw materials in protein synthesis as well as signal molecules in energy metabolism, participating in tumor growth process. In addition, amino acid metabolism also intertwines with signaling pathways, TME and epigenetic modification. Amino acids act as pivotal signaling molecules, stimulating signal pathways like mTORC1, MYC and KRAS to drive tumor growth and proliferation. Within the TME, a dynamic interplay occurs as nutrient competition and amino acid availability dictate tumor progression. Tumor and immune cells collaborate to regulate amino acid metabolism, which is crucial for sustaining the metabolic needs of proliferating tumor cells and sculpting the immune response. Moreover, amino acid-derived metabolites like α-KG, acetyl-CoA, NAD<sup>+</sup> and SAM wield influence over epigenetic regulation. Comprehending this metabolism network provides foundation for targeted therapeutic approaches which aim to disrupt amino acid metabolism.</p>\",\n \"<p id=\\\"Par77\\\">Compared with normal cells, tumor cells have more emphasis on amino acid requirement and dependence. Therefore, targeting amino acid metabolism to treat tumors is more effective and less damage. However, the field still faces many challenges. Some drugs targeting amino acid metabolism have shown promising effects in animal experiments but realize them clinically is still difficult. Insufficient amino acid depletion may merely maintain tumor cells in a state of cell quiescence, and once treatment is terminated, tumor cells will reappear. The effect of amino acid intervention on tumor development is not only related to tumor type but also depends on external factors even tumor location [##REF##31484055##233##]. Since most metabolism inhibitors are not effective as single agents, combination therapy may be a more reasonable strategy. Nevertheless, compared with other therapies, amino acid depletion therapy is safer to normal cells. It is hoped that with the wider understanding of amino acid metabolism in tumor and the advancement of metabolic analysis techniques, appropriate patients can be identified. Thus, fully understand of the metabolic flexibility in amino acid metabolism in cancer cells is of great significance, which provides further insight into metabolic dependences and liabilities that can be exploited therapeutically.</p>\",\n \"<title>Supplementary information</title>\",\n \"<p>\\n\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary information</title>","<p>The online version contains supplementary material available at 10.1038/s41419-024-06435-w.</p>","<title>Author contributions</title>","<p>Conceptualization: SX Compilation of literature: JC and LC. Article writing and editing: JC and LC. Figures: JC and LC. Supervision: JC and SL. All authors read and approved the final manuscript.</p>","<title>Funding</title>","<p>This work was funded by the National Natural Science Foundation of China (82071789, 31870910), Peak Disciplines (Type IV) of Institutions of Higher Learning in Shanghai, and Key Basic Research Projects (2021-JCJQ-ZD-077-11).</p>","<title>Competing interests</title>","<p id=\"Par78\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41419-024-06435-w.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>Conceptualization: SX Compilation of literature: JC and LC. Article writing and editing: JC and LC. Figures: JC and LC. Supervision: JC and SL. All authors read and approved the final manuscript.</p>\",\n \"<title>Funding</title>\",\n \"<p>This work was funded by the National Natural Science Foundation of China (82071789, 31870910), Peak Disciplines (Type IV) of Institutions of Higher Learning in Shanghai, and Key Basic Research Projects (2021-JCJQ-ZD-077-11).</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par78\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Amino acid metabolism in tumors.</title><p>ASCT2 serves as the primary glutamine transporter, importing glutamine to generate purines and pyrimidines. GLS1 catalyzes glutaminolysis, generating glutamate which synthesizes GSH to maintain redox balance. Glutamate further transfers to α-KG under the catalyze of transaminase or deaminase. Under glucose-limited condition, glutamate derived α-KG acts as an alternative for glucose through participating in TCA cycle. Elevated PCK2 prompts PEP production to fuel TCA cycle. α-KG also act as substrate for DNA dioxygenase in demethylation. LAT1/2 transports BCAAs intracellular, activating mTOR activity and producing BCKA under the catalyze of BCAT2. BCKA can be further catalyzed to acetyl-CoA and succinyl-CoA to fuel TCA cycle. Arginine can be obtained through CAT family transporters, and synthesized de novo under the catalysis of ASS1 and ASL from citrulline in urea cycle. In tumors, ornithine is metabolized by upregulated ODC into polyamines including putrescine, spermidine, and spermine. The overproduction of polyamines results in uncontrolled tumor growth. Besides, NO production under the catalyze of NOS-2 elevates angiogenesis and suppress immune. De novo serine synthesis is catalyzed by PHGDH, PSAT and PSPH. SHMT2 catalyzes glycine and 5,10-methylene-THF production, in which glycine can also be transferred to 5,10-methylene-THF under the catalyze of GLDC, participating in folate cycle and methionine cycle. Besides, glycine also directly supplies carbon for de novo purine biosynthesis. Tryptophan is mainly catalyzed by IDO1 and TDO to produce kynurenine, and finally generates NAD<sup>+</sup> and alanine to inhibit immune response and promote cancer progression. Seldom tryptophan metabolizes along the 5-HT and indole pathway, also function to suppress immune response. GLS1 Glutaminase1, GS Glutamine synthetase, GOT Glutamic oxaloacetic transaminase, GPT Glutamic pyruvic transaminase, GDH Glutamate dehydrogenase, α-KG α-ketoglutarate, OAA Oxaloacetic acid, PCK2 Phosphoenolpyruvate carboxykinase2, PEP Phosphoenolpyruvate, CP Carbamoyl phosphate, PRA Phosphoribosyl amine, ASNS Asparagine synthase, ASNase Asparaginase, CPS carbamoyl phosphate synthase, ATC Aspartate transcarbamylase, DHO Dihydroorotase, ASS1 Argininosuccinate synthase1, ASL Argininosuccinate lyase, ARG1 Arginase1, NOS-2 Nitric oxide synthase-2, ODC Ornithine decarboxylase, SPDS Spermidine synthase, SPMS Spermine synthase, 3PG 3-phosphoglyceric acid, 3PHP 3-phosphohydroxypyruvate, 3PS 3-phosphoserine, PHGDH Phosphoglycerate dehydrogenase, PSAT Phosphoserine aminotransferase, PSPH Phosphoserine phosphatase, SHMT2 Serine hydroxymethyltransferase2, GLDC Glycine dehydrogenase, MTHFD2 Methylenetetrahydrofolate dehydrogenase2, THF Tetrahydrofolate, SAM S-adenosyl methionine, IDO Indoleamine 2,3-dioxygenase, TDO Tryptophan 2,3-dioxygenase, AFMID arylformamidase, KMO Kynurenine 3-monooxygenase, KYNU Kynureninase, TPH Tryptophan hydroxylase, Gln Glutamine, Glu Glutamate, Val Valine, Ile Ileucine, Leu Leucine, Asp Asparate, Asn Asparagine, Arg Arginine, Ser Serine, Gly Glycine, Met Methionine, Trp Tryptophan.</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Signal pathways in amino acid metabolism.</title><p><bold>a</bold> mTOR signaling and amino acid metabolism. When amino acids are insufficient, GCN2 is activated by the uncharged tRNA and thereby leading to the activation of eIF2α and subsequently the upregulation of sestrins expression to regulate mTORC1 activity. Sestrins and SAR1B could be activated under leucine deprivation circumstance. After activation, sestrins and SAR1B bind and inhibit GATOR2, a positive regulator of mTORC1. In arginine-depleted condition, CASTOR1/2 forms dimers to inhibit mTORC1 activity by downregulating GATOR2 and upregulating GATOR1. SAMTOR senses methionine concentration in the form of SAM. When the concentration of methionine is down regulated, SAMTOR binds GATOR1 to inhibit mTORC1. LARS senses leucine concentration and interacts with Rag by directly mediating leucylation of RagA/B to activate mTORC1. Rag promotes mTORC1 to locate to lysosome which contains its activator Rheb. Ragulator provides a platform for lysosome to tether Rag. Besides, SLC38A9 participates in the activation of mTORC1 by influencing Rag. After activation, mTORC1 activates ATF4, stimulating serine synthesis and amino acids uptake. mTORC1 also promotes glutamine anaplerosis through regulating CREB2, SIRT4 and GDH. Furthermore, mTORC1 triggers phosphorylate 4EBP-1 and S6K1 to regulate protein synthesis and MYC translation. <bold>b</bold> MYC signaling and amino acid metabolism. MYC upregulates SLC7A5 and SLC43A1 to transport EAAs. These EAAs activate mTORC1 and Myc, forming a positive feedback loop. Meanwhile, SLC1A5 and SLC38A5 are also upregulated by MYC to transport NEAAs. Specifically, MYC promotes the conversion of tryptophan to kynurenine through inducing AFMID in the kynurenine pathway. MYC also upregulates GS to promote glutamine synthesis. Besides, MYC participates in glutamine catabolism by inducing miR-23a/b to suppress the expression of GLS. MYC induces P5CS and PYCR to promote proline synthesis. Meanwhile, it increases miR-23b to decrease the expression of POX/PRODH to inhibit proline catabolism. MYC upregulates BCAT1 and increases BCAAs synthesis. In addition, MYC can promote serine synthesis from glucose. It also upregulates SHMT2 to metabolize serine, producing glycine to participate in GSH production and 5,10-mTHF to participate in purine synthesis. <bold>c</bold> KRAS signaling and amino acid metabolism. Binding of KRAS-GTP to RAF stimulates its dimerization and activation, triggering the activation of MEK and ERK to drive cell cycle progression and proliferation. Binding of KRAS-GTP to PI3K activates AKT and mTOR, regulating apoptosis, metabolism and translation. Macropincytosis maintains intracellular glutamine levels following <italic>Kras</italic> activation. KRAS downregulates GLUD1, leading to alterations in glutamine metabolism to produce NEAAs instead of participating in TCA cycle. KRAS not only upregulates GOT1 but also upregulates GOT2, catabolizing glutamine-derived aspartate in mitochondria and aspartate into OAA in the cytoplasm and finally converted to pyruvate, resulting GSH production to maintain the cellular redox balance. mTOR Mammalian target of rapamycin, GCN2 General control nonderepressible2, LARS Leucyl-tRNA synthetase, S6K1 P70 ribosomal S6 kinase 1, 4EBP1 4E binding protein 1, SREBP Sterol regulatory element binding protein, CREB2 cAMP-responsive element binding 2, POX Proline oxidase, P5CS P5C synthase, PYCR P5C reductase, P5C Pyrroline-5-carboxylate, BCKA Branched chain α-keto acid, GSH Glutathione, GSSG Glutathione disulfide, GEFs Guanine nucleotide exchange factors, GAPs GTPase-activating proteins, GLUD1 Glutamate dehydrogenase1 EAAs Essential amino acids, NEAAs Non-essential amino acids, Pro Proline, Gly Glycine.</p></caption></fig>","<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Metabolic relationship between immune cells and cancer cells in TME.</title><p><bold>a</bold> Tumor macropinocytosis and tumor interaction with CAFs. Collagen fragments secreted by CAFs are presented in the TME, and are taken up by tumor cells through macropinocytosis and catabolized to support tumor cells survival. CAFs supply aspartate to cancer cells to synthesis DNA and protein via SLC1A3 while cancer cells in turn secrete glutamine-derived glutamate to CAFs via SLC1A3 to generate GSH and therefore maintain the redox state. In CAFs, both glutamine and arginine facilitates proline synthesis and ECM secretion. <bold>b</bold> Reprogramed amino acid metabolism in MDSCs inhibits T cell and NK cell. MDSCs highly express ARG1 to deprive arginine in TME, impairing T cell-mediated anti-tumor immunity. MDSCs express x<sub>c</sub><sup>-</sup> transporter and import cystine, causing depleted cysteine and impaired T cell function. Overexpression of IDO in MDSCs deprives tryptophan, leading to T cell stagnating in G0 and NK cell apoptosis. The differentiation of naive T cells into Th1 or Th17 cells depends on glutaminolysis. This is because lacked glutamine promotes Th1 cells while inhibits Th17 cells. MDSCs also highly express NOS-2, producing NO to impair anti-tumor effect of T cells. <bold>c</bold> Reprogramed amino acid metabolism in TAMs polarizes macrophage and suppresses T cell. The expression of GS in TAMs increased, promoting glutamine synthesis and M2-like macrophage polarization. TAMs also overexpress IDO to produce kynurenine, blocking T cell proliferation and inducing T cell death. ARG1 overexpression depletes arginine and thereby suppresses T cell function. It is worth noting that NOS-2 is also upregulated to produce NO and suppress T cell function. <bold>d</bold> Reprogrammed glutamine and tryptophan metabolism in DC influences T cell. Upregulated IDO expression in DC induces kynurenine production and tryptophan depletion as well as the activation of AhR and GCN2 respectively, leading to CD8<sup>+</sup> T cell dysfunction and Treg differentiation. DC and tumor cells both express SLC38A2 to mediate glutamine uptake to tune anti-tumor immunity. Glutamine in DC promotes the formation of the FLCN-FNIP2 complex and thereby restricts TFEB activity, promoting antigen cross-presentation and T cell immunity. Glutaminolysis underlies asymmetric T cell division. The daughter T cell proximal to the APC is accumulated with glutamine transporters and adopts an effector-like fate, while the distal T cell absence of glutamine transporters assumes a memory-like fate. CAFs Cancer-associated fibroblasts, ECM Extracellular matrix, MDSC Myeloid-derived suppressor cell, TAM Tumor-associated macrophage, GCN2 General control nonderepressible2, AhR Aryl hydrocarbon receptor, Kyn Kynurenine, DC Dendritic cell, NK Natural killer cell, Cys Cysteine.</p></caption></fig>","<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Amino acid metabolism and epigenetic modification.</title><p>Tumors often overexpress SLC7A5 to obtain methionine, competing with T cell to inhibit T cell function as well as participating in methionine cycle to synthesis SAM. The downregulation of glycine or the knock down of GNMTs in the methionine cycle reduces the consumption of SAM and thus promotes the methylation of histone by HMT and DNA by DNMT. Serine fuels folate cycle under the catalyze of SHMT2 and produces glycine. Glycine can also be metabolized to produce 5,10-mTHF to fuel the folate cycle. Threonine depletion from the culture medium decreases folate cycle and the levels of SAM as it serves as the precursor for 5-mTHF synthesis. Mutated IDH, in contrast with common IDH, generates 2-HG which suppresses DNA demethylation by KDMs, leading to a hypermethylation phenotype. Isoleucine and leucine catabolism directly generates acetyl-CoA in the mitochondria. Alanine, cystine, glycine and threonine convert to pyruvate to synthesis acetyl-CoA. Lysine, phenylalanine, tryptophan and tyrosine convert to acetoacetyl-CoA to synthesis acetyl-CoA. Collectively, above amino acids derived acetyl-CoA donates acetyl groups to modify histone proteins, which is catalyzed by HATs. An alternative pathway for isoleucine and leucine to activate mTORC1 involves providing acetyl-CoA to the EP300 acetyltransferase. This enzyme mediates inhibitory acetylation of the mTORC1 regulator Raptor, ultimately leading to mTORC1 activation. Deacetylation refers to acetyl groups remove from histones catalyzed by HDAC. The sirtuin proteins are classified as class III HDACs due to their dependence on NAD<sup>+</sup> for catalysis, in contrast to the zinc-dependent catalysis observed in class I, II, and IV enzymes. The tryptophan metabolism and NAD<sup>+</sup>/NADH ratio exhibits a close correlation with both the acetylation state and energy status. SAH S-Adenosyl-homocysteine, GNMTs glycine N-methyltransferase, DNMTs DNA methyltransferases, HMT Histone methyltransferase, 2-HG 2-hydroxyglutaric acid, IDH Isocitrate dehydrogenase, HATs Histone acetyltransferases, Thr Threonine, Ile Isoleucine.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><title>Amino acid metabolism in tumors.</title><p>ASCT2 serves as the primary glutamine transporter, importing glutamine to generate purines and pyrimidines. GLS1 catalyzes glutaminolysis, generating glutamate which synthesizes GSH to maintain redox balance. Glutamate further transfers to α-KG under the catalyze of transaminase or deaminase. Under glucose-limited condition, glutamate derived α-KG acts as an alternative for glucose through participating in TCA cycle. Elevated PCK2 prompts PEP production to fuel TCA cycle. α-KG also act as substrate for DNA dioxygenase in demethylation. LAT1/2 transports BCAAs intracellular, activating mTOR activity and producing BCKA under the catalyze of BCAT2. BCKA can be further catalyzed to acetyl-CoA and succinyl-CoA to fuel TCA cycle. Arginine can be obtained through CAT family transporters, and synthesized de novo under the catalysis of ASS1 and ASL from citrulline in urea cycle. In tumors, ornithine is metabolized by upregulated ODC into polyamines including putrescine, spermidine, and spermine. The overproduction of polyamines results in uncontrolled tumor growth. Besides, NO production under the catalyze of NOS-2 elevates angiogenesis and suppress immune. De novo serine synthesis is catalyzed by PHGDH, PSAT and PSPH. SHMT2 catalyzes glycine and 5,10-methylene-THF production, in which glycine can also be transferred to 5,10-methylene-THF under the catalyze of GLDC, participating in folate cycle and methionine cycle. Besides, glycine also directly supplies carbon for de novo purine biosynthesis. Tryptophan is mainly catalyzed by IDO1 and TDO to produce kynurenine, and finally generates NAD<sup>+</sup> and alanine to inhibit immune response and promote cancer progression. Seldom tryptophan metabolizes along the 5-HT and indole pathway, also function to suppress immune response. GLS1 Glutaminase1, GS Glutamine synthetase, GOT Glutamic oxaloacetic transaminase, GPT Glutamic pyruvic transaminase, GDH Glutamate dehydrogenase, α-KG α-ketoglutarate, OAA Oxaloacetic acid, PCK2 Phosphoenolpyruvate carboxykinase2, PEP Phosphoenolpyruvate, CP Carbamoyl phosphate, PRA Phosphoribosyl amine, ASNS Asparagine synthase, ASNase Asparaginase, CPS carbamoyl phosphate synthase, ATC Aspartate transcarbamylase, DHO Dihydroorotase, ASS1 Argininosuccinate synthase1, ASL Argininosuccinate lyase, ARG1 Arginase1, NOS-2 Nitric oxide synthase-2, ODC Ornithine decarboxylase, SPDS Spermidine synthase, SPMS Spermine synthase, 3PG 3-phosphoglyceric acid, 3PHP 3-phosphohydroxypyruvate, 3PS 3-phosphoserine, PHGDH Phosphoglycerate dehydrogenase, PSAT Phosphoserine aminotransferase, PSPH Phosphoserine phosphatase, SHMT2 Serine hydroxymethyltransferase2, GLDC Glycine dehydrogenase, MTHFD2 Methylenetetrahydrofolate dehydrogenase2, THF Tetrahydrofolate, SAM S-adenosyl methionine, IDO Indoleamine 2,3-dioxygenase, TDO Tryptophan 2,3-dioxygenase, AFMID arylformamidase, KMO Kynurenine 3-monooxygenase, KYNU Kynureninase, TPH Tryptophan hydroxylase, Gln Glutamine, Glu Glutamate, Val Valine, Ile Ileucine, Leu Leucine, Asp Asparate, Asn Asparagine, Arg Arginine, Ser Serine, Gly Glycine, Met Methionine, Trp Tryptophan.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><title>Signal pathways in amino acid metabolism.</title><p><bold>a</bold> mTOR signaling and amino acid metabolism. When amino acids are insufficient, GCN2 is activated by the uncharged tRNA and thereby leading to the activation of eIF2α and subsequently the upregulation of sestrins expression to regulate mTORC1 activity. Sestrins and SAR1B could be activated under leucine deprivation circumstance. After activation, sestrins and SAR1B bind and inhibit GATOR2, a positive regulator of mTORC1. In arginine-depleted condition, CASTOR1/2 forms dimers to inhibit mTORC1 activity by downregulating GATOR2 and upregulating GATOR1. SAMTOR senses methionine concentration in the form of SAM. When the concentration of methionine is down regulated, SAMTOR binds GATOR1 to inhibit mTORC1. LARS senses leucine concentration and interacts with Rag by directly mediating leucylation of RagA/B to activate mTORC1. Rag promotes mTORC1 to locate to lysosome which contains its activator Rheb. Ragulator provides a platform for lysosome to tether Rag. Besides, SLC38A9 participates in the activation of mTORC1 by influencing Rag. After activation, mTORC1 activates ATF4, stimulating serine synthesis and amino acids uptake. mTORC1 also promotes glutamine anaplerosis through regulating CREB2, SIRT4 and GDH. Furthermore, mTORC1 triggers phosphorylate 4EBP-1 and S6K1 to regulate protein synthesis and MYC translation. <bold>b</bold> MYC signaling and amino acid metabolism. MYC upregulates SLC7A5 and SLC43A1 to transport EAAs. These EAAs activate mTORC1 and Myc, forming a positive feedback loop. Meanwhile, SLC1A5 and SLC38A5 are also upregulated by MYC to transport NEAAs. Specifically, MYC promotes the conversion of tryptophan to kynurenine through inducing AFMID in the kynurenine pathway. MYC also upregulates GS to promote glutamine synthesis. Besides, MYC participates in glutamine catabolism by inducing miR-23a/b to suppress the expression of GLS. MYC induces P5CS and PYCR to promote proline synthesis. Meanwhile, it increases miR-23b to decrease the expression of POX/PRODH to inhibit proline catabolism. MYC upregulates BCAT1 and increases BCAAs synthesis. In addition, MYC can promote serine synthesis from glucose. It also upregulates SHMT2 to metabolize serine, producing glycine to participate in GSH production and 5,10-mTHF to participate in purine synthesis. <bold>c</bold> KRAS signaling and amino acid metabolism. Binding of KRAS-GTP to RAF stimulates its dimerization and activation, triggering the activation of MEK and ERK to drive cell cycle progression and proliferation. Binding of KRAS-GTP to PI3K activates AKT and mTOR, regulating apoptosis, metabolism and translation. Macropincytosis maintains intracellular glutamine levels following <italic>Kras</italic> activation. KRAS downregulates GLUD1, leading to alterations in glutamine metabolism to produce NEAAs instead of participating in TCA cycle. KRAS not only upregulates GOT1 but also upregulates GOT2, catabolizing glutamine-derived aspartate in mitochondria and aspartate into OAA in the cytoplasm and finally converted to pyruvate, resulting GSH production to maintain the cellular redox balance. mTOR Mammalian target of rapamycin, GCN2 General control nonderepressible2, LARS Leucyl-tRNA synthetase, S6K1 P70 ribosomal S6 kinase 1, 4EBP1 4E binding protein 1, SREBP Sterol regulatory element binding protein, CREB2 cAMP-responsive element binding 2, POX Proline oxidase, P5CS P5C synthase, PYCR P5C reductase, P5C Pyrroline-5-carboxylate, BCKA Branched chain α-keto acid, GSH Glutathione, GSSG Glutathione disulfide, GEFs Guanine nucleotide exchange factors, GAPs GTPase-activating proteins, GLUD1 Glutamate dehydrogenase1 EAAs Essential amino acids, NEAAs Non-essential amino acids, Pro Proline, Gly Glycine.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Fig. 3</label><caption><title>Metabolic relationship between immune cells and cancer cells in TME.</title><p><bold>a</bold> Tumor macropinocytosis and tumor interaction with CAFs. Collagen fragments secreted by CAFs are presented in the TME, and are taken up by tumor cells through macropinocytosis and catabolized to support tumor cells survival. CAFs supply aspartate to cancer cells to synthesis DNA and protein via SLC1A3 while cancer cells in turn secrete glutamine-derived glutamate to CAFs via SLC1A3 to generate GSH and therefore maintain the redox state. In CAFs, both glutamine and arginine facilitates proline synthesis and ECM secretion. <bold>b</bold> Reprogramed amino acid metabolism in MDSCs inhibits T cell and NK cell. MDSCs highly express ARG1 to deprive arginine in TME, impairing T cell-mediated anti-tumor immunity. MDSCs express x<sub>c</sub><sup>-</sup> transporter and import cystine, causing depleted cysteine and impaired T cell function. Overexpression of IDO in MDSCs deprives tryptophan, leading to T cell stagnating in G0 and NK cell apoptosis. The differentiation of naive T cells into Th1 or Th17 cells depends on glutaminolysis. This is because lacked glutamine promotes Th1 cells while inhibits Th17 cells. MDSCs also highly express NOS-2, producing NO to impair anti-tumor effect of T cells. <bold>c</bold> Reprogramed amino acid metabolism in TAMs polarizes macrophage and suppresses T cell. The expression of GS in TAMs increased, promoting glutamine synthesis and M2-like macrophage polarization. TAMs also overexpress IDO to produce kynurenine, blocking T cell proliferation and inducing T cell death. ARG1 overexpression depletes arginine and thereby suppresses T cell function. It is worth noting that NOS-2 is also upregulated to produce NO and suppress T cell function. <bold>d</bold> Reprogrammed glutamine and tryptophan metabolism in DC influences T cell. Upregulated IDO expression in DC induces kynurenine production and tryptophan depletion as well as the activation of AhR and GCN2 respectively, leading to CD8<sup>+</sup> T cell dysfunction and Treg differentiation. DC and tumor cells both express SLC38A2 to mediate glutamine uptake to tune anti-tumor immunity. Glutamine in DC promotes the formation of the FLCN-FNIP2 complex and thereby restricts TFEB activity, promoting antigen cross-presentation and T cell immunity. Glutaminolysis underlies asymmetric T cell division. The daughter T cell proximal to the APC is accumulated with glutamine transporters and adopts an effector-like fate, while the distal T cell absence of glutamine transporters assumes a memory-like fate. CAFs Cancer-associated fibroblasts, ECM Extracellular matrix, MDSC Myeloid-derived suppressor cell, TAM Tumor-associated macrophage, GCN2 General control nonderepressible2, AhR Aryl hydrocarbon receptor, Kyn Kynurenine, DC Dendritic cell, NK Natural killer cell, Cys Cysteine.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Fig. 4</label><caption><title>Amino acid metabolism and epigenetic modification.</title><p>Tumors often overexpress SLC7A5 to obtain methionine, competing with T cell to inhibit T cell function as well as participating in methionine cycle to synthesis SAM. The downregulation of glycine or the knock down of GNMTs in the methionine cycle reduces the consumption of SAM and thus promotes the methylation of histone by HMT and DNA by DNMT. Serine fuels folate cycle under the catalyze of SHMT2 and produces glycine. Glycine can also be metabolized to produce 5,10-mTHF to fuel the folate cycle. Threonine depletion from the culture medium decreases folate cycle and the levels of SAM as it serves as the precursor for 5-mTHF synthesis. Mutated IDH, in contrast with common IDH, generates 2-HG which suppresses DNA demethylation by KDMs, leading to a hypermethylation phenotype. Isoleucine and leucine catabolism directly generates acetyl-CoA in the mitochondria. Alanine, cystine, glycine and threonine convert to pyruvate to synthesis acetyl-CoA. Lysine, phenylalanine, tryptophan and tyrosine convert to acetoacetyl-CoA to synthesis acetyl-CoA. Collectively, above amino acids derived acetyl-CoA donates acetyl groups to modify histone proteins, which is catalyzed by HATs. An alternative pathway for isoleucine and leucine to activate mTORC1 involves providing acetyl-CoA to the EP300 acetyltransferase. This enzyme mediates inhibitory acetylation of the mTORC1 regulator Raptor, ultimately leading to mTORC1 activation. Deacetylation refers to acetyl groups remove from histones catalyzed by HDAC. The sirtuin proteins are classified as class III HDACs due to their dependence on NAD<sup>+</sup> for catalysis, in contrast to the zinc-dependent catalysis observed in class I, II, and IV enzymes. The tryptophan metabolism and NAD<sup>+</sup>/NADH ratio exhibits a close correlation with both the acetylation state and energy status. SAH S-Adenosyl-homocysteine, GNMTs glycine N-methyltransferase, DNMTs DNA methyltransferases, HMT Histone methyltransferase, 2-HG 2-hydroxyglutaric acid, IDH Isocitrate dehydrogenase, HATs Histone acetyltransferases, Thr Threonine, Ile Isoleucine.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":["<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Key enzymes/transporters and their drugs in cancer amino acid metabolism.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>Target</th><th>Drug</th><th>Cancer type</th><th>Clinical phases</th></tr></thead><tbody><tr><td>Amino acid transporters</td><td>ASCT2</td><td>Tamoxifen and Raloxifene</td><td>Breast cancer</td><td>Approved</td></tr><tr><td/><td>ASCT2</td><td>PGS-siRNA</td><td>NSCLC</td><td>Preclinical</td></tr><tr><td/><td>ASCT2</td><td>V-9302</td><td>HCC</td><td>Preclinical</td></tr><tr><td/><td>CATs</td><td>verapamil</td><td>Colorectal cancer</td><td>Preclinical</td></tr><tr><td/><td>SLC6A14</td><td><p>Aspartate-modified</p><p>docetaxel-loading stealth liposomes</p></td><td>Lung cancer</td><td>Preclinical</td></tr><tr><td><p>Asparagine</p><p>metabolism</p></td><td>ASNS</td><td>metformin</td><td>Multiple cancers</td><td>Preclinical</td></tr><tr><td/><td/><td>Asparlas</td><td>Acute lymphoblastic leukemia</td><td>Approved</td></tr><tr><td><p>Arginine</p><p>metabolism</p></td><td>ADI</td><td>ADI-PEG20</td><td>Ovarian cancer</td><td>Phase III</td></tr><tr><td/><td/><td>INCB001158</td><td>Advanced or metastatic solid tumors</td><td>Phase II</td></tr><tr><td><p>Glutamine</p><p>metabolism</p></td><td>GLS</td><td>Telaglenastat</td><td>NSCLC, lymphoma, glioma, breast cancer, pancreatic cancer, and kidney cancer [##REF##33361272##234##]</td><td>Phase II</td></tr><tr><td/><td>GLS1</td><td>968</td><td>NSCLC [##REF##28039459##235##]</td><td>Preclinical</td></tr><tr><td>One-carbon metabolism</td><td>DHFR</td><td>Methotrexate</td><td>Multiple cancers</td><td>Approved</td></tr><tr><td/><td>Thymidylate synthase</td><td>5-fluorouracil</td><td>Multiple cancers</td><td>Approved</td></tr></tbody></table></table-wrap>"],"string":"[\n \"<table-wrap id=\\\"Tab1\\\"><label>Table 1</label><caption><p>Key enzymes/transporters and their drugs in cancer amino acid metabolism.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th/><th>Target</th><th>Drug</th><th>Cancer type</th><th>Clinical phases</th></tr></thead><tbody><tr><td>Amino acid transporters</td><td>ASCT2</td><td>Tamoxifen and Raloxifene</td><td>Breast cancer</td><td>Approved</td></tr><tr><td/><td>ASCT2</td><td>PGS-siRNA</td><td>NSCLC</td><td>Preclinical</td></tr><tr><td/><td>ASCT2</td><td>V-9302</td><td>HCC</td><td>Preclinical</td></tr><tr><td/><td>CATs</td><td>verapamil</td><td>Colorectal cancer</td><td>Preclinical</td></tr><tr><td/><td>SLC6A14</td><td><p>Aspartate-modified</p><p>docetaxel-loading stealth liposomes</p></td><td>Lung cancer</td><td>Preclinical</td></tr><tr><td><p>Asparagine</p><p>metabolism</p></td><td>ASNS</td><td>metformin</td><td>Multiple cancers</td><td>Preclinical</td></tr><tr><td/><td/><td>Asparlas</td><td>Acute lymphoblastic leukemia</td><td>Approved</td></tr><tr><td><p>Arginine</p><p>metabolism</p></td><td>ADI</td><td>ADI-PEG20</td><td>Ovarian cancer</td><td>Phase III</td></tr><tr><td/><td/><td>INCB001158</td><td>Advanced or metastatic solid tumors</td><td>Phase II</td></tr><tr><td><p>Glutamine</p><p>metabolism</p></td><td>GLS</td><td>Telaglenastat</td><td>NSCLC, lymphoma, glioma, breast cancer, pancreatic cancer, and kidney cancer [##REF##33361272##234##]</td><td>Phase II</td></tr><tr><td/><td>GLS1</td><td>968</td><td>NSCLC [##REF##28039459##235##]</td><td>Preclinical</td></tr><tr><td>One-carbon metabolism</td><td>DHFR</td><td>Methotrexate</td><td>Multiple cancers</td><td>Approved</td></tr><tr><td/><td>Thymidylate synthase</td><td>5-fluorouracil</td><td>Multiple cancers</td><td>Approved</td></tr></tbody></table></table-wrap>\"\n]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":["<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"],"string":"[\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM1\\\"></supplementary-material>\"\n]","isConversational":false},"footnote":{"kind":"list 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Drugs\"], \"year\": [\"2021\"], \"volume\": [\"39\"], \"fpage\": [\"1671\"], \"lpage\": [\"81\"], \"pub-id\": [\"10.1007/s10637-021-01144-z\"]}, {\"label\": [\"132.\"], \"surname\": [\"Chakrabarti\", \"Gerber\", \"Boothman\"], \"given-names\": [\"G\", \"DE\", \"DA\"], \"article-title\": [\"Expanding antitumor therapeutic windows by targeting cancer-specific nicotinamide adenine dinucleotide phosphate-biogenesis pathways\"], \"source\": [\"Clin Pharm\"], \"year\": [\"2015\"], \"volume\": [\"7\"], \"fpage\": [\"57\"], \"lpage\": [\"68\"]}, {\"label\": [\"137.\"], \"surname\": [\"Recouvreux\", \"Commisso\"], \"given-names\": [\"MV\", \"C\"], \"article-title\": [\"Macropinocytosis: a metabolic adaptation to nutrient stress in cancer\"], \"source\": [\"Front Endocrinol\"], \"year\": [\"2017\"], \"volume\": [\"8\"], \"fpage\": [\"261\"], \"pub-id\": [\"10.3389/fendo.2017.00261\"]}, {\"label\": [\"217.\"], \"surname\": [\"Avramis\", \"Tiwari\"], \"given-names\": [\"VI\", \"PN\"], \"article-title\": [\"Asparaginase (native ASNase or pegylated ASNase) in the treatment of acute lymphoblastic leukemia\"], \"source\": [\"Int J Nanomed\"], \"year\": [\"2006\"], \"volume\": [\"1\"], \"fpage\": [\"241\"], \"lpage\": [\"54\"]}, {\"label\": [\"222.\"], \"surname\": [\"Holtsberg\", \"Ensor\", \"Steiner\", \"Bomalaski\", \"Clark\"], \"given-names\": [\"FW\", \"CM\", \"MR\", \"JS\", \"MA\"], \"article-title\": [\"Poly(ethylene glycol) (PEG) conjugated arginine deiminase: effects of PEG formulations on its pharmacological properties\"], \"source\": [\"J Controlled Release\"], \"year\": [\"2002\"], \"volume\": [\"80\"], \"fpage\": [\"259\"], \"lpage\": [\"71\"], \"pub-id\": [\"10.1016/S0168-3659(02)00042-1\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":235,"string":"235"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:57"},"citation":{"kind":"string","value":"Cell Death Dis. 2024 Jan 13; 15(1):42"},"package_file":{"kind":"string","value":"oa_package/c5/18/PMC10787762.tar.gz"}}},{"rowIdx":93,"cells":{"accession_id":{"kind":"string","value":"PMC10787763"},"pmid":{"kind":"string","value":"38218974"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par2\">Biocatalysts have long promised to play a major role in synthetic organic chemistry, offering potentially greener routes to high-value chemicals<sup>##UREF##0##1##,##REF##11196655##2##</sup>. However, their highly-valued specificity and selectivity also leads to limitations in their exploitation as they are more difficult to adapt to new substrates than traditional catalysts<sup>##REF##15929154##3##</sup>. The challenge is particularly acute for enzymes that use two substrates as their interactions with the enzyme can often occur at overlapping sites. As a result, few studies have reported the engineering of enzymes to modify both substrate specificities to access new products<sup>##REF##31647171##4##,##REF##25765309##5##</sup>.</p>","<p id=\"Par3\">The formation of asymmetric carbon–carbon bonds is a critical route in organic synthesis to access a variety of innovative and natural compounds that can be used as building blocks in additional synthesis<sup>##REF##21354781##6##–##REF##21417217##8##</sup>. Due to their great selectivity and specificity, asymmetric carbon–carbon bond-forming enzymes like transketolase (TK) (EC 2.2.1.1) have significant synthetic potential<sup>##REF##14662384##9##,##REF##16614860##10##</sup>. TK catalyses the reversible transfer of a C<sub>2</sub>-ketol unit from donor substrate <sc>d</sc>-xylulose-5-phosphate to aldose acceptor substrates of either <sc>d</sc>-ribose-5-phosphate or <sc>d</sc>-erythrose-4-phosphate in the pentose phosphate pathway of all cells<sup>##REF##7607225##11##,##UREF##1##12##</sup>. This metabolic pathway is critical in the cellular production of nucleotides, amino acids, and fatty acids.</p>","<p id=\"Par4\">In recent years, TK variants have been demonstrated in the synthesis of complex carbohydrates and other high-value compounds, including <sc>l</sc>-erythrulose<sup>##UREF##2##13##</sup>, deoxysugars<sup>##UREF##3##14##</sup>, <italic>N</italic>-Aryl Hydroxamic Acids<sup>##UREF##4##15##</sup>, 7-keto-octuronic acid<sup>##REF##34250527##16##</sup>. At large scale, TK has been used for the production of unusual sugars<sup>##REF##11102785##17##</sup>. Wild-type TK enzymes tend to accept a relatively limited range of hydroxyaldehyde aldol-acceptors with strict (2<italic>R</italic>)-specificity<sup>##REF##11102785##17##,##REF##9924800##18##</sup>. Donor substrates are similarly limited as they require an oxo group adjacent to the scissile C–C bond, and also typically a C-1 hydroxyl group. Furthermore, they tend to prefer donor substrates with a D-threo configuration of C-3 and C-4 hydroxyls<sup>##REF##11278369##19##</sup>. Wild-type TK enzymes also have a preference for substrates that are phosphorylated due to complementary positively charged side-chains at the active site entrance<sup>##REF##11278369##19##–##REF##17825449##21##</sup>. However, this is not an absolute requirement, and TK can accept β-hydroxypyruvate (HPA) as the ketol donor which is advantageous in biocatalysis as it renders the donor half-reaction quasi-irreversible through liberation of CO<sub>2</sub>. This has been particularly useful for <italic>E. coli</italic> TK which gives up to 30-fold higher specific activity using HPA compared to yeast and spinach orthologs<sup>##UREF##5##22##</sup>.</p>","<p id=\"Par5\">To broaden the synthetic capability of transketolase, and to make it more readily adaptable to new reactions, a toolbox of variants is desired that can accept a wider set of acceptor and donor substrates. TKs from various organisms have been subjected to extensive modifications through both rational engineering and directed evolution. <italic>E. coli</italic> TK has been engineered for improved and reversed enantioselectivity<sup>##REF##18342970##23##</sup>, stability<sup>##REF##28145457##24##,##REF##33193259##25##</sup>, and to progressively broaden the aldol-acceptor substrate range to polar aliphatics<sup>##REF##17825449##21##</sup>, non-polar aliphatics<sup>##REF##18342970##23##</sup>. TK from <italic>Geobacillus stearothermophilus</italic> (TK<sub>gst</sub>) has also been similarly engineered using related variants<sup>##REF##28005308##26##–##UREF##8##29##</sup>.</p>","<p id=\"Par6\">One key goal has been to engineer <italic>E. coli</italic> TK to accept aromatic aldehydes, as shown through the lineage of engineered variants in Fig. ##FIG##0##1##. Initial work used structure-guided engineering of active-site residues to obtain the “3M” variant (S385Y/D469T/R520Q) that accepts 3-formylbenzoic acid (3-FBA) when using HPA as the ketol donor<sup>##REF##25765309##5##</sup>. Crystal structural analysis of 3M and molecular docking of substrates revealed divergent binding modes<sup>##REF##27767080##30##</sup> for the three benzaldehyde analogues, 3-FBA, 4-formylbenzoic acid (4-FBA), and 3-hydroxybenzaldehyde (3-HBA). It was found that 3-FBA and 4-FBA oriented into two distinct binding pockets, whereas 3-HBA could bind into either with no obvious preference<sup>##REF##27767080##30##,##REF##23079923##31##</sup>.</p>","<p id=\"Par7\">The 3M variant has been further engineered to reverse the enatioselectivity<sup>##UREF##9##32##</sup>, while incorporating non-natural amino acids into the active site led to variants that were more stable, and more active towards 4-FBA and 3-HBA<sup>##REF##32897608##33##</sup>. In the latter study, the variant <bold>S385pCNF</bold>/D469T/R520Q which incorporated <italic>p-</italic>cyanophenyl alanine at residue 385, was found to have 43× higher activity towards 3-HBA compared to the original 3M variant<sup>##REF##32897608##33##</sup>. The stability of the 3M variant was also restored to wild-type levels using four previously known stabilising mutations to target residues within a network that was dynamically-correlated with active site residues<sup>##REF##30530661##34##</sup>. The new “7M” variant, <bold>H192P/A282P/I365L/</bold>S385Y/D469T<bold>/G506A</bold>/R520Q retained its activity with 3-FBA but also improved the activity with 4FBA by 3×.</p>","<p id=\"Par8\">Another recent focus has been to engineer variants that can simultaneously use aromatic aldehydes with pyruvate as the ketol donor instead of HPA. This would give synthetic access to precursors to drugs such as spisulosine and phenylpropanolamine, and to analogues of phenylacetylcarbinol (PAC), an important pharmaceutical intermediate (Scheme ##FIG##1##1##). Pyruvate is only different from HPA by the absence of the C-3-hydroxyl group. However, it is unable to serve as a donor substrate in the TK process, highlighting the crucial significance of the hydroxyl group. The <italic>E. coli</italic> TK variant “6M” (<bold>H100L</bold>/H192P/A282P/I365L/D469T/G506A) was developed by introducing H100L and the previously known propanal-accepting D469T mutation into the WT-stabilising “4M” variant (H192P/A282P/I365L/G506A)<sup>##REF##30404916##35##</sup> to enable pyruvate acceptance as the donor along with propanal as the acceptor. A similar result was achieved recently with glycolaldehyde as the acceptor, by using a related pyruvate-accepting enzyme 1-deoxy-<sc>d</sc>-xylulose-5-phosphate synthase (DXS) to guide the engineering of TK<sub>gst</sub> to accept pyruvate and other aliphatic analogues<sup>##UREF##10##36##,##UREF##11##37##</sup>.</p>","<p id=\"Par9\">Out of a series of variants, the “6M” <italic>E.coli</italic> TK variant was found previously to have the highest activity towards 3-FBA when using pyruvate, yielding just enough material in 48 h to isolate and characterise the product by NMR and LC/MS<sup>##REF##31647171##4##</sup>. Interestingly, combining 3-FBA accepting mutations S385Y and R520Q from “3M”, into pyruvate accepting variants such as 4M/D469T/H473N and 4M/D469T/H473N/R520Q, led to losses in activity for the 3-FBA and pyruvate reaction<sup>##REF##31647171##4##</sup>. The S385 and R520 residues both also form part of the entrance to the active site, and so their mutagenesis can impact not only on 3-FBA binding and orientation, but also may control the passage of pyruvate on its way towards the TPP cofactor.</p>","<p id=\"Par10\">Thus, the sites that influenced donor and acceptor substrate acceptance appeared to overlap or interact in a way that made combining beneficial mutations from each unpredictable. Furthermore, despite achieving success in obtaining activity with aromatic substrates and pyruvate, the activities obtained from the variants to date remained very low compared to those achieved with hydroxypyruvate (HPA) (Table ##TAB##0##1##).</p>","<p id=\"Par11\">To date mutations from “3M” had not been explored within the “6M” variant. Therefore, in this new study, we created a small library that recombined the 6M <italic>E. coli</italic> TK variant (H192P/A282P/I365L/G506A/D469T/H100L) with R520Q and a range of S385 mutations, including non-natural amino acids. These were then screened against reactions with pyruvate as donor and the three aromatic acceptor aldehydes 3-FBA, 4-FBA and 3-HBA. This led to new variants with significantly improved activities and higher levels of conversion. Kinetic analysis alongside molecular docking of the substrates into key variants revealed possible reasons for improved activities.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\">Biocatalysts have long promised to play a major role in synthetic organic chemistry, offering potentially greener routes to high-value chemicals<sup>##UREF##0##1##,##REF##11196655##2##</sup>. However, their highly-valued specificity and selectivity also leads to limitations in their exploitation as they are more difficult to adapt to new substrates than traditional catalysts<sup>##REF##15929154##3##</sup>. The challenge is particularly acute for enzymes that use two substrates as their interactions with the enzyme can often occur at overlapping sites. As a result, few studies have reported the engineering of enzymes to modify both substrate specificities to access new products<sup>##REF##31647171##4##,##REF##25765309##5##</sup>.</p>\",\n \"<p id=\\\"Par3\\\">The formation of asymmetric carbon–carbon bonds is a critical route in organic synthesis to access a variety of innovative and natural compounds that can be used as building blocks in additional synthesis<sup>##REF##21354781##6##–##REF##21417217##8##</sup>. Due to their great selectivity and specificity, asymmetric carbon–carbon bond-forming enzymes like transketolase (TK) (EC 2.2.1.1) have significant synthetic potential<sup>##REF##14662384##9##,##REF##16614860##10##</sup>. TK catalyses the reversible transfer of a C<sub>2</sub>-ketol unit from donor substrate <sc>d</sc>-xylulose-5-phosphate to aldose acceptor substrates of either <sc>d</sc>-ribose-5-phosphate or <sc>d</sc>-erythrose-4-phosphate in the pentose phosphate pathway of all cells<sup>##REF##7607225##11##,##UREF##1##12##</sup>. This metabolic pathway is critical in the cellular production of nucleotides, amino acids, and fatty acids.</p>\",\n \"<p id=\\\"Par4\\\">In recent years, TK variants have been demonstrated in the synthesis of complex carbohydrates and other high-value compounds, including <sc>l</sc>-erythrulose<sup>##UREF##2##13##</sup>, deoxysugars<sup>##UREF##3##14##</sup>, <italic>N</italic>-Aryl Hydroxamic Acids<sup>##UREF##4##15##</sup>, 7-keto-octuronic acid<sup>##REF##34250527##16##</sup>. At large scale, TK has been used for the production of unusual sugars<sup>##REF##11102785##17##</sup>. Wild-type TK enzymes tend to accept a relatively limited range of hydroxyaldehyde aldol-acceptors with strict (2<italic>R</italic>)-specificity<sup>##REF##11102785##17##,##REF##9924800##18##</sup>. Donor substrates are similarly limited as they require an oxo group adjacent to the scissile C–C bond, and also typically a C-1 hydroxyl group. Furthermore, they tend to prefer donor substrates with a D-threo configuration of C-3 and C-4 hydroxyls<sup>##REF##11278369##19##</sup>. Wild-type TK enzymes also have a preference for substrates that are phosphorylated due to complementary positively charged side-chains at the active site entrance<sup>##REF##11278369##19##–##REF##17825449##21##</sup>. However, this is not an absolute requirement, and TK can accept β-hydroxypyruvate (HPA) as the ketol donor which is advantageous in biocatalysis as it renders the donor half-reaction quasi-irreversible through liberation of CO<sub>2</sub>. This has been particularly useful for <italic>E. coli</italic> TK which gives up to 30-fold higher specific activity using HPA compared to yeast and spinach orthologs<sup>##UREF##5##22##</sup>.</p>\",\n \"<p id=\\\"Par5\\\">To broaden the synthetic capability of transketolase, and to make it more readily adaptable to new reactions, a toolbox of variants is desired that can accept a wider set of acceptor and donor substrates. TKs from various organisms have been subjected to extensive modifications through both rational engineering and directed evolution. <italic>E. coli</italic> TK has been engineered for improved and reversed enantioselectivity<sup>##REF##18342970##23##</sup>, stability<sup>##REF##28145457##24##,##REF##33193259##25##</sup>, and to progressively broaden the aldol-acceptor substrate range to polar aliphatics<sup>##REF##17825449##21##</sup>, non-polar aliphatics<sup>##REF##18342970##23##</sup>. TK from <italic>Geobacillus stearothermophilus</italic> (TK<sub>gst</sub>) has also been similarly engineered using related variants<sup>##REF##28005308##26##–##UREF##8##29##</sup>.</p>\",\n \"<p id=\\\"Par6\\\">One key goal has been to engineer <italic>E. coli</italic> TK to accept aromatic aldehydes, as shown through the lineage of engineered variants in Fig. ##FIG##0##1##. Initial work used structure-guided engineering of active-site residues to obtain the “3M” variant (S385Y/D469T/R520Q) that accepts 3-formylbenzoic acid (3-FBA) when using HPA as the ketol donor<sup>##REF##25765309##5##</sup>. Crystal structural analysis of 3M and molecular docking of substrates revealed divergent binding modes<sup>##REF##27767080##30##</sup> for the three benzaldehyde analogues, 3-FBA, 4-formylbenzoic acid (4-FBA), and 3-hydroxybenzaldehyde (3-HBA). It was found that 3-FBA and 4-FBA oriented into two distinct binding pockets, whereas 3-HBA could bind into either with no obvious preference<sup>##REF##27767080##30##,##REF##23079923##31##</sup>.</p>\",\n \"<p id=\\\"Par7\\\">The 3M variant has been further engineered to reverse the enatioselectivity<sup>##UREF##9##32##</sup>, while incorporating non-natural amino acids into the active site led to variants that were more stable, and more active towards 4-FBA and 3-HBA<sup>##REF##32897608##33##</sup>. In the latter study, the variant <bold>S385pCNF</bold>/D469T/R520Q which incorporated <italic>p-</italic>cyanophenyl alanine at residue 385, was found to have 43× higher activity towards 3-HBA compared to the original 3M variant<sup>##REF##32897608##33##</sup>. The stability of the 3M variant was also restored to wild-type levels using four previously known stabilising mutations to target residues within a network that was dynamically-correlated with active site residues<sup>##REF##30530661##34##</sup>. The new “7M” variant, <bold>H192P/A282P/I365L/</bold>S385Y/D469T<bold>/G506A</bold>/R520Q retained its activity with 3-FBA but also improved the activity with 4FBA by 3×.</p>\",\n \"<p id=\\\"Par8\\\">Another recent focus has been to engineer variants that can simultaneously use aromatic aldehydes with pyruvate as the ketol donor instead of HPA. This would give synthetic access to precursors to drugs such as spisulosine and phenylpropanolamine, and to analogues of phenylacetylcarbinol (PAC), an important pharmaceutical intermediate (Scheme ##FIG##1##1##). Pyruvate is only different from HPA by the absence of the C-3-hydroxyl group. However, it is unable to serve as a donor substrate in the TK process, highlighting the crucial significance of the hydroxyl group. The <italic>E. coli</italic> TK variant “6M” (<bold>H100L</bold>/H192P/A282P/I365L/D469T/G506A) was developed by introducing H100L and the previously known propanal-accepting D469T mutation into the WT-stabilising “4M” variant (H192P/A282P/I365L/G506A)<sup>##REF##30404916##35##</sup> to enable pyruvate acceptance as the donor along with propanal as the acceptor. A similar result was achieved recently with glycolaldehyde as the acceptor, by using a related pyruvate-accepting enzyme 1-deoxy-<sc>d</sc>-xylulose-5-phosphate synthase (DXS) to guide the engineering of TK<sub>gst</sub> to accept pyruvate and other aliphatic analogues<sup>##UREF##10##36##,##UREF##11##37##</sup>.</p>\",\n \"<p id=\\\"Par9\\\">Out of a series of variants, the “6M” <italic>E.coli</italic> TK variant was found previously to have the highest activity towards 3-FBA when using pyruvate, yielding just enough material in 48 h to isolate and characterise the product by NMR and LC/MS<sup>##REF##31647171##4##</sup>. Interestingly, combining 3-FBA accepting mutations S385Y and R520Q from “3M”, into pyruvate accepting variants such as 4M/D469T/H473N and 4M/D469T/H473N/R520Q, led to losses in activity for the 3-FBA and pyruvate reaction<sup>##REF##31647171##4##</sup>. The S385 and R520 residues both also form part of the entrance to the active site, and so their mutagenesis can impact not only on 3-FBA binding and orientation, but also may control the passage of pyruvate on its way towards the TPP cofactor.</p>\",\n \"<p id=\\\"Par10\\\">Thus, the sites that influenced donor and acceptor substrate acceptance appeared to overlap or interact in a way that made combining beneficial mutations from each unpredictable. Furthermore, despite achieving success in obtaining activity with aromatic substrates and pyruvate, the activities obtained from the variants to date remained very low compared to those achieved with hydroxypyruvate (HPA) (Table ##TAB##0##1##).</p>\",\n \"<p id=\\\"Par11\\\">To date mutations from “3M” had not been explored within the “6M” variant. Therefore, in this new study, we created a small library that recombined the 6M <italic>E. coli</italic> TK variant (H192P/A282P/I365L/G506A/D469T/H100L) with R520Q and a range of S385 mutations, including non-natural amino acids. These were then screened against reactions with pyruvate as donor and the three aromatic acceptor aldehydes 3-FBA, 4-FBA and 3-HBA. This led to new variants with significantly improved activities and higher levels of conversion. Kinetic analysis alongside molecular docking of the substrates into key variants revealed possible reasons for improved activities.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Materials and methods</title>","<title>Materials</title>","<p id=\"Par12\">All chemicals were obtained from Sigma-Aldrich, Merck, Germany. Non-natural amino acid, p-Amino <sc>l</sc>-phenylalanine (pAMF) and p-Cyano-<sc>l</sc>-Phenylalanine (pCNF) were obtained from Chem Cruz (Texas, USA).</p>","<p id=\"Par13\">Qiagen Ni-NTA column followed by Amicon ultra 10KD filter unit were used to purify and concentrate the proteins. Transketolase gene carrying plasmid pQR791 and suppression plasmid, pUltra were obtained from our lab master stock. For reverse phase HPLC (C18 HPLC) Agilent ACE5 C18 reverse-phase column (150 × 9 × 4.6 mm) was used in departmental Agilent 1200 HPLC, UPLC-SQD2 was used for Mass spectrometric analysis and NMR.</p>","<title>Methodologies</title>","<title>Preparation of a TK mutant library</title>","<p id=\"Par14\">TK variants were prepared by site directed mutagenesis using the Quick-Change site directed mutagenesis kit (Agilent technologies). Variants were obtained by sequential mutation from the 6M TK variant (H100L/H192P/A282P/I365L/D469T/G506A). A detailed list of mutants and the primers used is shown in ##SUPPL##0##Table S1 (supplementary information)##. This library consists of 8 variants in total. For the variants containing one of the two non-natural amino acids (nnAA) pAMF and pCNF at residue 385, we mutated the site to amber stop codon TAG. The nnAA incorporation was carried out by a standard amber suppression technique where the pUltra plasmid, pULTRA-CNF was a gift from Peter Schultz (Addgene plasmid # 48215)<sup>##REF##17061854##40##</sup> carrying the pAMF/pCNF specific aminoacyl synthetase gene, was co-transformed with the pQR729 plasmid for TK gene expression, into an amberless <italic>E. coli</italic> strain<sup>##REF##24136966##41##</sup> TK was then overexpressed using media supplemented with pAMF or pCNF. The nnAA incorporation was confirmed by ESI–MS experiments.</p>","<title>Transketolase purification</title>","<p id=\"Par15\">The cell pellets from 100-mL cell cultures for each variant or wild type were resuspended using cofactor solution containing 2.4 mM ThDP, 9 mM MgCl<sub>2</sub> and 50 mM Tris/HCl, pH 7.0. The suspended cells were then transferred to a 50 mL falcon tube for cell lysis by sonication, then centrifuged at 18,900 g for 15 min to collect the cell lysate supernatant. The protein expression level was assessed by 12% SDS/PAGE. TK with nnAAs were overexpressed using a 1 mM IPTG induction. The TK was C-terminally fused to a His6 tag, and so TK variants were purified using a Ni–NTA column. Purity was assessed by 12% SDS PAGE and the concentration measured using the Bradford method. Eluted holo-TK was buffer exchanged into 2.4 mM ThDP, 9 mM MgCl<sub>2</sub> and 50 mM Tris/HCl, pH 7.0, and diluted to 0.15 mg/mL or 0.2 mg/mL prior to enzyme activity reactions.</p>","<title>TK activities with aromatic aldehydes</title>","<p id=\"Par16\">Reactions between aromatic aldehydes and sodium pyruvate were initiated by adding 1 volume of substrate solution containing 150 mM aldehyde, 150 mM Na pyruvate, 50 mM Tris/HCl, pH 7.0, 2.4 mM ThDP, 9 mM MgCl<sub>2</sub>, to 2 volumes of the 0.15 mg/mL enzyme solution, to give final concentrations of 50 mM aromatic aldehydes and 50 mM sodium pyruvate. After reaction at 25 °C for 24 h, 20 μL of reaction mixture was transferred to 380 μL of 0.1% TFA to quench the reaction. Samples were then analysed by RP-HPLC with an ACE5 C18 reverse-phase column (150 × 9 × 4.6 mm), UV detection at 210 and 250 nm, and a mobile phase at a flow rate of 1.0 mL/min, starting with 70% of A (0.1% TFA) and 30% of B (100% acetonitrile) for 7 min.</p>","<title>Enzyme kinetics</title>","<p id=\"Par17\">Holo-TK variants at 0.2 mg/mL in 2.4 mM ThDP, 9 mM MgCl<sub>2</sub> and 50 mM Tris/HCl, pH 7.0 were combined 1:1 with 2× substrate solutions. Kinetic parameters were obtained at 50 mM pyruvate and a range of 25–150 mM 3-FBA, in final conditions of 0.1 mg/mL holo-TK, 50 mM Tris–HCl pH 7.0, 2.4 mM ThDP, 9 mM MgCl<sub>2</sub>, at 21 °C. Aliquots of 50 μL were quenched at various times within 24 h by adding 350 μL of 0.1% (v/v) TFA. Triplicate reactions were monitored using RP-HPLC as above. All data were fitted by non-linear regression in OriginPro9.0, using the Michaelis–Menten equation to determine the <italic>K</italic><sub>m</sub> and <italic>k</italic><sub>cat</sub> of wild-type TK and the variants.</p>","<title>NMR analysis</title>","<p id=\"Par18\">Product fractions were collected from multiple C18 RP-HPLC runs, pooled and concentrated using a vacuum centrifuge until > 1 mg was obtained. This was dissolved in D<sub>2</sub>O within NMR tubes. <sup>1</sup>H-NMR spectra were recorded on Bruker Avance 400, 500, 600 and 700 MHz spectrometers at 25 °C, using the residual protic solvent stated as the internal standard. Chemical shifts are quoted in p.p.m. to the nearest 0.01 p.p.m.</p>","<title>Sample preparation for capillary LC–MS/MS</title>","<p id=\"Par19\">For mass spectrometry, the C18 HPLC mobile phase was changed to 0.1% formic acid and 100% acetonitrile, and product fractions collected, concentrated using vacuum centrifugation, and then solid phase extraction with a Waters C<sub>18</sub> 100 mg cartridge (Waters, UK) used to remove trace salts. The C<sub>18</sub> cartridge was preconditioned following application of 100 µL of each sample, then washed with 1 mL of water and the sample components then desorbed with 1 mL methanol by gravity. 20 µL of each sample was then injected into the LC–MS instrument.</p>","<title>Capillary LC–MS/MS analysis</title>","<p id=\"Par20\">The LC–MS/MS system consisted of a Vanquish Liquid Chromatography LC system (Thermo Fisher Scientific, UK) coupled to a heated electrospray (HESI) probe connected to a Q Exactive mass spectrometer (Thermo Fisher Scientific, UK). The chromatographic separation was achieved on a Hypersil GOLD reverse-phase column (100 mm long, 2.1 mm internal diameter, particle size 1.9 µm) at a flow rate of 200 µL/min using a mobile phase A was 0.1% formic acid in water and mobile phase B was 0.1% formic acid in 80% acetonitrile. An eight minute gradient was as follows: After 1 min with 5% B, the proportion of B was raised to 95% B over the next 5 min, and maintained at 95% B for a further 1 min, after within 6 s the proportion of B was changed to 5% for 1.9 min to equilibrate the Hypersil C18 column. The LC column eluent was directed to the HESI source of the Q Exactive mass spectrometer. The HESI probe was operated with a sheath gas flow at 25 psi, an auxiliary gas flow at 10 psi, a spray voltage at 3.50 V, a capillary temperature at 320 °C, a S-lens RF level at 55, an auxiliary gas heater temperature was set at 50 °C. The Q Exactive mass spectrometer was operated in a positive ion mode. MS data was acquired using a data-dependent acquisition mode, and operated at 70,000 mass resolution (full width at half-maximum height, FWHM definition), and the top eight most abundant singly charged ions in the 80–800 m/z range were selected for MS/MS. The automatic gain control for the Q Exactive was set to 300,000 ions, and the automatic gain control for the MS/MS in the ion trap was set at 25,000 ions. For MS/MS, the isolation width was set at 1.2 amu and the collision energy was 30%. MS/MS scans were acquired at a mass resolution of 17,500 at 200 m/z. Three MS/MS microscans for each precursor were accumulated. Dynamic exclusion was enabled, and selected ions were excluded for 20 s before they could be selected for another round of MS/MS.</p>","<title>Computational molecular docking</title>","<p id=\"Par21\">The structures of the variants H192P/A282P/I365L/G506A/H100L/D469T/S385Y (TK1_7M), H192P/A282P/I365L/G506A/H100L/D469T/S385Y/R520Q (TK2_8M), H192P/A282P/I365L/G506A/H100L/D469T/S385F (TK3_7M) and H192P/A282P/I365L/G506A/H100L/D469T/S3854pCNF/R520Q (TK5_8M) were obtained using SWISS-MODEL and 5HHT.pdb as the template, and the S3854pCNF mutation introduced using the SwissSidechain Pymol plugin. The enamine-ThDP intermediate present in previously docked crystal structures was aligned into the WT and triple mutant (5HHT). The 3D conformation of 3-FBA was obtained from the PubChem (NIH) database and converted from a .sdf into a .pdb file using Open Babel. Autodock Vina was used to dock 3-FBA into WT and mutant TKs with the grid centred at − 11.25 Å, 25.858 Å and 40.198 Å (in 5HHT.pdb) and the exhaustiveness of 24<sup>##REF##27077332##42##</sup>. The grid sizes of 30 Å × 30 Å × 30 Å, was used for the docking of 3-FBA and includes the entire active-site while omitting other surface hydrophobic pockets. Three replicate dockings were carried out and the conformations with lowest energy were selected for analysis. To get a comparative view, TK-WT and the four variant structures were also docked with 3-FBA in Autodock 4.2<sup>##REF##19399780##43##</sup>. The explorable space for docking was defined using the same grid as above. For each search, a Lamarckian genetic algorithm was run 200 times with a maximum number of 25 million energy evaluations. The ligand (3-FBA) was flexible whereas the enzyme remained rigid. Resulting poses were analysed and checked for molecular interaction (hydrogen bonding, hydrophobic interaction, π-π stacking) in PyMOL Molecular Graphics System (Schrödinger, USA) and Protein Plus (TuHH).</p>"],"string":"[\n \"<title>Materials and methods</title>\",\n \"<title>Materials</title>\",\n \"<p id=\\\"Par12\\\">All chemicals were obtained from Sigma-Aldrich, Merck, Germany. Non-natural amino acid, p-Amino <sc>l</sc>-phenylalanine (pAMF) and p-Cyano-<sc>l</sc>-Phenylalanine (pCNF) were obtained from Chem Cruz (Texas, USA).</p>\",\n \"<p id=\\\"Par13\\\">Qiagen Ni-NTA column followed by Amicon ultra 10KD filter unit were used to purify and concentrate the proteins. Transketolase gene carrying plasmid pQR791 and suppression plasmid, pUltra were obtained from our lab master stock. For reverse phase HPLC (C18 HPLC) Agilent ACE5 C18 reverse-phase column (150 × 9 × 4.6 mm) was used in departmental Agilent 1200 HPLC, UPLC-SQD2 was used for Mass spectrometric analysis and NMR.</p>\",\n \"<title>Methodologies</title>\",\n \"<title>Preparation of a TK mutant library</title>\",\n \"<p id=\\\"Par14\\\">TK variants were prepared by site directed mutagenesis using the Quick-Change site directed mutagenesis kit (Agilent technologies). Variants were obtained by sequential mutation from the 6M TK variant (H100L/H192P/A282P/I365L/D469T/G506A). A detailed list of mutants and the primers used is shown in ##SUPPL##0##Table S1 (supplementary information)##. This library consists of 8 variants in total. For the variants containing one of the two non-natural amino acids (nnAA) pAMF and pCNF at residue 385, we mutated the site to amber stop codon TAG. The nnAA incorporation was carried out by a standard amber suppression technique where the pUltra plasmid, pULTRA-CNF was a gift from Peter Schultz (Addgene plasmid # 48215)<sup>##REF##17061854##40##</sup> carrying the pAMF/pCNF specific aminoacyl synthetase gene, was co-transformed with the pQR729 plasmid for TK gene expression, into an amberless <italic>E. coli</italic> strain<sup>##REF##24136966##41##</sup> TK was then overexpressed using media supplemented with pAMF or pCNF. The nnAA incorporation was confirmed by ESI–MS experiments.</p>\",\n \"<title>Transketolase purification</title>\",\n \"<p id=\\\"Par15\\\">The cell pellets from 100-mL cell cultures for each variant or wild type were resuspended using cofactor solution containing 2.4 mM ThDP, 9 mM MgCl<sub>2</sub> and 50 mM Tris/HCl, pH 7.0. The suspended cells were then transferred to a 50 mL falcon tube for cell lysis by sonication, then centrifuged at 18,900 g for 15 min to collect the cell lysate supernatant. The protein expression level was assessed by 12% SDS/PAGE. TK with nnAAs were overexpressed using a 1 mM IPTG induction. The TK was C-terminally fused to a His6 tag, and so TK variants were purified using a Ni–NTA column. Purity was assessed by 12% SDS PAGE and the concentration measured using the Bradford method. Eluted holo-TK was buffer exchanged into 2.4 mM ThDP, 9 mM MgCl<sub>2</sub> and 50 mM Tris/HCl, pH 7.0, and diluted to 0.15 mg/mL or 0.2 mg/mL prior to enzyme activity reactions.</p>\",\n \"<title>TK activities with aromatic aldehydes</title>\",\n \"<p id=\\\"Par16\\\">Reactions between aromatic aldehydes and sodium pyruvate were initiated by adding 1 volume of substrate solution containing 150 mM aldehyde, 150 mM Na pyruvate, 50 mM Tris/HCl, pH 7.0, 2.4 mM ThDP, 9 mM MgCl<sub>2</sub>, to 2 volumes of the 0.15 mg/mL enzyme solution, to give final concentrations of 50 mM aromatic aldehydes and 50 mM sodium pyruvate. After reaction at 25 °C for 24 h, 20 μL of reaction mixture was transferred to 380 μL of 0.1% TFA to quench the reaction. Samples were then analysed by RP-HPLC with an ACE5 C18 reverse-phase column (150 × 9 × 4.6 mm), UV detection at 210 and 250 nm, and a mobile phase at a flow rate of 1.0 mL/min, starting with 70% of A (0.1% TFA) and 30% of B (100% acetonitrile) for 7 min.</p>\",\n \"<title>Enzyme kinetics</title>\",\n \"<p id=\\\"Par17\\\">Holo-TK variants at 0.2 mg/mL in 2.4 mM ThDP, 9 mM MgCl<sub>2</sub> and 50 mM Tris/HCl, pH 7.0 were combined 1:1 with 2× substrate solutions. Kinetic parameters were obtained at 50 mM pyruvate and a range of 25–150 mM 3-FBA, in final conditions of 0.1 mg/mL holo-TK, 50 mM Tris–HCl pH 7.0, 2.4 mM ThDP, 9 mM MgCl<sub>2</sub>, at 21 °C. Aliquots of 50 μL were quenched at various times within 24 h by adding 350 μL of 0.1% (v/v) TFA. Triplicate reactions were monitored using RP-HPLC as above. All data were fitted by non-linear regression in OriginPro9.0, using the Michaelis–Menten equation to determine the <italic>K</italic><sub>m</sub> and <italic>k</italic><sub>cat</sub> of wild-type TK and the variants.</p>\",\n \"<title>NMR analysis</title>\",\n \"<p id=\\\"Par18\\\">Product fractions were collected from multiple C18 RP-HPLC runs, pooled and concentrated using a vacuum centrifuge until > 1 mg was obtained. This was dissolved in D<sub>2</sub>O within NMR tubes. <sup>1</sup>H-NMR spectra were recorded on Bruker Avance 400, 500, 600 and 700 MHz spectrometers at 25 °C, using the residual protic solvent stated as the internal standard. Chemical shifts are quoted in p.p.m. to the nearest 0.01 p.p.m.</p>\",\n \"<title>Sample preparation for capillary LC–MS/MS</title>\",\n \"<p id=\\\"Par19\\\">For mass spectrometry, the C18 HPLC mobile phase was changed to 0.1% formic acid and 100% acetonitrile, and product fractions collected, concentrated using vacuum centrifugation, and then solid phase extraction with a Waters C<sub>18</sub> 100 mg cartridge (Waters, UK) used to remove trace salts. The C<sub>18</sub> cartridge was preconditioned following application of 100 µL of each sample, then washed with 1 mL of water and the sample components then desorbed with 1 mL methanol by gravity. 20 µL of each sample was then injected into the LC–MS instrument.</p>\",\n \"<title>Capillary LC–MS/MS analysis</title>\",\n \"<p id=\\\"Par20\\\">The LC–MS/MS system consisted of a Vanquish Liquid Chromatography LC system (Thermo Fisher Scientific, UK) coupled to a heated electrospray (HESI) probe connected to a Q Exactive mass spectrometer (Thermo Fisher Scientific, UK). The chromatographic separation was achieved on a Hypersil GOLD reverse-phase column (100 mm long, 2.1 mm internal diameter, particle size 1.9 µm) at a flow rate of 200 µL/min using a mobile phase A was 0.1% formic acid in water and mobile phase B was 0.1% formic acid in 80% acetonitrile. An eight minute gradient was as follows: After 1 min with 5% B, the proportion of B was raised to 95% B over the next 5 min, and maintained at 95% B for a further 1 min, after within 6 s the proportion of B was changed to 5% for 1.9 min to equilibrate the Hypersil C18 column. The LC column eluent was directed to the HESI source of the Q Exactive mass spectrometer. The HESI probe was operated with a sheath gas flow at 25 psi, an auxiliary gas flow at 10 psi, a spray voltage at 3.50 V, a capillary temperature at 320 °C, a S-lens RF level at 55, an auxiliary gas heater temperature was set at 50 °C. The Q Exactive mass spectrometer was operated in a positive ion mode. MS data was acquired using a data-dependent acquisition mode, and operated at 70,000 mass resolution (full width at half-maximum height, FWHM definition), and the top eight most abundant singly charged ions in the 80–800 m/z range were selected for MS/MS. The automatic gain control for the Q Exactive was set to 300,000 ions, and the automatic gain control for the MS/MS in the ion trap was set at 25,000 ions. For MS/MS, the isolation width was set at 1.2 amu and the collision energy was 30%. MS/MS scans were acquired at a mass resolution of 17,500 at 200 m/z. Three MS/MS microscans for each precursor were accumulated. Dynamic exclusion was enabled, and selected ions were excluded for 20 s before they could be selected for another round of MS/MS.</p>\",\n \"<title>Computational molecular docking</title>\",\n \"<p id=\\\"Par21\\\">The structures of the variants H192P/A282P/I365L/G506A/H100L/D469T/S385Y (TK1_7M), H192P/A282P/I365L/G506A/H100L/D469T/S385Y/R520Q (TK2_8M), H192P/A282P/I365L/G506A/H100L/D469T/S385F (TK3_7M) and H192P/A282P/I365L/G506A/H100L/D469T/S3854pCNF/R520Q (TK5_8M) were obtained using SWISS-MODEL and 5HHT.pdb as the template, and the S3854pCNF mutation introduced using the SwissSidechain Pymol plugin. The enamine-ThDP intermediate present in previously docked crystal structures was aligned into the WT and triple mutant (5HHT). The 3D conformation of 3-FBA was obtained from the PubChem (NIH) database and converted from a .sdf into a .pdb file using Open Babel. Autodock Vina was used to dock 3-FBA into WT and mutant TKs with the grid centred at − 11.25 Å, 25.858 Å and 40.198 Å (in 5HHT.pdb) and the exhaustiveness of 24<sup>##REF##27077332##42##</sup>. The grid sizes of 30 Å × 30 Å × 30 Å, was used for the docking of 3-FBA and includes the entire active-site while omitting other surface hydrophobic pockets. Three replicate dockings were carried out and the conformations with lowest energy were selected for analysis. To get a comparative view, TK-WT and the four variant structures were also docked with 3-FBA in Autodock 4.2<sup>##REF##19399780##43##</sup>. The explorable space for docking was defined using the same grid as above. For each search, a Lamarckian genetic algorithm was run 200 times with a maximum number of 25 million energy evaluations. The ligand (3-FBA) was flexible whereas the enzyme remained rigid. Resulting poses were analysed and checked for molecular interaction (hydrogen bonding, hydrophobic interaction, π-π stacking) in PyMOL Molecular Graphics System (Schrödinger, USA) and Protein Plus (TuHH).</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results and discussion</title>","<p id=\"Par22\">Our goal was to find <italic>E. coli</italic> TK variants with improved activity towards aromatic acceptor aldehydes 3-FBA, 4-FBA and 3-HBA and with pyruvate as the donor, starting from the best variant to date, 6M (H192P/A282P/I365L/G506A/D469T/H100L). The 3M variant (S385Y/D469T/R520Q) was previously found to accept aromatic aldehydes, but only with hydroxypyruvate as donor. Previous attempts to introduce S385Y into 4M/D469T/H473N or 4M/D469T/H473N/R520Q actually led to a loss in activity towards 3-FBA with pyruvate<sup>##REF##31647171##4##</sup>. However, this mutation was not previously tested in 6M. Other mutations of S385, including non-natural substitutions within 3M (S385X/D469T/R520Q) were also found previously to improve the activity towards aromatic aldehydes, but again only when using HPA as donor substrate<sup>##REF##32897608##33##</sup>. The R520Q mutation has had a stabilising effect in several variants, often also improving activity<sup>##REF##25765309##5##,##REF##23079923##31##,##REF##22154561##38##</sup>, but it did not improve the activity towards 3-FBA when inserted into 4M/D469T/H473N previously<sup>##REF##31647171##4##</sup>. Therefore, we were interested now to explore whether any combinations of the various S385X mutations and also R520Q, could improve the activity of 6M towards aromatic aldehydes and pyruvate.</p>","<title>TK variant activities with aromatic substrates</title>","<p id=\"Par23\">A series of TK variants were generated, expressed, purified and then evaluated for their activities towards three aromatic aldehyde acceptor substrates at 50 mM, with 50 mM pyruvate as the donor substrate. The variant names, mutations and initial levels of conversion after 24 h, based on product peak areas as a fraction of total peak area for substrate and product, are shown in Table ##TAB##1##2##.</p>","<p id=\"Par24\">Among all eight variants TK-1, TK-2, TK-3 and TK-5C showed the greatest conversion to product after 24 h, and compared to no conversion with TK-WT. TK-3 and TK-5C also showed the greatest conversion when using 4FBA. The level of conversion for 3HBA remained relatively low with all variants. In previous work, the 6M variant (4M/H100L/D469T) gave only a 2.5% conversion of 3-FBA after 24 h. This could be increased at 1.3 mg/mL enzyme to 46.8%, but was still lower than the 62.2% achieved with the new TK-3 variant. Given the greatest activities with 3-FBA we focused further characterisation on that substrate, selecting the four most prominent variants TK-1, TK-2, TK-3 and TK-5C. We also used the conversion of 3-FBA with each TK variant to isolate the product and confirm its identity as previously<sup>##REF##31647171##4##</sup> using a combination of LC–MS and NMR (##SUPPL##0##Supplementary information, Figs. S1, S2, S3##).</p>","<title>Kinetic and stability studies of TK variants with substrate 3-FBA and pyruvate</title>","<p id=\"Par25\">The kinetic parameters for TK-1, TK-2, TK-3 and TK-5C were determined at 0.1 mg/mL enzyme and 50 mM pyruvate by varying the concentration of 3-FBA from 0 to 150 mM, and monitoring reactions in triplicate for up to 24 h. The kinetic parameters are shown in Table ##TAB##2##3##, alongside the specific activity at 50 mM 3-FBA, and the conversion at 24 h (taken from Table ##TAB##1##2##). For comparison, the parameters determined previously for 6 M are also shown, although the <italic>K</italic><sub>m</sub> for 3-FBA was not determined as that experiment varied pyruvate instead. It was not possible to obtain parameters for WT-TK as no activity was detected and so rates and rate constants are set to 0, while the <italic>K</italic><sub>m</sub> cannot be determined.</p>","<p id=\"Par26\">Compared to 6M, all four variants demonstrated significant increases in specific activity, <italic>V</italic><sub>m</sub> and <italic>k</italic><sub>cat</sub>. The specific activity of TK-1 in particular was 400× higher than for 6M, while the <italic>V</italic><sub>m</sub> was 900× greater. While the <italic>k</italic><sub>cat</sub> values for TK-1 and TK-3 were 2–3× higher than for TK-3 and TK-5C, their <italic>K</italic><sub>m</sub> values were correspondingly higher. The resulting <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub> values were fairly similar for TK-1, TK3 and TK-5C at 0.52–0.6/s mM, with TK-2 at a lower value of 0.2/s mM. Clearly the two larger <italic>K</italic><sub>m</sub> values (for TK-1 and TK-2) exceeded the studied range of 3-FBA (up to 150 mM), resulting in the larger errors on their estimated values.</p>","<p id=\"Par27\">The stability of the variants was also measured using thermal scanning fluorimetry with intrinsic fluorescence as the probe. TK is already known to aggregate rapidly upon unfolding<sup>##REF##21723889##44##</sup>, but comparisons can still be made between variants with consistent ramping protocols, and so an apparent thermal transition mid-point (<italic>T</italic><sub>m</sub>) is estimated. The <italic>T</italic><sub>m</sub> for wild-type TK was 57.8 °C, and consistent with a previous measurement of 58.3 °C under similar conditions (0.1 mg/mL, 25 mM Tris–HCl, pH 7.5, 0.5 mM MgCl<sub>2</sub>, 0.05 mM TPP)<sup>##REF##21723889##44##</sup>.</p>","<p id=\"Par28\">The variants all had increased <italic>T</italic><sub>m</sub> values, reaching 65.1 °C, and 63.7 °C for TK-3 and TK-5C respectively. TK-1 and TK-2 gave more modest increases to 59.2 °C and 61.4 °C respectively. It was previously shown that four of the mutations used within 6M increased the <italic>T</italic><sub>m</sub> over wild-type TK by 3.6 °C<sup>##REF##30404916##35##</sup>, whereas their introduction into 3M increased the <italic>T</italic><sub>m</sub> by 3 °C<sup>##REF##30530661##34##</sup>. These earlier experiments suggest that a significant part of the 1.4–7.3 °C increased stability in the new variants was likely to have already been present in 6M.</p>","<p id=\"Par29\">Overall, TK-3 was the most stable, had the lowest <italic>K</italic><sub>m</sub> for 3-FBA and performed well in terms of catalytic efficiency (<italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub>) and conversion (62%). However, for biocatalysis under conditions where substrate could be added in excess over <italic>K</italic><sub>m</sub>, then the <italic>k</italic><sub>cat</sub> and stability are more important factors. With that perspective, the variants TK-1 and TK-2 might be preferred for their elevated activity despite the slightly lower <italic>T</italic><sub>m</sub> values than TK-3.</p>","<p id=\"Par30\">It is worth comparing the impact of R520Q and S385X mutations here with their addition to previous variants. R520Q in TK-2 increased the <italic>T</italic><sub>m</sub> over TK-1 by 2.2 °C with no adverse impact on activity, consistent with its stabilising effects in previous studies. S385Y and S385F each had a significant impact on activity when added to 6M in the current study, leading to respective 630× and 200× increases in <italic>k</italic><sub>cat</sub> for the reaction with 3-FBA and pyruvate. This contrasts with the previous impact of adding S385Y into the D469T/R520Q variant (to form “3M”), which improved the catalytic efficiency towards 3-FBA and HPA by significantly decreasing the <italic>K</italic><sub>m</sub> for 3-FBA. Furthermore, when the S385Y mutation was incorporated into 4M/D469T/H473N and 4M/D469T/H473N/R520Q, it led to a loss of activity towards 3-FBA with pyruvate<sup>##REF##31647171##4##</sup>. The contextual mutations were clearly very important for the effects of S385Y. S385F gave a lower <italic>k</italic><sub>cat</sub>, but also lower <italic>K</italic><sub>m</sub> for 3-FBA, when compared to S385Y, which mirrored the same result in S385X/D469T/R520Q variants when tested on 3-HBA and hydroxpyruvate<sup>##REF##32897608##33##</sup>.</p>","<title>Computational docking of 3-FBA into TK-WT, 7M and 8M</title>","<p id=\"Par31\">To provide some structural insights into the effects of mutations on <italic>k</italic><sub>cat</sub> and <italic>K</italic><sub>m</sub>, we used computational docking of the 3-FBA substrate into the active site of the TK WT, TK-1 (7M), TK-2 (8M), TK-3 (7M) and TK-5C (8M) enzymes containing the enamine intermediate expected after prior reaction of pyruvate with the ThDP cofactor. Docking of the 3-FBA was performed in Autodock 4.2 which allowed both pose clustering (based on energy and RMSD for each pose) and an analysis of the relative populations of poses obtained for each cluster, from a total of 30. To analyse each pose and cluster in more detail, we calculated the distance, d, between the TPP enamine carbanion and the C-atom of the –CHO group of 3-FBA, to approximate the likelihood that a given pose was catalytically productive (not accounting for dynamics or angle of attack).</p>","<p id=\"Par32\">The poses for each variant are mapped in Fig. ##FIG##2##2##, and docking parameters shown in Table ##TAB##3##4##. The calculated cluster-averaged binding energies were all in the range − 3.2 to − 4.4 kcal/mol, indicating broad energetic similarity between substrates in all clusters such that all could potentially provide sufficient binding for catalysis. However, not all binding orientations and positions would necessarily be productive for catalysis, and significant populations of non-reactive complexes in the actual enzyme could contribute to either an increase in the apparent <italic>K</italic><sub>m</sub> or even lead to substrate inhibition. The maximum RMSD within each cluster varied from 0.1 to 1.0 Å, indicating a small combined degree of variance in substrate position, orientation and conformation within each cluster.</p>","<p id=\"Par33\">The docking produced two or three distinct clusters for each of the variants, but only a single cluster for WT (Table ##TAB##3##4##). We also analysed all poses together by visualising within aligned structures to determine structural similarities and differences between clusters from each variant. This revealed that across the four mutant variants there were actually only four clusters in total, with a few outlying poses, and that these were all distinct from the only cluster (cluster 0) in WT. Cluster 0, found only in WT, and for all WT poses, was unreactive with 7.5 < d < 8 Å. Results from Autodock Vina were consistent with Autodock 4.2 (Average d = 7.2 Å), although.</p>","<p id=\"Par34\">Vina also identified a rare pose with d = 4.8 Å suggesting catalysis might be theoretically possible in WT-TK but achieving the correct substrate binding may be frustrated due to binding predominantly into a non-productive location.</p>","<p id=\"Par35\">Of the four clusters in variants, cluster 4 was unreactive with 6.1 < d < 7.0 Å. TK2 contained the fewest potentially reactive poses (23%), with 67% in cluster 4. A further 10% for TK2 were in cluster 2b, a subcluster of cluster 2 but with a 180° relative rotation in the plane of the 3FBA ring leading to d > 9 Å. Clusters 1, 2 and 3 could be considered as “reactive”. Cluster 1 was preferred by TK1 and TK3, (even though TK3 found a single pose in cluster 2 with a lower binding energy). TK1 and TK3 each retained the wild-type arginine at residue 520. By contrast, Cluster 2 was preferred by TK2 and TK5, and these two variants contained the R520Q mutation.</p>","<p id=\"Par36\">To test the relationship between population, distance and experimentally determined catalytic efficiency, we created a score (Table ##TAB##3##4##) that weighted distance by population of poses, and linearly scaled between two cut-off values such that a distance of ≤ 4 Å scored 100% and ≥ 8 Å scored 0%. Given the limitations of docking methods this analysis does not account for dynamics or specific alignment of orbitals in favourable ways, such as at the Bergi-Dunitz angle for nucleophilic attack of a carbonyl<sup>##UREF##13##45##</sup>. However, intriguingly the score scaled well with <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub> (Fig. ##FIG##3##3##), confirming that control of the distance to enamine, and population distributions were both major factors in the observed kinetic differences between the variants.</p>","<title>How do mutations influence the binding populations?</title>","<p id=\"Par37\">Each pose was mapped to the active site structure by aligning them via their respective TPP-enamine cofactors, to then identify common interactions with the enzyme, and the potential role of mutations (Fig. ##FIG##4##4##). In previous studies, the WT enzyme was found to be inactive on the aromatic aldehyde substrate 3FBA<sup>##REF##27767080##30##,##REF##23079923##31##</sup>. The earlier 3M variant which included the S385Y and D469T mutations, first introduced activity towards 3-FBA in reactions with hydroxypyruvate<sup>##REF##31647171##4##,##REF##25765309##5##</sup>. A crystal structure of 3M and subsequent computational substrate docking of 3FBA, 4FBA and 3HBA, revealed that D469T mutation gave a hydrophobic surface for more favourable interactions with the nonpolar acceptor substrates<sup>##REF##27767080##30##</sup>. The S385Y mutation meanwhile created opportunities for π–π stacking with the new substrates, but also sterically filled the active-site space to create a more defined binding pocket. Finally, the R520Q mutation decreased the potential for interaction with the carboxylate of 3-FBA or 4-FBA. Interestingly the docking in 3M previously showed two clusters arising from the formation of two distinct binding pockets with the aromatic ring positioned on either side of D469T. 3-FBA and 4-FBA oriented into different binding pockets, whereas 3-HBA could bind into either with no obvious preference. For 3-FBA the carboxylate group was oriented towards R91 to form a salt bridge interaction.</p>","<p id=\"Par38\">In the present study, docking of 3-FBA into the four key variants and WT, again revealed multiple potentially reactive binding modes, dominated by cluster 1 (Fig. ##FIG##4##4##C and E) and cluster 2 (Fig. ##FIG##4##4##D and F). Interestingly, the 3-FBA orientation observed in 3 M was no longer populated in the current variants, none of which made use of the previous interaction between 3-FBA and R91. Instead, the carboxylate groups remained oriented towards R520 in TK-1 (Fig. ##FIG##4##4##C) and TK-3 (Fig. ##FIG##4##4##E) as might be expected, but surprisingly also towards R520Q in TK-2 (Fig. ##FIG##4##4##D) and TK-5C (Fig. ##FIG##4##4##F). This orientation in TK-2 and TK-5C was supported through interactions with nearby H461 and to a lesser degree with R358, which were both also found in the WT docking of 3-FBA. However, the key difference to 3M appears to be the H100L mutation in the current variants. The imidazole sidechain of H100 interacts directly with the guanidinium of R91 in both WT-TK and 3M, and so the H100L mutation would directly modify the pKa for R91, most likely disfavouring ionisation and formation of a salt bridge to the 3-FBA substrate. This would explain the loss of the 3-FBA substrate orientation via R91 observed previously in 3M docking.</p>","<p id=\"Par39\">When comparing cluster 1 favoured by TK-1 and TK-3, to cluster 2 (preferred by TK2 and TK5), the key change was the R520Q mutation in TK2 and TK5. This mutation pulls 3-FBA slightly further away from the TPP-enamine (see distance d in Table ##TAB##3##4##), but also pulls it across the surface created by the D469T sidechain, thus enabling the aromatic ring face to rotate approximately 90 degrees and fit better into the cluster 2 binding pocket. By contrast, rotation of the 3-FBA ring face in TK-1 and TK-5, while maintaining the interaction with R520 would result in an unfavourable steric clash with the side chain of D469T.</p>"],"string":"[\n \"<title>Results and discussion</title>\",\n \"<p id=\\\"Par22\\\">Our goal was to find <italic>E. coli</italic> TK variants with improved activity towards aromatic acceptor aldehydes 3-FBA, 4-FBA and 3-HBA and with pyruvate as the donor, starting from the best variant to date, 6M (H192P/A282P/I365L/G506A/D469T/H100L). The 3M variant (S385Y/D469T/R520Q) was previously found to accept aromatic aldehydes, but only with hydroxypyruvate as donor. Previous attempts to introduce S385Y into 4M/D469T/H473N or 4M/D469T/H473N/R520Q actually led to a loss in activity towards 3-FBA with pyruvate<sup>##REF##31647171##4##</sup>. However, this mutation was not previously tested in 6M. Other mutations of S385, including non-natural substitutions within 3M (S385X/D469T/R520Q) were also found previously to improve the activity towards aromatic aldehydes, but again only when using HPA as donor substrate<sup>##REF##32897608##33##</sup>. The R520Q mutation has had a stabilising effect in several variants, often also improving activity<sup>##REF##25765309##5##,##REF##23079923##31##,##REF##22154561##38##</sup>, but it did not improve the activity towards 3-FBA when inserted into 4M/D469T/H473N previously<sup>##REF##31647171##4##</sup>. Therefore, we were interested now to explore whether any combinations of the various S385X mutations and also R520Q, could improve the activity of 6M towards aromatic aldehydes and pyruvate.</p>\",\n \"<title>TK variant activities with aromatic substrates</title>\",\n \"<p id=\\\"Par23\\\">A series of TK variants were generated, expressed, purified and then evaluated for their activities towards three aromatic aldehyde acceptor substrates at 50 mM, with 50 mM pyruvate as the donor substrate. The variant names, mutations and initial levels of conversion after 24 h, based on product peak areas as a fraction of total peak area for substrate and product, are shown in Table ##TAB##1##2##.</p>\",\n \"<p id=\\\"Par24\\\">Among all eight variants TK-1, TK-2, TK-3 and TK-5C showed the greatest conversion to product after 24 h, and compared to no conversion with TK-WT. TK-3 and TK-5C also showed the greatest conversion when using 4FBA. The level of conversion for 3HBA remained relatively low with all variants. In previous work, the 6M variant (4M/H100L/D469T) gave only a 2.5% conversion of 3-FBA after 24 h. This could be increased at 1.3 mg/mL enzyme to 46.8%, but was still lower than the 62.2% achieved with the new TK-3 variant. Given the greatest activities with 3-FBA we focused further characterisation on that substrate, selecting the four most prominent variants TK-1, TK-2, TK-3 and TK-5C. We also used the conversion of 3-FBA with each TK variant to isolate the product and confirm its identity as previously<sup>##REF##31647171##4##</sup> using a combination of LC–MS and NMR (##SUPPL##0##Supplementary information, Figs. S1, S2, S3##).</p>\",\n \"<title>Kinetic and stability studies of TK variants with substrate 3-FBA and pyruvate</title>\",\n \"<p id=\\\"Par25\\\">The kinetic parameters for TK-1, TK-2, TK-3 and TK-5C were determined at 0.1 mg/mL enzyme and 50 mM pyruvate by varying the concentration of 3-FBA from 0 to 150 mM, and monitoring reactions in triplicate for up to 24 h. The kinetic parameters are shown in Table ##TAB##2##3##, alongside the specific activity at 50 mM 3-FBA, and the conversion at 24 h (taken from Table ##TAB##1##2##). For comparison, the parameters determined previously for 6 M are also shown, although the <italic>K</italic><sub>m</sub> for 3-FBA was not determined as that experiment varied pyruvate instead. It was not possible to obtain parameters for WT-TK as no activity was detected and so rates and rate constants are set to 0, while the <italic>K</italic><sub>m</sub> cannot be determined.</p>\",\n \"<p id=\\\"Par26\\\">Compared to 6M, all four variants demonstrated significant increases in specific activity, <italic>V</italic><sub>m</sub> and <italic>k</italic><sub>cat</sub>. The specific activity of TK-1 in particular was 400× higher than for 6M, while the <italic>V</italic><sub>m</sub> was 900× greater. While the <italic>k</italic><sub>cat</sub> values for TK-1 and TK-3 were 2–3× higher than for TK-3 and TK-5C, their <italic>K</italic><sub>m</sub> values were correspondingly higher. The resulting <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub> values were fairly similar for TK-1, TK3 and TK-5C at 0.52–0.6/s mM, with TK-2 at a lower value of 0.2/s mM. Clearly the two larger <italic>K</italic><sub>m</sub> values (for TK-1 and TK-2) exceeded the studied range of 3-FBA (up to 150 mM), resulting in the larger errors on their estimated values.</p>\",\n \"<p id=\\\"Par27\\\">The stability of the variants was also measured using thermal scanning fluorimetry with intrinsic fluorescence as the probe. TK is already known to aggregate rapidly upon unfolding<sup>##REF##21723889##44##</sup>, but comparisons can still be made between variants with consistent ramping protocols, and so an apparent thermal transition mid-point (<italic>T</italic><sub>m</sub>) is estimated. The <italic>T</italic><sub>m</sub> for wild-type TK was 57.8 °C, and consistent with a previous measurement of 58.3 °C under similar conditions (0.1 mg/mL, 25 mM Tris–HCl, pH 7.5, 0.5 mM MgCl<sub>2</sub>, 0.05 mM TPP)<sup>##REF##21723889##44##</sup>.</p>\",\n \"<p id=\\\"Par28\\\">The variants all had increased <italic>T</italic><sub>m</sub> values, reaching 65.1 °C, and 63.7 °C for TK-3 and TK-5C respectively. TK-1 and TK-2 gave more modest increases to 59.2 °C and 61.4 °C respectively. It was previously shown that four of the mutations used within 6M increased the <italic>T</italic><sub>m</sub> over wild-type TK by 3.6 °C<sup>##REF##30404916##35##</sup>, whereas their introduction into 3M increased the <italic>T</italic><sub>m</sub> by 3 °C<sup>##REF##30530661##34##</sup>. These earlier experiments suggest that a significant part of the 1.4–7.3 °C increased stability in the new variants was likely to have already been present in 6M.</p>\",\n \"<p id=\\\"Par29\\\">Overall, TK-3 was the most stable, had the lowest <italic>K</italic><sub>m</sub> for 3-FBA and performed well in terms of catalytic efficiency (<italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub>) and conversion (62%). However, for biocatalysis under conditions where substrate could be added in excess over <italic>K</italic><sub>m</sub>, then the <italic>k</italic><sub>cat</sub> and stability are more important factors. With that perspective, the variants TK-1 and TK-2 might be preferred for their elevated activity despite the slightly lower <italic>T</italic><sub>m</sub> values than TK-3.</p>\",\n \"<p id=\\\"Par30\\\">It is worth comparing the impact of R520Q and S385X mutations here with their addition to previous variants. R520Q in TK-2 increased the <italic>T</italic><sub>m</sub> over TK-1 by 2.2 °C with no adverse impact on activity, consistent with its stabilising effects in previous studies. S385Y and S385F each had a significant impact on activity when added to 6M in the current study, leading to respective 630× and 200× increases in <italic>k</italic><sub>cat</sub> for the reaction with 3-FBA and pyruvate. This contrasts with the previous impact of adding S385Y into the D469T/R520Q variant (to form “3M”), which improved the catalytic efficiency towards 3-FBA and HPA by significantly decreasing the <italic>K</italic><sub>m</sub> for 3-FBA. Furthermore, when the S385Y mutation was incorporated into 4M/D469T/H473N and 4M/D469T/H473N/R520Q, it led to a loss of activity towards 3-FBA with pyruvate<sup>##REF##31647171##4##</sup>. The contextual mutations were clearly very important for the effects of S385Y. S385F gave a lower <italic>k</italic><sub>cat</sub>, but also lower <italic>K</italic><sub>m</sub> for 3-FBA, when compared to S385Y, which mirrored the same result in S385X/D469T/R520Q variants when tested on 3-HBA and hydroxpyruvate<sup>##REF##32897608##33##</sup>.</p>\",\n \"<title>Computational docking of 3-FBA into TK-WT, 7M and 8M</title>\",\n \"<p id=\\\"Par31\\\">To provide some structural insights into the effects of mutations on <italic>k</italic><sub>cat</sub> and <italic>K</italic><sub>m</sub>, we used computational docking of the 3-FBA substrate into the active site of the TK WT, TK-1 (7M), TK-2 (8M), TK-3 (7M) and TK-5C (8M) enzymes containing the enamine intermediate expected after prior reaction of pyruvate with the ThDP cofactor. Docking of the 3-FBA was performed in Autodock 4.2 which allowed both pose clustering (based on energy and RMSD for each pose) and an analysis of the relative populations of poses obtained for each cluster, from a total of 30. To analyse each pose and cluster in more detail, we calculated the distance, d, between the TPP enamine carbanion and the C-atom of the –CHO group of 3-FBA, to approximate the likelihood that a given pose was catalytically productive (not accounting for dynamics or angle of attack).</p>\",\n \"<p id=\\\"Par32\\\">The poses for each variant are mapped in Fig. ##FIG##2##2##, and docking parameters shown in Table ##TAB##3##4##. The calculated cluster-averaged binding energies were all in the range − 3.2 to − 4.4 kcal/mol, indicating broad energetic similarity between substrates in all clusters such that all could potentially provide sufficient binding for catalysis. However, not all binding orientations and positions would necessarily be productive for catalysis, and significant populations of non-reactive complexes in the actual enzyme could contribute to either an increase in the apparent <italic>K</italic><sub>m</sub> or even lead to substrate inhibition. The maximum RMSD within each cluster varied from 0.1 to 1.0 Å, indicating a small combined degree of variance in substrate position, orientation and conformation within each cluster.</p>\",\n \"<p id=\\\"Par33\\\">The docking produced two or three distinct clusters for each of the variants, but only a single cluster for WT (Table ##TAB##3##4##). We also analysed all poses together by visualising within aligned structures to determine structural similarities and differences between clusters from each variant. This revealed that across the four mutant variants there were actually only four clusters in total, with a few outlying poses, and that these were all distinct from the only cluster (cluster 0) in WT. Cluster 0, found only in WT, and for all WT poses, was unreactive with 7.5 < d < 8 Å. Results from Autodock Vina were consistent with Autodock 4.2 (Average d = 7.2 Å), although.</p>\",\n \"<p id=\\\"Par34\\\">Vina also identified a rare pose with d = 4.8 Å suggesting catalysis might be theoretically possible in WT-TK but achieving the correct substrate binding may be frustrated due to binding predominantly into a non-productive location.</p>\",\n \"<p id=\\\"Par35\\\">Of the four clusters in variants, cluster 4 was unreactive with 6.1 < d < 7.0 Å. TK2 contained the fewest potentially reactive poses (23%), with 67% in cluster 4. A further 10% for TK2 were in cluster 2b, a subcluster of cluster 2 but with a 180° relative rotation in the plane of the 3FBA ring leading to d > 9 Å. Clusters 1, 2 and 3 could be considered as “reactive”. Cluster 1 was preferred by TK1 and TK3, (even though TK3 found a single pose in cluster 2 with a lower binding energy). TK1 and TK3 each retained the wild-type arginine at residue 520. By contrast, Cluster 2 was preferred by TK2 and TK5, and these two variants contained the R520Q mutation.</p>\",\n \"<p id=\\\"Par36\\\">To test the relationship between population, distance and experimentally determined catalytic efficiency, we created a score (Table ##TAB##3##4##) that weighted distance by population of poses, and linearly scaled between two cut-off values such that a distance of ≤ 4 Å scored 100% and ≥ 8 Å scored 0%. Given the limitations of docking methods this analysis does not account for dynamics or specific alignment of orbitals in favourable ways, such as at the Bergi-Dunitz angle for nucleophilic attack of a carbonyl<sup>##UREF##13##45##</sup>. However, intriguingly the score scaled well with <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub> (Fig. ##FIG##3##3##), confirming that control of the distance to enamine, and population distributions were both major factors in the observed kinetic differences between the variants.</p>\",\n \"<title>How do mutations influence the binding populations?</title>\",\n \"<p id=\\\"Par37\\\">Each pose was mapped to the active site structure by aligning them via their respective TPP-enamine cofactors, to then identify common interactions with the enzyme, and the potential role of mutations (Fig. ##FIG##4##4##). In previous studies, the WT enzyme was found to be inactive on the aromatic aldehyde substrate 3FBA<sup>##REF##27767080##30##,##REF##23079923##31##</sup>. The earlier 3M variant which included the S385Y and D469T mutations, first introduced activity towards 3-FBA in reactions with hydroxypyruvate<sup>##REF##31647171##4##,##REF##25765309##5##</sup>. A crystal structure of 3M and subsequent computational substrate docking of 3FBA, 4FBA and 3HBA, revealed that D469T mutation gave a hydrophobic surface for more favourable interactions with the nonpolar acceptor substrates<sup>##REF##27767080##30##</sup>. The S385Y mutation meanwhile created opportunities for π–π stacking with the new substrates, but also sterically filled the active-site space to create a more defined binding pocket. Finally, the R520Q mutation decreased the potential for interaction with the carboxylate of 3-FBA or 4-FBA. Interestingly the docking in 3M previously showed two clusters arising from the formation of two distinct binding pockets with the aromatic ring positioned on either side of D469T. 3-FBA and 4-FBA oriented into different binding pockets, whereas 3-HBA could bind into either with no obvious preference. For 3-FBA the carboxylate group was oriented towards R91 to form a salt bridge interaction.</p>\",\n \"<p id=\\\"Par38\\\">In the present study, docking of 3-FBA into the four key variants and WT, again revealed multiple potentially reactive binding modes, dominated by cluster 1 (Fig. ##FIG##4##4##C and E) and cluster 2 (Fig. ##FIG##4##4##D and F). Interestingly, the 3-FBA orientation observed in 3 M was no longer populated in the current variants, none of which made use of the previous interaction between 3-FBA and R91. Instead, the carboxylate groups remained oriented towards R520 in TK-1 (Fig. ##FIG##4##4##C) and TK-3 (Fig. ##FIG##4##4##E) as might be expected, but surprisingly also towards R520Q in TK-2 (Fig. ##FIG##4##4##D) and TK-5C (Fig. ##FIG##4##4##F). This orientation in TK-2 and TK-5C was supported through interactions with nearby H461 and to a lesser degree with R358, which were both also found in the WT docking of 3-FBA. However, the key difference to 3M appears to be the H100L mutation in the current variants. The imidazole sidechain of H100 interacts directly with the guanidinium of R91 in both WT-TK and 3M, and so the H100L mutation would directly modify the pKa for R91, most likely disfavouring ionisation and formation of a salt bridge to the 3-FBA substrate. This would explain the loss of the 3-FBA substrate orientation via R91 observed previously in 3M docking.</p>\",\n \"<p id=\\\"Par39\\\">When comparing cluster 1 favoured by TK-1 and TK-3, to cluster 2 (preferred by TK2 and TK5), the key change was the R520Q mutation in TK2 and TK5. This mutation pulls 3-FBA slightly further away from the TPP-enamine (see distance d in Table ##TAB##3##4##), but also pulls it across the surface created by the D469T sidechain, thus enabling the aromatic ring face to rotate approximately 90 degrees and fit better into the cluster 2 binding pocket. By contrast, rotation of the 3-FBA ring face in TK-1 and TK-5, while maintaining the interaction with R520 would result in an unfavourable steric clash with the side chain of D469T.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Results and discussion</title>","<p id=\"Par22\">Our goal was to find <italic>E. coli</italic> TK variants with improved activity towards aromatic acceptor aldehydes 3-FBA, 4-FBA and 3-HBA and with pyruvate as the donor, starting from the best variant to date, 6M (H192P/A282P/I365L/G506A/D469T/H100L). The 3M variant (S385Y/D469T/R520Q) was previously found to accept aromatic aldehydes, but only with hydroxypyruvate as donor. Previous attempts to introduce S385Y into 4M/D469T/H473N or 4M/D469T/H473N/R520Q actually led to a loss in activity towards 3-FBA with pyruvate<sup>##REF##31647171##4##</sup>. However, this mutation was not previously tested in 6M. Other mutations of S385, including non-natural substitutions within 3M (S385X/D469T/R520Q) were also found previously to improve the activity towards aromatic aldehydes, but again only when using HPA as donor substrate<sup>##REF##32897608##33##</sup>. The R520Q mutation has had a stabilising effect in several variants, often also improving activity<sup>##REF##25765309##5##,##REF##23079923##31##,##REF##22154561##38##</sup>, but it did not improve the activity towards 3-FBA when inserted into 4M/D469T/H473N previously<sup>##REF##31647171##4##</sup>. Therefore, we were interested now to explore whether any combinations of the various S385X mutations and also R520Q, could improve the activity of 6M towards aromatic aldehydes and pyruvate.</p>","<title>TK variant activities with aromatic substrates</title>","<p id=\"Par23\">A series of TK variants were generated, expressed, purified and then evaluated for their activities towards three aromatic aldehyde acceptor substrates at 50 mM, with 50 mM pyruvate as the donor substrate. The variant names, mutations and initial levels of conversion after 24 h, based on product peak areas as a fraction of total peak area for substrate and product, are shown in Table ##TAB##1##2##.</p>","<p id=\"Par24\">Among all eight variants TK-1, TK-2, TK-3 and TK-5C showed the greatest conversion to product after 24 h, and compared to no conversion with TK-WT. TK-3 and TK-5C also showed the greatest conversion when using 4FBA. The level of conversion for 3HBA remained relatively low with all variants. In previous work, the 6M variant (4M/H100L/D469T) gave only a 2.5% conversion of 3-FBA after 24 h. This could be increased at 1.3 mg/mL enzyme to 46.8%, but was still lower than the 62.2% achieved with the new TK-3 variant. Given the greatest activities with 3-FBA we focused further characterisation on that substrate, selecting the four most prominent variants TK-1, TK-2, TK-3 and TK-5C. We also used the conversion of 3-FBA with each TK variant to isolate the product and confirm its identity as previously<sup>##REF##31647171##4##</sup> using a combination of LC–MS and NMR (##SUPPL##0##Supplementary information, Figs. S1, S2, S3##).</p>","<title>Kinetic and stability studies of TK variants with substrate 3-FBA and pyruvate</title>","<p id=\"Par25\">The kinetic parameters for TK-1, TK-2, TK-3 and TK-5C were determined at 0.1 mg/mL enzyme and 50 mM pyruvate by varying the concentration of 3-FBA from 0 to 150 mM, and monitoring reactions in triplicate for up to 24 h. The kinetic parameters are shown in Table ##TAB##2##3##, alongside the specific activity at 50 mM 3-FBA, and the conversion at 24 h (taken from Table ##TAB##1##2##). For comparison, the parameters determined previously for 6 M are also shown, although the <italic>K</italic><sub>m</sub> for 3-FBA was not determined as that experiment varied pyruvate instead. It was not possible to obtain parameters for WT-TK as no activity was detected and so rates and rate constants are set to 0, while the <italic>K</italic><sub>m</sub> cannot be determined.</p>","<p id=\"Par26\">Compared to 6M, all four variants demonstrated significant increases in specific activity, <italic>V</italic><sub>m</sub> and <italic>k</italic><sub>cat</sub>. The specific activity of TK-1 in particular was 400× higher than for 6M, while the <italic>V</italic><sub>m</sub> was 900× greater. While the <italic>k</italic><sub>cat</sub> values for TK-1 and TK-3 were 2–3× higher than for TK-3 and TK-5C, their <italic>K</italic><sub>m</sub> values were correspondingly higher. The resulting <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub> values were fairly similar for TK-1, TK3 and TK-5C at 0.52–0.6/s mM, with TK-2 at a lower value of 0.2/s mM. Clearly the two larger <italic>K</italic><sub>m</sub> values (for TK-1 and TK-2) exceeded the studied range of 3-FBA (up to 150 mM), resulting in the larger errors on their estimated values.</p>","<p id=\"Par27\">The stability of the variants was also measured using thermal scanning fluorimetry with intrinsic fluorescence as the probe. TK is already known to aggregate rapidly upon unfolding<sup>##REF##21723889##44##</sup>, but comparisons can still be made between variants with consistent ramping protocols, and so an apparent thermal transition mid-point (<italic>T</italic><sub>m</sub>) is estimated. The <italic>T</italic><sub>m</sub> for wild-type TK was 57.8 °C, and consistent with a previous measurement of 58.3 °C under similar conditions (0.1 mg/mL, 25 mM Tris–HCl, pH 7.5, 0.5 mM MgCl<sub>2</sub>, 0.05 mM TPP)<sup>##REF##21723889##44##</sup>.</p>","<p id=\"Par28\">The variants all had increased <italic>T</italic><sub>m</sub> values, reaching 65.1 °C, and 63.7 °C for TK-3 and TK-5C respectively. TK-1 and TK-2 gave more modest increases to 59.2 °C and 61.4 °C respectively. It was previously shown that four of the mutations used within 6M increased the <italic>T</italic><sub>m</sub> over wild-type TK by 3.6 °C<sup>##REF##30404916##35##</sup>, whereas their introduction into 3M increased the <italic>T</italic><sub>m</sub> by 3 °C<sup>##REF##30530661##34##</sup>. These earlier experiments suggest that a significant part of the 1.4–7.3 °C increased stability in the new variants was likely to have already been present in 6M.</p>","<p id=\"Par29\">Overall, TK-3 was the most stable, had the lowest <italic>K</italic><sub>m</sub> for 3-FBA and performed well in terms of catalytic efficiency (<italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub>) and conversion (62%). However, for biocatalysis under conditions where substrate could be added in excess over <italic>K</italic><sub>m</sub>, then the <italic>k</italic><sub>cat</sub> and stability are more important factors. With that perspective, the variants TK-1 and TK-2 might be preferred for their elevated activity despite the slightly lower <italic>T</italic><sub>m</sub> values than TK-3.</p>","<p id=\"Par30\">It is worth comparing the impact of R520Q and S385X mutations here with their addition to previous variants. R520Q in TK-2 increased the <italic>T</italic><sub>m</sub> over TK-1 by 2.2 °C with no adverse impact on activity, consistent with its stabilising effects in previous studies. S385Y and S385F each had a significant impact on activity when added to 6M in the current study, leading to respective 630× and 200× increases in <italic>k</italic><sub>cat</sub> for the reaction with 3-FBA and pyruvate. This contrasts with the previous impact of adding S385Y into the D469T/R520Q variant (to form “3M”), which improved the catalytic efficiency towards 3-FBA and HPA by significantly decreasing the <italic>K</italic><sub>m</sub> for 3-FBA. Furthermore, when the S385Y mutation was incorporated into 4M/D469T/H473N and 4M/D469T/H473N/R520Q, it led to a loss of activity towards 3-FBA with pyruvate<sup>##REF##31647171##4##</sup>. The contextual mutations were clearly very important for the effects of S385Y. S385F gave a lower <italic>k</italic><sub>cat</sub>, but also lower <italic>K</italic><sub>m</sub> for 3-FBA, when compared to S385Y, which mirrored the same result in S385X/D469T/R520Q variants when tested on 3-HBA and hydroxpyruvate<sup>##REF##32897608##33##</sup>.</p>","<title>Computational docking of 3-FBA into TK-WT, 7M and 8M</title>","<p id=\"Par31\">To provide some structural insights into the effects of mutations on <italic>k</italic><sub>cat</sub> and <italic>K</italic><sub>m</sub>, we used computational docking of the 3-FBA substrate into the active site of the TK WT, TK-1 (7M), TK-2 (8M), TK-3 (7M) and TK-5C (8M) enzymes containing the enamine intermediate expected after prior reaction of pyruvate with the ThDP cofactor. Docking of the 3-FBA was performed in Autodock 4.2 which allowed both pose clustering (based on energy and RMSD for each pose) and an analysis of the relative populations of poses obtained for each cluster, from a total of 30. To analyse each pose and cluster in more detail, we calculated the distance, d, between the TPP enamine carbanion and the C-atom of the –CHO group of 3-FBA, to approximate the likelihood that a given pose was catalytically productive (not accounting for dynamics or angle of attack).</p>","<p id=\"Par32\">The poses for each variant are mapped in Fig. ##FIG##2##2##, and docking parameters shown in Table ##TAB##3##4##. The calculated cluster-averaged binding energies were all in the range − 3.2 to − 4.4 kcal/mol, indicating broad energetic similarity between substrates in all clusters such that all could potentially provide sufficient binding for catalysis. However, not all binding orientations and positions would necessarily be productive for catalysis, and significant populations of non-reactive complexes in the actual enzyme could contribute to either an increase in the apparent <italic>K</italic><sub>m</sub> or even lead to substrate inhibition. The maximum RMSD within each cluster varied from 0.1 to 1.0 Å, indicating a small combined degree of variance in substrate position, orientation and conformation within each cluster.</p>","<p id=\"Par33\">The docking produced two or three distinct clusters for each of the variants, but only a single cluster for WT (Table ##TAB##3##4##). We also analysed all poses together by visualising within aligned structures to determine structural similarities and differences between clusters from each variant. This revealed that across the four mutant variants there were actually only four clusters in total, with a few outlying poses, and that these were all distinct from the only cluster (cluster 0) in WT. Cluster 0, found only in WT, and for all WT poses, was unreactive with 7.5 < d < 8 Å. Results from Autodock Vina were consistent with Autodock 4.2 (Average d = 7.2 Å), although.</p>","<p id=\"Par34\">Vina also identified a rare pose with d = 4.8 Å suggesting catalysis might be theoretically possible in WT-TK but achieving the correct substrate binding may be frustrated due to binding predominantly into a non-productive location.</p>","<p id=\"Par35\">Of the four clusters in variants, cluster 4 was unreactive with 6.1 < d < 7.0 Å. TK2 contained the fewest potentially reactive poses (23%), with 67% in cluster 4. A further 10% for TK2 were in cluster 2b, a subcluster of cluster 2 but with a 180° relative rotation in the plane of the 3FBA ring leading to d > 9 Å. Clusters 1, 2 and 3 could be considered as “reactive”. Cluster 1 was preferred by TK1 and TK3, (even though TK3 found a single pose in cluster 2 with a lower binding energy). TK1 and TK3 each retained the wild-type arginine at residue 520. By contrast, Cluster 2 was preferred by TK2 and TK5, and these two variants contained the R520Q mutation.</p>","<p id=\"Par36\">To test the relationship between population, distance and experimentally determined catalytic efficiency, we created a score (Table ##TAB##3##4##) that weighted distance by population of poses, and linearly scaled between two cut-off values such that a distance of ≤ 4 Å scored 100% and ≥ 8 Å scored 0%. Given the limitations of docking methods this analysis does not account for dynamics or specific alignment of orbitals in favourable ways, such as at the Bergi-Dunitz angle for nucleophilic attack of a carbonyl<sup>##UREF##13##45##</sup>. However, intriguingly the score scaled well with <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub> (Fig. ##FIG##3##3##), confirming that control of the distance to enamine, and population distributions were both major factors in the observed kinetic differences between the variants.</p>","<title>How do mutations influence the binding populations?</title>","<p id=\"Par37\">Each pose was mapped to the active site structure by aligning them via their respective TPP-enamine cofactors, to then identify common interactions with the enzyme, and the potential role of mutations (Fig. ##FIG##4##4##). In previous studies, the WT enzyme was found to be inactive on the aromatic aldehyde substrate 3FBA<sup>##REF##27767080##30##,##REF##23079923##31##</sup>. The earlier 3M variant which included the S385Y and D469T mutations, first introduced activity towards 3-FBA in reactions with hydroxypyruvate<sup>##REF##31647171##4##,##REF##25765309##5##</sup>. A crystal structure of 3M and subsequent computational substrate docking of 3FBA, 4FBA and 3HBA, revealed that D469T mutation gave a hydrophobic surface for more favourable interactions with the nonpolar acceptor substrates<sup>##REF##27767080##30##</sup>. The S385Y mutation meanwhile created opportunities for π–π stacking with the new substrates, but also sterically filled the active-site space to create a more defined binding pocket. Finally, the R520Q mutation decreased the potential for interaction with the carboxylate of 3-FBA or 4-FBA. Interestingly the docking in 3M previously showed two clusters arising from the formation of two distinct binding pockets with the aromatic ring positioned on either side of D469T. 3-FBA and 4-FBA oriented into different binding pockets, whereas 3-HBA could bind into either with no obvious preference. For 3-FBA the carboxylate group was oriented towards R91 to form a salt bridge interaction.</p>","<p id=\"Par38\">In the present study, docking of 3-FBA into the four key variants and WT, again revealed multiple potentially reactive binding modes, dominated by cluster 1 (Fig. ##FIG##4##4##C and E) and cluster 2 (Fig. ##FIG##4##4##D and F). Interestingly, the 3-FBA orientation observed in 3 M was no longer populated in the current variants, none of which made use of the previous interaction between 3-FBA and R91. Instead, the carboxylate groups remained oriented towards R520 in TK-1 (Fig. ##FIG##4##4##C) and TK-3 (Fig. ##FIG##4##4##E) as might be expected, but surprisingly also towards R520Q in TK-2 (Fig. ##FIG##4##4##D) and TK-5C (Fig. ##FIG##4##4##F). This orientation in TK-2 and TK-5C was supported through interactions with nearby H461 and to a lesser degree with R358, which were both also found in the WT docking of 3-FBA. However, the key difference to 3M appears to be the H100L mutation in the current variants. The imidazole sidechain of H100 interacts directly with the guanidinium of R91 in both WT-TK and 3M, and so the H100L mutation would directly modify the pKa for R91, most likely disfavouring ionisation and formation of a salt bridge to the 3-FBA substrate. This would explain the loss of the 3-FBA substrate orientation via R91 observed previously in 3M docking.</p>","<p id=\"Par39\">When comparing cluster 1 favoured by TK-1 and TK-3, to cluster 2 (preferred by TK2 and TK5), the key change was the R520Q mutation in TK2 and TK5. This mutation pulls 3-FBA slightly further away from the TPP-enamine (see distance d in Table ##TAB##3##4##), but also pulls it across the surface created by the D469T sidechain, thus enabling the aromatic ring face to rotate approximately 90 degrees and fit better into the cluster 2 binding pocket. By contrast, rotation of the 3-FBA ring face in TK-1 and TK-5, while maintaining the interaction with R520 would result in an unfavourable steric clash with the side chain of D469T.</p>"],"string":"[\n \"<title>Results and discussion</title>\",\n \"<p id=\\\"Par22\\\">Our goal was to find <italic>E. coli</italic> TK variants with improved activity towards aromatic acceptor aldehydes 3-FBA, 4-FBA and 3-HBA and with pyruvate as the donor, starting from the best variant to date, 6M (H192P/A282P/I365L/G506A/D469T/H100L). The 3M variant (S385Y/D469T/R520Q) was previously found to accept aromatic aldehydes, but only with hydroxypyruvate as donor. Previous attempts to introduce S385Y into 4M/D469T/H473N or 4M/D469T/H473N/R520Q actually led to a loss in activity towards 3-FBA with pyruvate<sup>##REF##31647171##4##</sup>. However, this mutation was not previously tested in 6M. Other mutations of S385, including non-natural substitutions within 3M (S385X/D469T/R520Q) were also found previously to improve the activity towards aromatic aldehydes, but again only when using HPA as donor substrate<sup>##REF##32897608##33##</sup>. The R520Q mutation has had a stabilising effect in several variants, often also improving activity<sup>##REF##25765309##5##,##REF##23079923##31##,##REF##22154561##38##</sup>, but it did not improve the activity towards 3-FBA when inserted into 4M/D469T/H473N previously<sup>##REF##31647171##4##</sup>. Therefore, we were interested now to explore whether any combinations of the various S385X mutations and also R520Q, could improve the activity of 6M towards aromatic aldehydes and pyruvate.</p>\",\n \"<title>TK variant activities with aromatic substrates</title>\",\n \"<p id=\\\"Par23\\\">A series of TK variants were generated, expressed, purified and then evaluated for their activities towards three aromatic aldehyde acceptor substrates at 50 mM, with 50 mM pyruvate as the donor substrate. The variant names, mutations and initial levels of conversion after 24 h, based on product peak areas as a fraction of total peak area for substrate and product, are shown in Table ##TAB##1##2##.</p>\",\n \"<p id=\\\"Par24\\\">Among all eight variants TK-1, TK-2, TK-3 and TK-5C showed the greatest conversion to product after 24 h, and compared to no conversion with TK-WT. TK-3 and TK-5C also showed the greatest conversion when using 4FBA. The level of conversion for 3HBA remained relatively low with all variants. In previous work, the 6M variant (4M/H100L/D469T) gave only a 2.5% conversion of 3-FBA after 24 h. This could be increased at 1.3 mg/mL enzyme to 46.8%, but was still lower than the 62.2% achieved with the new TK-3 variant. Given the greatest activities with 3-FBA we focused further characterisation on that substrate, selecting the four most prominent variants TK-1, TK-2, TK-3 and TK-5C. We also used the conversion of 3-FBA with each TK variant to isolate the product and confirm its identity as previously<sup>##REF##31647171##4##</sup> using a combination of LC–MS and NMR (##SUPPL##0##Supplementary information, Figs. S1, S2, S3##).</p>\",\n \"<title>Kinetic and stability studies of TK variants with substrate 3-FBA and pyruvate</title>\",\n \"<p id=\\\"Par25\\\">The kinetic parameters for TK-1, TK-2, TK-3 and TK-5C were determined at 0.1 mg/mL enzyme and 50 mM pyruvate by varying the concentration of 3-FBA from 0 to 150 mM, and monitoring reactions in triplicate for up to 24 h. The kinetic parameters are shown in Table ##TAB##2##3##, alongside the specific activity at 50 mM 3-FBA, and the conversion at 24 h (taken from Table ##TAB##1##2##). For comparison, the parameters determined previously for 6 M are also shown, although the <italic>K</italic><sub>m</sub> for 3-FBA was not determined as that experiment varied pyruvate instead. It was not possible to obtain parameters for WT-TK as no activity was detected and so rates and rate constants are set to 0, while the <italic>K</italic><sub>m</sub> cannot be determined.</p>\",\n \"<p id=\\\"Par26\\\">Compared to 6M, all four variants demonstrated significant increases in specific activity, <italic>V</italic><sub>m</sub> and <italic>k</italic><sub>cat</sub>. The specific activity of TK-1 in particular was 400× higher than for 6M, while the <italic>V</italic><sub>m</sub> was 900× greater. While the <italic>k</italic><sub>cat</sub> values for TK-1 and TK-3 were 2–3× higher than for TK-3 and TK-5C, their <italic>K</italic><sub>m</sub> values were correspondingly higher. The resulting <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub> values were fairly similar for TK-1, TK3 and TK-5C at 0.52–0.6/s mM, with TK-2 at a lower value of 0.2/s mM. Clearly the two larger <italic>K</italic><sub>m</sub> values (for TK-1 and TK-2) exceeded the studied range of 3-FBA (up to 150 mM), resulting in the larger errors on their estimated values.</p>\",\n \"<p id=\\\"Par27\\\">The stability of the variants was also measured using thermal scanning fluorimetry with intrinsic fluorescence as the probe. TK is already known to aggregate rapidly upon unfolding<sup>##REF##21723889##44##</sup>, but comparisons can still be made between variants with consistent ramping protocols, and so an apparent thermal transition mid-point (<italic>T</italic><sub>m</sub>) is estimated. The <italic>T</italic><sub>m</sub> for wild-type TK was 57.8 °C, and consistent with a previous measurement of 58.3 °C under similar conditions (0.1 mg/mL, 25 mM Tris–HCl, pH 7.5, 0.5 mM MgCl<sub>2</sub>, 0.05 mM TPP)<sup>##REF##21723889##44##</sup>.</p>\",\n \"<p id=\\\"Par28\\\">The variants all had increased <italic>T</italic><sub>m</sub> values, reaching 65.1 °C, and 63.7 °C for TK-3 and TK-5C respectively. TK-1 and TK-2 gave more modest increases to 59.2 °C and 61.4 °C respectively. It was previously shown that four of the mutations used within 6M increased the <italic>T</italic><sub>m</sub> over wild-type TK by 3.6 °C<sup>##REF##30404916##35##</sup>, whereas their introduction into 3M increased the <italic>T</italic><sub>m</sub> by 3 °C<sup>##REF##30530661##34##</sup>. These earlier experiments suggest that a significant part of the 1.4–7.3 °C increased stability in the new variants was likely to have already been present in 6M.</p>\",\n \"<p id=\\\"Par29\\\">Overall, TK-3 was the most stable, had the lowest <italic>K</italic><sub>m</sub> for 3-FBA and performed well in terms of catalytic efficiency (<italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub>) and conversion (62%). However, for biocatalysis under conditions where substrate could be added in excess over <italic>K</italic><sub>m</sub>, then the <italic>k</italic><sub>cat</sub> and stability are more important factors. With that perspective, the variants TK-1 and TK-2 might be preferred for their elevated activity despite the slightly lower <italic>T</italic><sub>m</sub> values than TK-3.</p>\",\n \"<p id=\\\"Par30\\\">It is worth comparing the impact of R520Q and S385X mutations here with their addition to previous variants. R520Q in TK-2 increased the <italic>T</italic><sub>m</sub> over TK-1 by 2.2 °C with no adverse impact on activity, consistent with its stabilising effects in previous studies. S385Y and S385F each had a significant impact on activity when added to 6M in the current study, leading to respective 630× and 200× increases in <italic>k</italic><sub>cat</sub> for the reaction with 3-FBA and pyruvate. This contrasts with the previous impact of adding S385Y into the D469T/R520Q variant (to form “3M”), which improved the catalytic efficiency towards 3-FBA and HPA by significantly decreasing the <italic>K</italic><sub>m</sub> for 3-FBA. Furthermore, when the S385Y mutation was incorporated into 4M/D469T/H473N and 4M/D469T/H473N/R520Q, it led to a loss of activity towards 3-FBA with pyruvate<sup>##REF##31647171##4##</sup>. The contextual mutations were clearly very important for the effects of S385Y. S385F gave a lower <italic>k</italic><sub>cat</sub>, but also lower <italic>K</italic><sub>m</sub> for 3-FBA, when compared to S385Y, which mirrored the same result in S385X/D469T/R520Q variants when tested on 3-HBA and hydroxpyruvate<sup>##REF##32897608##33##</sup>.</p>\",\n \"<title>Computational docking of 3-FBA into TK-WT, 7M and 8M</title>\",\n \"<p id=\\\"Par31\\\">To provide some structural insights into the effects of mutations on <italic>k</italic><sub>cat</sub> and <italic>K</italic><sub>m</sub>, we used computational docking of the 3-FBA substrate into the active site of the TK WT, TK-1 (7M), TK-2 (8M), TK-3 (7M) and TK-5C (8M) enzymes containing the enamine intermediate expected after prior reaction of pyruvate with the ThDP cofactor. Docking of the 3-FBA was performed in Autodock 4.2 which allowed both pose clustering (based on energy and RMSD for each pose) and an analysis of the relative populations of poses obtained for each cluster, from a total of 30. To analyse each pose and cluster in more detail, we calculated the distance, d, between the TPP enamine carbanion and the C-atom of the –CHO group of 3-FBA, to approximate the likelihood that a given pose was catalytically productive (not accounting for dynamics or angle of attack).</p>\",\n \"<p id=\\\"Par32\\\">The poses for each variant are mapped in Fig. ##FIG##2##2##, and docking parameters shown in Table ##TAB##3##4##. The calculated cluster-averaged binding energies were all in the range − 3.2 to − 4.4 kcal/mol, indicating broad energetic similarity between substrates in all clusters such that all could potentially provide sufficient binding for catalysis. However, not all binding orientations and positions would necessarily be productive for catalysis, and significant populations of non-reactive complexes in the actual enzyme could contribute to either an increase in the apparent <italic>K</italic><sub>m</sub> or even lead to substrate inhibition. The maximum RMSD within each cluster varied from 0.1 to 1.0 Å, indicating a small combined degree of variance in substrate position, orientation and conformation within each cluster.</p>\",\n \"<p id=\\\"Par33\\\">The docking produced two or three distinct clusters for each of the variants, but only a single cluster for WT (Table ##TAB##3##4##). We also analysed all poses together by visualising within aligned structures to determine structural similarities and differences between clusters from each variant. This revealed that across the four mutant variants there were actually only four clusters in total, with a few outlying poses, and that these were all distinct from the only cluster (cluster 0) in WT. Cluster 0, found only in WT, and for all WT poses, was unreactive with 7.5 < d < 8 Å. Results from Autodock Vina were consistent with Autodock 4.2 (Average d = 7.2 Å), although.</p>\",\n \"<p id=\\\"Par34\\\">Vina also identified a rare pose with d = 4.8 Å suggesting catalysis might be theoretically possible in WT-TK but achieving the correct substrate binding may be frustrated due to binding predominantly into a non-productive location.</p>\",\n \"<p id=\\\"Par35\\\">Of the four clusters in variants, cluster 4 was unreactive with 6.1 < d < 7.0 Å. TK2 contained the fewest potentially reactive poses (23%), with 67% in cluster 4. A further 10% for TK2 were in cluster 2b, a subcluster of cluster 2 but with a 180° relative rotation in the plane of the 3FBA ring leading to d > 9 Å. Clusters 1, 2 and 3 could be considered as “reactive”. Cluster 1 was preferred by TK1 and TK3, (even though TK3 found a single pose in cluster 2 with a lower binding energy). TK1 and TK3 each retained the wild-type arginine at residue 520. By contrast, Cluster 2 was preferred by TK2 and TK5, and these two variants contained the R520Q mutation.</p>\",\n \"<p id=\\\"Par36\\\">To test the relationship between population, distance and experimentally determined catalytic efficiency, we created a score (Table ##TAB##3##4##) that weighted distance by population of poses, and linearly scaled between two cut-off values such that a distance of ≤ 4 Å scored 100% and ≥ 8 Å scored 0%. Given the limitations of docking methods this analysis does not account for dynamics or specific alignment of orbitals in favourable ways, such as at the Bergi-Dunitz angle for nucleophilic attack of a carbonyl<sup>##UREF##13##45##</sup>. However, intriguingly the score scaled well with <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub> (Fig. ##FIG##3##3##), confirming that control of the distance to enamine, and population distributions were both major factors in the observed kinetic differences between the variants.</p>\",\n \"<title>How do mutations influence the binding populations?</title>\",\n \"<p id=\\\"Par37\\\">Each pose was mapped to the active site structure by aligning them via their respective TPP-enamine cofactors, to then identify common interactions with the enzyme, and the potential role of mutations (Fig. ##FIG##4##4##). In previous studies, the WT enzyme was found to be inactive on the aromatic aldehyde substrate 3FBA<sup>##REF##27767080##30##,##REF##23079923##31##</sup>. The earlier 3M variant which included the S385Y and D469T mutations, first introduced activity towards 3-FBA in reactions with hydroxypyruvate<sup>##REF##31647171##4##,##REF##25765309##5##</sup>. A crystal structure of 3M and subsequent computational substrate docking of 3FBA, 4FBA and 3HBA, revealed that D469T mutation gave a hydrophobic surface for more favourable interactions with the nonpolar acceptor substrates<sup>##REF##27767080##30##</sup>. The S385Y mutation meanwhile created opportunities for π–π stacking with the new substrates, but also sterically filled the active-site space to create a more defined binding pocket. Finally, the R520Q mutation decreased the potential for interaction with the carboxylate of 3-FBA or 4-FBA. Interestingly the docking in 3M previously showed two clusters arising from the formation of two distinct binding pockets with the aromatic ring positioned on either side of D469T. 3-FBA and 4-FBA oriented into different binding pockets, whereas 3-HBA could bind into either with no obvious preference. For 3-FBA the carboxylate group was oriented towards R91 to form a salt bridge interaction.</p>\",\n \"<p id=\\\"Par38\\\">In the present study, docking of 3-FBA into the four key variants and WT, again revealed multiple potentially reactive binding modes, dominated by cluster 1 (Fig. ##FIG##4##4##C and E) and cluster 2 (Fig. ##FIG##4##4##D and F). Interestingly, the 3-FBA orientation observed in 3 M was no longer populated in the current variants, none of which made use of the previous interaction between 3-FBA and R91. Instead, the carboxylate groups remained oriented towards R520 in TK-1 (Fig. ##FIG##4##4##C) and TK-3 (Fig. ##FIG##4##4##E) as might be expected, but surprisingly also towards R520Q in TK-2 (Fig. ##FIG##4##4##D) and TK-5C (Fig. ##FIG##4##4##F). This orientation in TK-2 and TK-5C was supported through interactions with nearby H461 and to a lesser degree with R358, which were both also found in the WT docking of 3-FBA. However, the key difference to 3M appears to be the H100L mutation in the current variants. The imidazole sidechain of H100 interacts directly with the guanidinium of R91 in both WT-TK and 3M, and so the H100L mutation would directly modify the pKa for R91, most likely disfavouring ionisation and formation of a salt bridge to the 3-FBA substrate. This would explain the loss of the 3-FBA substrate orientation via R91 observed previously in 3M docking.</p>\",\n \"<p id=\\\"Par39\\\">When comparing cluster 1 favoured by TK-1 and TK-3, to cluster 2 (preferred by TK2 and TK5), the key change was the R520Q mutation in TK2 and TK5. This mutation pulls 3-FBA slightly further away from the TPP-enamine (see distance d in Table ##TAB##3##4##), but also pulls it across the surface created by the D469T sidechain, thus enabling the aromatic ring face to rotate approximately 90 degrees and fit better into the cluster 2 binding pocket. By contrast, rotation of the 3-FBA ring face in TK-1 and TK-5, while maintaining the interaction with R520 would result in an unfavourable steric clash with the side chain of D469T.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":["<title>Conclusions</title>","<p id=\"Par40\">While earlier attempts to combine aromatic aldehyde-accepting mutations (S385Y, R520Q) into pyruvate accepting variants led to losses in activity, the current small library of S385X and R520Q mutations within the “6M” scaffold resulted in variants with up to 630× increases in <italic>k</italic><sub>cat</sub> for the reaction with 3-FBA and pyruvate. The best variants also retained enzyme stability as measured by their thermal transition midpoints. Variants with more modest 200× increases in <italic>k</italic><sub>cat</sub> had stabilities 4–5 °C higher. The <italic>K</italic><sub>m</sub> values of variants remained relatively high (80–530 mM) suggesting significant scope for further improvement, although in a biocatalytic process the aim would be to use high substrate concentrations that exceed the <italic>K</italic><sub>m</sub> values achieved.</p>","<p id=\"Par41\">This work demonstrates that rational recombination of mutations does not always combine their respective attributes, e.g. improved acceptance of new donor and acceptor substrates in the current case. However, by using small libraries based on the mutational sites identified previously, we can often find a successful solution to combining the multiple attributes.</p>","<p id=\"Par42\">Computational docking of substrates into the variant enzyme active sites explained the effects of the mutations in terms of shaping the active site pocket as well as in guiding the orientation of the aromatic aldehyde and its proximity to the enamine-TPP intermediate. The distance to the enamine achieved, weighted according to the population of poses found in each clustered position, gave a good correlation to the observed <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub>, indicating a strong role of that distance in improving enzyme activity, even before considering the more detailed effects of protein dynamics or specific orbital alignments.</p>"],"string":"[\n \"<title>Conclusions</title>\",\n \"<p id=\\\"Par40\\\">While earlier attempts to combine aromatic aldehyde-accepting mutations (S385Y, R520Q) into pyruvate accepting variants led to losses in activity, the current small library of S385X and R520Q mutations within the “6M” scaffold resulted in variants with up to 630× increases in <italic>k</italic><sub>cat</sub> for the reaction with 3-FBA and pyruvate. The best variants also retained enzyme stability as measured by their thermal transition midpoints. Variants with more modest 200× increases in <italic>k</italic><sub>cat</sub> had stabilities 4–5 °C higher. The <italic>K</italic><sub>m</sub> values of variants remained relatively high (80–530 mM) suggesting significant scope for further improvement, although in a biocatalytic process the aim would be to use high substrate concentrations that exceed the <italic>K</italic><sub>m</sub> values achieved.</p>\",\n \"<p id=\\\"Par41\\\">This work demonstrates that rational recombination of mutations does not always combine their respective attributes, e.g. improved acceptance of new donor and acceptor substrates in the current case. However, by using small libraries based on the mutational sites identified previously, we can often find a successful solution to combining the multiple attributes.</p>\",\n \"<p id=\\\"Par42\\\">Computational docking of substrates into the variant enzyme active sites explained the effects of the mutations in terms of shaping the active site pocket as well as in guiding the orientation of the aromatic aldehyde and its proximity to the enamine-TPP intermediate. The distance to the enamine achieved, weighted according to the population of poses found in each clustered position, gave a good correlation to the observed <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub>, indicating a strong role of that distance in improving enzyme activity, even before considering the more detailed effects of protein dynamics or specific orbital alignments.</p>\"\n]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Improving the range of substrates accepted by enzymes with high catalytic activity remains an important goal for the industrialisation of biocatalysis. Many enzymes catalyse two-substrate reactions which increases the complexity in engineering them for the synthesis of alternative products. Often mutations are found independently that can improve the acceptance of alternatives to each of the two substrates. Ideally, we would be able to combine mutations identified for each of the two alternative substrates, and so reprogramme new enzyme variants that synthesise specific products from their respective two-substrate combinations. However, as we have previously observed for <italic>E. coli</italic> transketolase, the mutations that improved activity towards aromatic acceptor aldehydes, did not successfully recombine with mutations that switched the donor substrate to pyruvate. This likely results from several active site residues having multiple roles that can affect both of the substrates, as well as structural interactions between the mutations themselves. Here, we have designed small libraries, including both natural and non-natural amino acids, based on the previous mutational sites that impact on acceptance of the two substrates, to achieve up to 630× increases in <italic>k</italic><sub>cat</sub> for the reaction with 3-formylbenzoic acid (3-FBA) and pyruvate. Computational docking was able to determine how the mutations shaped the active site to improve the proximity of the 3-FBA substrate relative to the enamine-TPP intermediate, formed after the initial reaction with pyruvate. This work opens the way for small libraries to rapidly reprogramme enzyme active sites in a plug and play approach to catalyse new combinations of two-substrate reactions.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Improving the range of substrates accepted by enzymes with high catalytic activity remains an important goal for the industrialisation of biocatalysis. Many enzymes catalyse two-substrate reactions which increases the complexity in engineering them for the synthesis of alternative products. Often mutations are found independently that can improve the acceptance of alternatives to each of the two substrates. Ideally, we would be able to combine mutations identified for each of the two alternative substrates, and so reprogramme new enzyme variants that synthesise specific products from their respective two-substrate combinations. However, as we have previously observed for <italic>E. coli</italic> transketolase, the mutations that improved activity towards aromatic acceptor aldehydes, did not successfully recombine with mutations that switched the donor substrate to pyruvate. This likely results from several active site residues having multiple roles that can affect both of the substrates, as well as structural interactions between the mutations themselves. Here, we have designed small libraries, including both natural and non-natural amino acids, based on the previous mutational sites that impact on acceptance of the two substrates, to achieve up to 630× increases in <italic>k</italic><sub>cat</sub> for the reaction with 3-formylbenzoic acid (3-FBA) and pyruvate. Computational docking was able to determine how the mutations shaped the active site to improve the proximity of the 3-FBA substrate relative to the enamine-TPP intermediate, formed after the initial reaction with pyruvate. This work opens the way for small libraries to rapidly reprogramme enzyme active sites in a plug and play approach to catalyse new combinations of two-substrate reactions.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary Information</title>","<p>\n</p>"],"string":"[\n \"<title>Supplementary Information</title>\",\n \"<p>\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary Information</title>","<p>The online version contains supplementary material available at 10.1038/s41598-024-51831-z.</p>","<title>Acknowledgements</title>","<p>We gratefully acknowledge the Engineering and Physical Sciences Research Council (EPSRC) for funding (EP/P006485/1) and also Marie Skłodowska-Curie Individual Fellowship (795539) that supported Arka Mukhopadhyay.</p>","<title>Author contributions</title>","<p>A.M. carried out the mutagenesis, purification and enzyme kinetics. A.M. and P.A.D performed data analyses and wrote the manuscript. P.A.D designed and supervised research.</p>","<title>Data availability</title>","<p>All data reported here are available on request to the corresponding author.</p>","<title>Competing interests</title>","<p id=\"Par43\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary Information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41598-024-51831-z.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>We gratefully acknowledge the Engineering and Physical Sciences Research Council (EPSRC) for funding (EP/P006485/1) and also Marie Skłodowska-Curie Individual Fellowship (795539) that supported Arka Mukhopadhyay.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>A.M. carried out the mutagenesis, purification and enzyme kinetics. A.M. and P.A.D performed data analyses and wrote the manuscript. P.A.D designed and supervised research.</p>\",\n \"<title>Data availability</title>\",\n \"<p>All data reported here are available on request to the corresponding author.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par43\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Lineages of <italic>E. coli</italic> TK variants. Multiple phases of rational, and semi-rational design guided by structure, bioinformatics and molecular dynamics simulations have introduced mutations that altered enzyme stability and the specificity for both aldol acceptor and ketol donor substrates. The 3M lineage first achieved activity on 3-FBA but with hydroxypyruvate as donor. The 6M lineage shown at the top right corner, was developed on acceptance of pyruvate as the donor, but achieved only low activity with 3-FBA. The current work explored combining the 3M and 6M lineages to improve activity of the reaction with 3-FBA and pyruvate.</p></caption></fig>","<fig id=\"Sch1\"><label>Scheme 1</label><caption><p>TK-catalysed synthesis of Phenylacetylcarbinol (PAC) analogue 3-(1-hydroxy-2-oxopropyl) benzoic acid from 3-formyl benzoic acid and pyruvate.</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Distribution of binding energy and reactive distance for 3-FBA docking poses. Distributions are shown by (<bold>A</bold>) TK variant and (<bold>B</bold>) Docking cluster.</p></caption></fig>","<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Correlation between <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub> and docking weighted score. The docking score is a linear function derived from up to three clustered populations and their mean distance between enamine and acceptor carbonyl, as Score = ∑ Pop<sub>i</sub>*(2–0.25*d<sub>i</sub>), limited 0–100% such that a distance of 4 Å or less scores as 100% while distances exceeding 8 Å score zero.</p></caption></fig>","<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Alignment of the top docking poses for each TK variant and WT. Alignments show (<bold>A</bold>) all five top poses aligned within the WT structure, and then top poses in their respective structures for (<bold>B</bold>) WT; (<bold>C</bold>) TK-1; (<bold>D</bold>) TK-2; (<bold>E</bold>) TK-3; (<bold>F</bold>) TK-5. 3-FBA is represented as thick lines. TPP-enamine is shown as salmon sticks. Mg<sup>2+</sup> cofactor is shown as green spheres. Docked 3-FBA is colour coded according the top structure in each cluster as (Green) Cluster 0 in WT only; (Magenta) Cluster 1; (Blue) Cluster 2.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>Lineages of <italic>E. coli</italic> TK variants. Multiple phases of rational, and semi-rational design guided by structure, bioinformatics and molecular dynamics simulations have introduced mutations that altered enzyme stability and the specificity for both aldol acceptor and ketol donor substrates. The 3M lineage first achieved activity on 3-FBA but with hydroxypyruvate as donor. The 6M lineage shown at the top right corner, was developed on acceptance of pyruvate as the donor, but achieved only low activity with 3-FBA. The current work explored combining the 3M and 6M lineages to improve activity of the reaction with 3-FBA and pyruvate.</p></caption></fig>\",\n \"<fig id=\\\"Sch1\\\"><label>Scheme 1</label><caption><p>TK-catalysed synthesis of Phenylacetylcarbinol (PAC) analogue 3-(1-hydroxy-2-oxopropyl) benzoic acid from 3-formyl benzoic acid and pyruvate.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>Distribution of binding energy and reactive distance for 3-FBA docking poses. Distributions are shown by (<bold>A</bold>) TK variant and (<bold>B</bold>) Docking cluster.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Figure 3</label><caption><p>Correlation between <italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>m</sub> and docking weighted score. The docking score is a linear function derived from up to three clustered populations and their mean distance between enamine and acceptor carbonyl, as Score = ∑ Pop<sub>i</sub>*(2–0.25*d<sub>i</sub>), limited 0–100% such that a distance of 4 Å or less scores as 100% while distances exceeding 8 Å score zero.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Figure 4</label><caption><p>Alignment of the top docking poses for each TK variant and WT. Alignments show (<bold>A</bold>) all five top poses aligned within the WT structure, and then top poses in their respective structures for (<bold>B</bold>) WT; (<bold>C</bold>) TK-1; (<bold>D</bold>) TK-2; (<bold>E</bold>) TK-3; (<bold>F</bold>) TK-5. 3-FBA is represented as thick lines. TPP-enamine is shown as salmon sticks. Mg<sup>2+</sup> cofactor is shown as green spheres. Docked 3-FBA is colour coded according the top structure in each cluster as (Green) Cluster 0 in WT only; (Magenta) Cluster 1; (Blue) Cluster 2.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":["<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Specific activities of previous variants towards aromatic aldehydes.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variant</th><th align=\"left\">Donor</th><th align=\"left\">Acceptor</th><th align=\"left\"><italic>k</italic><sub>cat</sub><break/>(/s)</th><th align=\"left\">Specific activity<break/>(µmol/mg min)</th><th align=\"left\">References</th></tr></thead><tbody><tr><td align=\"left\">3M: S385Y/D469T/R520Q</td><td align=\"left\">HPA</td><td align=\"left\">3-FBA</td><td align=\"left\">7.0</td><td char=\".\" align=\"char\">2.3</td><td align=\"left\"><sup>##REF##25765309##5##</sup></td></tr><tr><td align=\"left\">3M: S385Y/D469T/R520Q</td><td align=\"left\">HPA</td><td align=\"left\">4-FBA</td><td align=\"left\">n.d.</td><td char=\".\" align=\"char\">1.5</td><td align=\"left\"><sup>##REF##25765309##5##</sup></td></tr><tr><td align=\"left\">3M: S385Y/D469T/R520Q</td><td align=\"left\">HPA</td><td align=\"left\">3-HBA</td><td align=\"left\">n.d.</td><td char=\".\" align=\"char\">0.09</td><td align=\"left\"><sup>##REF##25765309##5##</sup></td></tr><tr><td align=\"left\">S385pCNF/D469T/R520Q</td><td align=\"left\">HPA</td><td align=\"left\">3-HBA</td><td align=\"left\">0.6</td><td char=\".\" align=\"char\">0.047</td><td align=\"left\"><sup>##REF##32897608##33##</sup></td></tr><tr><td align=\"left\">6M: H100L/H192P/A282P/I365L/D469T/G506A</td><td align=\"left\">PA</td><td align=\"left\">3-FBA</td><td align=\"left\">0.2</td><td char=\".\" align=\"char\">0.048</td><td align=\"left\"><sup>##REF##31647171##4##</sup></td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Conversion @24 h for all variants for 3FBA, 4FBA and 3HBA.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Name</th><th align=\"left\" rowspan=\"2\">Mutations</th><th align=\"left\" colspan=\"3\">Conversion @24 h (%)</th></tr><tr><th align=\"left\">3-FBA</th><th align=\"left\">4-FBA</th><th align=\"left\">3-HBA</th></tr></thead><tbody><tr><td align=\"left\">WT</td><td align=\"left\"/><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\">6M</td><td align=\"left\">4M+H100L+D469T</td><td align=\"left\">2.5</td><td align=\"left\">n.d.</td><td align=\"left\">n.d.</td></tr><tr><td align=\"left\">TK-1</td><td align=\"left\">6M+S385Y</td><td align=\"left\">61</td><td align=\"left\">15</td><td align=\"left\">41</td></tr><tr><td align=\"left\">TK-2</td><td align=\"left\">6M+R520Q/S385Y</td><td align=\"left\">61</td><td align=\"left\">15</td><td align=\"left\">27</td></tr><tr><td align=\"left\">TK-3</td><td align=\"left\">6M+S385F</td><td align=\"left\">62</td><td align=\"left\">9</td><td align=\"left\">42</td></tr><tr><td align=\"left\">TK-4A</td><td align=\"left\">6M+S385pAMF</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\">TK-4C</td><td align=\"left\">6M+S385pCNF</td><td align=\"left\">58</td><td align=\"left\">18</td><td align=\"left\">40</td></tr><tr><td align=\"left\">TK5A</td><td align=\"left\">6M+R520Q/S385pAMF</td><td align=\"left\">59</td><td align=\"left\">46</td><td align=\"left\">47</td></tr><tr><td align=\"left\">TK5C</td><td align=\"left\">6M+R520Q/S385pCNF</td><td align=\"left\">62</td><td align=\"left\">19</td><td align=\"left\">0</td></tr><tr><td align=\"left\">TK-6</td><td align=\"left\">6M+R520Q/S385F</td><td align=\"left\">62</td><td align=\"left\">15</td><td align=\"left\">44</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Enzyme kinetics and stability of selected variants.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variant</th><th align=\"left\">Mutations</th><th align=\"left\"><italic>V</italic><sub>m</sub> (mM/min)</th><th align=\"left\"><italic>k</italic><sub>cat</sub> (/s)</th><th align=\"left\"><italic>K</italic><sub>m</sub> (mM)</th><th align=\"left\">Specific activity<break/>at 50 mM 3-FBA<break/>(µmol/mg min)</th><th align=\"left\">Conversion @24 h<break/>(%)</th><th align=\"left\"><italic>T</italic><sub>m</sub> (°C)</th></tr></thead><tbody><tr><td align=\"left\">WT</td><td align=\"left\"/><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">n.d.</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">57.8</td></tr><tr><td align=\"left\">6M<sup>a</sup></td><td align=\"left\">4M+H100L+D469T</td><td align=\"left\">0.012</td><td align=\"left\">0.22 (0.01)</td><td align=\"left\">n.d.</td><td align=\"left\">0.048 (0.005)</td><td align=\"left\">2.5</td><td align=\"left\">n.d.</td></tr><tr><td align=\"left\">TK-1</td><td align=\"left\">6M+S385Y</td><td align=\"left\">11 (7)</td><td align=\"left\">134 (85)</td><td align=\"left\">220 (180)</td><td align=\"left\">20.3 (5)</td><td align=\"left\">61</td><td align=\"left\">59.2</td></tr><tr><td align=\"left\">TK-2</td><td align=\"left\">6M+R520Q/S385Y</td><td align=\"left\">9.7 (3)</td><td align=\"left\">118 (41)</td><td align=\"left\">530 (190)</td><td align=\"left\">8.3 (0.3)</td><td align=\"left\">61</td><td align=\"left\">61.4</td></tr><tr><td align=\"left\">TK-3</td><td align=\"left\">6M+S385F</td><td align=\"left\">3.5 (0.2)</td><td align=\"left\">43 (3)</td><td align=\"left\">82 (11)</td><td align=\"left\">13.3 (1.5)</td><td align=\"left\">62</td><td align=\"left\">65.1</td></tr><tr><td align=\"left\">TK-5C</td><td align=\"left\">6M+R520Q/S385pCNF</td><td align=\"left\">4.2 (1.1)</td><td align=\"left\">51 (13)</td><td align=\"left\">95 (45)</td><td align=\"left\">14.6 (3)</td><td align=\"left\">62</td><td align=\"left\">63.7</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Docking data for all variants with 3-FBA.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variant</th><th align=\"left\">Mutations</th><th align=\"left\">Cluster</th><th align=\"left\">population<break/>(%)</th><th align=\"left\">Δ<italic>G</italic><sub>av</sub><break/>(kcal/mol)</th><th align=\"left\">RMSD<sub>max</sub><break/>(Å)</th><th align=\"left\">d<break/>(Å)</th><th align=\"left\">Score</th></tr></thead><tbody><tr><td align=\"left\">WT</td><td align=\"left\"/><td align=\"left\">0</td><td align=\"left\">100</td><td align=\"left\">− 3.54</td><td align=\"left\">0.45</td><td align=\"left\">7.7</td><td align=\"left\">7.0</td></tr><tr><td align=\"left\" rowspan=\"5\">TK-1</td><td align=\"left\" rowspan=\"5\">6M+S385Y</td><td align=\"left\">1a</td><td align=\"left\">3.3</td><td align=\"left\">− 4.38</td><td align=\"left\">0</td><td align=\"left\">4.2</td><td align=\"left\">76</td></tr><tr><td align=\"left\">1</td><td align=\"left\">26.7</td><td align=\"left\">− 4.24</td><td align=\"left\">0.26</td><td align=\"left\">3.8</td><td align=\"left\"/></tr><tr><td align=\"left\">3</td><td align=\"left\">26.7</td><td align=\"left\">− 3.99</td><td align=\"left\">0.17</td><td align=\"left\">4.4</td><td align=\"left\"/></tr><tr><td align=\"left\">1b</td><td align=\"left\">6.7</td><td align=\"left\">− 3.61</td><td align=\"left\">0.19</td><td align=\"left\">4.0</td><td align=\"left\"/></tr><tr><td align=\"left\">4</td><td align=\"left\">36.7</td><td align=\"left\">− 3.54</td><td align=\"left\">0.19</td><td align=\"left\">6.5</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">TK-2</td><td align=\"left\" rowspan=\"4\">6M+R520Q/S385Y</td><td align=\"left\">2</td><td align=\"left\">20.0</td><td align=\"left\">− 3.89</td><td align=\"left\">0.35</td><td align=\"left\">4.7</td><td align=\"left\">44</td></tr><tr><td align=\"left\">2b</td><td align=\"left\">10.0</td><td align=\"left\">− 3.47</td><td align=\"left\">0.68</td><td align=\"left\">9.2</td><td align=\"left\"/></tr><tr><td align=\"left\">4</td><td align=\"left\">66.7</td><td align=\"left\">− 3.39</td><td align=\"left\">0.5</td><td align=\"left\">6.3</td><td align=\"left\"/></tr><tr><td align=\"left\">1b</td><td align=\"left\">3.3</td><td align=\"left\">− 3.22</td><td align=\"left\">0</td><td align=\"left\">4.6</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"4\">TK-3</td><td align=\"left\" rowspan=\"4\">6M+S385F</td><td align=\"left\">2</td><td align=\"left\">3.3</td><td align=\"left\">− 4.29</td><td align=\"left\">0</td><td align=\"left\">4.5</td><td align=\"left\">68</td></tr><tr><td align=\"left\">1</td><td align=\"left\">46.7</td><td align=\"left\">− 4.04</td><td align=\"left\">0.96</td><td align=\"left\">4.2</td><td align=\"left\"/></tr><tr><td align=\"left\">4</td><td align=\"left\">46.7</td><td align=\"left\">− 3.63</td><td align=\"left\">0.11</td><td align=\"left\">6.4</td><td align=\"left\"/></tr><tr><td align=\"left\">5*</td><td align=\"left\">3.3</td><td align=\"left\">− 3.51</td><td align=\"left\">0</td><td align=\"left\">6.1</td><td align=\"left\"/></tr><tr><td align=\"left\" rowspan=\"3\">TK-5C</td><td align=\"left\" rowspan=\"3\">6M+R520Q/S385pCNF</td><td align=\"left\">2</td><td align=\"left\">36.7</td><td align=\"left\">− 4.00</td><td align=\"left\">0.60</td><td align=\"left\">5</td><td align=\"left\">60</td></tr><tr><td align=\"left\">1</td><td align=\"left\">23.3</td><td align=\"left\">− 3.51</td><td align=\"left\">1.0</td><td align=\"left\">4.3</td><td align=\"left\"/></tr><tr><td align=\"left\">4</td><td align=\"left\">40.0</td><td align=\"left\">− 3.40</td><td align=\"left\">0.11</td><td align=\"left\">6.9</td><td align=\"left\"/></tr></tbody></table></table-wrap>"],"string":"[\n \"<table-wrap id=\\\"Tab1\\\"><label>Table 1</label><caption><p>Specific activities of previous variants towards aromatic aldehydes.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Variant</th><th align=\\\"left\\\">Donor</th><th align=\\\"left\\\">Acceptor</th><th align=\\\"left\\\"><italic>k</italic><sub>cat</sub><break/>(/s)</th><th align=\\\"left\\\">Specific activity<break/>(µmol/mg min)</th><th align=\\\"left\\\">References</th></tr></thead><tbody><tr><td align=\\\"left\\\">3M: S385Y/D469T/R520Q</td><td align=\\\"left\\\">HPA</td><td align=\\\"left\\\">3-FBA</td><td align=\\\"left\\\">7.0</td><td char=\\\".\\\" align=\\\"char\\\">2.3</td><td align=\\\"left\\\"><sup>##REF##25765309##5##</sup></td></tr><tr><td align=\\\"left\\\">3M: S385Y/D469T/R520Q</td><td align=\\\"left\\\">HPA</td><td align=\\\"left\\\">4-FBA</td><td align=\\\"left\\\">n.d.</td><td char=\\\".\\\" align=\\\"char\\\">1.5</td><td align=\\\"left\\\"><sup>##REF##25765309##5##</sup></td></tr><tr><td align=\\\"left\\\">3M: S385Y/D469T/R520Q</td><td align=\\\"left\\\">HPA</td><td align=\\\"left\\\">3-HBA</td><td align=\\\"left\\\">n.d.</td><td char=\\\".\\\" align=\\\"char\\\">0.09</td><td align=\\\"left\\\"><sup>##REF##25765309##5##</sup></td></tr><tr><td align=\\\"left\\\">S385pCNF/D469T/R520Q</td><td align=\\\"left\\\">HPA</td><td align=\\\"left\\\">3-HBA</td><td align=\\\"left\\\">0.6</td><td char=\\\".\\\" align=\\\"char\\\">0.047</td><td align=\\\"left\\\"><sup>##REF##32897608##33##</sup></td></tr><tr><td align=\\\"left\\\">6M: H100L/H192P/A282P/I365L/D469T/G506A</td><td align=\\\"left\\\">PA</td><td align=\\\"left\\\">3-FBA</td><td align=\\\"left\\\">0.2</td><td char=\\\".\\\" align=\\\"char\\\">0.048</td><td align=\\\"left\\\"><sup>##REF##31647171##4##</sup></td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab2\\\"><label>Table 2</label><caption><p>Conversion @24 h for all variants for 3FBA, 4FBA and 3HBA.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\" rowspan=\\\"2\\\">Name</th><th align=\\\"left\\\" rowspan=\\\"2\\\">Mutations</th><th align=\\\"left\\\" colspan=\\\"3\\\">Conversion @24 h (%)</th></tr><tr><th align=\\\"left\\\">3-FBA</th><th align=\\\"left\\\">4-FBA</th><th align=\\\"left\\\">3-HBA</th></tr></thead><tbody><tr><td align=\\\"left\\\">WT</td><td align=\\\"left\\\"/><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">6M</td><td align=\\\"left\\\">4M+H100L+D469T</td><td align=\\\"left\\\">2.5</td><td align=\\\"left\\\">n.d.</td><td align=\\\"left\\\">n.d.</td></tr><tr><td align=\\\"left\\\">TK-1</td><td align=\\\"left\\\">6M+S385Y</td><td align=\\\"left\\\">61</td><td align=\\\"left\\\">15</td><td align=\\\"left\\\">41</td></tr><tr><td align=\\\"left\\\">TK-2</td><td align=\\\"left\\\">6M+R520Q/S385Y</td><td align=\\\"left\\\">61</td><td align=\\\"left\\\">15</td><td align=\\\"left\\\">27</td></tr><tr><td align=\\\"left\\\">TK-3</td><td align=\\\"left\\\">6M+S385F</td><td align=\\\"left\\\">62</td><td align=\\\"left\\\">9</td><td align=\\\"left\\\">42</td></tr><tr><td align=\\\"left\\\">TK-4A</td><td align=\\\"left\\\">6M+S385pAMF</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">TK-4C</td><td align=\\\"left\\\">6M+S385pCNF</td><td align=\\\"left\\\">58</td><td align=\\\"left\\\">18</td><td align=\\\"left\\\">40</td></tr><tr><td align=\\\"left\\\">TK5A</td><td align=\\\"left\\\">6M+R520Q/S385pAMF</td><td align=\\\"left\\\">59</td><td align=\\\"left\\\">46</td><td align=\\\"left\\\">47</td></tr><tr><td align=\\\"left\\\">TK5C</td><td align=\\\"left\\\">6M+R520Q/S385pCNF</td><td align=\\\"left\\\">62</td><td align=\\\"left\\\">19</td><td align=\\\"left\\\">0</td></tr><tr><td align=\\\"left\\\">TK-6</td><td align=\\\"left\\\">6M+R520Q/S385F</td><td align=\\\"left\\\">62</td><td align=\\\"left\\\">15</td><td align=\\\"left\\\">44</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab3\\\"><label>Table 3</label><caption><p>Enzyme kinetics and stability of selected variants.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Variant</th><th align=\\\"left\\\">Mutations</th><th align=\\\"left\\\"><italic>V</italic><sub>m</sub> (mM/min)</th><th align=\\\"left\\\"><italic>k</italic><sub>cat</sub> (/s)</th><th align=\\\"left\\\"><italic>K</italic><sub>m</sub> (mM)</th><th align=\\\"left\\\">Specific activity<break/>at 50 mM 3-FBA<break/>(µmol/mg min)</th><th align=\\\"left\\\">Conversion @24 h<break/>(%)</th><th align=\\\"left\\\"><italic>T</italic><sub>m</sub> (°C)</th></tr></thead><tbody><tr><td align=\\\"left\\\">WT</td><td align=\\\"left\\\"/><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">n.d.</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">57.8</td></tr><tr><td align=\\\"left\\\">6M<sup>a</sup></td><td align=\\\"left\\\">4M+H100L+D469T</td><td align=\\\"left\\\">0.012</td><td align=\\\"left\\\">0.22 (0.01)</td><td align=\\\"left\\\">n.d.</td><td align=\\\"left\\\">0.048 (0.005)</td><td align=\\\"left\\\">2.5</td><td align=\\\"left\\\">n.d.</td></tr><tr><td align=\\\"left\\\">TK-1</td><td align=\\\"left\\\">6M+S385Y</td><td align=\\\"left\\\">11 (7)</td><td align=\\\"left\\\">134 (85)</td><td align=\\\"left\\\">220 (180)</td><td align=\\\"left\\\">20.3 (5)</td><td align=\\\"left\\\">61</td><td align=\\\"left\\\">59.2</td></tr><tr><td align=\\\"left\\\">TK-2</td><td align=\\\"left\\\">6M+R520Q/S385Y</td><td align=\\\"left\\\">9.7 (3)</td><td align=\\\"left\\\">118 (41)</td><td align=\\\"left\\\">530 (190)</td><td align=\\\"left\\\">8.3 (0.3)</td><td align=\\\"left\\\">61</td><td align=\\\"left\\\">61.4</td></tr><tr><td align=\\\"left\\\">TK-3</td><td align=\\\"left\\\">6M+S385F</td><td align=\\\"left\\\">3.5 (0.2)</td><td align=\\\"left\\\">43 (3)</td><td align=\\\"left\\\">82 (11)</td><td align=\\\"left\\\">13.3 (1.5)</td><td align=\\\"left\\\">62</td><td align=\\\"left\\\">65.1</td></tr><tr><td align=\\\"left\\\">TK-5C</td><td align=\\\"left\\\">6M+R520Q/S385pCNF</td><td align=\\\"left\\\">4.2 (1.1)</td><td align=\\\"left\\\">51 (13)</td><td align=\\\"left\\\">95 (45)</td><td align=\\\"left\\\">14.6 (3)</td><td align=\\\"left\\\">62</td><td align=\\\"left\\\">63.7</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab4\\\"><label>Table 4</label><caption><p>Docking data for all variants with 3-FBA.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th align=\\\"left\\\">Variant</th><th align=\\\"left\\\">Mutations</th><th align=\\\"left\\\">Cluster</th><th align=\\\"left\\\">population<break/>(%)</th><th align=\\\"left\\\">Δ<italic>G</italic><sub>av</sub><break/>(kcal/mol)</th><th align=\\\"left\\\">RMSD<sub>max</sub><break/>(Å)</th><th align=\\\"left\\\">d<break/>(Å)</th><th align=\\\"left\\\">Score</th></tr></thead><tbody><tr><td align=\\\"left\\\">WT</td><td align=\\\"left\\\"/><td align=\\\"left\\\">0</td><td align=\\\"left\\\">100</td><td align=\\\"left\\\">− 3.54</td><td align=\\\"left\\\">0.45</td><td align=\\\"left\\\">7.7</td><td align=\\\"left\\\">7.0</td></tr><tr><td align=\\\"left\\\" rowspan=\\\"5\\\">TK-1</td><td align=\\\"left\\\" rowspan=\\\"5\\\">6M+S385Y</td><td align=\\\"left\\\">1a</td><td align=\\\"left\\\">3.3</td><td align=\\\"left\\\">− 4.38</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">4.2</td><td align=\\\"left\\\">76</td></tr><tr><td align=\\\"left\\\">1</td><td align=\\\"left\\\">26.7</td><td align=\\\"left\\\">− 4.24</td><td align=\\\"left\\\">0.26</td><td align=\\\"left\\\">3.8</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">3</td><td align=\\\"left\\\">26.7</td><td align=\\\"left\\\">− 3.99</td><td align=\\\"left\\\">0.17</td><td align=\\\"left\\\">4.4</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">1b</td><td align=\\\"left\\\">6.7</td><td align=\\\"left\\\">− 3.61</td><td align=\\\"left\\\">0.19</td><td align=\\\"left\\\">4.0</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">4</td><td align=\\\"left\\\">36.7</td><td align=\\\"left\\\">− 3.54</td><td align=\\\"left\\\">0.19</td><td align=\\\"left\\\">6.5</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"4\\\">TK-2</td><td align=\\\"left\\\" rowspan=\\\"4\\\">6M+R520Q/S385Y</td><td align=\\\"left\\\">2</td><td align=\\\"left\\\">20.0</td><td align=\\\"left\\\">− 3.89</td><td align=\\\"left\\\">0.35</td><td align=\\\"left\\\">4.7</td><td align=\\\"left\\\">44</td></tr><tr><td align=\\\"left\\\">2b</td><td align=\\\"left\\\">10.0</td><td align=\\\"left\\\">− 3.47</td><td align=\\\"left\\\">0.68</td><td align=\\\"left\\\">9.2</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">4</td><td align=\\\"left\\\">66.7</td><td align=\\\"left\\\">− 3.39</td><td align=\\\"left\\\">0.5</td><td align=\\\"left\\\">6.3</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">1b</td><td align=\\\"left\\\">3.3</td><td align=\\\"left\\\">− 3.22</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">4.6</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"4\\\">TK-3</td><td align=\\\"left\\\" rowspan=\\\"4\\\">6M+S385F</td><td align=\\\"left\\\">2</td><td align=\\\"left\\\">3.3</td><td align=\\\"left\\\">− 4.29</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">4.5</td><td align=\\\"left\\\">68</td></tr><tr><td align=\\\"left\\\">1</td><td align=\\\"left\\\">46.7</td><td align=\\\"left\\\">− 4.04</td><td align=\\\"left\\\">0.96</td><td align=\\\"left\\\">4.2</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">4</td><td align=\\\"left\\\">46.7</td><td align=\\\"left\\\">− 3.63</td><td align=\\\"left\\\">0.11</td><td align=\\\"left\\\">6.4</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">5*</td><td align=\\\"left\\\">3.3</td><td align=\\\"left\\\">− 3.51</td><td align=\\\"left\\\">0</td><td align=\\\"left\\\">6.1</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\" rowspan=\\\"3\\\">TK-5C</td><td align=\\\"left\\\" rowspan=\\\"3\\\">6M+R520Q/S385pCNF</td><td align=\\\"left\\\">2</td><td align=\\\"left\\\">36.7</td><td align=\\\"left\\\">− 4.00</td><td align=\\\"left\\\">0.60</td><td align=\\\"left\\\">5</td><td align=\\\"left\\\">60</td></tr><tr><td align=\\\"left\\\">1</td><td align=\\\"left\\\">23.3</td><td align=\\\"left\\\">− 3.51</td><td align=\\\"left\\\">1.0</td><td align=\\\"left\\\">4.3</td><td align=\\\"left\\\"/></tr><tr><td align=\\\"left\\\">4</td><td align=\\\"left\\\">40.0</td><td align=\\\"left\\\">− 3.40</td><td align=\\\"left\\\">0.11</td><td align=\\\"left\\\">6.9</td><td align=\\\"left\\\"/></tr></tbody></table></table-wrap>\"\n]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":["<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"],"string":"[\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM1\\\"></supplementary-material>\"\n]","isConversational":false},"footnote":{"kind":"list like","value":["<table-wrap-foot><p>6M: H100L/H192P/A282P/I365L/D469T/G506A.</p><p>All enzymes purified. Reactions at 25 °C with 0.1 mg/mL TK, 50 mM Tris–HCl, 2.4 mM ThDP, 9 mM MgCl<sub>2</sub>, pH 7.0, and final concentrations of 50 mM aromatic aldehydes and 50 mM sodium pyruvate. 6 M reaction was at 0.07 mg/mL enzyme<sup>##REF##31647171##4##</sup>.</p></table-wrap-foot>","<table-wrap-foot><p><sup>a</sup>Kinetic parameters for 6M (H100L/H192P/A282P/I365L/D469T/G506A) were published previously<sup>##REF##31647171##4##</sup>, using 0.067 mg/mL enzyme and by varying pyruvate at 50 mM 3-FBA. <italic>K</italic><sub>m</sub> is for 3-FBA at 50 mM pyruvate. Errors (as SEM) are shown in parentheses.</p></table-wrap-foot>","<table-wrap-foot><p>Population, average binding energy, RMSD, distance to ThDP-enamine, and docking score are given for each pose cluster.</p><p>Score = ∑ Pop<sub>i</sub>*(2–0.25*d<sub>i</sub>), limited 0–100% such that a distance of 4 Å or less scores as 100% while distances exceeding 8 Å score zero. RMSD<sub>max</sub> is the highest RMSD for any pose within the cluster. *Cluster 5 only had a population of 1 pose across all variants.</p></table-wrap-foot>","<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"],"string":"[\n \"<table-wrap-foot><p>6M: H100L/H192P/A282P/I365L/D469T/G506A.</p><p>All enzymes purified. Reactions at 25 °C with 0.1 mg/mL TK, 50 mM Tris–HCl, 2.4 mM ThDP, 9 mM MgCl<sub>2</sub>, pH 7.0, and final concentrations of 50 mM aromatic aldehydes and 50 mM sodium pyruvate. 6 M reaction was at 0.07 mg/mL enzyme<sup>##REF##31647171##4##</sup>.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p><sup>a</sup>Kinetic parameters for 6M (H100L/H192P/A282P/I365L/D469T/G506A) were published previously<sup>##REF##31647171##4##</sup>, using 0.067 mg/mL enzyme and by varying pyruvate at 50 mM 3-FBA. <italic>K</italic><sub>m</sub> is for 3-FBA at 50 mM pyruvate. Errors (as SEM) are shown in parentheses.</p></table-wrap-foot>\",\n \"<table-wrap-foot><p>Population, average binding energy, RMSD, distance to ThDP-enamine, and docking score are given for each pose cluster.</p><p>Score = ∑ Pop<sub>i</sub>*(2–0.25*d<sub>i</sub>), limited 0–100% such that a distance of 4 Å or less scores as 100% while distances exceeding 8 Å score zero. RMSD<sub>max</sub> is the highest RMSD for any pose within the cluster. *Cluster 5 only had a population of 1 pose across all variants.</p></table-wrap-foot>\",\n \"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41598_2024_51831_Fig1_HTML\" id=\"MO1\"/>","<graphic xlink:href=\"41598_2024_51831_Sch1_HTML\" id=\"MO2\"/>","<graphic xlink:href=\"41598_2024_51831_Fig2_HTML\" id=\"MO3\"/>","<graphic xlink:href=\"41598_2024_51831_Fig3_HTML\" id=\"MO4\"/>","<graphic xlink:href=\"41598_2024_51831_Fig4_HTML\" id=\"MO5\"/>"],"string":"[\n \"<graphic xlink:href=\\\"41598_2024_51831_Fig1_HTML\\\" id=\\\"MO1\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51831_Sch1_HTML\\\" id=\\\"MO2\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51831_Fig2_HTML\\\" id=\\\"MO3\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51831_Fig3_HTML\\\" id=\\\"MO4\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51831_Fig4_HTML\\\" id=\\\"MO5\\\"/>\"\n]","isConversational":false},"media":{"kind":"list like","value":["<media xlink:href=\"41598_2024_51831_MOESM1_ESM.docx\"><caption><p>Supplementary Information.</p></caption></media>"],"string":"[\n \"<media xlink:href=\\\"41598_2024_51831_MOESM1_ESM.docx\\\"><caption><p>Supplementary Information.</p></caption></media>\"\n]","isConversational":false},"unknown_pub":{"kind":"string","value":"[{\"label\": [\"1.\"], \"surname\": [\"Albarr\\u00e1n-Velo\", \"Gonz\\u00e1lez-Mart\\u00ednez\", \"Gotor-Fern\\u00e1ndez\"], \"given-names\": [\"J\", \"D\", \"V\"], \"article-title\": [\"Stereoselective biocatalysis: A mature technology for the asymmetric synthesis of pharmaceutical building blocks\"], \"source\": [\"Biocatal. Biotrans.\"], \"year\": [\"2018\"], \"volume\": [\"36\"], \"fpage\": [\"102\"], \"lpage\": [\"130\"], \"pub-id\": [\"10.1080/10242422.2017.1340457\"]}, {\"label\": [\"12.\"], \"surname\": [\"Draths\"], \"given-names\": [\"KM\"], \"article-title\": [\"Biocatalytic synthesis of aromatics from D-glucose: The role of transketolase\"], \"source\": [\"J. Am. Chem. Soc.\"], \"year\": [\"1992\"], \"volume\": [\"114\"], \"fpage\": [\"3956\"], \"lpage\": [\"3962\"], \"pub-id\": [\"10.1021/ja00036a050\"]}, {\"label\": [\"13.\"], \"surname\": [\"\\u015awi\\u0119tochowska\", \"\\u0141ochowicz\", \"Ocal\", \"Pollegioni\", \"Charmantray\", \"Hecquet\", \"Szyma\\u0144ska\"], \"given-names\": [\"D\", \"A\", \"N\", \"L\", \"F\", \"L\", \"K\"], \"article-title\": [\"Co-immobilization of D-amino acid oxidase, catalase, and transketolase for one-pot, two-step synthesis of L-erythrulose\"], \"source\": [\"Catalysts\"], \"year\": [\"2023\"], \"volume\": [\"13\"], \"fpage\": [\"95\"], \"pub-id\": [\"10.3390/catal13010095\"]}, {\"label\": [\"14.\"], \"surname\": [\"Ocal\", \"Arbia\", \"Lagarde\", \"Joly\", \"Gittings\", \"Graham\", \"Charmantray\", \"Hecquet\"], \"given-names\": [\"N\", \"G\", \"A\", \"M\", \"S\", \"KM\", \"F\", \"L\"], \"article-title\": [\"Synthesis of deoxysugars from aliphatic \\u03b1-ketoacids and aldoses catalyzed by thermostable transketolase variants from \"], \"italic\": [\"Geobacillus\", \"stearothermophilus\"], \"source\": [\"Adv. Syn. Catal.\"], \"year\": [\"2023\"], \"volume\": [\"365\"], \"fpage\": [\"78\"], \"lpage\": [\"87\"], \"pub-id\": [\"10.1002/adsc.202201190\"]}, {\"label\": [\"15.\"], \"surname\": [\"F\\u00faster-Fern\\u00e1ndez\", \"Hecquet\", \"Fessner\"], \"given-names\": [\"I\", \"L\", \"WD\"], \"article-title\": [\"Transketolase catalyzed synthesis of N-aryl hydroxamic acids\"], \"source\": [\"Adv. Syn. Catal.\"], \"year\": [\"2022\"], \"volume\": [\"364\"], \"fpage\": [\"612\"], \"lpage\": [\"621\"], \"pub-id\": [\"10.1002/adsc.202101100\"]}, {\"label\": [\"22.\"], \"surname\": [\"Sprenger\", \"Pohl\"], \"given-names\": [\"GA\", \"M\"], \"article-title\": [\"Synthetic potential of thiamin diphosphate-dependent enzymes\"], \"source\": [\"J. Mol. Catal. B: Enzym.\"], \"year\": [\"1999\"], \"volume\": [\"6\"], \"fpage\": [\"145\"], \"lpage\": [\"159\"], \"pub-id\": [\"10.1016/S1381-1177(98)00107-6\"]}, {\"label\": [\"27.\"], \"surname\": [\"Zabar\", \"Lorilliere\", \"Yi\", \"Thangavelu\", \"Devamani\", \"Nauton\", \"Charmantray\", \"Helaine\", \"Fessner\", \"Hecquet\"], \"given-names\": [\"JA\", \"M\", \"D\", \"S\", \"T\", \"L\", \"F\", \"V\", \"WD\", \"L\"], \"article-title\": [\"Engineering a thermostable transketolase for unnatural conversion of (2S)-hydroxyaldehydes\"], \"source\": [\"Adv. Syn. Catal.\"], \"year\": [\"2015\"], \"volume\": [\"357\"], \"fpage\": [\"1715\"], \"lpage\": [\"1720\"], \"pub-id\": [\"10.1002/adsc.201500207\"]}, {\"label\": [\"28.\"], \"surname\": [\"Yi\", \"Saravanan\", \"Devamani\", \"Charmantray\", \"Hecquet\", \"Fessner\"], \"given-names\": [\"D\", \"T\", \"T\", \"F\", \"L\", \"WD\"], \"article-title\": [\"A thermostable transketolase evolved for aliphatic aldehyde acceptors\"], \"source\": [\"Chem. Commun.\"], \"year\": [\"2015\"], \"volume\": [\"51\"], \"fpage\": [\"480\"], \"lpage\": [\"483\"], \"pub-id\": [\"10.1039/C4CC08436E\"]}, {\"label\": [\"29.\"], \"surname\": [\"Saravanan\", \"Reif\", \"Yi\", \"Lorilliere\", \"Charmantray\", \"Hecquet\", \"Fessner\"], \"given-names\": [\"T\", \"ML\", \"D\", \"M\", \"F\", \"L\", \"WD\"], \"article-title\": [\"Engineering a thermostable transketolase for arylated substrates\"], \"source\": [\"Green Chem.\"], \"year\": [\"2017\"], \"volume\": [\"19\"], \"fpage\": [\"481\"], \"lpage\": [\"489\"], \"pub-id\": [\"10.1039/C6GC02017H\"]}, {\"label\": [\"32.\"], \"surname\": [\"Liu\", \"Xie\", \"Tang\", \"Tao\", \"Shi\", \"Zhang\", \"Huang\", \"Zhao\", \"Deng\", \"Lin\"], \"given-names\": [\"Q\", \"X\", \"M\", \"W\", \"T\", \"Y\", \"T\", \"Y\", \"Z\", \"S\"], \"article-title\": [\"One-pot asymmetric synthesis of an aminodiol intermediate of florfenicol using engineered transketolase and transaminase\"], \"source\": [\"ACS Catal.\"], \"year\": [\"2021\"], \"volume\": [\"11\"], \"fpage\": [\"7477\"], \"lpage\": [\"7488\"], \"pub-id\": [\"10.1021/acscatal.1c01229\"]}, {\"label\": [\"36.\"], \"surname\": [\"Saravanan\", \"Junker\", \"Kickstein\", \"Hein\", \"Link\", \"Ranglack\", \"Witt\", \"Lorilliere\", \"Hecquet\", \"Fessner\"], \"given-names\": [\"T\", \"S\", \"M\", \"S\", \"M-K\", \"J\", \"S\", \"M\", \"L\", \"WD\"], \"article-title\": [\"Donor promiscuity of a thermostable transketolase by directed evolution: Efficient complementation of 1-deoxy-d-xylulose-5-phosphate synthase activity\"], \"source\": [\"Angew. Intl. Ed.\"], \"year\": [\"2017\"], \"volume\": [\"56\"], \"fpage\": [\"5358\"], \"lpage\": [\"5362\"], \"pub-id\": [\"10.1002/anie.201701169\"]}, {\"label\": [\"37.\"], \"surname\": [\"Casajus\", \"Lagarde\", \"Leremboure\", \"Miguel\", \"Nauton\", \"Thery\", \"Fessner\", \"Duguet\", \"Charmantray\", \"Hecquet\"], \"given-names\": [\"H\", \"A\", \"M\", \"TD\", \"L\", \"V\", \"WD\", \"N\", \"F\", \"L\"], \"article-title\": [\"Enzymatic synthesis of aliphatic acyloins catalyzed by thermostable transketolase\"], \"source\": [\"Chem. Cat. Chem.\"], \"year\": [\"2020\"], \"volume\": [\"12\"], \"fpage\": [\"5772\"], \"lpage\": [\"5779\"]}, {\"label\": [\"39.\"], \"surname\": [\"Morris\", \"Rios-Solis\", \"Garc\\u00eda-Arrazola\", \"Lye\", \"Dalby\"], \"given-names\": [\"P\", \"L\", \"R\", \"GJ\", \"PA\"], \"article-title\": [\"Impact of cofactor-binding loop mutations on thermotolerance and activity of \"], \"italic\": [\"E. coli\"], \"source\": [\"Enzym. Microb. Technol.\"], \"year\": [\"2016\"], \"volume\": [\"89\"], \"fpage\": [\"85\"], \"lpage\": [\"91\"], \"pub-id\": [\"10.1016/j.enzmictec.2016.04.003\"]}, {\"label\": [\"45.\"], \"surname\": [\"Burgi\", \"Dunitz\", \"Lehn\", \"Wipff\"], \"given-names\": [\"HB\", \"JD\", \"JM\", \"G\"], \"article-title\": [\"Stereochemistry of reaction paths at carbonyl centres\"], \"source\": [\"Tetrahedron\"], \"year\": [\"1974\"], \"volume\": [\"30\"], \"fpage\": [\"1563\"], \"lpage\": [\"1572\"], \"pub-id\": [\"10.1016/S0040-4020(01)90678-7\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":45,"string":"45"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:57"},"citation":{"kind":"string","value":"Sci Rep. 2024 Jan 14; 14:1287"},"package_file":{"kind":"string","value":"oa_package/1e/64/PMC10787763.tar.gz"}}},{"rowIdx":94,"cells":{"accession_id":{"kind":"string","value":"PMC10787764"},"pmid":{"kind":"string","value":"38218910"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par3\">The contribution of multiple individuals to group decision-making can bring substantial benefits<sup>##REF##12520299##1##</sup>. Shared decisions can be more accurate<sup>##UREF##0##2##,##REF##16701304##3##</sup>, for example homing pigeons (<italic>Columbia livia</italic>) have more direct homing routes when flying in dyads than when flying alone<sup>##REF##17084696##4##</sup>. Shared decisions also allow all group members to acquire vital resources while remaining part of the group<sup>##UREF##1##5##</sup>, as they allow individuals in a state of need to influence group decisions<sup>##REF##33239284##6##</sup>. While many studies have found evidence in support for shared decision-making, for example by observing a range of different individuals initiating movements<sup>##REF##34754018##7##–##UREF##3##9##</sup>, the extent to which collective decision-making is governed by similar movement rules across species requires further investigation<sup>##REF##23197836##10##,##REF##34880130##11##</sup>.</p>","<p id=\"Par4\">Collective decisions can be an emergent outcome of the movement interactions among individuals<sup>##REF##15690039##12##</sup>. The classic theoretical model that proposes this hypothesis provides two sets of testable predictions: (i) that the geometry of a conflict in preferences among initiators (the angle of their directional vectors) should determine the actions of followers, and (ii) that individuals should follow a majority rule when choosing which direction to follow<sup>##REF##12520299##1##,##REF##34880130##11##,##REF##15690039##12##</sup>. The first prediction is that when faced with differences in the preferred direction of movement of group members, followers should average between directions if the disagreement among initiators is small (i.e. ‘compromise’) or choose one option over the other (i.e. ‘choose’) if the disagreement is large (above a critical angle<sup>##REF##15690039##12##</sup>). Greater disagreement (e.g. a larger angle between initiators and/or having more initiators proposing different directions) should also reduce the probability of following<sup>##UREF##4##13##</sup>. The second prediction is that when choosing a direction, followers should move where the majority of preferences are directed<sup>##REF##15690039##12##</sup>. These key predictions allow quantitative comparisons of the processes driving collective decisions across different species. However, testing these predictions is challenging, as they require information about how potential decision-makers—both initiators and followers—move relative to one-another<sup>##UREF##5##14##</sup>.</p>","<p id=\"Par5\">Two studies have provided evidence for the geometric prediction of the aforementioned classic model of collective motion in semi-wild or wild animal groups<sup>##REF##17084696##4##,##UREF##4##13##</sup>. GPS-tracking of pairs of homing pigeons showed that if the disagreement between the two birds’ directional preferences when flying back home was small, individuals averaged their routes. Instead, if disagreement was over a critical threshold, either the dyad split or one of the two birds became the leader<sup>##REF##17084696##4##</sup>. However, as the study was conducted on dyads, there was no test of the classic model’s prediction on which direction individuals would choose when faced with large disagreement and a numerical difference between the clusters of concurrent initiators. That gap was covered by a study<sup>##UREF##4##13##</sup> that fitted GPS trackers to the majority of individuals in a troop of olive baboons, a species in which individuals form groups with very stable membership. By analysing the relative movements of individuals, and extracting initiations and following behaviours, the study showed support for the two sets of predictions for shared decision-making emerging from interactions among individuals. First, individuals were less likely to follow when there was greatest directional conflict among initiators, but when following, individual baboons averaged proposed directions by initiators when the disagreement was small and chose one or the other when the disagreement was large<sup>##UREF##4##13##</sup>. Second, when choosing a direction, individual baboons used a majority rule—moving in the direction with the largest number of initiators<sup>##UREF##4##13##</sup>. Thus, evidence is beginning to suggest that emergent decision-making processes might be relatively common across animals that move as groups, and could potentially be underpinned by a consistent set of individual decision rules.</p>","<p id=\"Par6\">One challenge with determining whether species use similar rules when making decisions is that careful replication is required. While the replication crisis in biology<sup>##REF##26510635##15##–##REF##27879038##17##</sup> largely stems from the incentive structures favouring novelty<sup>##REF##27703703##18##</sup>, there are also logistical barriers to replication. For example, one recent study<sup>##REF##31915382##19##</sup> testing whether the increase of CO<sub>2</sub> in the ocean impacts the behaviour of coral reel fish replicated previous experiments by examining a large number of captive fish (900) from multiple species (6) and across several years (3), matching the conditions of older experiments and finding low support for the original results. However, critics—right or wrongly—noted that methodological differences could also contribute differences in the results<sup>##REF##33087911##20##</sup>, meaning that the true answer remains largely unknown. The challenges that are inherent with working with whole organisms, and with the different ecological conditions that they might experience in different studies, means that replications remain relatively rare. While large-scale collaborative networks<sup>##REF##33205462##21##,##REF##31648222##22##</sup> can overcome some of the barriers to making comparative studies, large-scale and long-term studies conducted in the wild often cannot be replicated, despite these being among the most influential<sup>##REF##30033518##23##–##REF##28105722##26##</sup>. A consequence of this is not only a lack of certainty in our scientific results, but also a lack of data on the generality of our findings.</p>","<p id=\"Par7\">Here, we conduct a within- and between-species replication study to investigate how consensus is achieved when individuals are faced with conflicting directional preferences among group members. We study vulturine guineafowl, a sympatric species to olive baboons that also forms stable and cohesive groups. A previous observational study on collective departures suggested that every member of a vulturine guineafowl group can initiate movement from a scattered food resource but that individuals excluded from clumped food patches are more likely to lead their group after receiving aggression<sup>##REF##33239284##6##</sup>. These findings indicated that dominance can play a role in modulating leadership—at least in the context of departures from food patches. In the present study, we fit high-resolution solar-powered GPS-trackers to almost all adults from two groups of vulturine guineafowl and implement the same analytical procedure as the previous study on baboons<sup>##UREF##4##13##</sup> to determine how group members reach consensus across a broader range of movements. We first confirm that all group members can successfully initiate movement, and that males (who are on top of the dominance hierarchy<sup>##UREF##7##27##</sup>) have greater influence on group movements than females. We then show that vulturine guineafowl express the same geometric properties and majority rule as predicted by the classic theoretical model of leadership and collective decision-making<sup>##REF##15690039##12##</sup>, and match almost exactly the empirical results observed in wild olive baboons<sup>##UREF##4##13##</sup>. Our study provides a powerful replication of previous empirical work, enabling quantitative comparisons between observational and GPS-based methods, and between two taxonomically distant species that live in the same habitat.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par3\\\">The contribution of multiple individuals to group decision-making can bring substantial benefits<sup>##REF##12520299##1##</sup>. Shared decisions can be more accurate<sup>##UREF##0##2##,##REF##16701304##3##</sup>, for example homing pigeons (<italic>Columbia livia</italic>) have more direct homing routes when flying in dyads than when flying alone<sup>##REF##17084696##4##</sup>. Shared decisions also allow all group members to acquire vital resources while remaining part of the group<sup>##UREF##1##5##</sup>, as they allow individuals in a state of need to influence group decisions<sup>##REF##33239284##6##</sup>. While many studies have found evidence in support for shared decision-making, for example by observing a range of different individuals initiating movements<sup>##REF##34754018##7##–##UREF##3##9##</sup>, the extent to which collective decision-making is governed by similar movement rules across species requires further investigation<sup>##REF##23197836##10##,##REF##34880130##11##</sup>.</p>\",\n \"<p id=\\\"Par4\\\">Collective decisions can be an emergent outcome of the movement interactions among individuals<sup>##REF##15690039##12##</sup>. The classic theoretical model that proposes this hypothesis provides two sets of testable predictions: (i) that the geometry of a conflict in preferences among initiators (the angle of their directional vectors) should determine the actions of followers, and (ii) that individuals should follow a majority rule when choosing which direction to follow<sup>##REF##12520299##1##,##REF##34880130##11##,##REF##15690039##12##</sup>. The first prediction is that when faced with differences in the preferred direction of movement of group members, followers should average between directions if the disagreement among initiators is small (i.e. ‘compromise’) or choose one option over the other (i.e. ‘choose’) if the disagreement is large (above a critical angle<sup>##REF##15690039##12##</sup>). Greater disagreement (e.g. a larger angle between initiators and/or having more initiators proposing different directions) should also reduce the probability of following<sup>##UREF##4##13##</sup>. The second prediction is that when choosing a direction, followers should move where the majority of preferences are directed<sup>##REF##15690039##12##</sup>. These key predictions allow quantitative comparisons of the processes driving collective decisions across different species. However, testing these predictions is challenging, as they require information about how potential decision-makers—both initiators and followers—move relative to one-another<sup>##UREF##5##14##</sup>.</p>\",\n \"<p id=\\\"Par5\\\">Two studies have provided evidence for the geometric prediction of the aforementioned classic model of collective motion in semi-wild or wild animal groups<sup>##REF##17084696##4##,##UREF##4##13##</sup>. GPS-tracking of pairs of homing pigeons showed that if the disagreement between the two birds’ directional preferences when flying back home was small, individuals averaged their routes. Instead, if disagreement was over a critical threshold, either the dyad split or one of the two birds became the leader<sup>##REF##17084696##4##</sup>. However, as the study was conducted on dyads, there was no test of the classic model’s prediction on which direction individuals would choose when faced with large disagreement and a numerical difference between the clusters of concurrent initiators. That gap was covered by a study<sup>##UREF##4##13##</sup> that fitted GPS trackers to the majority of individuals in a troop of olive baboons, a species in which individuals form groups with very stable membership. By analysing the relative movements of individuals, and extracting initiations and following behaviours, the study showed support for the two sets of predictions for shared decision-making emerging from interactions among individuals. First, individuals were less likely to follow when there was greatest directional conflict among initiators, but when following, individual baboons averaged proposed directions by initiators when the disagreement was small and chose one or the other when the disagreement was large<sup>##UREF##4##13##</sup>. Second, when choosing a direction, individual baboons used a majority rule—moving in the direction with the largest number of initiators<sup>##UREF##4##13##</sup>. Thus, evidence is beginning to suggest that emergent decision-making processes might be relatively common across animals that move as groups, and could potentially be underpinned by a consistent set of individual decision rules.</p>\",\n \"<p id=\\\"Par6\\\">One challenge with determining whether species use similar rules when making decisions is that careful replication is required. While the replication crisis in biology<sup>##REF##26510635##15##–##REF##27879038##17##</sup> largely stems from the incentive structures favouring novelty<sup>##REF##27703703##18##</sup>, there are also logistical barriers to replication. For example, one recent study<sup>##REF##31915382##19##</sup> testing whether the increase of CO<sub>2</sub> in the ocean impacts the behaviour of coral reel fish replicated previous experiments by examining a large number of captive fish (900) from multiple species (6) and across several years (3), matching the conditions of older experiments and finding low support for the original results. However, critics—right or wrongly—noted that methodological differences could also contribute differences in the results<sup>##REF##33087911##20##</sup>, meaning that the true answer remains largely unknown. The challenges that are inherent with working with whole organisms, and with the different ecological conditions that they might experience in different studies, means that replications remain relatively rare. While large-scale collaborative networks<sup>##REF##33205462##21##,##REF##31648222##22##</sup> can overcome some of the barriers to making comparative studies, large-scale and long-term studies conducted in the wild often cannot be replicated, despite these being among the most influential<sup>##REF##30033518##23##–##REF##28105722##26##</sup>. A consequence of this is not only a lack of certainty in our scientific results, but also a lack of data on the generality of our findings.</p>\",\n \"<p id=\\\"Par7\\\">Here, we conduct a within- and between-species replication study to investigate how consensus is achieved when individuals are faced with conflicting directional preferences among group members. We study vulturine guineafowl, a sympatric species to olive baboons that also forms stable and cohesive groups. A previous observational study on collective departures suggested that every member of a vulturine guineafowl group can initiate movement from a scattered food resource but that individuals excluded from clumped food patches are more likely to lead their group after receiving aggression<sup>##REF##33239284##6##</sup>. These findings indicated that dominance can play a role in modulating leadership—at least in the context of departures from food patches. In the present study, we fit high-resolution solar-powered GPS-trackers to almost all adults from two groups of vulturine guineafowl and implement the same analytical procedure as the previous study on baboons<sup>##UREF##4##13##</sup> to determine how group members reach consensus across a broader range of movements. We first confirm that all group members can successfully initiate movement, and that males (who are on top of the dominance hierarchy<sup>##UREF##7##27##</sup>) have greater influence on group movements than females. We then show that vulturine guineafowl express the same geometric properties and majority rule as predicted by the classic theoretical model of leadership and collective decision-making<sup>##REF##15690039##12##</sup>, and match almost exactly the empirical results observed in wild olive baboons<sup>##UREF##4##13##</sup>. Our study provides a powerful replication of previous empirical work, enabling quantitative comparisons between observational and GPS-based methods, and between two taxonomically distant species that live in the same habitat.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Methods</title>","<title>Data collection</title>","<p id=\"Par27\">Our study population of vulturine guineafowl resides in a savannah-woodland ecosystem of approximately 12 km<sup>2</sup> in the southern part of the Mpala Research Centre (MRC) in Laikipia, Kenya. Vulturine guineafowls are large (~1.5 kg), predominantly terrestrial, and live in relatively large groups (13–65 adults) with largely stable membership<sup>##REF##31689393##52##</sup>. Groups are not territorial and associate preferentially with specific other groups<sup>##REF##31689393##52##</sup>.</p>","<title>GPS trackers</title>","<p id=\"Par28\">We fitted with GPS solar-powered tags almost all adult members from two groups of vulturine guineafowl. We programmed the GPS tags to simultaneously collect 1 Hz data every fourth day from 06:00 to 19:00 by allowing them to fully recharge over three days before starting a full day of operating. For the purposes of other research projects running at the same time<sup>##UREF##20##53##</sup>, we set one to two tags in each group to work on a daily schedule and during some months the tags of all individuals in focal Group 2 where programmed to work on a daily basis (see Supplementary Table ##SUPPL##1##4## for the group size per month, number of tagged individuals, how long they were tracked and GPS tag programming setting, see also Supplementary Movies ##SUPPL##3##1##–##SUPPL##8##6## for a demonstration of the whole-group tracking datasets of Groups 1 and 2). This ‘daily’ setting recorded one data point (date, time, coordinates) every second when the battery had a high charge (approximately every second to third day, for up to 8 h continuously). When the battery was at the next highest threshold, tags recorded 10 points spanning the first 10 s of every fifth minute. At the lowest battery threshold, tags recorded one point every 15 min (this setting was used less than 1% of the time). We downloaded data remotely every two to three days using a BaseStation II (e-obs Digital Telemetry, Grünwald, Germany).</p>","<p id=\"Par29\">We conducted census observations every two days (on average) to record changes on group size and the number of tagged individuals per group across the study period, as some individuals got predated or lost their tags. We summarise this information in Supplementary Table ##SUPPL##1##4##.</p>","<title>Dominance hierarchies</title>","<p id=\"Par30\">First, to estimate the dominance hierarchy, we conducted all-occurrence sampling in each group, recording different types of agonistic interactions, as described by Papageorgiou & Farine<sup>##REF##33239284##6##</sup> and Dehnen et al. <sup>##UREF##7##27##</sup>. For each observed interaction, we recorded the time, the winner, and the loser. We recorded data over at least 3 sessions, lasting 2-3 h each, per group, per week across the study period (restricted to days when simultaneous GPS tracking was not taking place). From the agonistic interactions data, we calculated a dominance hierarchy for each group using the randomised Elo scores method<sup>##REF##29083030##54##</sup>.</p>","<p id=\"Par31\">To test if the dominance hierarchy remained stable during the study period, we calculated the repeatability score of ranks by randomising the order of the data, splitting the dataset in two halves, and calculating the Spearman rank correlation coefficients across the estimates of ranks from each half. We repeated this process 1000 times, using the function ‘estimate_uncertainty_by_splitting’ from the ‘aniDom’ R package<sup>##REF##29083030##54##</sup>, to estimate a mean and 95% confidence intervals of the correlation values.</p>","<title>Data processing</title>","<p id=\"Par32\">We used (and adapted where necessary) the methods and published code developed by Strandburg-Peshkin et al.<sup>##UREF##4##13##</sup>. We repeated each of the following steps on the data from the two study groups separately.</p>","<title>Pre-processing GPS data</title>","<p id=\"Par33\">We used the built-in features from the Movebank data repository to remove the outliers from our dataset that were falling outside of our study area (<0.001% of the data, corresponding to points that were often outside of Kenya). In the rare cases when a tag failed to log one point (e.g. skipping one second, 0.16–0.21% of the data in both groups), we linearly interpolated missing points based on the existing data around that point from the same tag. More specifically, if there was a missing value at time t, between t − 1 s and t + 1 s, we added one point in time t, in the middle of the straight line connecting the two known points of t − 1 s and t + 1 s.</p>","<title>Extracting successful and failed initiation attempts at the dyadic level</title>","<p id=\"Par34\">We extracted movement initiations, and their outcomes by identifying maxima and minima in the dyadic distance between a given pair of individuals. The data between a minima and a maxima identified cases when an individual moved away from another (i.e. an initiation). The subsequent behaviour of individual between the maxima and the following minima determined the interpretation of the event. If moved towards the direction of , the outcome was defined as a “pull”, whereas if moved back towards , then the outcome was defined as an “anchor”.</p>","<p id=\"Par35\">We used a set of thresholds to remove pulls and anchors potentially arising from GPS noise or small movements. Specifically, we defined initiation events as only those in which the minimum change in distance between and was more than 3.5 m. We believe this threshold to be biologically relevant considering the scale that the movements of vulturine guineafowl take place, especially given their high degree of spatial cohesion. It is also above the error of the GPS tags, as our field testing suggested that the estimated relative position of two GPS tags is accurate to within 1 m more than 95% of the time<sup>##REF##31689393##52##</sup>. Further, we determined that pull or anchor events required one individual doing a disproportionate amount of movement, setting a “disparity” threshold of 0.1, whereby 0 represents both individuals having moved equally during an event and 1 represents a single individual having done all of the moving during the event. Finally, we set a “strength” threshold to 0.1, which could range from 0 when the change in dyadic distance was very small relative to the total dyadic distance (i.e. small movements by individuals far away) to 1 when the change in dyadic distance was very large compared to the total dyadic distance (large movements by individuals that are in the same spot). The latter two are the same settings as the original study by Strandburg-Peshkin et al., whereas we set the minimum change in distance to a smaller value (3.5 m instead of 5 m) as vulturine guineafowl are substantially smaller and more cohesive in their movements than baboons. The dyadic distances throughout the process of initiation are shown in Fig. ##FIG##0##1##, confirming the small distances over which leadership interactions take place in vulturine guineafowl.</p>","<p id=\"Par36\">We only kept in subsequent analysis events that took place when at least half of group members’ tags were collecting data, which largely matched the distribution of the data in the original baboon study. In that study, 80% of adults and subadults were tagged, however some tags stopped working for periods of data collection, meaning that as few as 16 of the 26 collared baboons (55%) collected data on some days<sup>##UREF##4##13##</sup>. We also applied, and present, the results using a threshold keeping only events when at least 80% of group members’ tags collected data at the same time. The results are presented in the Supplementary Note 1 of the Supplementary Materials (Supplementary Tables ##SUPPL##1##5##–##SUPPL##1##8## and Supplementary Figs. ##SUPPL##1##4##–##SUPPL##1##8##) and show that the patterns in our results are not sensitive to the choice of threshold.</p>","<title>Identifying simultaneous initiation events</title>","<p id=\"Par37\">To investigate pulls and anchors beyond the dyadic level, we grouped together interactions (potential pulls and anchors) that operated simultaneously (i.e. involving one or more initiation attempts that overlapped in time) on one potential follower, and we defined this as an event. We considered interactions as overlapping in time using a chain rule, meaning that if interaction A overlapped with B, and interaction B overlapped with C, then all three would be combined into one event regardless of whether interaction A overlapped with interaction C. For each event, we calculated the direction of the initiators in relation to the position of the potential follower, whether the potential follower was pulled or not, and the direction of the subsequent movement of the follower if the follower was pulled. We defined events as successful if, and only if, at least one initiator was recorded as having pulled the potential follower. To test for a majority rule—whether followers moved in the direction with most initiators, we also clustered of initiators according to their direction using Gaussian Mixture Models<sup>##REF##22696481##55##</sup>.</p>","<title>Statistics and reproducibility</title>","<title>Does dominance predict influence?</title>","<p id=\"Par38\">To investigate if dominance predicts influence within each group, we created a matrix representing the relative influence among dyads, with the influence index in dyad, and defined as , where represents the number of events individual pulled individual . The index ranges from −1 ( pulled in all events) to 1 ( pulled in all events), with 0 representing no difference in influence among the two individuals. From these data, we calculated influence ranks by summing each individual’s indices and ranking these sums such that individuals with a larger sum were considered to be more influential.</p>","<p id=\"Par39\">We examined the effects of dominance and sex on influence rank by running four permutation tests:</p>","<p id=\"Par40\">(i) We calculated the mean absolute difference between dominance rank and influence rank for each individual. If dominant individuals were more highly ranked in the influence matrix, then we expected this value to approach 0. We evaluated the significance of our measure by recalculating the same value 1000 times after randomising the order of individuals’ dominance ranks relative to their influence ranks.</p>","<p id=\"Par41\">(ii–iii) We tested whether there was a within-sex effect of dominance by conducting the same test as (i) in males and females independently.</p>","<p id=\"Par42\">(iv) We tested whether males were more likely to be on the top of the influence hierarchy by calculating the mean of the absolute difference between two binary variables. The first variable represented whether an individual was within the top n-ranked more influential individuals, where n represented the number of males in the group. The second binary variable represented whether the individual was a male or not. We then re-calculated this value in 1000 permutations randomising the link between the two binary variables.</p>","<p id=\"Par43\">Then, we also examined the effects of dominance and sex on the successful initiation rates (pulls) per hour. To do this, we ran permutation tests similar to (i-iv), but we replaced influence rank with the individuals ranked according to the rates of successful initiations per hour.</p>","<p id=\"Par44\">In all the permutation tests, we considered an effect to be significant at α = 0.05 if the observed value was closer to 0 than 95% of the values generated by the permuted datasets.</p>","<p id=\"Par45\">Finally, to examine the effect of sex in the success rate of initiating, we extracted cases in which there were two simultaneous pullers comprising one male and one female initiator. We then calculated the proportion of time the male was the successful puller. We tested the significance of this measure by randomising the sexes across all of these events 1000 times. This allowed us to test if males were more successful pullers than expected by chance. As above, we considered an effect to be significant at α = 0.05 if the observed value was smaller than the probabilities of 95% of the permuted datasets.</p>","<title>How do the agreement and number of initiators affect whether initiators are successful?</title>","<p id=\"Par46\">We first tested the factors that contributed to individuals’ decisions about whether to follow or not. From the full set of events, we constructed a generalised estimating equations (GEE) model testing whether a focal individual would follow or not (binary response variable where pull = 1 and anchor = 0) was predicted by the level of agreement, and the number of initiators. We quantified directional agreement among simultaneous initiators using the circular variance (cv) of the unit vectors pointing from the potential follower to each initiator in the event, and defined agreement as 1 − cv. Values of agreement are close to 0 when individuals initiate in opposing directions and approach 1 when all individuals initiate in the same direction. Given that events, include all simultaneous initiations by default, our GEE did not include an autocorrelation structure. We used the R package ‘geepack’<sup>##UREF##21##56##</sup> to fit the GEE model.</p>","<title>How does the angle between initiators affect where followers move?</title>","<p id=\"Par47\">We tested whether the angle between initiators predicted where individuals moved in cases where a guineafowl did follow an initiation, focusing on events comprising two initiators. To identify which regime followers used (compromise or choose) for a given angle of disagreement, we ran a dip test of bimodality and a converging modes test (using the method developed by Hartigan & Hartigan<sup>##UREF##22##57##</sup>, and the code from Strandburg-Peshkin et al.<sup>##UREF##4##13##</sup>). If vulturine guineafowl were in the compromise regime, then the distribution of angles taken by the follower would not be significantly bimodal (according to the dip test) and would be more unimodal than expected by chance (according to the converging modes test). If neither of these conditions held, then vulturine guineafowl were in the choose regime. We interpreted situations in which one condition held but not the other as demarking a transition between the compromise and choose regimes. We ran these analyses by combining events into 12 degree bins of angular disagreement. We conducted this analysis independently on both groups, using code developed by Strandburg-Peshkin et al.<sup>##UREF##4##13##</sup>.</p>","<title>Where do guineafowl move when they choose one direction versus the other?</title>","<p id=\"Par48\">Finally, we investigated which direction followers chose when in the choose regime by examining the numerical difference among clusters of simultaneous pullers. Specifically, we expand the analysis in the previous section by looking at all events with more than one puller. In each of these events, we used a circular clustering algorithm<sup>##UREF##4##13##</sup> to identify clusters of individuals pulling in similar directions. We then extracted all of the events containing two clusters, and counted the number of individuals in each of the clusters. We then identified which of these clusters was successful, and related this to the numerical difference in the size of each cluster.</p>","<p id=\"Par49\">If guineafowl follow a majority rule, then they should be much more likely to follow numerically larger clusters. Following Strandburg-Peshkin et al. 2015<sup>##UREF##4##13##</sup>, we first fit a non-linear least square model where the response variable was the probability of choosing a randomly allocated cluster 1, while the predictor was the numerical difference between the number of individuals in cluster 1 minus the number of individuals in cluster 2. We then estimated the uncertainty for each bin (i.e. for each numerical difference between the size of initiating cluster 1 minus the size of initiating cluster 2) by drawing n samples from a uniform distribution, where n is the number of events in that bin, and calculating the probability that the random values are less than or equal to the observed probability. We repeated this process 1000 times, and extracted the lower 2.5th and upper 97.5th quantile of these probabilities as a measure of the 95% confidence intervals.</p>","<title>Reporting summary</title>","<p id=\"Par50\">Further information on research design is available in the ##SUPPL##9##Nature Portfolio Reporting Summary## linked to this article.</p>"],"string":"[\n \"<title>Methods</title>\",\n \"<title>Data collection</title>\",\n \"<p id=\\\"Par27\\\">Our study population of vulturine guineafowl resides in a savannah-woodland ecosystem of approximately 12 km<sup>2</sup> in the southern part of the Mpala Research Centre (MRC) in Laikipia, Kenya. Vulturine guineafowls are large (~1.5 kg), predominantly terrestrial, and live in relatively large groups (13–65 adults) with largely stable membership<sup>##REF##31689393##52##</sup>. Groups are not territorial and associate preferentially with specific other groups<sup>##REF##31689393##52##</sup>.</p>\",\n \"<title>GPS trackers</title>\",\n \"<p id=\\\"Par28\\\">We fitted with GPS solar-powered tags almost all adult members from two groups of vulturine guineafowl. We programmed the GPS tags to simultaneously collect 1 Hz data every fourth day from 06:00 to 19:00 by allowing them to fully recharge over three days before starting a full day of operating. For the purposes of other research projects running at the same time<sup>##UREF##20##53##</sup>, we set one to two tags in each group to work on a daily schedule and during some months the tags of all individuals in focal Group 2 where programmed to work on a daily basis (see Supplementary Table ##SUPPL##1##4## for the group size per month, number of tagged individuals, how long they were tracked and GPS tag programming setting, see also Supplementary Movies ##SUPPL##3##1##–##SUPPL##8##6## for a demonstration of the whole-group tracking datasets of Groups 1 and 2). This ‘daily’ setting recorded one data point (date, time, coordinates) every second when the battery had a high charge (approximately every second to third day, for up to 8 h continuously). When the battery was at the next highest threshold, tags recorded 10 points spanning the first 10 s of every fifth minute. At the lowest battery threshold, tags recorded one point every 15 min (this setting was used less than 1% of the time). We downloaded data remotely every two to three days using a BaseStation II (e-obs Digital Telemetry, Grünwald, Germany).</p>\",\n \"<p id=\\\"Par29\\\">We conducted census observations every two days (on average) to record changes on group size and the number of tagged individuals per group across the study period, as some individuals got predated or lost their tags. We summarise this information in Supplementary Table ##SUPPL##1##4##.</p>\",\n \"<title>Dominance hierarchies</title>\",\n \"<p id=\\\"Par30\\\">First, to estimate the dominance hierarchy, we conducted all-occurrence sampling in each group, recording different types of agonistic interactions, as described by Papageorgiou & Farine<sup>##REF##33239284##6##</sup> and Dehnen et al. <sup>##UREF##7##27##</sup>. For each observed interaction, we recorded the time, the winner, and the loser. We recorded data over at least 3 sessions, lasting 2-3 h each, per group, per week across the study period (restricted to days when simultaneous GPS tracking was not taking place). From the agonistic interactions data, we calculated a dominance hierarchy for each group using the randomised Elo scores method<sup>##REF##29083030##54##</sup>.</p>\",\n \"<p id=\\\"Par31\\\">To test if the dominance hierarchy remained stable during the study period, we calculated the repeatability score of ranks by randomising the order of the data, splitting the dataset in two halves, and calculating the Spearman rank correlation coefficients across the estimates of ranks from each half. We repeated this process 1000 times, using the function ‘estimate_uncertainty_by_splitting’ from the ‘aniDom’ R package<sup>##REF##29083030##54##</sup>, to estimate a mean and 95% confidence intervals of the correlation values.</p>\",\n \"<title>Data processing</title>\",\n \"<p id=\\\"Par32\\\">We used (and adapted where necessary) the methods and published code developed by Strandburg-Peshkin et al.<sup>##UREF##4##13##</sup>. We repeated each of the following steps on the data from the two study groups separately.</p>\",\n \"<title>Pre-processing GPS data</title>\",\n \"<p id=\\\"Par33\\\">We used the built-in features from the Movebank data repository to remove the outliers from our dataset that were falling outside of our study area (<0.001% of the data, corresponding to points that were often outside of Kenya). In the rare cases when a tag failed to log one point (e.g. skipping one second, 0.16–0.21% of the data in both groups), we linearly interpolated missing points based on the existing data around that point from the same tag. More specifically, if there was a missing value at time t, between t − 1 s and t + 1 s, we added one point in time t, in the middle of the straight line connecting the two known points of t − 1 s and t + 1 s.</p>\",\n \"<title>Extracting successful and failed initiation attempts at the dyadic level</title>\",\n \"<p id=\\\"Par34\\\">We extracted movement initiations, and their outcomes by identifying maxima and minima in the dyadic distance between a given pair of individuals. The data between a minima and a maxima identified cases when an individual moved away from another (i.e. an initiation). The subsequent behaviour of individual between the maxima and the following minima determined the interpretation of the event. If moved towards the direction of , the outcome was defined as a “pull”, whereas if moved back towards , then the outcome was defined as an “anchor”.</p>\",\n \"<p id=\\\"Par35\\\">We used a set of thresholds to remove pulls and anchors potentially arising from GPS noise or small movements. Specifically, we defined initiation events as only those in which the minimum change in distance between and was more than 3.5 m. We believe this threshold to be biologically relevant considering the scale that the movements of vulturine guineafowl take place, especially given their high degree of spatial cohesion. It is also above the error of the GPS tags, as our field testing suggested that the estimated relative position of two GPS tags is accurate to within 1 m more than 95% of the time<sup>##REF##31689393##52##</sup>. Further, we determined that pull or anchor events required one individual doing a disproportionate amount of movement, setting a “disparity” threshold of 0.1, whereby 0 represents both individuals having moved equally during an event and 1 represents a single individual having done all of the moving during the event. Finally, we set a “strength” threshold to 0.1, which could range from 0 when the change in dyadic distance was very small relative to the total dyadic distance (i.e. small movements by individuals far away) to 1 when the change in dyadic distance was very large compared to the total dyadic distance (large movements by individuals that are in the same spot). The latter two are the same settings as the original study by Strandburg-Peshkin et al., whereas we set the minimum change in distance to a smaller value (3.5 m instead of 5 m) as vulturine guineafowl are substantially smaller and more cohesive in their movements than baboons. The dyadic distances throughout the process of initiation are shown in Fig. ##FIG##0##1##, confirming the small distances over which leadership interactions take place in vulturine guineafowl.</p>\",\n \"<p id=\\\"Par36\\\">We only kept in subsequent analysis events that took place when at least half of group members’ tags were collecting data, which largely matched the distribution of the data in the original baboon study. In that study, 80% of adults and subadults were tagged, however some tags stopped working for periods of data collection, meaning that as few as 16 of the 26 collared baboons (55%) collected data on some days<sup>##UREF##4##13##</sup>. We also applied, and present, the results using a threshold keeping only events when at least 80% of group members’ tags collected data at the same time. The results are presented in the Supplementary Note 1 of the Supplementary Materials (Supplementary Tables ##SUPPL##1##5##–##SUPPL##1##8## and Supplementary Figs. ##SUPPL##1##4##–##SUPPL##1##8##) and show that the patterns in our results are not sensitive to the choice of threshold.</p>\",\n \"<title>Identifying simultaneous initiation events</title>\",\n \"<p id=\\\"Par37\\\">To investigate pulls and anchors beyond the dyadic level, we grouped together interactions (potential pulls and anchors) that operated simultaneously (i.e. involving one or more initiation attempts that overlapped in time) on one potential follower, and we defined this as an event. We considered interactions as overlapping in time using a chain rule, meaning that if interaction A overlapped with B, and interaction B overlapped with C, then all three would be combined into one event regardless of whether interaction A overlapped with interaction C. For each event, we calculated the direction of the initiators in relation to the position of the potential follower, whether the potential follower was pulled or not, and the direction of the subsequent movement of the follower if the follower was pulled. We defined events as successful if, and only if, at least one initiator was recorded as having pulled the potential follower. To test for a majority rule—whether followers moved in the direction with most initiators, we also clustered of initiators according to their direction using Gaussian Mixture Models<sup>##REF##22696481##55##</sup>.</p>\",\n \"<title>Statistics and reproducibility</title>\",\n \"<title>Does dominance predict influence?</title>\",\n \"<p id=\\\"Par38\\\">To investigate if dominance predicts influence within each group, we created a matrix representing the relative influence among dyads, with the influence index in dyad, and defined as , where represents the number of events individual pulled individual . The index ranges from −1 ( pulled in all events) to 1 ( pulled in all events), with 0 representing no difference in influence among the two individuals. From these data, we calculated influence ranks by summing each individual’s indices and ranking these sums such that individuals with a larger sum were considered to be more influential.</p>\",\n \"<p id=\\\"Par39\\\">We examined the effects of dominance and sex on influence rank by running four permutation tests:</p>\",\n \"<p id=\\\"Par40\\\">(i) We calculated the mean absolute difference between dominance rank and influence rank for each individual. If dominant individuals were more highly ranked in the influence matrix, then we expected this value to approach 0. We evaluated the significance of our measure by recalculating the same value 1000 times after randomising the order of individuals’ dominance ranks relative to their influence ranks.</p>\",\n \"<p id=\\\"Par41\\\">(ii–iii) We tested whether there was a within-sex effect of dominance by conducting the same test as (i) in males and females independently.</p>\",\n \"<p id=\\\"Par42\\\">(iv) We tested whether males were more likely to be on the top of the influence hierarchy by calculating the mean of the absolute difference between two binary variables. The first variable represented whether an individual was within the top n-ranked more influential individuals, where n represented the number of males in the group. The second binary variable represented whether the individual was a male or not. We then re-calculated this value in 1000 permutations randomising the link between the two binary variables.</p>\",\n \"<p id=\\\"Par43\\\">Then, we also examined the effects of dominance and sex on the successful initiation rates (pulls) per hour. To do this, we ran permutation tests similar to (i-iv), but we replaced influence rank with the individuals ranked according to the rates of successful initiations per hour.</p>\",\n \"<p id=\\\"Par44\\\">In all the permutation tests, we considered an effect to be significant at α = 0.05 if the observed value was closer to 0 than 95% of the values generated by the permuted datasets.</p>\",\n \"<p id=\\\"Par45\\\">Finally, to examine the effect of sex in the success rate of initiating, we extracted cases in which there were two simultaneous pullers comprising one male and one female initiator. We then calculated the proportion of time the male was the successful puller. We tested the significance of this measure by randomising the sexes across all of these events 1000 times. This allowed us to test if males were more successful pullers than expected by chance. As above, we considered an effect to be significant at α = 0.05 if the observed value was smaller than the probabilities of 95% of the permuted datasets.</p>\",\n \"<title>How do the agreement and number of initiators affect whether initiators are successful?</title>\",\n \"<p id=\\\"Par46\\\">We first tested the factors that contributed to individuals’ decisions about whether to follow or not. From the full set of events, we constructed a generalised estimating equations (GEE) model testing whether a focal individual would follow or not (binary response variable where pull = 1 and anchor = 0) was predicted by the level of agreement, and the number of initiators. We quantified directional agreement among simultaneous initiators using the circular variance (cv) of the unit vectors pointing from the potential follower to each initiator in the event, and defined agreement as 1 − cv. Values of agreement are close to 0 when individuals initiate in opposing directions and approach 1 when all individuals initiate in the same direction. Given that events, include all simultaneous initiations by default, our GEE did not include an autocorrelation structure. We used the R package ‘geepack’<sup>##UREF##21##56##</sup> to fit the GEE model.</p>\",\n \"<title>How does the angle between initiators affect where followers move?</title>\",\n \"<p id=\\\"Par47\\\">We tested whether the angle between initiators predicted where individuals moved in cases where a guineafowl did follow an initiation, focusing on events comprising two initiators. To identify which regime followers used (compromise or choose) for a given angle of disagreement, we ran a dip test of bimodality and a converging modes test (using the method developed by Hartigan & Hartigan<sup>##UREF##22##57##</sup>, and the code from Strandburg-Peshkin et al.<sup>##UREF##4##13##</sup>). If vulturine guineafowl were in the compromise regime, then the distribution of angles taken by the follower would not be significantly bimodal (according to the dip test) and would be more unimodal than expected by chance (according to the converging modes test). If neither of these conditions held, then vulturine guineafowl were in the choose regime. We interpreted situations in which one condition held but not the other as demarking a transition between the compromise and choose regimes. We ran these analyses by combining events into 12 degree bins of angular disagreement. We conducted this analysis independently on both groups, using code developed by Strandburg-Peshkin et al.<sup>##UREF##4##13##</sup>.</p>\",\n \"<title>Where do guineafowl move when they choose one direction versus the other?</title>\",\n \"<p id=\\\"Par48\\\">Finally, we investigated which direction followers chose when in the choose regime by examining the numerical difference among clusters of simultaneous pullers. Specifically, we expand the analysis in the previous section by looking at all events with more than one puller. In each of these events, we used a circular clustering algorithm<sup>##UREF##4##13##</sup> to identify clusters of individuals pulling in similar directions. We then extracted all of the events containing two clusters, and counted the number of individuals in each of the clusters. We then identified which of these clusters was successful, and related this to the numerical difference in the size of each cluster.</p>\",\n \"<p id=\\\"Par49\\\">If guineafowl follow a majority rule, then they should be much more likely to follow numerically larger clusters. Following Strandburg-Peshkin et al. 2015<sup>##UREF##4##13##</sup>, we first fit a non-linear least square model where the response variable was the probability of choosing a randomly allocated cluster 1, while the predictor was the numerical difference between the number of individuals in cluster 1 minus the number of individuals in cluster 2. We then estimated the uncertainty for each bin (i.e. for each numerical difference between the size of initiating cluster 1 minus the size of initiating cluster 2) by drawing n samples from a uniform distribution, where n is the number of events in that bin, and calculating the probability that the random values are less than or equal to the observed probability. We repeated this process 1000 times, and extracted the lower 2.5th and upper 97.5th quantile of these probabilities as a measure of the 95% confidence intervals.</p>\",\n \"<title>Reporting summary</title>\",\n \"<p id=\\\"Par50\\\">Further information on research design is available in the ##SUPPL##9##Nature Portfolio Reporting Summary## linked to this article.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<title>Sex but not dominance determine who has influence</title>","<p id=\"Par8\">Decisions by animal groups can be despotic, where one individual decides<sup>##REF##19026539##28##</sup>, partially shared (or graded), where some individuals contribute to decisions more than others<sup>##UREF##8##29##</sup>, or fully shared, where all individuals have an equal influence<sup>##UREF##4##13##</sup>. When contribution to decision-making is unequal, it has generally been predicted that dominant individuals should have greater influence<sup>##REF##28607061##30##</sup>, but this has received mixed support<sup>##REF##19026539##28##,##REF##32675244##31##–##UREF##9##34##</sup>.</p>","<p id=\"Par9\">We first explored whether leadership in vulturine guineafowl is fully shared or graded by quantifying the role of dominance on influence. Vulturine guineafowl groups have steep dominance hierarchies (see Supplementary Fig. ##SUPPL##1##1##), which remain stable for several months<sup>##REF##33239284##6##,##UREF##7##27##</sup>. We applied the approach described by Strandburg-Peshkin et al.<sup>##UREF##4##13##</sup> to infer who initiates movements and who follows, based on dyadic movement patterns from the GPS tracks collected simultaneously across group members (see Methods for details on GPS tracking). Initiation attempts were characterised by an increasing inter-individual distance followed by a decreasing inter-individual distance (see Methods and Fig. ##FIG##0##1##). Depending on the relative contribution of each individual to the change in distance, initiations were classified as being successful (‘pulls’, where A moves to increase the distance and B moves to subsequently reduce the distance) or unsuccessful (‘anchors’, where A moves to increase the distance but, subsequently, decreases it by moving back towards B).</p>","<p id=\"Par10\">Summarising 502,253 leader-follower cases from two social groups, we confirm that all group members can initiate movement and pull others, but that there is a distinct subset of individuals that are more likely to be followed (Fig. ##FIG##1##2A, D##). To investigate the relationship between dominance and the probability of being followed, we ran permutation tests within and between sexes (Fig. ##FIG##1##2B, C, E, F##, see the Methods section for details on the permutations). While it appears that more dominant individuals are more likely to be followed (Fig. ##FIG##1##2B i, E i##), analyses controlling for sex show it was rather that males, who are dominant over females, are more likely to be followed (Fig. ##FIG##1##2B ii-iii, C, E ii-iii, F##), and that there is no effect of dominance within sex. In a two-puller context comprising one male and one female initiator and where followers choose one direction (see below), the effect of sex translates to a difference in success rate of approximately 10% (Group 1; <italic>P</italic><sub>male success</sub> = 0.543, Group 2; <italic>P</italic><sub>male success</sub> = 0.553). However, success is not only determined by the probability of being followed, but also by the rate of initiating. When considering the number of successful initiations for each individual, we again find no effect of dominance but a consistent effect of sex (Fig. ##FIG##2##3##).</p>","<p id=\"Par11\">Our results show that leadership in vulturine guineafowl is shared, aligning with the previous work on olive baboons<sup>##UREF##4##13##</sup> and with direct observations in this system<sup>##REF##33239284##6##</sup>. However, unlike in baboons, leadership in vulturine guineafowl is not completely equal. Instead, it is graded, with males being more likely to be followed and initiating at higher rates (on average) relative to females. This difference to baboons may relate to the fact that males, who are dominant, are also the philopatric sex in vulturine guineafowl<sup>##REF##33977638##35##</sup>. Staying in their natal group potentially allows guineafowl males to maintain life-long, and thus stronger, influence relationships with other members of their group. The natal sex also has more information about the local landscape than the dispersing sex, which may contribute to observed differences (though we note that the baboon study<sup>##UREF##4##13##</sup> did not explicitly tested for a sex difference). This difference may not, however, always play a role in decision-making. Females were often as successful at initiating as male group members, and many females initiated movements more often than some males. The relatively small differences between male and female vulturine guineafowl is likely to reflect the relatively low rates of conflict in most of their collective movements. We found that guineafowl are substantially more likely to follow initiators (<italic>P</italic><sub>success</sub> range: 0.7–0.9) than baboons (<italic>P</italic><sub>success</sub> range: 0.2–0.8)<sup>##UREF##4##13##</sup>. This is likely to explain the high degree of cohesion and small intra-group dispersion of vulturine guineafowl.</p>","<title>Individuals are more likely to follow when initiators agree</title>","<p id=\"Par12\">We aggregated the simultaneous initiation attempts acting on single candidate follower individuals into ‘events’ based on their overlapping start and finish timestamps, following Strandburg-Peshkin et al. <sup>##UREF##4##13##</sup>. While initiation attempts lasted on average for 2.4 min (SD = 2.7), the temporal overlapping nature of initiations that were combined into events meant that events lasted longer than the initiation attempts themselves, with an average of 7.0 min (SD = 6.2; Supplementary Fig. ##SUPPL##1##2##). For each event, we calculated the direction of the initiators in relation to the position of the potential follower and their directional agreement. Directional agreement ranged from 0, when the movement vectors of initiators were equally distributed over potential directions, to 1 when the movement vectors were perfectly aligned (see Methods section for more details). We also noted whether the potential follower was subsequently pulled or not, and calculated the direction of the movement of the follower if the follower was pulled. We defined events as successful if at least one initiator pulled the potential follower.</p>","<p id=\"Par13\">We found that the number of simultaneous initiators, the level of their directional agreement, and the interaction between these two, all predict the probability of following a given initiation. In both groups, increasing the number of initiators has a positive effect on following when the angular agreement was high, but a negative effect on following when the agreement was low (Fig. ##FIG##3##4##; Supplementary Table ##SUPPL##1##1##). Although the results are consistent across both groups, only the interaction is significant in Group 2 (for which we collected substantially fewer data; see Supplementary Table ##SUPPL##1##2##). Supplementary analyses that account for changes in the number of tracked individuals and the distance between initiators and potential followers confirm that our results are robust to variation in data collection and to the assumptions of the methods.</p>","<p id=\"Par14\">The interaction between agreement and the number of initiators on the tendency for vulturine guineafowl to follow, matches closely with the behaviour of baboons. Specifically, baboons also require greater agreement when there are more initiators in order to follow<sup>##UREF##4##13##</sup>. In vulturine guineafowl, the patterns are also very similar across both groups: having more simultaneous initiators requires a higher agreement for individuals to follow, and high levels of agreement (>0.6) generally result in a better-than-chance (>0.5) probability of an event being successful. Baboons appear to be more tolerant of disagreement, with any agreement over 0.3 producing a better-than-chance probability of an initiation being successful<sup>##UREF##4##13##</sup>.</p>","<title>Followers compromise the initiation directions when initiators agree but choose a direction when initiators disagree</title>","<p id=\"Par15\">For each successful event, we tested the theoretical prediction<sup>##REF##15690039##12##</sup> that the angular agreement of the initiators should determine where a follower moves next. For simplicity, in this particular test we focused on events comprising two initiators (17.160% of all events for Group 1 and 18.157% of all events for Group 2, see Supplementary Table ##SUPPL##1##2##), allowing us to calculate the angle between the initiators relative to the potential follower. If a follower moves in a direction that averaged the angle between initiators (i.e. ‘compromise’), then we expected a unimodal distribution in the directions taken by followers across repeated observations at a given angular disagreement. By contrast, if a follower ‘chooses’ one or the other direction, then we expected a bimodal distribution in the directions taken by followers across repeated observations with the same angle of disagreement.</p>","<p id=\"Par16\">We found that the direction taken by vulturine guineafowl followers has identical properties to those predicted by theory and those found in baboons. Specifically, in both guineafowl groups, followers compromise the initiated directions when the disagreement between initiators is below a critical threshold that separates the two regimes, and choose one direction versus the other when the disagreement is above the threshold (Fig. ##FIG##4##5##, Supplementary Fig. ##SUPPL##1##3##).</p>","<p id=\"Par17\">As with previous results, we found strong concurrence between vulturine guineafowl and baboons. Baboons also express a transitional phase from compromise to choose, which is estimated to range between 72 and 96 degrees<sup>##UREF##4##13##</sup>. In Group 1, we found that the lower end of the transitional phase from compromise to choose is almost identical to that of baboons (78 degrees), but that the upper end is much higher (130 degrees). In Group 2, we could only find a transition threshold, which is estimated to be 117 degrees. But, as estimates for Group 2 are based on substantially fewer data, we expect that adding more data will reveal a larger range of uncertainty as in Group 1. The data from Group 2 do, however, also suggest that the upper end of the transition phase to the choose regime takes place at a larger angle in vulturine guineafowl than in baboons.</p>","<title>Followers move in the direction of the majority when choosing</title>","<p id=\"Par18\">To find where a follower moves when in the choose regime, we focused on cases when two or more individuals initiated toward different directions. We used a spatial clustering algorithm to identify sets of individuals co-initiating in similar directions, extracted cases in which there were exactly two clusters, and counted the number of individuals initiating in each of the two directions.</p>","<p id=\"Par19\">As predicted, we found support that vulturine guineafowl employ a majority rule when choosing one versus the other direction to move in (i.e. at high levels of disagreement, on the right side of each panel of Fig. ##FIG##4##5##). Specifically, in both Group 1 and Group 2, followers are disproportionately more likely to move in the direction containing the largest cluster of initiators (Fig. ##FIG##5##6##, Supplementary Table ##SUPPL##1##3##).</p>","<p id=\"Par20\">Our results confirm that vulturine guineafowl use a similar majority rule to baboons when choosing between directions. In baboons, individuals have an 80% chance of choosing the majority when the difference between the number of initiators in each cluster is three or more. By contrast, the model fits predict that vulturine guineafowl require a larger numerical difference (a difference of 8–9 for Group 1 and 4–5 for Group 2) to reach the same level of discrimination.</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>Sex but not dominance determine who has influence</title>\",\n \"<p id=\\\"Par8\\\">Decisions by animal groups can be despotic, where one individual decides<sup>##REF##19026539##28##</sup>, partially shared (or graded), where some individuals contribute to decisions more than others<sup>##UREF##8##29##</sup>, or fully shared, where all individuals have an equal influence<sup>##UREF##4##13##</sup>. When contribution to decision-making is unequal, it has generally been predicted that dominant individuals should have greater influence<sup>##REF##28607061##30##</sup>, but this has received mixed support<sup>##REF##19026539##28##,##REF##32675244##31##–##UREF##9##34##</sup>.</p>\",\n \"<p id=\\\"Par9\\\">We first explored whether leadership in vulturine guineafowl is fully shared or graded by quantifying the role of dominance on influence. Vulturine guineafowl groups have steep dominance hierarchies (see Supplementary Fig. ##SUPPL##1##1##), which remain stable for several months<sup>##REF##33239284##6##,##UREF##7##27##</sup>. We applied the approach described by Strandburg-Peshkin et al.<sup>##UREF##4##13##</sup> to infer who initiates movements and who follows, based on dyadic movement patterns from the GPS tracks collected simultaneously across group members (see Methods for details on GPS tracking). Initiation attempts were characterised by an increasing inter-individual distance followed by a decreasing inter-individual distance (see Methods and Fig. ##FIG##0##1##). Depending on the relative contribution of each individual to the change in distance, initiations were classified as being successful (‘pulls’, where A moves to increase the distance and B moves to subsequently reduce the distance) or unsuccessful (‘anchors’, where A moves to increase the distance but, subsequently, decreases it by moving back towards B).</p>\",\n \"<p id=\\\"Par10\\\">Summarising 502,253 leader-follower cases from two social groups, we confirm that all group members can initiate movement and pull others, but that there is a distinct subset of individuals that are more likely to be followed (Fig. ##FIG##1##2A, D##). To investigate the relationship between dominance and the probability of being followed, we ran permutation tests within and between sexes (Fig. ##FIG##1##2B, C, E, F##, see the Methods section for details on the permutations). While it appears that more dominant individuals are more likely to be followed (Fig. ##FIG##1##2B i, E i##), analyses controlling for sex show it was rather that males, who are dominant over females, are more likely to be followed (Fig. ##FIG##1##2B ii-iii, C, E ii-iii, F##), and that there is no effect of dominance within sex. In a two-puller context comprising one male and one female initiator and where followers choose one direction (see below), the effect of sex translates to a difference in success rate of approximately 10% (Group 1; <italic>P</italic><sub>male success</sub> = 0.543, Group 2; <italic>P</italic><sub>male success</sub> = 0.553). However, success is not only determined by the probability of being followed, but also by the rate of initiating. When considering the number of successful initiations for each individual, we again find no effect of dominance but a consistent effect of sex (Fig. ##FIG##2##3##).</p>\",\n \"<p id=\\\"Par11\\\">Our results show that leadership in vulturine guineafowl is shared, aligning with the previous work on olive baboons<sup>##UREF##4##13##</sup> and with direct observations in this system<sup>##REF##33239284##6##</sup>. However, unlike in baboons, leadership in vulturine guineafowl is not completely equal. Instead, it is graded, with males being more likely to be followed and initiating at higher rates (on average) relative to females. This difference to baboons may relate to the fact that males, who are dominant, are also the philopatric sex in vulturine guineafowl<sup>##REF##33977638##35##</sup>. Staying in their natal group potentially allows guineafowl males to maintain life-long, and thus stronger, influence relationships with other members of their group. The natal sex also has more information about the local landscape than the dispersing sex, which may contribute to observed differences (though we note that the baboon study<sup>##UREF##4##13##</sup> did not explicitly tested for a sex difference). This difference may not, however, always play a role in decision-making. Females were often as successful at initiating as male group members, and many females initiated movements more often than some males. The relatively small differences between male and female vulturine guineafowl is likely to reflect the relatively low rates of conflict in most of their collective movements. We found that guineafowl are substantially more likely to follow initiators (<italic>P</italic><sub>success</sub> range: 0.7–0.9) than baboons (<italic>P</italic><sub>success</sub> range: 0.2–0.8)<sup>##UREF##4##13##</sup>. This is likely to explain the high degree of cohesion and small intra-group dispersion of vulturine guineafowl.</p>\",\n \"<title>Individuals are more likely to follow when initiators agree</title>\",\n \"<p id=\\\"Par12\\\">We aggregated the simultaneous initiation attempts acting on single candidate follower individuals into ‘events’ based on their overlapping start and finish timestamps, following Strandburg-Peshkin et al. <sup>##UREF##4##13##</sup>. While initiation attempts lasted on average for 2.4 min (SD = 2.7), the temporal overlapping nature of initiations that were combined into events meant that events lasted longer than the initiation attempts themselves, with an average of 7.0 min (SD = 6.2; Supplementary Fig. ##SUPPL##1##2##). For each event, we calculated the direction of the initiators in relation to the position of the potential follower and their directional agreement. Directional agreement ranged from 0, when the movement vectors of initiators were equally distributed over potential directions, to 1 when the movement vectors were perfectly aligned (see Methods section for more details). We also noted whether the potential follower was subsequently pulled or not, and calculated the direction of the movement of the follower if the follower was pulled. We defined events as successful if at least one initiator pulled the potential follower.</p>\",\n \"<p id=\\\"Par13\\\">We found that the number of simultaneous initiators, the level of their directional agreement, and the interaction between these two, all predict the probability of following a given initiation. In both groups, increasing the number of initiators has a positive effect on following when the angular agreement was high, but a negative effect on following when the agreement was low (Fig. ##FIG##3##4##; Supplementary Table ##SUPPL##1##1##). Although the results are consistent across both groups, only the interaction is significant in Group 2 (for which we collected substantially fewer data; see Supplementary Table ##SUPPL##1##2##). Supplementary analyses that account for changes in the number of tracked individuals and the distance between initiators and potential followers confirm that our results are robust to variation in data collection and to the assumptions of the methods.</p>\",\n \"<p id=\\\"Par14\\\">The interaction between agreement and the number of initiators on the tendency for vulturine guineafowl to follow, matches closely with the behaviour of baboons. Specifically, baboons also require greater agreement when there are more initiators in order to follow<sup>##UREF##4##13##</sup>. In vulturine guineafowl, the patterns are also very similar across both groups: having more simultaneous initiators requires a higher agreement for individuals to follow, and high levels of agreement (>0.6) generally result in a better-than-chance (>0.5) probability of an event being successful. Baboons appear to be more tolerant of disagreement, with any agreement over 0.3 producing a better-than-chance probability of an initiation being successful<sup>##UREF##4##13##</sup>.</p>\",\n \"<title>Followers compromise the initiation directions when initiators agree but choose a direction when initiators disagree</title>\",\n \"<p id=\\\"Par15\\\">For each successful event, we tested the theoretical prediction<sup>##REF##15690039##12##</sup> that the angular agreement of the initiators should determine where a follower moves next. For simplicity, in this particular test we focused on events comprising two initiators (17.160% of all events for Group 1 and 18.157% of all events for Group 2, see Supplementary Table ##SUPPL##1##2##), allowing us to calculate the angle between the initiators relative to the potential follower. If a follower moves in a direction that averaged the angle between initiators (i.e. ‘compromise’), then we expected a unimodal distribution in the directions taken by followers across repeated observations at a given angular disagreement. By contrast, if a follower ‘chooses’ one or the other direction, then we expected a bimodal distribution in the directions taken by followers across repeated observations with the same angle of disagreement.</p>\",\n \"<p id=\\\"Par16\\\">We found that the direction taken by vulturine guineafowl followers has identical properties to those predicted by theory and those found in baboons. Specifically, in both guineafowl groups, followers compromise the initiated directions when the disagreement between initiators is below a critical threshold that separates the two regimes, and choose one direction versus the other when the disagreement is above the threshold (Fig. ##FIG##4##5##, Supplementary Fig. ##SUPPL##1##3##).</p>\",\n \"<p id=\\\"Par17\\\">As with previous results, we found strong concurrence between vulturine guineafowl and baboons. Baboons also express a transitional phase from compromise to choose, which is estimated to range between 72 and 96 degrees<sup>##UREF##4##13##</sup>. In Group 1, we found that the lower end of the transitional phase from compromise to choose is almost identical to that of baboons (78 degrees), but that the upper end is much higher (130 degrees). In Group 2, we could only find a transition threshold, which is estimated to be 117 degrees. But, as estimates for Group 2 are based on substantially fewer data, we expect that adding more data will reveal a larger range of uncertainty as in Group 1. The data from Group 2 do, however, also suggest that the upper end of the transition phase to the choose regime takes place at a larger angle in vulturine guineafowl than in baboons.</p>\",\n \"<title>Followers move in the direction of the majority when choosing</title>\",\n \"<p id=\\\"Par18\\\">To find where a follower moves when in the choose regime, we focused on cases when two or more individuals initiated toward different directions. We used a spatial clustering algorithm to identify sets of individuals co-initiating in similar directions, extracted cases in which there were exactly two clusters, and counted the number of individuals initiating in each of the two directions.</p>\",\n \"<p id=\\\"Par19\\\">As predicted, we found support that vulturine guineafowl employ a majority rule when choosing one versus the other direction to move in (i.e. at high levels of disagreement, on the right side of each panel of Fig. ##FIG##4##5##). Specifically, in both Group 1 and Group 2, followers are disproportionately more likely to move in the direction containing the largest cluster of initiators (Fig. ##FIG##5##6##, Supplementary Table ##SUPPL##1##3##).</p>\",\n \"<p id=\\\"Par20\\\">Our results confirm that vulturine guineafowl use a similar majority rule to baboons when choosing between directions. In baboons, individuals have an 80% chance of choosing the majority when the difference between the number of initiators in each cluster is three or more. By contrast, the model fits predict that vulturine guineafowl require a larger numerical difference (a difference of 8–9 for Group 1 and 4–5 for Group 2) to reach the same level of discrimination.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par21\">Our study shows that the movements of vulturine guineafowl are consistent with the predictions from a classic theoretical model of leadership and collective decision-making, and have striking similarities to the movements described in taxonomically distant but sympatric olive baboons<sup>##UREF##4##13##</sup>. In both our guineafowl study groups, we found that any individual could initiate movement, with no direct link between dominance and influence. Male guineafowl are more likely to be followed than females, and also have slightly higher rates of initiations. However, females still initiated often, and many had a high number of successful attempts. Like in baboons, conflicts in vulturine guineafowl group decisions affect the probability that initiators are followed and, when they do, follower movements fall into one of two regimes: when the disagreement between concurrent initiators is small followers average the directions of the initiators and when the disagreement is large they choose the direction with the most initiators. Our study also demonstrates the importance of replication in ecology and animal behaviour<sup>##REF##26510635##15##,##REF##32607143##16##,##UREF##10##36##</sup>, showing that by following the same methods and conducting the same statistical tests we could reveal that the emergence of collective decisions from simple rules governing group cohesion are likely to be consistent across very distinct taxonomic groups.</p>","<p id=\"Par22\">While influence can be distributed within the group<sup>##UREF##11##37##</sup>, whereby all individuals can initiate movement and be followed, it is not necessarily equal among group members<sup>##UREF##8##29##,##UREF##12##38##,##UREF##13##39##</sup>. For example, homing pigeons form influence hierarchies during flight and these hierarchies determine whom an individual is likely to lead and are most likely to be led by Nagy et al.<sup>##REF##20376149##32##</sup>. However, these influence hierarchies are independent from dominance hierarchies<sup>##REF##23878247##33##</sup>. In vulturine guineafowl, we found that leadership is generally shared, but that males are more likely to be followed and initiate more often than females. This difference reflects the social structure of vulturine guineafowl societies, where males are dominant over all females<sup>##REF##33239284##6##,##UREF##7##27##</sup>, who are also the dispersing sex<sup>##REF##33977638##35##</sup>. However, males have been found to be more influential than females in collective departures also in species in which female matrilines dominate aggression hierarchies<sup>##REF##34754018##7##,##UREF##14##40##</sup>, suggesting that neither the dominance hierarchy alone, nor the dispersal tendency of the sexes, always determine which individuals influence group coordination. Further, while the differences we found between males and females are significant, females still had substantial influence over where groups moved. One key outstanding question is therefore to identify whether there are specific contexts in which the ability to exert influence (i.e. have a higher probability of being followed) may be important.</p>","<p id=\"Par23\">Within each sex group of vulturine guineafowl, we found no evidence for dominance playing a role on influence (although in both groups, the lowest ranking female initiated less often than almost any other group member). While it appears that female vulturine guineafowl have overall less influence than males on where their group goes, unlike in other species<sup>##UREF##15##41##–##UREF##17##45##</sup>, it is also possible that females exhibit specific strategies to influence decisions<sup>##REF##33168743##46##,##REF##35597702##47##</sup>. For example, they could influence when groups leave<sup>##REF##33239284##6##</sup>, as has been suggested in baboons<sup>##UREF##18##48##</sup>, with timing decisions potentially reflecting a distinct axis of decision-making<sup>##UREF##4##13##,##UREF##19##49##</sup>. Such routes of influence would not be obvious from the analytical approach that was employed in the current and baboon studies<sup>##UREF##4##13##</sup>. Rather, our approach likely captured a number of very general collective movements, including many moment-by-moment decisions (e.g. which way to move around a tree). Identifying the functional importance of each decision (e.g. those that dictate where groups move next at larger spatial scales) remains a challenge in the field.</p>","<p id=\"Par24\">Despite the properties of follower movements in response to initiators being very similar across vulturine guineafowl and baboons, details may vary from group to group<sup>##REF##34703618##50##</sup> and from one context to the next<sup>##REF##24618695##51##</sup>. In theory, larger group sizes should be associated with a decrease in the angle at which follower movements transition from compromise to choose<sup>##REF##15690039##12##</sup>. Group 1 was similar in size to the previously studied baboon group, and their transitional phase overlapped<sup>##UREF##4##13##</sup>. Further, the transitional phase of Group 1 started at values that were almost 40 degrees smaller than the smaller Group 2, albeit it also went beyond that of Group 2 (Fig. ##FIG##5##6##). Given this overlap, we can’t safely draw conclusions on whether our findings support the theoretical predictions that indicate that larger groups show a smaller transitional angle, and therefore data on more groups of different sizes are required to address this question. The potential influence of the social, as well as the physical environment is worth exploring, given that environmental effects have already been documented across various facets of collective behaviour<sup>##REF##24618695##51##</sup>.</p>","<p id=\"Par25\">One behaviour where we could find some clearer differences between groups is when looking at the majority rule employed by each. In vulturine guineafowl, the smaller group (Group 2) appeared to require a smaller threshold in order to identify a clear majority. A key question is whether the shift in the threshold scales with group size. Our data suggest that the larger group (Group 1) reliably chose the majority (80% of the time) when there was a higher proportional difference (approximately one third of the group 1) in initiators compared to the smaller group (approximately one quarter of the group 2). Baboons required a much smaller majority to reach the same 80% threshold<sup>##UREF##4##13##</sup>. Given that the baboon troop that was studied was larger than Group 1 (and had a very similar proportion of GPS-tracked group members), these results suggest that discrimination may be harder in larger groups, and that baboons could have a better capacity to discriminate smaller relative differences than vulturine guineafowl.</p>","<p id=\"Par26\">Our results show that the processes driving the movement patterns of wild group-living vulturine guineafowl are largely consistent with those previously described in a group of wild baboons<sup>##UREF##4##13##</sup> and in dyads of homing pigeons<sup>##REF##17084696##4##</sup>, with the specific directions of movements by individuals and responses to conflict when following in particular matching those of olive baboons. Our work adds to the weight of support for predictions arising from a classic theoretical model of leadership and collective decision-making<sup>##REF##15690039##12##</sup>. Further, by carefully conducting a large-scale within- and between-species replication, we propose that a multitude of group-living species could exhibit highly convergent processes governing how they reach consensus on where to move.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par21\\\">Our study shows that the movements of vulturine guineafowl are consistent with the predictions from a classic theoretical model of leadership and collective decision-making, and have striking similarities to the movements described in taxonomically distant but sympatric olive baboons<sup>##UREF##4##13##</sup>. In both our guineafowl study groups, we found that any individual could initiate movement, with no direct link between dominance and influence. Male guineafowl are more likely to be followed than females, and also have slightly higher rates of initiations. However, females still initiated often, and many had a high number of successful attempts. Like in baboons, conflicts in vulturine guineafowl group decisions affect the probability that initiators are followed and, when they do, follower movements fall into one of two regimes: when the disagreement between concurrent initiators is small followers average the directions of the initiators and when the disagreement is large they choose the direction with the most initiators. Our study also demonstrates the importance of replication in ecology and animal behaviour<sup>##REF##26510635##15##,##REF##32607143##16##,##UREF##10##36##</sup>, showing that by following the same methods and conducting the same statistical tests we could reveal that the emergence of collective decisions from simple rules governing group cohesion are likely to be consistent across very distinct taxonomic groups.</p>\",\n \"<p id=\\\"Par22\\\">While influence can be distributed within the group<sup>##UREF##11##37##</sup>, whereby all individuals can initiate movement and be followed, it is not necessarily equal among group members<sup>##UREF##8##29##,##UREF##12##38##,##UREF##13##39##</sup>. For example, homing pigeons form influence hierarchies during flight and these hierarchies determine whom an individual is likely to lead and are most likely to be led by Nagy et al.<sup>##REF##20376149##32##</sup>. However, these influence hierarchies are independent from dominance hierarchies<sup>##REF##23878247##33##</sup>. In vulturine guineafowl, we found that leadership is generally shared, but that males are more likely to be followed and initiate more often than females. This difference reflects the social structure of vulturine guineafowl societies, where males are dominant over all females<sup>##REF##33239284##6##,##UREF##7##27##</sup>, who are also the dispersing sex<sup>##REF##33977638##35##</sup>. However, males have been found to be more influential than females in collective departures also in species in which female matrilines dominate aggression hierarchies<sup>##REF##34754018##7##,##UREF##14##40##</sup>, suggesting that neither the dominance hierarchy alone, nor the dispersal tendency of the sexes, always determine which individuals influence group coordination. Further, while the differences we found between males and females are significant, females still had substantial influence over where groups moved. One key outstanding question is therefore to identify whether there are specific contexts in which the ability to exert influence (i.e. have a higher probability of being followed) may be important.</p>\",\n \"<p id=\\\"Par23\\\">Within each sex group of vulturine guineafowl, we found no evidence for dominance playing a role on influence (although in both groups, the lowest ranking female initiated less often than almost any other group member). While it appears that female vulturine guineafowl have overall less influence than males on where their group goes, unlike in other species<sup>##UREF##15##41##–##UREF##17##45##</sup>, it is also possible that females exhibit specific strategies to influence decisions<sup>##REF##33168743##46##,##REF##35597702##47##</sup>. For example, they could influence when groups leave<sup>##REF##33239284##6##</sup>, as has been suggested in baboons<sup>##UREF##18##48##</sup>, with timing decisions potentially reflecting a distinct axis of decision-making<sup>##UREF##4##13##,##UREF##19##49##</sup>. Such routes of influence would not be obvious from the analytical approach that was employed in the current and baboon studies<sup>##UREF##4##13##</sup>. Rather, our approach likely captured a number of very general collective movements, including many moment-by-moment decisions (e.g. which way to move around a tree). Identifying the functional importance of each decision (e.g. those that dictate where groups move next at larger spatial scales) remains a challenge in the field.</p>\",\n \"<p id=\\\"Par24\\\">Despite the properties of follower movements in response to initiators being very similar across vulturine guineafowl and baboons, details may vary from group to group<sup>##REF##34703618##50##</sup> and from one context to the next<sup>##REF##24618695##51##</sup>. In theory, larger group sizes should be associated with a decrease in the angle at which follower movements transition from compromise to choose<sup>##REF##15690039##12##</sup>. Group 1 was similar in size to the previously studied baboon group, and their transitional phase overlapped<sup>##UREF##4##13##</sup>. Further, the transitional phase of Group 1 started at values that were almost 40 degrees smaller than the smaller Group 2, albeit it also went beyond that of Group 2 (Fig. ##FIG##5##6##). Given this overlap, we can’t safely draw conclusions on whether our findings support the theoretical predictions that indicate that larger groups show a smaller transitional angle, and therefore data on more groups of different sizes are required to address this question. The potential influence of the social, as well as the physical environment is worth exploring, given that environmental effects have already been documented across various facets of collective behaviour<sup>##REF##24618695##51##</sup>.</p>\",\n \"<p id=\\\"Par25\\\">One behaviour where we could find some clearer differences between groups is when looking at the majority rule employed by each. In vulturine guineafowl, the smaller group (Group 2) appeared to require a smaller threshold in order to identify a clear majority. A key question is whether the shift in the threshold scales with group size. Our data suggest that the larger group (Group 1) reliably chose the majority (80% of the time) when there was a higher proportional difference (approximately one third of the group 1) in initiators compared to the smaller group (approximately one quarter of the group 2). Baboons required a much smaller majority to reach the same 80% threshold<sup>##UREF##4##13##</sup>. Given that the baboon troop that was studied was larger than Group 1 (and had a very similar proportion of GPS-tracked group members), these results suggest that discrimination may be harder in larger groups, and that baboons could have a better capacity to discriminate smaller relative differences than vulturine guineafowl.</p>\",\n \"<p id=\\\"Par26\\\">Our results show that the processes driving the movement patterns of wild group-living vulturine guineafowl are largely consistent with those previously described in a group of wild baboons<sup>##UREF##4##13##</sup> and in dyads of homing pigeons<sup>##REF##17084696##4##</sup>, with the specific directions of movements by individuals and responses to conflict when following in particular matching those of olive baboons. Our work adds to the weight of support for predictions arising from a classic theoretical model of leadership and collective decision-making<sup>##REF##15690039##12##</sup>. Further, by carefully conducting a large-scale within- and between-species replication, we propose that a multitude of group-living species could exhibit highly convergent processes governing how they reach consensus on where to move.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Shared-decision making is beneficial for the maintenance of group-living. However, little is known about whether consensus decision-making follows similar processes across different species. Addressing this question requires robust quantification of how individuals move relative to each other. Here we use high-resolution GPS-tracking of two vulturine guineafowl (<italic>Acryllium vulturinum</italic>) groups to test the predictions from a classic theoretical model of collective motion. We show that, in both groups, all individuals can successfully initiate directional movements, although males are more likely to be followed than females. When multiple group members initiate simultaneously, follower decisions depend on directional agreement, with followers compromising directions if the difference between them is small or choosing the majority direction if the difference is large. By aligning with model predictions and replicating the findings of a previous field study on olive baboons (<italic>Papio anubis</italic>), our results suggest that a common process governs collective decision-making in moving animal groups.</p>","<p id=\"Par2\">A GPS study on wild vulturine guineafowl reveals shared decision-making similar to that in baboons and theoretical models. In directional conflicts, followers compromise for small differences and choose the majority’s direction for large differences.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Shared-decision making is beneficial for the maintenance of group-living. However, little is known about whether consensus decision-making follows similar processes across different species. Addressing this question requires robust quantification of how individuals move relative to each other. Here we use high-resolution GPS-tracking of two vulturine guineafowl (<italic>Acryllium vulturinum</italic>) groups to test the predictions from a classic theoretical model of collective motion. We show that, in both groups, all individuals can successfully initiate directional movements, although males are more likely to be followed than females. When multiple group members initiate simultaneously, follower decisions depend on directional agreement, with followers compromising directions if the difference between them is small or choosing the majority direction if the difference is large. By aligning with model predictions and replicating the findings of a previous field study on olive baboons (<italic>Papio anubis</italic>), our results suggest that a common process governs collective decision-making in moving animal groups.</p>\",\n \"<p id=\\\"Par2\\\">A GPS study on wild vulturine guineafowl reveals shared decision-making similar to that in baboons and theoretical models. In directional conflicts, followers compromise for small differences and choose the majority’s direction for large differences.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary information</title>","<p>\n\n\n\n\n\n\n\n\n\n\n</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary information</title>","<p>The online version contains supplementary material available at 10.1038/s42003-024-05782-w.</p>","<title>Acknowledgements</title>","<p>We are grateful to Ariana Strandburg-Peshkin, Vivek Hari Sridhar, Margaret C. Crofoot, and Dora Biro for feedback on early versions of the manuscript as well as to five anonymous reviewers. We also thank Wismer Cherono and John Ewoi for field assistance. The research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 850859 awarded to D.R.F.), the Max Planck Society, and grants awarded to D.R.F. from the Daimler und Benz Stiftung (32-03/16) and the Association for the Study of Animal Behaviour. D.P. received additional funding from a DAAD PhD fellowship and an Early Career Grant from the National Geographic Society (WW-175ER-17).</p>","<title>Author contributions</title>","<p>D.P. and D.R.F. conceived, designed the study and performed the analysis. D.P. and B.N. collected the data. D.P. and D.R.F. drafted the manuscript. D.R.F. supervised all aspects of the study. All authors contributed to revisions.</p>","<title>Peer review</title>","<title>Peer review information</title>","<p id=\"Par51\"><italic>Communications Biology</italic> thanks Tamas Vicsek, Lisa O’Bryan, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Joao Valente. A peer review file is available.</p>","<title>Funding</title>","<p>Open Access funding enabled and organized by Projekt DEAL.</p>","<title>Data availability</title>","<p>Processed data can be found on Figshare. 10.6084/m9.figshare.24850551. Raw GPS data are stored on <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.movebank.org/\">https://www.movebank.org</ext-link>.</p>","<title>Code availability</title>","<p>We used the code from Strandburg-Peshkin et al. 2015 Science. Our adjusted code can be found on Figshare. 10.6084/m9.figshare.24850551.</p>","<title>Competing interests</title>","<p id=\"Par52\">The authors declare no competing interests.</p>","<title>Ethics approval</title>","<p id=\"Par53\">Our study involves critical participation reflected on authorship and supervision by local (i.e. Kenyan) academics. We have complied with all relevant ethical regulations for animal use and we obtained permits from the National Science and Technology Council (NACOSTI permit: NACOSTI/P/16/3706/6465), the National Environment Management Authority (NEMA permit: NEMA/AGR/67/2017), the Kenya Wildlife Service (Research Authorizations and Capture Permits), the National Museums of Kenya (NMK). We especially thank Dr. Peter Njoroge from the Ornithological Section of NMK for providing useful feedback and reviewing our work but also the Mpala Research Centre, and the Max Planck Society’s Ethikrat Committee for ethical permission to conduct this research.</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s42003-024-05782-w.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>We are grateful to Ariana Strandburg-Peshkin, Vivek Hari Sridhar, Margaret C. Crofoot, and Dora Biro for feedback on early versions of the manuscript as well as to five anonymous reviewers. We also thank Wismer Cherono and John Ewoi for field assistance. The research was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 850859 awarded to D.R.F.), the Max Planck Society, and grants awarded to D.R.F. from the Daimler und Benz Stiftung (32-03/16) and the Association for the Study of Animal Behaviour. D.P. received additional funding from a DAAD PhD fellowship and an Early Career Grant from the National Geographic Society (WW-175ER-17).</p>\",\n \"<title>Author contributions</title>\",\n \"<p>D.P. and D.R.F. conceived, designed the study and performed the analysis. D.P. and B.N. collected the data. D.P. and D.R.F. drafted the manuscript. D.R.F. supervised all aspects of the study. All authors contributed to revisions.</p>\",\n \"<title>Peer review</title>\",\n \"<title>Peer review information</title>\",\n \"<p id=\\\"Par51\\\"><italic>Communications Biology</italic> thanks Tamas Vicsek, Lisa O’Bryan, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Joao Valente. A peer review file is available.</p>\",\n \"<title>Funding</title>\",\n \"<p>Open Access funding enabled and organized by Projekt DEAL.</p>\",\n \"<title>Data availability</title>\",\n \"<p>Processed data can be found on Figshare. 10.6084/m9.figshare.24850551. Raw GPS data are stored on <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.movebank.org/\\\">https://www.movebank.org</ext-link>.</p>\",\n \"<title>Code availability</title>\",\n \"<p>We used the code from Strandburg-Peshkin et al. 2015 Science. Our adjusted code can be found on Figshare. 10.6084/m9.figshare.24850551.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par52\\\">The authors declare no competing interests.</p>\",\n \"<title>Ethics approval</title>\",\n \"<p id=\\\"Par53\\\">Our study involves critical participation reflected on authorship and supervision by local (i.e. Kenyan) academics. We have complied with all relevant ethical regulations for animal use and we obtained permits from the National Science and Technology Council (NACOSTI permit: NACOSTI/P/16/3706/6465), the National Environment Management Authority (NEMA permit: NEMA/AGR/67/2017), the Kenya Wildlife Service (Research Authorizations and Capture Permits), the National Museums of Kenya (NMK). We especially thank Dr. Peter Njoroge from the Ornithological Section of NMK for providing useful feedback and reviewing our work but also the Mpala Research Centre, and the Max Planck Society’s Ethikrat Committee for ethical permission to conduct this research.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>The distribution of distances (d, in metres) between initiators and potential followers, during pulls and anchors at three different times.</title><p>First, when the initiation starts (first column; <bold>A</bold>, <bold>D</bold>, <bold>G</bold>, <bold>J</bold>); second, when the initiator reaches the maximum distance away from the potential follower (second column; <bold>B</bold>, <bold>E</bold>, <bold>H</bold>, <bold>K</bold>); and third, when the potential follower starts moving towards the leader (in pulls; <bold>C</bold>, <bold>I</bold>) or the leader returns by moving towards the potential follower (in anchors; F, L). Distributions of inter-individual distances are shown separately for groups 1 (<bold>A</bold>–<bold>F</bold>) and 2 (<bold>G</bold>–<bold>L</bold>).</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Dyadic influence, and relationships with dominance and sex, across two social groups.</title><p><bold>A</bold>, <bold>D</bold> In each group, we calculated the dyadic influence index, ranging from 1 (red) when the individual in the column always leads the individual in the row to −1 (blue) when the relationship is reversed. Individuals are plotted in descending dominance rank, e.g. the alpha male of Group 1 is “W1316” and the lowest ranking group member being the female “W1544”. Individuals in rectangles are males. <bold>B</bold>, <bold>E</bold> The result of permutation tests on the relationship between dominance and leadership across all individuals (i), within males (ii), and within females (iii). While there appears to be an effect of dominance on individual influence (i), this effect is not present within sexes (ii-iii). The y-axis corresponds to the absolute difference between dominance rank and influence rank (see Methods). <bold>C</bold>, <bold>F</bold> A permutation test on the relationship between sex and influence confirms that the relationship is driven by males being more influential than females. The y-axis shows the absolute difference between two binary variables; whether an individual was within the top n-ranked individuals, where n represented the number of males in the group and whether the individual was a male or not (see Methods). Panels <bold>B</bold>, <bold>C</bold>, <bold>E</bold>, <bold>F</bold> show the observed value (red star) relative to the mean (black dot) and 95% range from 1000 permutations of the datasets. <italic>P</italic> values are calculated as the proportion of the number of values of the randomised data being larger than the observed values divided by the number of randomised values (i.e. 1000).</p></caption></fig>","<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Initiation rates, and relationships with dominance and sex, across two social groups.</title><p>Panels <bold>A</bold> and <bold>D</bold> show the number of times each individual pulled another group member (black) or was anchored (grey). Individuals are plotted in descending dominance rank and those in rectangles are males. <bold>B</bold>, <bold>E</bold> The result of permutation tests on the relationship between dominance and successful initiation rates per hour (i.e. pulls) across all individuals (i), within males (ii), and within females (iii). While there appears to be a weak effect of dominance (i), this effect is not present within sexes (ii-iii). <bold>C</bold>, <bold>F</bold> A permutation test on the relationship between sex and successful initiation rates per hour (i.e. pulls) confirms that the relationship is driven by males initiating more often than females. The y-axis shows the absolute difference between two binary variables; whether an individual was within the top n-ranked individuals, where n represented the number of males in the group and whether the individual was a male or not (see Methods). The y-axes in <bold>B</bold>, <bold>C</bold>, <bold>E</bold>, <bold>F</bold> correspond to the absolute difference between dominance rank and ranked values of initiation rates. Permutation tests and panels are as per Fig. ##FIG##1##2##.</p></caption></fig>","<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>An individual is more likely to follow when there are few simultaneous initiators, and, as the number of concurrent initiators increases, when initiators have high agreement.</title><p>As the number of initiators increases, then the directional agreement becomes increasingly important in determining the decision of a follower, as revealed by the positive interaction term in the GEE models (Supplementary Table ##SUPPL##1##1##).</p></caption></fig>","<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>When following, vulturine guineafowl transition from compromise to choose depending on the angular disagreement between initiators.</title><p>Plots show the angle between two initiators relative to a potential follower (x-axis) and the resulting direction taken by the follower (y-axis). When the angular difference between initiators is above a critical threshold (see grey rectangle for transitional zone), follower directions are significantly bimodal (see Supplementary Fig. ##SUPPL##1##3##), suggesting that followers choose one direction or the other. Colours blue, white and red show the probability of a direction to be chosen by a follower. Solid white lines represent the median of the chosen direction(s) under the compromise and choose regimes (or both in the transitional zone).</p></caption></fig>","<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>When two clusters of initiators propose different directions with a large angle of disagreement, followers disproportionately choose the direction of the largest cluster of initiators, thus following a majority rule.</title><p>Black dots represent the empirical data and error bars are 95% confidence intervals estimated by 1000 bootstrapped replications of the data. The red line shows a significant sigmoidal fit to the data. Model summaries are presented in Supplementary Table ##SUPPL##1##3##.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><title>The distribution of distances (d, in metres) between initiators and potential followers, during pulls and anchors at three different times.</title><p>First, when the initiation starts (first column; <bold>A</bold>, <bold>D</bold>, <bold>G</bold>, <bold>J</bold>); second, when the initiator reaches the maximum distance away from the potential follower (second column; <bold>B</bold>, <bold>E</bold>, <bold>H</bold>, <bold>K</bold>); and third, when the potential follower starts moving towards the leader (in pulls; <bold>C</bold>, <bold>I</bold>) or the leader returns by moving towards the potential follower (in anchors; F, L). Distributions of inter-individual distances are shown separately for groups 1 (<bold>A</bold>–<bold>F</bold>) and 2 (<bold>G</bold>–<bold>L</bold>).</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><title>Dyadic influence, and relationships with dominance and sex, across two social groups.</title><p><bold>A</bold>, <bold>D</bold> In each group, we calculated the dyadic influence index, ranging from 1 (red) when the individual in the column always leads the individual in the row to −1 (blue) when the relationship is reversed. Individuals are plotted in descending dominance rank, e.g. the alpha male of Group 1 is “W1316” and the lowest ranking group member being the female “W1544”. Individuals in rectangles are males. <bold>B</bold>, <bold>E</bold> The result of permutation tests on the relationship between dominance and leadership across all individuals (i), within males (ii), and within females (iii). While there appears to be an effect of dominance on individual influence (i), this effect is not present within sexes (ii-iii). The y-axis corresponds to the absolute difference between dominance rank and influence rank (see Methods). <bold>C</bold>, <bold>F</bold> A permutation test on the relationship between sex and influence confirms that the relationship is driven by males being more influential than females. The y-axis shows the absolute difference between two binary variables; whether an individual was within the top n-ranked individuals, where n represented the number of males in the group and whether the individual was a male or not (see Methods). Panels <bold>B</bold>, <bold>C</bold>, <bold>E</bold>, <bold>F</bold> show the observed value (red star) relative to the mean (black dot) and 95% range from 1000 permutations of the datasets. <italic>P</italic> values are calculated as the proportion of the number of values of the randomised data being larger than the observed values divided by the number of randomised values (i.e. 1000).</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Fig. 3</label><caption><title>Initiation rates, and relationships with dominance and sex, across two social groups.</title><p>Panels <bold>A</bold> and <bold>D</bold> show the number of times each individual pulled another group member (black) or was anchored (grey). Individuals are plotted in descending dominance rank and those in rectangles are males. <bold>B</bold>, <bold>E</bold> The result of permutation tests on the relationship between dominance and successful initiation rates per hour (i.e. pulls) across all individuals (i), within males (ii), and within females (iii). While there appears to be a weak effect of dominance (i), this effect is not present within sexes (ii-iii). <bold>C</bold>, <bold>F</bold> A permutation test on the relationship between sex and successful initiation rates per hour (i.e. pulls) confirms that the relationship is driven by males initiating more often than females. The y-axis shows the absolute difference between two binary variables; whether an individual was within the top n-ranked individuals, where n represented the number of males in the group and whether the individual was a male or not (see Methods). The y-axes in <bold>B</bold>, <bold>C</bold>, <bold>E</bold>, <bold>F</bold> correspond to the absolute difference between dominance rank and ranked values of initiation rates. Permutation tests and panels are as per Fig. ##FIG##1##2##.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Fig. 4</label><caption><title>An individual is more likely to follow when there are few simultaneous initiators, and, as the number of concurrent initiators increases, when initiators have high agreement.</title><p>As the number of initiators increases, then the directional agreement becomes increasingly important in determining the decision of a follower, as revealed by the positive interaction term in the GEE models (Supplementary Table ##SUPPL##1##1##).</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Fig. 5</label><caption><title>When following, vulturine guineafowl transition from compromise to choose depending on the angular disagreement between initiators.</title><p>Plots show the angle between two initiators relative to a potential follower (x-axis) and the resulting direction taken by the follower (y-axis). When the angular difference between initiators is above a critical threshold (see grey rectangle for transitional zone), follower directions are significantly bimodal (see Supplementary Fig. ##SUPPL##1##3##), suggesting that followers choose one direction or the other. Colours blue, white and red show the probability of a direction to be chosen by a follower. Solid white lines represent the median of the chosen direction(s) under the compromise and choose regimes (or both in the transitional zone).</p></caption></fig>\",\n \"<fig id=\\\"Fig6\\\"><label>Fig. 6</label><caption><title>When two clusters of initiators propose different directions with a large angle of disagreement, followers disproportionately choose the direction of the largest cluster of initiators, thus following a majority rule.</title><p>Black dots represent the empirical data and error bars are 95% confidence intervals estimated by 1000 bootstrapped replications of the data. The red line shows a significant sigmoidal fit to the data. Model summaries are presented in Supplementary Table ##SUPPL##1##3##.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":["<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$i$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mi>i</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} 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Behav.\"], \"year\": [\"2010\"], \"volume\": [\"79\"], \"fpage\": [\"1037\"], \"lpage\": [\"1045\"], \"pub-id\": [\"10.1016/j.anbehav.2010.01.019\"]}, {\"label\": [\"34.\"], \"surname\": [\"Kurvers\"], \"given-names\": [\"RHJM\"], \"article-title\": [\"Personality differences explain leadership in barnacle geese\"], \"source\": [\"Anim. Behav.\"], \"year\": [\"2009\"], \"volume\": [\"78\"], \"fpage\": [\"447\"], \"lpage\": [\"453\"], \"pub-id\": [\"10.1016/j.anbehav.2009.06.002\"]}, {\"label\": [\"36.\"], \"surname\": [\"Brandl\", \"Farine\", \"Funghi\", \"Schuett\", \"Griffith\"], \"given-names\": [\"HB\", \"DR\", \"C\", \"W\", \"SC\"], \"article-title\": [\"Early-life social environment predicts social network position in wild zebra finches\"], \"source\": [\"Proc. R. Soc. B: Biol. Sci.\"], \"year\": [\"2019\"], \"volume\": [\"286\"], \"fpage\": [\"20182579\"], \"pub-id\": [\"10.1098/rspb.2018.2579\"]}, {\"label\": [\"37.\"], \"surname\": [\"Strandburg-Peshkin\", \"Papageorgiou\", \"Crofoot\", \"Farine\"], \"given-names\": [\"A\", \"D\", \"MC\", \"DR\"], \"article-title\": [\"Inferring influence and leadership in moving animal groups\"], \"source\": [\"Philos. Trans. R. Soc. B: Biol. Sci.\"], \"year\": [\"2018\"], \"volume\": [\"373\"], \"fpage\": [\"20170006\"], \"pub-id\": [\"10.1098/rstb.2017.0006\"]}, {\"label\": [\"38.\"], \"surname\": [\"Sueur\", \"Petit\"], \"given-names\": [\"C\", \"O\"], \"article-title\": [\"Shared or unshared consensus decision in macaques?\"], \"source\": [\"Behav. Process.\"], \"year\": [\"2008\"], \"volume\": [\"78\"], \"fpage\": [\"84\"], \"lpage\": [\"92\"], \"pub-id\": [\"10.1016/j.beproc.2008.01.004\"]}, {\"label\": [\"39.\"], \"mixed-citation\": [\"Bracken, A. M., Christensen, C., O\\u2019Riain, M. J., F\\u00fcrtbauer, I. & King, A. J. Flexible group cohesion and coordination, but robust leader\\u2013follower roles, in a wild social primate using urban space. \"], \"italic\": [\"Proc. R. Soc. B Biol. Sci.\"], \"bold\": [\"289\"]}, {\"label\": [\"40.\"], \"surname\": [\"Dal Pesco\", \"Trede\", \"Zinner\", \"Fischer\"], \"given-names\": [\"F\", \"F\", \"D\", \"J\"], \"article-title\": [\"Male\\u2013male social bonding, coalitionary support and reproductive success in wild Guinea baboons\"], \"source\": [\"Proc. R. Soc. B Biol. Sci.\"], \"year\": [\"2022\"], \"volume\": [\"289\"], \"fpage\": [\"20220347\"], \"pub-id\": [\"10.1098/rspb.2022.0347\"]}, {\"label\": [\"41.\"], \"surname\": [\"Tokuyama\", \"Furuichi\"], \"given-names\": [\"N\", \"T\"], \"article-title\": [\"Leadership of old females in collective departures in wild bonobos (\"], \"italic\": [\"Pan paniscus\"], \"source\": [\"Behav. Ecol. Sociobiol.\"], \"year\": [\"2017\"], \"volume\": [\"71\"], \"fpage\": [\"55\"], \"pub-id\": [\"10.1007/s00265-017-2277-5\"]}, {\"label\": [\"44.\"], \"surname\": [\"McComb\"], \"given-names\": [\"K\"], \"article-title\": [\"Leadership in elephants: the adaptive value of age\"], \"source\": [\"Proc. R. Soc. B: Biol. Sci.\"], \"year\": [\"2011\"], \"volume\": [\"278\"], \"fpage\": [\"3270\"], \"lpage\": [\"3276\"], \"pub-id\": [\"10.1098/rspb.2011.0168\"]}, {\"label\": [\"45.\"], \"surname\": [\"Fischhoff\"], \"given-names\": [\"IR\"], \"article-title\": [\"Social relationships and reproductive state influence leadership roles in movements of plains zebra, \"], \"italic\": [\"Equus burchellii\"], \"source\": [\"Anim. Behav.\"], \"year\": [\"2007\"], \"volume\": [\"73\"], \"fpage\": [\"825\"], \"lpage\": [\"831\"], \"pub-id\": [\"10.1016/j.anbehav.2006.10.012\"]}, {\"label\": [\"48.\"], \"surname\": [\"Rowell\"], \"given-names\": [\"TE\"], \"article-title\": [\"Female reproduction cycles and social behavior in primates\"], \"source\": [\"Adv. Study Behav.\"], \"year\": [\"1972\"], \"volume\": [\"4\"], \"fpage\": [\"69\"], \"lpage\": [\"105\"], \"pub-id\": [\"10.1016/S0065-3454(08)60007-8\"]}, {\"label\": [\"49.\"], \"surname\": [\"Conradt\", \"Roper\"], \"given-names\": [\"L\", \"TJ\"], \"article-title\": [\"Deciding group movements: where and when to go\"], \"source\": [\"Behav. Process.\"], \"year\": [\"2010\"], \"volume\": [\"84\"], \"fpage\": [\"675\"], \"lpage\": [\"677\"], \"pub-id\": [\"10.1016/j.beproc.2010.03.005\"]}, {\"label\": [\"53.\"], \"mixed-citation\": [\"He, P. et al. A guide to sampling design for GPS\\u2010based studies of animal societies. \"], \"italic\": [\"Methods Ecol. Evol.\"], \"bold\": [\"14\"]}, {\"label\": [\"56.\"], \"surname\": [\"Halekoh\", \"H\\u00f8jsgaard\", \"Yan\"], \"given-names\": [\"U\", \"S\", \"J\"], \"article-title\": [\"The R package geepack for generalized estimating equations\"], \"source\": [\"J. Stat. Softw.\"], \"year\": [\"2006\"], \"volume\": [\"15\"], \"fpage\": [\"1\"], \"lpage\": [\"11\"], \"pub-id\": [\"10.18637/jss.v015.i02\"]}, {\"label\": [\"57.\"], \"surname\": [\"Hartigan\", \"Hartigan\"], \"given-names\": [\"JA\", \"PM\"], \"article-title\": [\"The dip test of unimodality\"], \"source\": [\"Ann. Stat.\"], \"year\": [\"1985\"], \"volume\": [\"13\"], \"fpage\": [\"70\"], \"lpage\": [\"84\"], \"pub-id\": [\"10.1214/aos/1176346577\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":57,"string":"57"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:57"},"citation":{"kind":"string","value":"Commun Biol. 2024 Jan 13; 7:95"},"package_file":{"kind":"string","value":"oa_package/85/38/PMC10787764.tar.gz"}}},{"rowIdx":95,"cells":{"accession_id":{"kind":"string","value":"PMC10787765"},"pmid":{"kind":"string","value":"38218947"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par3\">Implantable bioelectronic devices play an increasingly important role in disease prevention, monitoring, and treatment<sup>##REF##32592227##1##–##UREF##0##3##</sup>, and it is one of the most rapidly emerging fields in medicine<sup>##REF##32895515##4##–##REF##35324282##6##</sup>. As a representative of bioelectronics therapy, the cardiac pacemaker is a powerful tool for bradycardia and heart block therapies<sup>##REF##30792165##7##</sup>. With the upgrading and iteration of technology, pacemakers are becoming intelligent and multi-functional<sup>##REF##11297981##8##–##REF##31310403##11##</sup>. Nevertheless, conventional pacemakers are associated with several complications, such as lead insulation breaks, pocket hematoma, local bulges, and scarring of the skin<sup>##REF##17578774##12##</sup>. Moreover, some patients cannot be implanted with conventional pacemakers due to their comorbidities and venous system defects. To address these lead- and device-pocket-related issues, leadless pacing technology has been proposed. A Leadless pacemaker<sup>##REF##28396380##13##</sup> integrating pacing leads with a pulse generator is very small and can be implanted into the cardiac chamber only through a catheter, which not only reduces pain, and prevents trauma and related complications, but also decreases the risk of infection. Additionally, patients can hardly feel the presence of the pacemaker after implantation, which greatly improves their quality of life.</p>","<p id=\"Par4\">However, it should be noted that the application of implantable bioelectronic devices, including leadless pacemakers, faces various challenges related to power supply in long-term operation<sup>##UREF##1##14##,##UREF##2##15##</sup> due to battery capacity limitations<sup>##UREF##3##16##–##REF##29540439##18##</sup>. Besides, leadless pacemakers are costly and difficult to remove after implantation. Meanwhile, it is difficult to wirelessly recharge a leadless pacemaker because cardiac chambers located in the mediastinum are flooded with blood. In recent years, technologies based on electromagnetic<sup>##UREF##4##19##–##UREF##5##21##</sup> or piezoelectric effects<sup>##REF##20446305##22##–##UREF##7##27##</sup> for converting biomechanical energy into electrical energy have been proposed. For electromagnetic technology, the permanent magnet is heavy and not suitable for magnetic resonance imaging (MRI) examinations<sup>##REF##18552230##28##</sup>, which may affect the normal physiological beating of the heart. Its performance is also limited by the beating frequency of the heart<sup>##REF##24677413##29##</sup>. For piezoelectric technology, due to the inherent strength of the heartbeat, the output voltage of piezoelectric devices is generally difficult to meet the pacing threshold requirements<sup>##UREF##8##30##</sup>. Therefore, it is necessary to develop new energy supply strategies for leadless pacemakers. As a new generation of biomechanical energy harvesting technology, triboelectric nanogenerator<sup>##UREF##9##31##–##REF##31371614##34##</sup> provide an effective solution for a leadless pacemaker. Currently, triboelectric nanogenerators with different structures and materials have successfully converted biomechanical energy into electrical energy<sup>##REF##34272375##35##–##REF##30559435##37##</sup>.</p>","<p id=\"Par5\">Herein, we proposed a self-powered intracardiac pacemaker (SICP) with a capsule structure for harvesting biomechanical energy from cardiac motion based on the nanogenerator technology. The device can be placed in the right ventricle through an intravenous route by a delivery catheter. The SICP integrates the energy harvesting unit (EHU) and power management unit (PMU) & pacemaker module (PM). In a laboratory experiment, the open-circuit voltage (<italic>V</italic><sub>oc</sub>), short-circuit current (<italic>I</italic><sub>sc</sub>), and short-circuit charge (<italic>Q</italic><sub>sc</sub>) of the device were about 21.8 V, 0.25 μA and 6.4 nC, respectively. We demonstrate that SICP can recharge its PMU by EHU. The material and structural design creates a lightweight, miniature device that maintains stable energy harvesting performance and excellent biocompatibility in vivo. Testing in swine models showed capabilities in the treatment of arrhythmia. Taken together, this work provides an alternative strategy for harvesting biomechanical energy via a minimally invasive approach, which may effectively improve the service life of leadless pacemakers. These findings show that SICP lays the energy foundation for the development of next-generation implantable bioelectronics.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par3\\\">Implantable bioelectronic devices play an increasingly important role in disease prevention, monitoring, and treatment<sup>##REF##32592227##1##–##UREF##0##3##</sup>, and it is one of the most rapidly emerging fields in medicine<sup>##REF##32895515##4##–##REF##35324282##6##</sup>. As a representative of bioelectronics therapy, the cardiac pacemaker is a powerful tool for bradycardia and heart block therapies<sup>##REF##30792165##7##</sup>. With the upgrading and iteration of technology, pacemakers are becoming intelligent and multi-functional<sup>##REF##11297981##8##–##REF##31310403##11##</sup>. Nevertheless, conventional pacemakers are associated with several complications, such as lead insulation breaks, pocket hematoma, local bulges, and scarring of the skin<sup>##REF##17578774##12##</sup>. Moreover, some patients cannot be implanted with conventional pacemakers due to their comorbidities and venous system defects. To address these lead- and device-pocket-related issues, leadless pacing technology has been proposed. A Leadless pacemaker<sup>##REF##28396380##13##</sup> integrating pacing leads with a pulse generator is very small and can be implanted into the cardiac chamber only through a catheter, which not only reduces pain, and prevents trauma and related complications, but also decreases the risk of infection. Additionally, patients can hardly feel the presence of the pacemaker after implantation, which greatly improves their quality of life.</p>\",\n \"<p id=\\\"Par4\\\">However, it should be noted that the application of implantable bioelectronic devices, including leadless pacemakers, faces various challenges related to power supply in long-term operation<sup>##UREF##1##14##,##UREF##2##15##</sup> due to battery capacity limitations<sup>##UREF##3##16##–##REF##29540439##18##</sup>. Besides, leadless pacemakers are costly and difficult to remove after implantation. Meanwhile, it is difficult to wirelessly recharge a leadless pacemaker because cardiac chambers located in the mediastinum are flooded with blood. In recent years, technologies based on electromagnetic<sup>##UREF##4##19##–##UREF##5##21##</sup> or piezoelectric effects<sup>##REF##20446305##22##–##UREF##7##27##</sup> for converting biomechanical energy into electrical energy have been proposed. For electromagnetic technology, the permanent magnet is heavy and not suitable for magnetic resonance imaging (MRI) examinations<sup>##REF##18552230##28##</sup>, which may affect the normal physiological beating of the heart. Its performance is also limited by the beating frequency of the heart<sup>##REF##24677413##29##</sup>. For piezoelectric technology, due to the inherent strength of the heartbeat, the output voltage of piezoelectric devices is generally difficult to meet the pacing threshold requirements<sup>##UREF##8##30##</sup>. Therefore, it is necessary to develop new energy supply strategies for leadless pacemakers. As a new generation of biomechanical energy harvesting technology, triboelectric nanogenerator<sup>##UREF##9##31##–##REF##31371614##34##</sup> provide an effective solution for a leadless pacemaker. Currently, triboelectric nanogenerators with different structures and materials have successfully converted biomechanical energy into electrical energy<sup>##REF##34272375##35##–##REF##30559435##37##</sup>.</p>\",\n \"<p id=\\\"Par5\\\">Herein, we proposed a self-powered intracardiac pacemaker (SICP) with a capsule structure for harvesting biomechanical energy from cardiac motion based on the nanogenerator technology. The device can be placed in the right ventricle through an intravenous route by a delivery catheter. The SICP integrates the energy harvesting unit (EHU) and power management unit (PMU) & pacemaker module (PM). In a laboratory experiment, the open-circuit voltage (<italic>V</italic><sub>oc</sub>), short-circuit current (<italic>I</italic><sub>sc</sub>), and short-circuit charge (<italic>Q</italic><sub>sc</sub>) of the device were about 21.8 V, 0.25 μA and 6.4 nC, respectively. We demonstrate that SICP can recharge its PMU by EHU. The material and structural design creates a lightweight, miniature device that maintains stable energy harvesting performance and excellent biocompatibility in vivo. Testing in swine models showed capabilities in the treatment of arrhythmia. Taken together, this work provides an alternative strategy for harvesting biomechanical energy via a minimally invasive approach, which may effectively improve the service life of leadless pacemakers. These findings show that SICP lays the energy foundation for the development of next-generation implantable bioelectronics.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Methods</title>","<title>Fabrication of SICP</title>","<p id=\"Par19\">The enclosure of SICP was printed by a 3D printer (PHTON MONO) using UV-sensitive resins (ANYCUBIC Photon). Nanostructured PTFE film (50 μm) and POM pellets (diameter: 1.588 mm) were employed as triboelectric layers, processed by an inductively coupled plasma etching system (SENTECH/SI 500). In detail, the PTFE films and POM pellets were rinsed with alcohol and deionized water. The Au, which acted as the mask for the etching process, was sputtered onto their surfaces for about 30 s. Then the PTFE films and POM pellets were etched by ICP reactive ionic etching for 300 s (ICP power: 400 W and 100 W) and 150 s (ICP power: 500 W and 150 W), respectively. The reaction gas in the ICP process was CF4 (30.0 sccm), O<sub>2</sub> (10.0 sccm), and Ar (15.0 sccm). The two Au electrodes were deposited on the back of nano-PTFE by magnetron sputter (Denton Discovery 635) parallelly for 15 min (sputter power 50 W), the gap between them was about 1 mm. The Au bottom electrodes were polarized by connecting with wire to ground with a voltage of 4.5 kV for 15 min through the corona needle. The composite film was attached to the inner surface of the enclosure. The POM pellets were placed in the cavity of the energy harvesting unit. Altium Designer software was used to design the power management unit and pacemaker module. The custom control chip is an ultra-low power chip, which has the characteristic of a low power supply voltage range. The circuit board adopts the welding process of surface mounted technology (SMT). The spiral platinum-iridium alloy was attached to the bottom of SICP as cathode and the ring of platinum-iridium alloy was attached to the waist of SICP as anode. Narrow strips of tungsten sheet (thickness: 50 μm, width: 1 mm) were rolled into rings and fixed to both ends of SICP as a radiopaque marker. Nickel alloy wires were bent into an arch (diameter: 0.3 mm, length: 10 mm) and crossed through holes in the bottom of SICP as a hook to anchor in the myocardial wall.</p>","<title>Encapsulation of the SICP</title>","<p id=\"Par20\">The ethyl cyanoacrylate (Aibida, Guangzhou, China) was employed to close the seams of the enclosure. Then the one-component UV light-curing adhesive (8500 Metal, Switzerland) was spin-coated on the enclosure as the package layer and then cured under UV light for 10 s. The holes in the enclosure that led out the wires need to be sealed with light-curing glue several times. Finally, the parylene-C particles were steamed at 135 °C/690 °C and then deposited on the surface of SICP where the thickness of parylene is 3 μm.</p>","<title>Cell viability</title>","<p id=\"Par21\">The L929 cells (fibroblasts, GNM28) were acquired from the Cell Bank of the Chinese Academy of Sciences in Beijing, China. After being cultured to a stable stage, L929 cells were collected and seeded on 96-well tissue culture polystyrenes (TCPs, Corning, USA) with a density of 1 × 10<sup>6</sup> cells/ml. The culture dish of the experimental group deposited encapsulation materials. 20 μL CCK-8 solution (Solarbio, China) was mixed with 280 μL cell culture medium for each well. The cells were cultured for 1, 2, and 3 days and evaluate the cell viability every day. After incubating the cells with CCK-8 solution for 1 h at 37 °C, 200 μL cell culture supernatant was transferred into a 96-well plate (set at least three repetitions for each group). The absorbance of the solution in the 96-well plate was measured at 450 nm with a microplate absorbance assay instrument (Bio-rad iMark, USA).</p>","<title>Cell morphology and immunofluorescent staining</title>","<p id=\"Par22\">Cell morphology could show the cellular growth condition on different substrates. The cytoskeleton and nucleus were stained with Phalloidin (Abcam, USA) and 4′, 6-diamidino-2-phenylindole (DAPI, Solarbio, China). The L929 were cultured for 1, 2, and 3 days on the naked 96-well disposable confocal dish, and the dish deposited encapsulation materials for cell morphologic observation. Before staining, the cells were rinsed with phosphate buffer solution (PBS, Solarbio, China) three times gently, then fixed with 4% paraformaldehyde (Solarbio, China) for 10 min and permeabilized with 0.1% Triton X-100 (Solarbio, China) for 10 min. Finally, the fixed cells were stained with Phalloidin for 40 min, DAPI for 10 min at room temperature, and washed with PBS three times. The stained cells were visualized by the laser scanning confocal microscope (SP8, Leica, Germany) under the filter at <italic>E</italic><sub>x</sub>/<italic>E</italic><sub>m</sub> = 493/517 nm.</p>","<title>Platelet adhesion tests</title>","<p id=\"Par23\">Rats were anesthetized with 2% isoflurane (RWD, R510-22-4). After the location of the abdominal aorta was determined by laparotomy, 2 ml of fresh blood from arterial blood was collected using a blood collection needle. The collected blood was placed in a centrifuge tube for 30 min. The cells were centrifuged at 110 × <italic>g</italic> for 10 min in a centrifuge. Platelets were removed and incubated on sterilized encapsulation coating material for 90 min at room temperature. After 30 min of fixation with 4% paraformaldehyde (Solarbio, China), platelets were dehydrated in serial concentration gradients of ethanol (50%, 60%, 70%, 80%, 90%, 95%, and 100%) for 30 min. After evaporation to dryness at room temperature, the surface of the encapsulation coating material with platelets was visualized by SEM.</p>","<title>Hemolysis assay</title>","<p id=\"Par24\">Working solution: positive group: 0.3% Triton X-100 (Solarbio, China); Negative group: normal saline (0.9% NaCl); Material group: 1 mg/ml encapsulation coating material leaching solution (1 mg encapsulation coating material was immersed in 1 ml of normal saline for 3 days). 1 ml of fresh blood from male 6-week-old SD rats (220 g) was placed in a 15 ml centrifuge tube, and the supernatant was removed after 4 ml PBS (Solarbio, China) was washed 4–5 times (170 g, 5 min). The washed red blood cells (RBCs) were resuspended by 10 ml PBS (Solarbio, China). 0.2 ml resuspended red blood cells were mixed with 0.8 ml working solution and incubated for 4 h. The mixture was centrifuged in a centrifuge at 170 × <italic>g</italic> for 5 min and photographed for recording. The supernatant was removed and the OD value (541 nm) was measured by a microplate reader. %hemolysis = (OD<sub>test</sub> – OD<sub>neg</sub>)/(OD<sub>pos</sub> – OD<sub>neg</sub>) × 100%.</p>","<title>Histology</title>","<p id=\"Par25\">The tissues were fixed in a 4% paraformaldehyde (Solarbio, China) overnight at room temperature, followed by dehydration using a series of graded ethanol and xylene solutions (Solarbio, China). Subsequently, routine paraffin embedding was carried out, and tissue sections with a thickness of 4 μm were obtained using a microtome. To visualize the tissue morphology and extracellular matrix, hematoxylin-eosin (HE) and Masson’s trichrome staining (Solarbio, China) were performed with standard procedures. The resulting images were observed under a light microscope.</p>","<title>Animal preparation</title>","<p id=\"Par26\">All experimental processes were strictly in line with the institutional and national guidelines for the care and use of laboratory animals and the study protocol was reviewed and approved by the Ethical Committee of the Animal Experimental Center in the State Key Laboratory of Cardiovascular Disease and Fuwai Hospital (0103-1-1-ZX(Y)−3). The swine were anesthetized and intubated with respirators for artificial respiration, and then ECG and femoral artery pressure were recorded by the data acquisition hardware (MP150, BIOPAC System, INC.). Adult swine (age: 1.2–2.0 years; weight: 50–65 kg) were used (<italic>n</italic> = 8, female = 5, male = 3). Rats (male, 220 g, 6 weeks of age, <italic>n</italic> = 10) were purchased from the Beijing Vital River Laboratory Animal Technology Co., Ltd., China.</p>","<title>Induction of AVB</title>","<p id=\"Par27\">Complete atrioventricular block (AVB) in the swine model was induced by radiofrequency ablation. The right femoral vein was dissected and cannulated with an 8F sheath to introduce a contact force catheter (Thermocool SmartTouch, Biosense Webster Inc., Diamond Bar, CA). Electroanatomic reconstructions of the cardiac structures (superior vena cava (SVC), inferior vena cava (IVC), and right atrial (RA)) were performed using a three-dimensional mapping system (CARTO3, Biosense Webster, Inc., Diamond Bar, CA). After mapping a (or cluster of) near field His-bundle (HB) potential, radiofrequency ablation with a power of 35 W and a duration of 120 s was performed in this HB region (ablation target). The signs of successful ablation were as follows: (1) the emergence of complete AVB; (2) the occurrence of the escape rhythm. The ablation catheter with the 8F sheath was then withdrawn and the incision of the right femoral vein was sutured.</p>","<title>Implantation of SICP</title>","<p id=\"Par28\">First, the right-side neck skin was prepared with a tincture of iodine solution. Then, the external jugular vein was exposed with a small incision. Next, a low-dose bolus of heparin (2000 to 4000 units) was delivered intravenously. After the vein was punctured, the next step was to guide a stiff guidewire (RF*GA35153M: Terumo Corporation, Tokyo, Japan) down into the IVC. Under fluoroscopic guidance, the delivery sheath was advanced through the external jugular vein and into the right atrium (RA) over the guidewire. The radiopaque marker band on the tip of the outside sheath should be positioned in the mid-RA, followed by retracting the inside sheath. Then the homemade delivery catheter integrated with SICP was advanced across the tricuspid valve into the right ventricle via the outside sheath. Once SICP was deployed with adequate fixation, the homemade delivery catheter with the outside sheath was retracted from the external jugular vein leaving SICP in the final position of the right ventricular apex. Upon removal of the sheath, the right-side neck skin and the vein were sutured.</p>","<title>Characterization methods</title>","<p id=\"Par29\">The scanning electron microscopy (SEM) images were taken by a Hitachi field emission scanning electron microscope (SU 8020). The <italic>V</italic><sub>oc</sub>, <italic>I</italic><sub>sc</sub>, and <italic>Q</italic><sub>sc</sub> were detected by an electrometer (Keithley 6517B) and recorded by an oscilloscope (Teledyne LeCroy HDO6104). The tensile test of SICP was performed by the ESM301/Mark-10 system. Optical photographs of the movement of the pellets in the cavity were exhibited by a high-speed digital video camera (FASTCAM Mini AX200 JAPAN). Optical photographs and movies of SICP fixed on the endocardium of the right ventricle in an isolated heart were recorded by an endoscope (BRK pt50).</p>","<title>Statistical analysis</title>","<p id=\"Par30\">All experiments were repeated at least three times. Data were analyzed as mean ± standard deviation (SD). The statistical significance of the differences is determined by a tailed <italic>t</italic>-test. ns was considered no significant differences. Origin 2018, GraphPad Prism version 8.0. and Excel were used for data analysis and plotting.</p>","<title>Reporting summary</title>","<p id=\"Par31\">Further information on research design is available in the ##SUPPL##11##Nature Portfolio Reporting Summary## linked to this article.</p>"],"string":"[\n \"<title>Methods</title>\",\n \"<title>Fabrication of SICP</title>\",\n \"<p id=\\\"Par19\\\">The enclosure of SICP was printed by a 3D printer (PHTON MONO) using UV-sensitive resins (ANYCUBIC Photon). Nanostructured PTFE film (50 μm) and POM pellets (diameter: 1.588 mm) were employed as triboelectric layers, processed by an inductively coupled plasma etching system (SENTECH/SI 500). In detail, the PTFE films and POM pellets were rinsed with alcohol and deionized water. The Au, which acted as the mask for the etching process, was sputtered onto their surfaces for about 30 s. Then the PTFE films and POM pellets were etched by ICP reactive ionic etching for 300 s (ICP power: 400 W and 100 W) and 150 s (ICP power: 500 W and 150 W), respectively. The reaction gas in the ICP process was CF4 (30.0 sccm), O<sub>2</sub> (10.0 sccm), and Ar (15.0 sccm). The two Au electrodes were deposited on the back of nano-PTFE by magnetron sputter (Denton Discovery 635) parallelly for 15 min (sputter power 50 W), the gap between them was about 1 mm. The Au bottom electrodes were polarized by connecting with wire to ground with a voltage of 4.5 kV for 15 min through the corona needle. The composite film was attached to the inner surface of the enclosure. The POM pellets were placed in the cavity of the energy harvesting unit. Altium Designer software was used to design the power management unit and pacemaker module. The custom control chip is an ultra-low power chip, which has the characteristic of a low power supply voltage range. The circuit board adopts the welding process of surface mounted technology (SMT). The spiral platinum-iridium alloy was attached to the bottom of SICP as cathode and the ring of platinum-iridium alloy was attached to the waist of SICP as anode. Narrow strips of tungsten sheet (thickness: 50 μm, width: 1 mm) were rolled into rings and fixed to both ends of SICP as a radiopaque marker. Nickel alloy wires were bent into an arch (diameter: 0.3 mm, length: 10 mm) and crossed through holes in the bottom of SICP as a hook to anchor in the myocardial wall.</p>\",\n \"<title>Encapsulation of the SICP</title>\",\n \"<p id=\\\"Par20\\\">The ethyl cyanoacrylate (Aibida, Guangzhou, China) was employed to close the seams of the enclosure. Then the one-component UV light-curing adhesive (8500 Metal, Switzerland) was spin-coated on the enclosure as the package layer and then cured under UV light for 10 s. The holes in the enclosure that led out the wires need to be sealed with light-curing glue several times. Finally, the parylene-C particles were steamed at 135 °C/690 °C and then deposited on the surface of SICP where the thickness of parylene is 3 μm.</p>\",\n \"<title>Cell viability</title>\",\n \"<p id=\\\"Par21\\\">The L929 cells (fibroblasts, GNM28) were acquired from the Cell Bank of the Chinese Academy of Sciences in Beijing, China. After being cultured to a stable stage, L929 cells were collected and seeded on 96-well tissue culture polystyrenes (TCPs, Corning, USA) with a density of 1 × 10<sup>6</sup> cells/ml. The culture dish of the experimental group deposited encapsulation materials. 20 μL CCK-8 solution (Solarbio, China) was mixed with 280 μL cell culture medium for each well. The cells were cultured for 1, 2, and 3 days and evaluate the cell viability every day. After incubating the cells with CCK-8 solution for 1 h at 37 °C, 200 μL cell culture supernatant was transferred into a 96-well plate (set at least three repetitions for each group). The absorbance of the solution in the 96-well plate was measured at 450 nm with a microplate absorbance assay instrument (Bio-rad iMark, USA).</p>\",\n \"<title>Cell morphology and immunofluorescent staining</title>\",\n \"<p id=\\\"Par22\\\">Cell morphology could show the cellular growth condition on different substrates. The cytoskeleton and nucleus were stained with Phalloidin (Abcam, USA) and 4′, 6-diamidino-2-phenylindole (DAPI, Solarbio, China). The L929 were cultured for 1, 2, and 3 days on the naked 96-well disposable confocal dish, and the dish deposited encapsulation materials for cell morphologic observation. Before staining, the cells were rinsed with phosphate buffer solution (PBS, Solarbio, China) three times gently, then fixed with 4% paraformaldehyde (Solarbio, China) for 10 min and permeabilized with 0.1% Triton X-100 (Solarbio, China) for 10 min. Finally, the fixed cells were stained with Phalloidin for 40 min, DAPI for 10 min at room temperature, and washed with PBS three times. The stained cells were visualized by the laser scanning confocal microscope (SP8, Leica, Germany) under the filter at <italic>E</italic><sub>x</sub>/<italic>E</italic><sub>m</sub> = 493/517 nm.</p>\",\n \"<title>Platelet adhesion tests</title>\",\n \"<p id=\\\"Par23\\\">Rats were anesthetized with 2% isoflurane (RWD, R510-22-4). After the location of the abdominal aorta was determined by laparotomy, 2 ml of fresh blood from arterial blood was collected using a blood collection needle. The collected blood was placed in a centrifuge tube for 30 min. The cells were centrifuged at 110 × <italic>g</italic> for 10 min in a centrifuge. Platelets were removed and incubated on sterilized encapsulation coating material for 90 min at room temperature. After 30 min of fixation with 4% paraformaldehyde (Solarbio, China), platelets were dehydrated in serial concentration gradients of ethanol (50%, 60%, 70%, 80%, 90%, 95%, and 100%) for 30 min. After evaporation to dryness at room temperature, the surface of the encapsulation coating material with platelets was visualized by SEM.</p>\",\n \"<title>Hemolysis assay</title>\",\n \"<p id=\\\"Par24\\\">Working solution: positive group: 0.3% Triton X-100 (Solarbio, China); Negative group: normal saline (0.9% NaCl); Material group: 1 mg/ml encapsulation coating material leaching solution (1 mg encapsulation coating material was immersed in 1 ml of normal saline for 3 days). 1 ml of fresh blood from male 6-week-old SD rats (220 g) was placed in a 15 ml centrifuge tube, and the supernatant was removed after 4 ml PBS (Solarbio, China) was washed 4–5 times (170 g, 5 min). The washed red blood cells (RBCs) were resuspended by 10 ml PBS (Solarbio, China). 0.2 ml resuspended red blood cells were mixed with 0.8 ml working solution and incubated for 4 h. The mixture was centrifuged in a centrifuge at 170 × <italic>g</italic> for 5 min and photographed for recording. The supernatant was removed and the OD value (541 nm) was measured by a microplate reader. %hemolysis = (OD<sub>test</sub> – OD<sub>neg</sub>)/(OD<sub>pos</sub> – OD<sub>neg</sub>) × 100%.</p>\",\n \"<title>Histology</title>\",\n \"<p id=\\\"Par25\\\">The tissues were fixed in a 4% paraformaldehyde (Solarbio, China) overnight at room temperature, followed by dehydration using a series of graded ethanol and xylene solutions (Solarbio, China). Subsequently, routine paraffin embedding was carried out, and tissue sections with a thickness of 4 μm were obtained using a microtome. To visualize the tissue morphology and extracellular matrix, hematoxylin-eosin (HE) and Masson’s trichrome staining (Solarbio, China) were performed with standard procedures. The resulting images were observed under a light microscope.</p>\",\n \"<title>Animal preparation</title>\",\n \"<p id=\\\"Par26\\\">All experimental processes were strictly in line with the institutional and national guidelines for the care and use of laboratory animals and the study protocol was reviewed and approved by the Ethical Committee of the Animal Experimental Center in the State Key Laboratory of Cardiovascular Disease and Fuwai Hospital (0103-1-1-ZX(Y)−3). The swine were anesthetized and intubated with respirators for artificial respiration, and then ECG and femoral artery pressure were recorded by the data acquisition hardware (MP150, BIOPAC System, INC.). Adult swine (age: 1.2–2.0 years; weight: 50–65 kg) were used (<italic>n</italic> = 8, female = 5, male = 3). Rats (male, 220 g, 6 weeks of age, <italic>n</italic> = 10) were purchased from the Beijing Vital River Laboratory Animal Technology Co., Ltd., China.</p>\",\n \"<title>Induction of AVB</title>\",\n \"<p id=\\\"Par27\\\">Complete atrioventricular block (AVB) in the swine model was induced by radiofrequency ablation. The right femoral vein was dissected and cannulated with an 8F sheath to introduce a contact force catheter (Thermocool SmartTouch, Biosense Webster Inc., Diamond Bar, CA). Electroanatomic reconstructions of the cardiac structures (superior vena cava (SVC), inferior vena cava (IVC), and right atrial (RA)) were performed using a three-dimensional mapping system (CARTO3, Biosense Webster, Inc., Diamond Bar, CA). After mapping a (or cluster of) near field His-bundle (HB) potential, radiofrequency ablation with a power of 35 W and a duration of 120 s was performed in this HB region (ablation target). The signs of successful ablation were as follows: (1) the emergence of complete AVB; (2) the occurrence of the escape rhythm. The ablation catheter with the 8F sheath was then withdrawn and the incision of the right femoral vein was sutured.</p>\",\n \"<title>Implantation of SICP</title>\",\n \"<p id=\\\"Par28\\\">First, the right-side neck skin was prepared with a tincture of iodine solution. Then, the external jugular vein was exposed with a small incision. Next, a low-dose bolus of heparin (2000 to 4000 units) was delivered intravenously. After the vein was punctured, the next step was to guide a stiff guidewire (RF*GA35153M: Terumo Corporation, Tokyo, Japan) down into the IVC. Under fluoroscopic guidance, the delivery sheath was advanced through the external jugular vein and into the right atrium (RA) over the guidewire. The radiopaque marker band on the tip of the outside sheath should be positioned in the mid-RA, followed by retracting the inside sheath. Then the homemade delivery catheter integrated with SICP was advanced across the tricuspid valve into the right ventricle via the outside sheath. Once SICP was deployed with adequate fixation, the homemade delivery catheter with the outside sheath was retracted from the external jugular vein leaving SICP in the final position of the right ventricular apex. Upon removal of the sheath, the right-side neck skin and the vein were sutured.</p>\",\n \"<title>Characterization methods</title>\",\n \"<p id=\\\"Par29\\\">The scanning electron microscopy (SEM) images were taken by a Hitachi field emission scanning electron microscope (SU 8020). The <italic>V</italic><sub>oc</sub>, <italic>I</italic><sub>sc</sub>, and <italic>Q</italic><sub>sc</sub> were detected by an electrometer (Keithley 6517B) and recorded by an oscilloscope (Teledyne LeCroy HDO6104). The tensile test of SICP was performed by the ESM301/Mark-10 system. Optical photographs of the movement of the pellets in the cavity were exhibited by a high-speed digital video camera (FASTCAM Mini AX200 JAPAN). Optical photographs and movies of SICP fixed on the endocardium of the right ventricle in an isolated heart were recorded by an endoscope (BRK pt50).</p>\",\n \"<title>Statistical analysis</title>\",\n \"<p id=\\\"Par30\\\">All experiments were repeated at least three times. Data were analyzed as mean ± standard deviation (SD). The statistical significance of the differences is determined by a tailed <italic>t</italic>-test. ns was considered no significant differences. Origin 2018, GraphPad Prism version 8.0. and Excel were used for data analysis and plotting.</p>\",\n \"<title>Reporting summary</title>\",\n \"<p id=\\\"Par31\\\">Further information on research design is available in the ##SUPPL##11##Nature Portfolio Reporting Summary## linked to this article.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<title>Design features and materials of SICP</title>","<p id=\"Par6\">The schematic illustration in Fig. ##FIG##0##1a## shows a battery-free and transcatheter SICP in the right ventricle. As shown in the partially enlarged drawing, this capsule-shaped device consists of EHU, PMU&PM, hooks, and radiopaque markers, which can be integrated with a customized delivery catheter system for interventional implantation in the heart through the intravenous route. The SICP fixed on the right ventricular endocardium through the design of the hook structure converts biomechanical energy from cardiac motion to electricity. Figure ##FIG##0##1b## displays the internal perspective structure of the device. The resin shell of SICP is constructed by stereolithography, the surface of which is deposited Parylene-C by the parylene coating system to form a waterproof coating with good biocompatibility (Supplementary Fig. ##SUPPL##1##1##). Polyformaldehyde (POM) pellets, and polytetrafluoroethylene (PTFE) film deposited gold electrodes are employed for the fabrication of the EHU. The POM pellets roll back and forth between the two electrodes under heart beating, which produces an alternating current to the external based on contact electrification (CE) and electrostatic induction. The illustration in Fig. ##FIG##0##1c## shows CE between POM and PTFE materials can be presented using the surface state model<sup>##UREF##11##38##</sup>. Material surface electron transfer is the main cause of CE<sup>##UREF##12##39##</sup>. Based on the CE theory, the basic theory of triboelectric nanogenerator was further proposed by introducing the displacement current () term into Maxwell’s equation, where the polarization density is mainly due to the presence of surface electrostatic charges caused by CE. The total displacement current from Maxwell’s equation is stated as follows:where denotes the density of free conduction current density, is the electric displacement vector. represents the electric field, and is the displacement current due to time variation of the electric field. As the theoretical origin of the triboelectric nanogenerator, represents displacement current due to the movement of the changed media as driven by an external mechanical agitation or force. This term is the key to converting mechanical energy into electrical energy in the nanogenerator.</p>","<p id=\"Par7\">As a three-dimensional explosive view of SICP, Fig. ##FIG##0##1d## shows various unit components of the device. The integration of PMU&PM achieved the miniaturization for placing in the cylindrical cavity with an outer diameter of 6.8 mm (inner diameter of 5.8 mm) and is, respectively, connected to the tail-end energy harvesting unit and the head-end pacing electrode (Fig. ##FIG##0##1e##). The overall length of the device was 42 mm and the volume was only 1.52 cc. To prevent reflecting the normal physiological contraction of the heart, the device combined with the heart should typically be <1–2% of the weight of the heart (<3–6 g)<sup>##REF##23949656##40##</sup>. The overall weight of our device was only 1.75 g, which was better than the above indicators. Besides, scanning electron microscope (SEM) images of POM and PTFE before treatment by inductively coupled plasma (ICP) are shown in Supplementary Fig. ##SUPPL##1##2## and Fig. ##FIG##0##1f##, respectively. The micro-nano structure on the pellet and the PTFE surface was constructed by the inductively coupled plasma technology for increasing the contact area between the pellet and the surface of the PTFE film during the movement, which will effectively enhance the electrical output performance of the device.</p>","<title>Working principle and electrical output performance of EHM</title>","<p id=\"Par8\">To understand the movement process of POM pellets more intuitively, a high-speed camera was employed to capture pellets movement trajectory, which clearly shows that pellets move sequentially from one end to the other on the side wall under the excitation of the external mechanical motion (Fig. ##FIG##1##2a##). The working principle of EHU is shown in Fig. ##FIG##1##2b## and Supplementary Movie ##SUPPL##3##1##. Under the excitation of cardiac motion, POM pellets roll back and forth on the PTFE surface. After multiple cycles of contact with POM pellets, the PTFE film was negatively charged. Due to the electret properties of the PTFE film, charges can remain on the surface. When POM pellets roll to the left, negative charges are induced on the left electrode. Subsequently, the current is generated in the loop and flows to the left electrode with POM pellets rolling back. With the periodic beating of the heart, under the combined action of gravity and supporting force, POM pellets periodically roll on the curved surface of the PTFE film to generate an alternating current based on the freestanding mode of the triboelectric nanogenerator. A triboelectric nanogenerator can be considered as a capacitor and an ideal voltage source in series. Therefore, the differential equation of triboelectric nanogenerator with an external pure resistance R can be represented by Kirchhoff’s law<sup>##REF##24038597##41##</sup> as follows:Where denotes the transferred charge, is the triboelectric nanogenerator’s internal capacitance, and represents the open-circuit voltage.</p>","<p id=\"Par9\">The open-circuit voltage is proportional to the transferred charge. Furthermore, the short-circuit current can be represented as follows:</p>","<p id=\"Par10\">The short-circuit current also relies on the external motion velocity (movement frequency). We compared the electrical performance of EHU at different numbers of pellets and tilt angles. As shown in Fig. ##FIG##1##2c##, for the volume ratio (<italic>V</italic><sub>pellets</sub>/<italic>V</italic><sub>cavity</sub>) from 10% to 90%, the maximum value of the <italic>V</italic><sub>oc</sub> was about 21.8 V at 40%. It was found that the overall height of the globules under a 40% volume ratio is consistent with the height of the unilateral electrode, which enables a maximum effective contact area during movement. The motion direction is consistent with the direction of the long axis of the device which was defined as 0° (Supplementary Fig. ##SUPPL##1##3##). The electrical output performance of the device gradually decreases with an increase in the angle (Fig. ##FIG##1##2d##). The highest performance of EHU was obtained at the angle of 0°. More importantly, when the tilt angle was 30°, the output voltage of the device was maintained at >65% of the maximum output. The <italic>V</italic><sub>oc</sub> of EHU could still reach 6.0 V when the tilt angle reached 45°. The good compliance of the device to the angle is mainly due to the design of the spherical structure for POM materials. As shown in Fig. ##FIG##1##2e##, under the optimal motion angle and volume ratio, the <italic>V</italic><sub>oc</sub>, <italic>I</italic><sub>sc</sub>, and <italic>Q</italic><sub>sc</sub> of the device were about 21.8 V, 0.25 μA, and 6.4 nC, respectively. The excellent fixation effect of the device is a key factor in ensuring good electrical output in vivo. In addition, the tensile test was employed for SICP. As shown in Fig. ##FIG##1##2f##, the device can withstand a cyclic action of 1.5 N tensile force (Supplementary Movie ##SUPPL##4##2##). The isolated heart simulation experiment also shows that SICP achieves good fixation in the heart (Supplementary Fig. ##SUPPL##1##4## and Supplementary Movie ##SUPPL##5##3##). The <italic>V</italic><sub>oc</sub> and <italic>I</italic><sub>sc</sub> of EHU show opposite trends with different load resistance (Supplementary Fig. ##SUPPL##1##5a##). The EHU reached a maximum output power density of 2200 mW/m<sup>3</sup> at 100 MΩ (Supplementary Fig. ##SUPPL##1##5b##). After 6 million stimuli cycles by a linear motor, the <italic>V</italic><sub>oc</sub> of EHU maintained stable in compared with its initial state (Supplementary Fig. ##SUPPL##1##6##), exhibiting outstanding stability and durability, which enables for long-term harvesting biomechanical energy in vivo. In addition, the output performance of EHU was further improved when the motion frequency was increased within a certain range (Supplementary Fig. ##SUPPL##1##7##).</p>","<title>Characterizations for PMU&PM</title>","<p id=\"Par11\">We developed an integral power management unit & pacemaker module (PMU&PM) (Fig. ##FIG##2##3a##). The length and width of a PMU&PM were 9.5 mm and 5.6 mm, respectively. The PMU&PM consisted of a rectifier bridge, capacitor, reed switch, electric pulse chip, and peripheral circuit (Fig. ##FIG##2##3b##). The extremely narrow width design enables PMU&PM to be integrated with EHU for establishing SICP. The pulse frequency of PM can be modulated on demand (Fig. ##FIG##2##3c##), such as 1.5 Hz, 1.8 Hz, etc., which induces myocardial contraction and regulates heart rate via pacing electrodes. Here, the output voltage and pulse width of the electrical pulses were set to 3 V and 0.5 ms (Fig. ##FIG##2##3d##), respectively. Charging a 10 μF capacitor to 3 V by EHU can continuously power the PM to work for nearly 40 s (Fig. ##FIG##2##3e##). Moreover, we also verified that using a capacitor with a larger capacity (47 μF) can significantly increase the duration of the pacing pulse release for the PM (Supplementary Fig. ##SUPPL##1##8##). To further explore the pacing efficiency for PM in animals, complete atrioventricular block (AVB) was induced by radiofrequency ablation (Fig. ##FIG##2##3f##). Figure ##FIG##2##3g## shows the electrocardiogram (ECG) before and after atrioventricular node ablation in a swine model. The heart rate decreased from 96 bpm to 33 bpm, showing that the bradycardia animal model was built successfully. The PM with 1.5 Hz was used for pacing the AVB animal model. The driving voltage of the pacing chip was increased to 1.5 V, and effective pacing in AVB animals was achieved (Fig. ##FIG##2##3h##), which is consistent with the threshold voltage of the PM of 1.5 V in large-animal models.</p>","<p id=\"Par12\">Biocompatibility is a critical aspect of the success of implantable bioelectronic devices, which depends on the encapsulation strategy and materials. Before implanting the device, we systematically evaluated the biocompatibility of the encapsulation material of SICP. The cytoskeletal structures and cell nucleus were detected by immunofluorescence staining on days 1, 2, and 3, respectively (Supplementary Fig. ##SUPPL##1##9##). The viability of a fibroblast cell line L929 on the encapsulation film was tested by the Cell Counting Kit-8 (CCK-8) (Supplementary Fig. ##SUPPL##1##10##). Compared with the control group, the results revealed that encapsulation material had no negative impact on cells growth and proliferation. Meanwhile, localized tissues of the skin to deep layer muscle from the implantation location of the materials were stained by Hematoxylin and Eosin (H&E). The tissues surrounding the materials of the device showed no observable differences compared with the surrounding tissue (Supplementary Fig. ##SUPPL##1##11##). Moreover, acceptable blood compatibility was required for the encapsulation materials in the present study. Hemolysis and coagulation on the materials were also demonstrated. The average hemolysis rate of the encapsulation materials was remarkably lower than the National Standards Organization (ISO) standard (5%) (Supplementary Fig. ##SUPPL##1##12a##). Platelets on the material that maintained a round shape without obvious deformation and aggregation, indicating a low degree of activation (Supplementary Fig. ##SUPPL##1##12b##).</p>","<title>Energy harvesting of SICP in vivo</title>","<p id=\"Par13\">To simulate the function of SICP for biomedical energy harvesting from cardiac chambers in human bodies, adult swine (age: 1.2–2.0 years; weight: 50–65 kg) were selected as our animal model. After swine were anesthetized and intubated with respirators for artificial respiration, ECG and femoral artery pressure were recorded using the data acquisition hardware (MP150, BIOPAC System, Inc.). The right-side neck skin was prepared by tincture of iodine solution, and the external jugular vein was exposed with a small incision (Supplementary Fig. ##SUPPL##1##13##). The diameter of the device was smaller than that of the external jugular vein, indicating that the device could be successfully delivery into the right ventricle through the venous system (Supplementary Fig. ##SUPPL##1##14##).</p>","<p id=\"Par14\">Once the vein access was obtained by the homemade introducer and dilator advancement over a guidewire (RF*GA35153M: Terumo Corporation, Tokyo, Japan), the homemade delivery catheter by integrating SICP was advanced across the tricuspid valve into the right ventricle, and SICP was delivered into the right ventricular endocardium. Figure ##FIG##3##4a## shows fluoroscopy images of minimally invasive delivery of SICP to the right ventricle via the intravenous route. The schematic diagram corresponding to the fluoroscopy images is shown in Supplementary Fig. ##SUPPL##1##15##. The electrodes and PMU&PM of the device could be seen clearly through dynamic Movie (Supplementary Movie ##SUPPL##6##4## and Supplementary Movie ##SUPPL##7##5##). The constructed radiopaque marker could accurately judge that the device successfully reached the right ventricle. The hook on the front end of the device interacts with the endocardium to hold the device firmly on the heart, and the helical electrode (cathode) makes good contact with the endocardium (Supplementary Movie ##SUPPL##8##6##).</p>","<p id=\"Par15\">After implanting the device, no significant changes in ECG and blood pressure of the animal model (Fig. ##FIG##3##4b##). The delivery process did not affect the physiological state of the experimental animals. The cardiac motion caused the pellets to move back and forth on the surface of the PTFE membrane (Supplementary Movie ##SUPPL##9##7##). The EHU of SICP generates periodic alternating current based on a coupling effect of triboelectrification and electrostatic induction. <italic>V</italic><sub>oc</sub>, <italic>I</italic><sub>sc</sub>, and <italic>Q</italic><sub>sc</sub> of SICP were about 6.0 V, 0.25 μA, 8.5 nC in vivo (Fig. ##FIG##3##4c##). With the periodic physiological contraction and relaxation of the heart, globules move freely in SICP, and the electrical signal exhibits a certain volatility. We speculate that fluctuations in the electrical signal may also be affected by the blood flow. Nonetheless, our statistical analysis showed that the average voltage and current can reach 4 V and 0.2 μA (Fig. ##FIG##3##4d##), respectively, and the energy conversion efficiency of the voltage exceeding 1.5 V per unit time accounted for over 82%. The output voltage EHU meets the requirement of the threshold voltage of PM. Actually, cardiac contraction intensity is also affected by respiratory status and exercise. Therefore, appropriately enhancing cardiac functional status may be beneficial to improving energy harvesting efficiency.</p>","<title>Illustration pacing performance of SICP in vivo</title>","<p id=\"Par16\">The EHU of SICP converts the biomechanical energy from heart beating to electricity, which can be stored in the capacitor of the PMU for powering the PM after the reed switch is turned on by the magnet (Fig. ##FIG##4##5a##). That is, after the device is integrated with the heart, self-sufficiency of electrical energy is achieved. The voltage of a capacitor was charged from 0 V to 3 V within 9000 s with the same electrical output of SICP in vivo (Supplementary Fig. ##SUPPL##1##16##). Corresponding to the ECG and blood pressure, transthoracic echocardiograms also showed that no tricuspid regurgitation was observed after SICP implantation (Fig. ##FIG##4##5b##). This result further suggests that the physical structure of the device itself has no effect on the heart. To demonstrate pacing the performance of SICP, ECG signals of the swine during SICP operation are shown in Fig. ##FIG##4##5c##. The typical P wave, QRS complex, and T wave appear in sequence on the intrinsic ECG. When SICP was in operation, the premature-paced QRS complex was induced by the electrical pulse stimulus (Supplementary Movie ##SUPPL##10##8##). The ECG showed regular-paced QRS complex occurrence ahead of the P wave (atrial contraction), indicating that the ventricle was effectively captured by SICP. The heart returned to intrinsic rhythm after the PM of SICP stopped working. The above results demonstrate the effectiveness of SICP for pacing the heart rhythm. Furthermore, the wound was sutured after implanting the device into the right ventricle, and long-term experiments were performed. As shown in Fig. ##FIG##4##5d##, ##FIG##4##e##, we monitored the ECG signals of the experimental animals for 2 and 3 weeks, respectively. The results showed that the heart rhythm remained stable, and the experimental animals were eating and living normally without the occurrence of complications (Supplementary Fig. ##SUPPL##1##17##). Meanwhile, EMU was controlled by switching in the third week, and SICP effectively paced the heart in vivo, the heart rate of the animal increased from 90 bpm to 108 bpm, demonstrating that the device still maintained normal pacing function (Fig. ##FIG##4##5f##). Three weeks after implantation, the swine was sacrificed by injection of a medium with high potassium concentration, and then the chest was opened to remove the heart with SICP. We found that the device was firmly anchored to myocardial tissues (Fig. ##FIG##4##5g##). Masson’s trichrome stain was applied after prolonged implantation, which revealed that the extent of cardiac tissue injury and inflammatory fibrous hyperplasia occurred only at the device fixation site, and Hematoxylin and eosin (H&E) staining showed no detectable infiltration of lymphocytes in other sites, prompting the conclusion that neither humoral nor cellular rejection to the device occurred in the myocardium from the implantation site (Fig. ##FIG##4##5h##). Overall, these in vivo tests demonstrated that SICP can achieve long-term pacing in large-animal models.</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>Design features and materials of SICP</title>\",\n \"<p id=\\\"Par6\\\">The schematic illustration in Fig. ##FIG##0##1a## shows a battery-free and transcatheter SICP in the right ventricle. As shown in the partially enlarged drawing, this capsule-shaped device consists of EHU, PMU&PM, hooks, and radiopaque markers, which can be integrated with a customized delivery catheter system for interventional implantation in the heart through the intravenous route. The SICP fixed on the right ventricular endocardium through the design of the hook structure converts biomechanical energy from cardiac motion to electricity. Figure ##FIG##0##1b## displays the internal perspective structure of the device. The resin shell of SICP is constructed by stereolithography, the surface of which is deposited Parylene-C by the parylene coating system to form a waterproof coating with good biocompatibility (Supplementary Fig. ##SUPPL##1##1##). Polyformaldehyde (POM) pellets, and polytetrafluoroethylene (PTFE) film deposited gold electrodes are employed for the fabrication of the EHU. The POM pellets roll back and forth between the two electrodes under heart beating, which produces an alternating current to the external based on contact electrification (CE) and electrostatic induction. The illustration in Fig. ##FIG##0##1c## shows CE between POM and PTFE materials can be presented using the surface state model<sup>##UREF##11##38##</sup>. Material surface electron transfer is the main cause of CE<sup>##UREF##12##39##</sup>. Based on the CE theory, the basic theory of triboelectric nanogenerator was further proposed by introducing the displacement current () term into Maxwell’s equation, where the polarization density is mainly due to the presence of surface electrostatic charges caused by CE. The total displacement current from Maxwell’s equation is stated as follows:where denotes the density of free conduction current density, is the electric displacement vector. represents the electric field, and is the displacement current due to time variation of the electric field. As the theoretical origin of the triboelectric nanogenerator, represents displacement current due to the movement of the changed media as driven by an external mechanical agitation or force. This term is the key to converting mechanical energy into electrical energy in the nanogenerator.</p>\",\n \"<p id=\\\"Par7\\\">As a three-dimensional explosive view of SICP, Fig. ##FIG##0##1d## shows various unit components of the device. The integration of PMU&PM achieved the miniaturization for placing in the cylindrical cavity with an outer diameter of 6.8 mm (inner diameter of 5.8 mm) and is, respectively, connected to the tail-end energy harvesting unit and the head-end pacing electrode (Fig. ##FIG##0##1e##). The overall length of the device was 42 mm and the volume was only 1.52 cc. To prevent reflecting the normal physiological contraction of the heart, the device combined with the heart should typically be <1–2% of the weight of the heart (<3–6 g)<sup>##REF##23949656##40##</sup>. The overall weight of our device was only 1.75 g, which was better than the above indicators. Besides, scanning electron microscope (SEM) images of POM and PTFE before treatment by inductively coupled plasma (ICP) are shown in Supplementary Fig. ##SUPPL##1##2## and Fig. ##FIG##0##1f##, respectively. The micro-nano structure on the pellet and the PTFE surface was constructed by the inductively coupled plasma technology for increasing the contact area between the pellet and the surface of the PTFE film during the movement, which will effectively enhance the electrical output performance of the device.</p>\",\n \"<title>Working principle and electrical output performance of EHM</title>\",\n \"<p id=\\\"Par8\\\">To understand the movement process of POM pellets more intuitively, a high-speed camera was employed to capture pellets movement trajectory, which clearly shows that pellets move sequentially from one end to the other on the side wall under the excitation of the external mechanical motion (Fig. ##FIG##1##2a##). The working principle of EHU is shown in Fig. ##FIG##1##2b## and Supplementary Movie ##SUPPL##3##1##. Under the excitation of cardiac motion, POM pellets roll back and forth on the PTFE surface. After multiple cycles of contact with POM pellets, the PTFE film was negatively charged. Due to the electret properties of the PTFE film, charges can remain on the surface. When POM pellets roll to the left, negative charges are induced on the left electrode. Subsequently, the current is generated in the loop and flows to the left electrode with POM pellets rolling back. With the periodic beating of the heart, under the combined action of gravity and supporting force, POM pellets periodically roll on the curved surface of the PTFE film to generate an alternating current based on the freestanding mode of the triboelectric nanogenerator. A triboelectric nanogenerator can be considered as a capacitor and an ideal voltage source in series. Therefore, the differential equation of triboelectric nanogenerator with an external pure resistance R can be represented by Kirchhoff’s law<sup>##REF##24038597##41##</sup> as follows:Where denotes the transferred charge, is the triboelectric nanogenerator’s internal capacitance, and represents the open-circuit voltage.</p>\",\n \"<p id=\\\"Par9\\\">The open-circuit voltage is proportional to the transferred charge. Furthermore, the short-circuit current can be represented as follows:</p>\",\n \"<p id=\\\"Par10\\\">The short-circuit current also relies on the external motion velocity (movement frequency). We compared the electrical performance of EHU at different numbers of pellets and tilt angles. As shown in Fig. ##FIG##1##2c##, for the volume ratio (<italic>V</italic><sub>pellets</sub>/<italic>V</italic><sub>cavity</sub>) from 10% to 90%, the maximum value of the <italic>V</italic><sub>oc</sub> was about 21.8 V at 40%. It was found that the overall height of the globules under a 40% volume ratio is consistent with the height of the unilateral electrode, which enables a maximum effective contact area during movement. The motion direction is consistent with the direction of the long axis of the device which was defined as 0° (Supplementary Fig. ##SUPPL##1##3##). The electrical output performance of the device gradually decreases with an increase in the angle (Fig. ##FIG##1##2d##). The highest performance of EHU was obtained at the angle of 0°. More importantly, when the tilt angle was 30°, the output voltage of the device was maintained at >65% of the maximum output. The <italic>V</italic><sub>oc</sub> of EHU could still reach 6.0 V when the tilt angle reached 45°. The good compliance of the device to the angle is mainly due to the design of the spherical structure for POM materials. As shown in Fig. ##FIG##1##2e##, under the optimal motion angle and volume ratio, the <italic>V</italic><sub>oc</sub>, <italic>I</italic><sub>sc</sub>, and <italic>Q</italic><sub>sc</sub> of the device were about 21.8 V, 0.25 μA, and 6.4 nC, respectively. The excellent fixation effect of the device is a key factor in ensuring good electrical output in vivo. In addition, the tensile test was employed for SICP. As shown in Fig. ##FIG##1##2f##, the device can withstand a cyclic action of 1.5 N tensile force (Supplementary Movie ##SUPPL##4##2##). The isolated heart simulation experiment also shows that SICP achieves good fixation in the heart (Supplementary Fig. ##SUPPL##1##4## and Supplementary Movie ##SUPPL##5##3##). The <italic>V</italic><sub>oc</sub> and <italic>I</italic><sub>sc</sub> of EHU show opposite trends with different load resistance (Supplementary Fig. ##SUPPL##1##5a##). The EHU reached a maximum output power density of 2200 mW/m<sup>3</sup> at 100 MΩ (Supplementary Fig. ##SUPPL##1##5b##). After 6 million stimuli cycles by a linear motor, the <italic>V</italic><sub>oc</sub> of EHU maintained stable in compared with its initial state (Supplementary Fig. ##SUPPL##1##6##), exhibiting outstanding stability and durability, which enables for long-term harvesting biomechanical energy in vivo. In addition, the output performance of EHU was further improved when the motion frequency was increased within a certain range (Supplementary Fig. ##SUPPL##1##7##).</p>\",\n \"<title>Characterizations for PMU&PM</title>\",\n \"<p id=\\\"Par11\\\">We developed an integral power management unit & pacemaker module (PMU&PM) (Fig. ##FIG##2##3a##). The length and width of a PMU&PM were 9.5 mm and 5.6 mm, respectively. The PMU&PM consisted of a rectifier bridge, capacitor, reed switch, electric pulse chip, and peripheral circuit (Fig. ##FIG##2##3b##). The extremely narrow width design enables PMU&PM to be integrated with EHU for establishing SICP. The pulse frequency of PM can be modulated on demand (Fig. ##FIG##2##3c##), such as 1.5 Hz, 1.8 Hz, etc., which induces myocardial contraction and regulates heart rate via pacing electrodes. Here, the output voltage and pulse width of the electrical pulses were set to 3 V and 0.5 ms (Fig. ##FIG##2##3d##), respectively. Charging a 10 μF capacitor to 3 V by EHU can continuously power the PM to work for nearly 40 s (Fig. ##FIG##2##3e##). Moreover, we also verified that using a capacitor with a larger capacity (47 μF) can significantly increase the duration of the pacing pulse release for the PM (Supplementary Fig. ##SUPPL##1##8##). To further explore the pacing efficiency for PM in animals, complete atrioventricular block (AVB) was induced by radiofrequency ablation (Fig. ##FIG##2##3f##). Figure ##FIG##2##3g## shows the electrocardiogram (ECG) before and after atrioventricular node ablation in a swine model. The heart rate decreased from 96 bpm to 33 bpm, showing that the bradycardia animal model was built successfully. The PM with 1.5 Hz was used for pacing the AVB animal model. The driving voltage of the pacing chip was increased to 1.5 V, and effective pacing in AVB animals was achieved (Fig. ##FIG##2##3h##), which is consistent with the threshold voltage of the PM of 1.5 V in large-animal models.</p>\",\n \"<p id=\\\"Par12\\\">Biocompatibility is a critical aspect of the success of implantable bioelectronic devices, which depends on the encapsulation strategy and materials. Before implanting the device, we systematically evaluated the biocompatibility of the encapsulation material of SICP. The cytoskeletal structures and cell nucleus were detected by immunofluorescence staining on days 1, 2, and 3, respectively (Supplementary Fig. ##SUPPL##1##9##). The viability of a fibroblast cell line L929 on the encapsulation film was tested by the Cell Counting Kit-8 (CCK-8) (Supplementary Fig. ##SUPPL##1##10##). Compared with the control group, the results revealed that encapsulation material had no negative impact on cells growth and proliferation. Meanwhile, localized tissues of the skin to deep layer muscle from the implantation location of the materials were stained by Hematoxylin and Eosin (H&E). The tissues surrounding the materials of the device showed no observable differences compared with the surrounding tissue (Supplementary Fig. ##SUPPL##1##11##). Moreover, acceptable blood compatibility was required for the encapsulation materials in the present study. Hemolysis and coagulation on the materials were also demonstrated. The average hemolysis rate of the encapsulation materials was remarkably lower than the National Standards Organization (ISO) standard (5%) (Supplementary Fig. ##SUPPL##1##12a##). Platelets on the material that maintained a round shape without obvious deformation and aggregation, indicating a low degree of activation (Supplementary Fig. ##SUPPL##1##12b##).</p>\",\n \"<title>Energy harvesting of SICP in vivo</title>\",\n \"<p id=\\\"Par13\\\">To simulate the function of SICP for biomedical energy harvesting from cardiac chambers in human bodies, adult swine (age: 1.2–2.0 years; weight: 50–65 kg) were selected as our animal model. After swine were anesthetized and intubated with respirators for artificial respiration, ECG and femoral artery pressure were recorded using the data acquisition hardware (MP150, BIOPAC System, Inc.). The right-side neck skin was prepared by tincture of iodine solution, and the external jugular vein was exposed with a small incision (Supplementary Fig. ##SUPPL##1##13##). The diameter of the device was smaller than that of the external jugular vein, indicating that the device could be successfully delivery into the right ventricle through the venous system (Supplementary Fig. ##SUPPL##1##14##).</p>\",\n \"<p id=\\\"Par14\\\">Once the vein access was obtained by the homemade introducer and dilator advancement over a guidewire (RF*GA35153M: Terumo Corporation, Tokyo, Japan), the homemade delivery catheter by integrating SICP was advanced across the tricuspid valve into the right ventricle, and SICP was delivered into the right ventricular endocardium. Figure ##FIG##3##4a## shows fluoroscopy images of minimally invasive delivery of SICP to the right ventricle via the intravenous route. The schematic diagram corresponding to the fluoroscopy images is shown in Supplementary Fig. ##SUPPL##1##15##. The electrodes and PMU&PM of the device could be seen clearly through dynamic Movie (Supplementary Movie ##SUPPL##6##4## and Supplementary Movie ##SUPPL##7##5##). The constructed radiopaque marker could accurately judge that the device successfully reached the right ventricle. The hook on the front end of the device interacts with the endocardium to hold the device firmly on the heart, and the helical electrode (cathode) makes good contact with the endocardium (Supplementary Movie ##SUPPL##8##6##).</p>\",\n \"<p id=\\\"Par15\\\">After implanting the device, no significant changes in ECG and blood pressure of the animal model (Fig. ##FIG##3##4b##). The delivery process did not affect the physiological state of the experimental animals. The cardiac motion caused the pellets to move back and forth on the surface of the PTFE membrane (Supplementary Movie ##SUPPL##9##7##). The EHU of SICP generates periodic alternating current based on a coupling effect of triboelectrification and electrostatic induction. <italic>V</italic><sub>oc</sub>, <italic>I</italic><sub>sc</sub>, and <italic>Q</italic><sub>sc</sub> of SICP were about 6.0 V, 0.25 μA, 8.5 nC in vivo (Fig. ##FIG##3##4c##). With the periodic physiological contraction and relaxation of the heart, globules move freely in SICP, and the electrical signal exhibits a certain volatility. We speculate that fluctuations in the electrical signal may also be affected by the blood flow. Nonetheless, our statistical analysis showed that the average voltage and current can reach 4 V and 0.2 μA (Fig. ##FIG##3##4d##), respectively, and the energy conversion efficiency of the voltage exceeding 1.5 V per unit time accounted for over 82%. The output voltage EHU meets the requirement of the threshold voltage of PM. Actually, cardiac contraction intensity is also affected by respiratory status and exercise. Therefore, appropriately enhancing cardiac functional status may be beneficial to improving energy harvesting efficiency.</p>\",\n \"<title>Illustration pacing performance of SICP in vivo</title>\",\n \"<p id=\\\"Par16\\\">The EHU of SICP converts the biomechanical energy from heart beating to electricity, which can be stored in the capacitor of the PMU for powering the PM after the reed switch is turned on by the magnet (Fig. ##FIG##4##5a##). That is, after the device is integrated with the heart, self-sufficiency of electrical energy is achieved. The voltage of a capacitor was charged from 0 V to 3 V within 9000 s with the same electrical output of SICP in vivo (Supplementary Fig. ##SUPPL##1##16##). Corresponding to the ECG and blood pressure, transthoracic echocardiograms also showed that no tricuspid regurgitation was observed after SICP implantation (Fig. ##FIG##4##5b##). This result further suggests that the physical structure of the device itself has no effect on the heart. To demonstrate pacing the performance of SICP, ECG signals of the swine during SICP operation are shown in Fig. ##FIG##4##5c##. The typical P wave, QRS complex, and T wave appear in sequence on the intrinsic ECG. When SICP was in operation, the premature-paced QRS complex was induced by the electrical pulse stimulus (Supplementary Movie ##SUPPL##10##8##). The ECG showed regular-paced QRS complex occurrence ahead of the P wave (atrial contraction), indicating that the ventricle was effectively captured by SICP. The heart returned to intrinsic rhythm after the PM of SICP stopped working. The above results demonstrate the effectiveness of SICP for pacing the heart rhythm. Furthermore, the wound was sutured after implanting the device into the right ventricle, and long-term experiments were performed. As shown in Fig. ##FIG##4##5d##, ##FIG##4##e##, we monitored the ECG signals of the experimental animals for 2 and 3 weeks, respectively. The results showed that the heart rhythm remained stable, and the experimental animals were eating and living normally without the occurrence of complications (Supplementary Fig. ##SUPPL##1##17##). Meanwhile, EMU was controlled by switching in the third week, and SICP effectively paced the heart in vivo, the heart rate of the animal increased from 90 bpm to 108 bpm, demonstrating that the device still maintained normal pacing function (Fig. ##FIG##4##5f##). Three weeks after implantation, the swine was sacrificed by injection of a medium with high potassium concentration, and then the chest was opened to remove the heart with SICP. We found that the device was firmly anchored to myocardial tissues (Fig. ##FIG##4##5g##). Masson’s trichrome stain was applied after prolonged implantation, which revealed that the extent of cardiac tissue injury and inflammatory fibrous hyperplasia occurred only at the device fixation site, and Hematoxylin and eosin (H&E) staining showed no detectable infiltration of lymphocytes in other sites, prompting the conclusion that neither humoral nor cellular rejection to the device occurred in the myocardium from the implantation site (Fig. ##FIG##4##5h##). Overall, these in vivo tests demonstrated that SICP can achieve long-term pacing in large-animal models.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par17\">We develop a SICP and demonstrate its efficacy and safety in the cardiac pacing of large-animal models. This device provides a promising method to harvest biomechanical energy from cardiac motion for powering the pacemaker module with the significant advantages of being leadless, battery-free, transcatheter-intervention, and lightweight. Based on the synergy between EHU and cardiac motion, the constant supply of energy enables the pacemaker to work stably, thereby preventing the perioperative risk caused by the replacement of devices due to energy depletion. In addition, to ensure cardiac physiological activity, the overall device adopts lightweight materials to reduce the load on the heart. Minimally invasive intervention with delivery technology decreases the risk of surgical exogenous infection and tissue trauma. The capsule structure facilitates implantation through the venous system. Such a capsule structure also substantially improved the energy conversion efficiency for EHU in vivo based on the freestanding triboelectric-layer mode of the triboelectric nanogenerator. The biomechanical energy harvested from SICP from each cardiac cycle is about 0.026 μJ (Supplementary Note ##SUPPL##1##1##). Maximum power output of SICP is about 0.039 μW (Supplementary Note ##SUPPL##1##2##). Theoretically, it means that the energy harvested by SICP from four heartbeats will be higher than the pacing threshold energy of a commercial leadless cardiac pacemaker (Supplementary Note ##SUPPL##1##1##).</p>","<p id=\"Par18\">Meanwhile, EHU is mainly constructed of polymer materials, which provides feasibility for SICP to be compatible with MRI examinations during clinical applications. Specifically, SICP has good blood and tissue compatibility and does not cause significant inflammation in the endocardium. Three weeks after the operation, the experimental animals maintained a normal survival state, and the device exhibited excellent output performance. Large-animal experimental models effectively simulate clinical applications and may provide more valuable and comparable results. Although SICP has certain limitations in long-term constant pacing on clinical criterion, this work provides a proof-of-concept demonstration for the next-generation pacemaker and will facilitate the upgrade of existing commercial leadless pacemakers (Table ##SUPPL##1##S1##). Furthermore, with the in-depth follow-up research and the improvement of the efficiency of the EHU, we believe that the energy collected by SICP from one heartbeat can fully satisfy the leadless pacemaker for one pacing. Going forward, further research is required to investigate the self-powered closed-loop operation system integrating arrhythmia active monitoring and stimulation regulation.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par17\\\">We develop a SICP and demonstrate its efficacy and safety in the cardiac pacing of large-animal models. This device provides a promising method to harvest biomechanical energy from cardiac motion for powering the pacemaker module with the significant advantages of being leadless, battery-free, transcatheter-intervention, and lightweight. Based on the synergy between EHU and cardiac motion, the constant supply of energy enables the pacemaker to work stably, thereby preventing the perioperative risk caused by the replacement of devices due to energy depletion. In addition, to ensure cardiac physiological activity, the overall device adopts lightweight materials to reduce the load on the heart. Minimally invasive intervention with delivery technology decreases the risk of surgical exogenous infection and tissue trauma. The capsule structure facilitates implantation through the venous system. Such a capsule structure also substantially improved the energy conversion efficiency for EHU in vivo based on the freestanding triboelectric-layer mode of the triboelectric nanogenerator. The biomechanical energy harvested from SICP from each cardiac cycle is about 0.026 μJ (Supplementary Note ##SUPPL##1##1##). Maximum power output of SICP is about 0.039 μW (Supplementary Note ##SUPPL##1##2##). Theoretically, it means that the energy harvested by SICP from four heartbeats will be higher than the pacing threshold energy of a commercial leadless cardiac pacemaker (Supplementary Note ##SUPPL##1##1##).</p>\",\n \"<p id=\\\"Par18\\\">Meanwhile, EHU is mainly constructed of polymer materials, which provides feasibility for SICP to be compatible with MRI examinations during clinical applications. Specifically, SICP has good blood and tissue compatibility and does not cause significant inflammation in the endocardium. Three weeks after the operation, the experimental animals maintained a normal survival state, and the device exhibited excellent output performance. Large-animal experimental models effectively simulate clinical applications and may provide more valuable and comparable results. Although SICP has certain limitations in long-term constant pacing on clinical criterion, this work provides a proof-of-concept demonstration for the next-generation pacemaker and will facilitate the upgrade of existing commercial leadless pacemakers (Table ##SUPPL##1##S1##). Furthermore, with the in-depth follow-up research and the improvement of the efficiency of the EHU, we believe that the energy collected by SICP from one heartbeat can fully satisfy the leadless pacemaker for one pacing. Going forward, further research is required to investigate the self-powered closed-loop operation system integrating arrhythmia active monitoring and stimulation regulation.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Harvesting biomechanical energy from cardiac motion is an attractive power source for implantable bioelectronic devices. Here, we report a battery-free, transcatheter, self-powered intracardiac pacemaker based on the coupled effect of triboelectrification and electrostatic induction for the treatment of arrhythmia in large animal models. We show that the capsule-shaped device (1.75 g, 1.52 cc) can be integrated with a delivery catheter for implanting in the right ventricle of a swine through the intravenous route, which effectively converts cardiac motion energy to electricity and maintains endocardial pacing function during the three-week follow-up period. We measure in vivo open circuit voltage and short circuit current of the self-powered intracardiac pacemaker of about 6.0 V and 0.2 μA, respectively. This approach exhibits up-to-date progress in self-powered medical devices and it may overcome the inherent energy shortcomings of implantable pacemakers and other bioelectronic devices for therapy and sensing.</p>","<p id=\"Par2\">Harvesting biomechanical energy from cardiac motion is an attractive power source for implantable bioelectronic devices. Here, the authors report a battery-free, transcatheter, self-powered intracardiac pacemaker for the treatment of arrhythmia in large animal models.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Harvesting biomechanical energy from cardiac motion is an attractive power source for implantable bioelectronic devices. Here, we report a battery-free, transcatheter, self-powered intracardiac pacemaker based on the coupled effect of triboelectrification and electrostatic induction for the treatment of arrhythmia in large animal models. We show that the capsule-shaped device (1.75 g, 1.52 cc) can be integrated with a delivery catheter for implanting in the right ventricle of a swine through the intravenous route, which effectively converts cardiac motion energy to electricity and maintains endocardial pacing function during the three-week follow-up period. We measure in vivo open circuit voltage and short circuit current of the self-powered intracardiac pacemaker of about 6.0 V and 0.2 μA, respectively. This approach exhibits up-to-date progress in self-powered medical devices and it may overcome the inherent energy shortcomings of implantable pacemakers and other bioelectronic devices for therapy and sensing.</p>\",\n \"<p id=\\\"Par2\\\">Harvesting biomechanical energy from cardiac motion is an attractive power source for implantable bioelectronic devices. Here, the authors report a battery-free, transcatheter, self-powered intracardiac pacemaker for the treatment of arrhythmia in large animal models.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary information</title>","<p>\n\n\n\n\n\n\n\n\n\n\n\n\n</p>","<title>Source data</title>","<p>\n\n</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n</p>\",\n \"<title>Source data</title>\",\n \"<p>\\n\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary information</title>","<p>The online version contains supplementary material available at 10.1038/s41467-023-44510-6.</p>","<title>Acknowledgements</title>","<p>We are grateful to the laboratory members, Prof. Yubo Fan (Beihang University), Dr. Lingling Xu (National Center for Nanoscience and Technology), and Dr. Pengkang He (Peking University First Hospital, Beijing) for their cooperation in this study. This work was financially supported by grants from the National Natural Science Foundation of China (T2125003 to Z.L., 82102231 and 82372141 to Z. Liu, 61875015 to Z.L. and 82100325 to Y.H.), the National Key Research and Development Program of China (2022YFE0111700 to Z.L.), Beijing Natural Science Foundation (JQ20038 to Z.L.), Beijing Natural Science Foundation (L212010 to Z.L.), High-level hospital clinical research funding of Fuwai Hospital, Chinese Academy of Medical Sciences (No.2022-GSP-GG-11 to W.H.), the Fundamental Research Funds for the General Universities to Z. Liu, the China Postdoctoral Science Foundation (2023M731943 and BX20230169 to X.Q.), The Beijing Gold-bridge project (No. ZZ21055 to Y.H.).</p>","<title>Author contributions</title>","<p>Z. Liu, Y.H., X.Q., and Y.L. contributed equally to this work. Z. Li, W.H. and Z. Liu conceived the idea and guided the project. Z. Liu, Y.H., X.Q., and Y.L. designed the experiment and analyzed the results. Z.L.W. directed the preparation of EHU. Z. Liu, X.Q., Y.L., N.W., Z.Z. and B.S. fabricated SICP and performed the electrical characterization. Y.S., R.L., and Z. Liu performed the cell experiments. Z. Liu, Y.H., X.Q., Y.L., S.C., S.W., H.L., H.N., M.G. and Y.Y. performed in vivo surgery. Z. Liu, Y.H., X.Q. and Y.L. wrote the paper. All authors read and approved the final manuscript.</p>","<title>Peer review</title>","<title>Peer review information</title>","<p id=\"Par32\"><italic>Nature Communications</italic> thanks Igor Efimov, Tasneem Naqvi, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.</p>","<title>Data availability</title>","<p>The authors declare that all data supporting the results of this study are available within the paper and its Supplementary Information. The source data underlying Figs. ##FIG##1##2c–f##, ##FIG##2##3c–e##, ##FIG##2##3g##, ##FIG##2##3h##, ##FIG##3##4b–d##, and ##FIG##4##5c–f## and Supplementary Figs. ##SUPPL##1##5##–##SUPPL##1##8##, ##SUPPL##1##10##, ##SUPPL##1##12##, and ##SUPPL##1##16## are provided as a Source Data file (10.6084/m9.figshare.24715311). Any additional requests for information can be directed to, and will be fulfilled by, the corresponding authors. <xref ref-type=\"sec\" rid=\"Sec24\">Source data</xref> are provided in this paper.</p>","<title>Competing interests</title>","<p id=\"Par33\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41467-023-44510-6.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>We are grateful to the laboratory members, Prof. Yubo Fan (Beihang University), Dr. Lingling Xu (National Center for Nanoscience and Technology), and Dr. Pengkang He (Peking University First Hospital, Beijing) for their cooperation in this study. This work was financially supported by grants from the National Natural Science Foundation of China (T2125003 to Z.L., 82102231 and 82372141 to Z. Liu, 61875015 to Z.L. and 82100325 to Y.H.), the National Key Research and Development Program of China (2022YFE0111700 to Z.L.), Beijing Natural Science Foundation (JQ20038 to Z.L.), Beijing Natural Science Foundation (L212010 to Z.L.), High-level hospital clinical research funding of Fuwai Hospital, Chinese Academy of Medical Sciences (No.2022-GSP-GG-11 to W.H.), the Fundamental Research Funds for the General Universities to Z. Liu, the China Postdoctoral Science Foundation (2023M731943 and BX20230169 to X.Q.), The Beijing Gold-bridge project (No. ZZ21055 to Y.H.).</p>\",\n \"<title>Author contributions</title>\",\n \"<p>Z. Liu, Y.H., X.Q., and Y.L. contributed equally to this work. Z. Li, W.H. and Z. Liu conceived the idea and guided the project. Z. Liu, Y.H., X.Q., and Y.L. designed the experiment and analyzed the results. Z.L.W. directed the preparation of EHU. Z. Liu, X.Q., Y.L., N.W., Z.Z. and B.S. fabricated SICP and performed the electrical characterization. Y.S., R.L., and Z. Liu performed the cell experiments. Z. Liu, Y.H., X.Q., Y.L., S.C., S.W., H.L., H.N., M.G. and Y.Y. performed in vivo surgery. Z. Liu, Y.H., X.Q. and Y.L. wrote the paper. All authors read and approved the final manuscript.</p>\",\n \"<title>Peer review</title>\",\n \"<title>Peer review information</title>\",\n \"<p id=\\\"Par32\\\"><italic>Nature Communications</italic> thanks Igor Efimov, Tasneem Naqvi, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.</p>\",\n \"<title>Data availability</title>\",\n \"<p>The authors declare that all data supporting the results of this study are available within the paper and its Supplementary Information. The source data underlying Figs. ##FIG##1##2c–f##, ##FIG##2##3c–e##, ##FIG##2##3g##, ##FIG##2##3h##, ##FIG##3##4b–d##, and ##FIG##4##5c–f## and Supplementary Figs. ##SUPPL##1##5##–##SUPPL##1##8##, ##SUPPL##1##10##, ##SUPPL##1##12##, and ##SUPPL##1##16## are provided as a Source Data file (10.6084/m9.figshare.24715311). Any additional requests for information can be directed to, and will be fulfilled by, the corresponding authors. <xref ref-type=\\\"sec\\\" rid=\\\"Sec24\\\">Source data</xref> are provided in this paper.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par33\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Design features and materials of a self-powered intracardiac pacemaker (SICP).</title><p><bold>a</bold> Schematic illustration of the device in the right ventricle (RV) for converting biomechanical energy from cardiac motion to electricity and regulating arrhythmia, which is composed of an energy harvesting unit (EHU), a power management unit & pacemaker module (PMU&PM), hooks and radiopaque markers. <bold>b</bold> Internal perspective structure of the device. <bold>c</bold> Charge transfer (i) before contact, (ii) in contact, and (iii) after contact between two different dielectrics- polyformaldehyde (POM) and polytetrafluoroethylene (PTFE). <bold>d</bold> Three-dimensional explosive view of SICP. <bold>e</bold> Photograph of SICP with a weight of 1.75 g and volume of 1.52 cc (diameter = 6.8 mm; length = 42 mm) (Scale bar = 2 cm). <bold>f</bold> Scanning electron microscope (SEM) images of the surface of POM pellet and PTFE film after treatment by inductively coupled plasma (ICP) technology (Scale bar = 5 μm). The abbreviations are the same in other figures unless otherwise stated.</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Working principle and electrical output performance of energy harvesting unit (EHU).</title><p><bold>a</bold> Photographs of the movement process of the pellets in EHU. <bold>b</bold> Schematic diagram of the working principle of EHU. <bold>c</bold> Open-circuit voltage (<italic>V</italic><sub>oc</sub>) of EHU when working under different volumes of pellets. (<italic>n</italic> = 3, Data are presented as mean ± SD) <bold>d</bold>\n<italic>V</italic><sub>oc</sub> of EHU when working under different tilt angles. (<italic>n</italic> = 3, Data are presented as mean ± SD) <bold>e</bold>\n<italic>V</italic><sub>oc</sub>, short-circuit current (<italic>I</italic><sub>sc</sub>), and short-circuit charge (<italic>Q</italic><sub>sc</sub>) of EHU in vitro. <bold>f</bold> Tensile test for SICP (Scale bar = 2 cm). Source data are provided as a Source Data file.</p></caption></fig>","<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Characterizations for power management unit & pacemaker module (PMU&PM) of SICP.</title><p><bold>a</bold> Photographs of the integrated PMU&PM (Scale bar = 5 mm). <bold>b</bold> Circuit diagram of PMU&PM. <bold>c</bold> Stimulation pulse generated by PM with different frequencies. <bold>d</bold> Enlarged view of the stimulation pulse. <bold>e</bold> PM powered by the capacitor (10 μF, 3 V) that continuously emits stimulating electrical pulses. <bold>f</bold> Electroanatomic reconstructions of the cardiac structures and ablation target (red dots) by three-dimensional mapping system (superior vena cava (SVC); right atrial (RA); inferior vena cava (IVC); anterior-posterior (AP), right-left (RL) projections). <bold>g</bold> Electrocardiogram (ECG) before and after construction of an AVB animal model. <bold>h</bold> ECG of an atrioventricular block (AVB) animal model before and after pacing by PM with a driving voltage of 1.5 V. Source data are provided as a Source Data file.</p></caption></fig>","<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Energy harvesting of SICP in vivo.</title><p><bold>a</bold> Fluoroscopy images of minimally invasive delivery of SICP to the right ventricle via the venous system under fluoroscopic guidance. <bold>b</bold> ECG and blood pressure signals of the animal before and after SICP implantation. <bold>c</bold>\n<italic>I</italic><sub>sc</sub>, <italic>Q</italic><sub>sc</sub>, and <italic>V</italic><sub>oc</sub> of SICP in vivo. <bold>d</bold> Statistical analysis of <italic>V</italic><sub>oc</sub> and <italic>I</italic><sub>sc</sub> (there are 34 data points for each test group. The box ends represent the 25th and 75th percentiles. The horizontal line in each box represents the median. The upper and lower whiskers refer to the range of non-outlier data values. Outliers were plotted as individual points). Source data are provided as a Source Data file.</p></caption></fig>","<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Illustration pacing performance of SICP in vivo.</title><p><bold>a</bold> Block diagram of the components of SICP. <bold>b</bold> Transthoracic echocardiograms of SICP before and after implantation. <bold>c</bold> Animal ECG signal when SICP works during minimally invasive implantation. <bold>d</bold> Animal ECG signal of 2 weeks and <bold>e</bold> 3 weeks after implantation. <bold>f</bold> Animal ECG signal when SICP works at 3 weeks. <bold>g</bold> Photographs of SICP fixed to the endocardium at 3 weeks (Scale bar = 0.5 cm). <bold>h</bold> Photographs of Masson’s trichrome and Hematoxylin−eosin (HE) staining for myocardial tissue at the fixation site (Scale bar = 200 μm). Source data are provided as a Source Data file.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><title>Design features and materials of a self-powered intracardiac pacemaker (SICP).</title><p><bold>a</bold> Schematic illustration of the device in the right ventricle (RV) for converting biomechanical energy from cardiac motion to electricity and regulating arrhythmia, which is composed of an energy harvesting unit (EHU), a power management unit & pacemaker module (PMU&PM), hooks and radiopaque markers. <bold>b</bold> Internal perspective structure of the device. <bold>c</bold> Charge transfer (i) before contact, (ii) in contact, and (iii) after contact between two different dielectrics- polyformaldehyde (POM) and polytetrafluoroethylene (PTFE). <bold>d</bold> Three-dimensional explosive view of SICP. <bold>e</bold> Photograph of SICP with a weight of 1.75 g and volume of 1.52 cc (diameter = 6.8 mm; length = 42 mm) (Scale bar = 2 cm). <bold>f</bold> Scanning electron microscope (SEM) images of the surface of POM pellet and PTFE film after treatment by inductively coupled plasma (ICP) technology (Scale bar = 5 μm). The abbreviations are the same in other figures unless otherwise stated.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><title>Working principle and electrical output performance of energy harvesting unit (EHU).</title><p><bold>a</bold> Photographs of the movement process of the pellets in EHU. <bold>b</bold> Schematic diagram of the working principle of EHU. <bold>c</bold> Open-circuit voltage (<italic>V</italic><sub>oc</sub>) of EHU when working under different volumes of pellets. (<italic>n</italic> = 3, Data are presented as mean ± SD) <bold>d</bold>\\n<italic>V</italic><sub>oc</sub> of EHU when working under different tilt angles. (<italic>n</italic> = 3, Data are presented as mean ± SD) <bold>e</bold>\\n<italic>V</italic><sub>oc</sub>, short-circuit current (<italic>I</italic><sub>sc</sub>), and short-circuit charge (<italic>Q</italic><sub>sc</sub>) of EHU in vitro. <bold>f</bold> Tensile test for SICP (Scale bar = 2 cm). Source data are provided as a Source Data file.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Fig. 3</label><caption><title>Characterizations for power management unit & pacemaker module (PMU&PM) of SICP.</title><p><bold>a</bold> Photographs of the integrated PMU&PM (Scale bar = 5 mm). <bold>b</bold> Circuit diagram of PMU&PM. <bold>c</bold> Stimulation pulse generated by PM with different frequencies. <bold>d</bold> Enlarged view of the stimulation pulse. <bold>e</bold> PM powered by the capacitor (10 μF, 3 V) that continuously emits stimulating electrical pulses. <bold>f</bold> Electroanatomic reconstructions of the cardiac structures and ablation target (red dots) by three-dimensional mapping system (superior vena cava (SVC); right atrial (RA); inferior vena cava (IVC); anterior-posterior (AP), right-left (RL) projections). <bold>g</bold> Electrocardiogram (ECG) before and after construction of an AVB animal model. <bold>h</bold> ECG of an atrioventricular block (AVB) animal model before and after pacing by PM with a driving voltage of 1.5 V. Source data are provided as a Source Data file.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Fig. 4</label><caption><title>Energy harvesting of SICP in vivo.</title><p><bold>a</bold> Fluoroscopy images of minimally invasive delivery of SICP to the right ventricle via the venous system under fluoroscopic guidance. <bold>b</bold> ECG and blood pressure signals of the animal before and after SICP implantation. <bold>c</bold>\\n<italic>I</italic><sub>sc</sub>, <italic>Q</italic><sub>sc</sub>, and <italic>V</italic><sub>oc</sub> of SICP in vivo. <bold>d</bold> Statistical analysis of <italic>V</italic><sub>oc</sub> and <italic>I</italic><sub>sc</sub> (there are 34 data points for each test group. The box ends represent the 25th and 75th percentiles. The horizontal line in each box represents the median. The upper and lower whiskers refer to the range of non-outlier data values. Outliers were plotted as individual points). Source data are provided as a Source Data file.</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Fig. 5</label><caption><title>Illustration pacing performance of SICP in vivo.</title><p><bold>a</bold> Block diagram of the components of SICP. <bold>b</bold> Transthoracic echocardiograms of SICP before and after implantation. <bold>c</bold> Animal ECG signal when SICP works during minimally invasive implantation. <bold>d</bold> Animal ECG signal of 2 weeks and <bold>e</bold> 3 weeks after implantation. <bold>f</bold> Animal ECG signal when SICP works at 3 weeks. <bold>g</bold> Photographs of SICP fixed to the endocardium at 3 weeks (Scale bar = 0.5 cm). <bold>h</bold> Photographs of Masson’s trichrome and Hematoxylin−eosin (HE) staining for myocardial tissue at the fixation site (Scale bar = 200 μm). Source data are provided as a Source Data file.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":["<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{\\partial {P}_{s}}{\\partial t}$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mfrac><mml:mrow><mml:mi>∂</mml:mi><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>∂</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>","<inline-formula 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id=\"IEq5\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$E$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:mi>E</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{\\partial {D}^{{\\prime} }}{\\partial t}$$\\end{document}</tex-math><mml:math id=\"M14\"><mml:mfrac><mml:mrow><mml:mi>∂</mml:mi><mml:msup><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:mrow><mml:mrow><mml:mi>∂</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{\\partial {P}_{s}}{\\partial t}$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:mfrac><mml:mrow><mml:mi>∂</mml:mi><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>∂</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R\\frac{{dQ}}{{dt}}=-\\frac{1}{C}Q+{V}_{{oc}}$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:mi>R</mml:mi><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mi>Q</mml:mi></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>C</mml:mi></mml:mrow></mml:mfrac><mml:mi>Q</mml:mi><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>V</mml:mi></mml:mrow><mml:mrow><mml:mi>o</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Q$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:mi>Q</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$C$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:mi>C</mml:mi></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${V}_{{oc}}$$\\end{document}</tex-math><mml:math id=\"M24\"><mml:msub><mml:mrow><mml:mi>V</mml:mi></mml:mrow><mml:mrow><mml:mi>o</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$I$$\\end{document}</tex-math><mml:math id=\"M26\"><mml:mi>I</mml:mi></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$I=\\frac{{dQ}}{{dt}}=\\frac{{dQ}}{{dx}}\\frac{{dx}}{{dt}}=\\frac{{dQ}}{{dx}}v$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:mi>I</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mi>Q</mml:mi></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mi>Q</mml:mi></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:mfrac><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mi>Q</mml:mi></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>x</mml:mi></mml:mrow></mml:mfrac><mml:mi>v</mml:mi></mml:math></alternatives></disp-formula>"],"string":"[\n \"<inline-formula id=\\\"IEq1\\\"><alternatives><tex-math id=\\\"M1\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\frac{\\\\partial {P}_{s}}{\\\\partial t}$$\\\\end{document}</tex-math><mml:math id=\\\"M2\\\"><mml:mfrac><mml:mrow><mml:mi>∂</mml:mi><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>∂</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq2\\\"><alternatives><tex-math id=\\\"M3\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${P}_{s}$$\\\\end{document}</tex-math><mml:math id=\\\"M4\\\"><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ1\\\"><label>1</label><alternatives><tex-math id=\\\"M5\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${J}_{D}=\\\\frac{\\\\partial {D}^{{\\\\prime} }}{\\\\partial t}+\\\\frac{\\\\partial {P}_{S}}{\\\\partial t}=\\\\varepsilon \\\\frac{\\\\partial E}{\\\\partial t}+\\\\frac{\\\\partial {P}_{S}}{\\\\partial t}$$\\\\end{document}</tex-math><mml:math id=\\\"M6\\\"><mml:msub><mml:mrow><mml:mi>J</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>∂</mml:mi><mml:msup><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:mrow><mml:mrow><mml:mi>∂</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mi>∂</mml:mi><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>∂</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mi>ε</mml:mi><mml:mfrac><mml:mrow><mml:mi>∂</mml:mi><mml:mi>E</mml:mi></mml:mrow><mml:mrow><mml:mi>∂</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:mfrac><mml:mrow><mml:mi>∂</mml:mi><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>∂</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq3\\\"><alternatives><tex-math id=\\\"M7\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$J$$\\\\end{document}</tex-math><mml:math id=\\\"M8\\\"><mml:mi>J</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq4\\\"><alternatives><tex-math id=\\\"M9\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$D$$\\\\end{document}</tex-math><mml:math id=\\\"M10\\\"><mml:mi>D</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq5\\\"><alternatives><tex-math id=\\\"M11\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$E$$\\\\end{document}</tex-math><mml:math id=\\\"M12\\\"><mml:mi>E</mml:mi></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq6\\\"><alternatives><tex-math id=\\\"M13\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\frac{\\\\partial {D}^{{\\\\prime} }}{\\\\partial t}$$\\\\end{document}</tex-math><mml:math id=\\\"M14\\\"><mml:mfrac><mml:mrow><mml:mi>∂</mml:mi><mml:msup><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:mrow><mml:mrow><mml:mi>∂</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq7\\\"><alternatives><tex-math id=\\\"M15\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\frac{\\\\partial {P}_{s}}{\\\\partial t}$$\\\\end{document}</tex-math><mml:math id=\\\"M16\\\"><mml:mfrac><mml:mrow><mml:mi>∂</mml:mi><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi>∂</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ2\\\"><label>2</label><alternatives><tex-math id=\\\"M17\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} 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affiliations.</p></fn><fn><p>These authors contributed equally: Zhuo Liu, Yiran Hu, Xuecheng Qu, Ying Liu.</p></fn></fn-group>"],"string":"[\n \"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Zhuo Liu, Yiran Hu, Xuecheng Qu, Ying Liu.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<inline-graphic xlink:href=\"41467_2023_44510_Article_IEq1.gif\"/>","<inline-graphic xlink:href=\"41467_2023_44510_Article_IEq2.gif\"/>","<graphic xlink:href=\"41467_2023_44510_Article_Equ1.gif\" position=\"anchor\"/>","<inline-graphic xlink:href=\"41467_2023_44510_Article_IEq3.gif\"/>","<inline-graphic xlink:href=\"41467_2023_44510_Article_IEq4.gif\"/>","<inline-graphic xlink:href=\"41467_2023_44510_Article_IEq5.gif\"/>","<inline-graphic 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implants: system design\"], \"source\": [\"Integr. 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An effective vaccine that provides protection and prevents transmission is considered the most cost-effective tool for malaria control and would greatly facilitate <italic>P. vivax</italic> elimination. The best targets for malaria vaccine development are parasite antigens that can induce an effective immune response in natural and experimental infections that is capable of inhibiting host cell invasion and parasite development<sup>##REF##23176656##4##,##REF##25120545##5##</sup>. To this respect, promising candidates are parasite proteins that play an important role in targeting cell infection.</p>","<p id=\"Par3\">Research into <italic>P. vivax</italic> vaccine targets have focused mainly on blood stage antigens, with very few studies on pre-erythrocytic (PE) antigens. Sporozoites, the infective stage of the malarial parasite are considered ideal targets for antimalarial strategies and protective immunity<sup>##REF##7045272##6##</sup>. Sporozoites constitute a bottleneck in the parasite complex life cycle as only a very few sporozoites are injected by an infected mosquito<sup>##REF##15972531##7##</sup>, they have a longer exposure time to the host immune system than antigens of blood-stage invasive stages<sup>##REF##23053392##8##</sup>, and the PE stages are clinically silent. Liver infection is an obligatory step in malarial transmission. Once injected into the skin, sporozoites actively migrate in the dermis, traverse the capillary epithelium into the bloodstream and through the liver sinusoids into the parenchyma where they invade host hepatocytes, proliferate, and develop into exoerythrocytic forms (EEFs) inside a parasitophorous vacuole. Thus, PE vaccines are aimed at targeting the sporozoites and the EEFs, thereby preventing progression of the parasite to the blood stage. In other <italic>Plasmodium</italic> spp., sporozoite antigens are considered good vaccine candidates as antibodies against these antigens represent the first line of defense against infection. Studies have demonstrated that subunit vaccines based on sporozoite surface antigens and attenuated whole sporozoites can induce protection in both animal models and humans<sup>##REF##30001524##9##</sup>. Orthologues of sporozoite antigens from other <italic>Plasmodium</italic> spp. are also present in <italic>P. vivax</italic> and have been shown to play critical roles during hepatocyte infection. Amongst them, the <italic>P. vivax</italic> circumsporozoite surface protein (CSP), the dominant molecule on the sporozoite surface is a leading vaccine candidate and a prime target in irradiated sporozoite immunity<sup>##REF##7045272##6##,##REF##6084251##10##–##REF##6189951##14##</sup>. CSP plays multiple essential functions throughout pre-erythrocytic stage development including motility, cell traversal, and liver stage development (reviewed in<sup>##REF##19575563##15##</sup>). Recent studies have demonstrated that CSP-based vaccines can elicit significant protection after sporozoite challenge<sup>##REF##1517574##16##,##REF##26074888##17##</sup> and attenuate liver-stage (LS) development<sup>##REF##29743474##18##</sup>. This molecule forms the basis of the <italic>Plasmodium falciparum</italic> malaria vaccines (RTS,S/AS01 and R21/MM) currently authorized for use in children in endemic regions<sup>##REF##25913272##19##–##REF##34432975##21##</sup>. However, very limited progress has been achieved towards a <italic>P. vivax</italic> CSP based vaccine<sup>##REF##26919472##22##–##REF##32402755##29##</sup>. Other studies suggested protective anti-PvCSP memory B-cells are short lived due to poor immunogenicity<sup>##REF##33554669##30##,##REF##33481638##31##</sup>. Thus, there is a need to identify other potential candidates to partner with CSP in a multivalent vaccine to protect against infection and disease.</p>","<p id=\"Par4\">The cell-traversal protein for ookinetes and sporozoites (CelTOS) is another PE antigen, which is highly conserved among <italic>Plasmodium</italic> spp.<sup>##REF##21504890##32##</sup> and considered an attractive vaccine candidate. It plays a critical role in sporozoite egress from host cells during traversal<sup>##REF##16468982##33##–##REF##31487334##36##</sup>, which is a necessary part of ookinete infection of the mosquito midgut and sporozoite infection of the liver. <italic>P. vivax</italic> CelTOS is naturally immunogenic<sup>##REF##28223984##37##,##REF##32266169##38##</sup> and immunization of mice with recombinant CelTOS elicits both humoral and cellular immune responses that reduced hepatocyte infection<sup>##REF##27906127##34##,##REF##28223984##37##,##REF##20808868##39##–##REF##27895131##52##</sup>. Antibodies targeting CelTOS can inhibit gliding motility, cell traversal, sporozoite hepatocyte infection, and impaired parasite development in the mosquito<sup>##REF##20808868##39##,##REF##27895131##52##</sup>. Parasites lacking expression of CelTOS are defective in mosquito and liver stage development<sup>##REF##16468982##33##</sup>, suggesting its importance as both a transmission blocking and PE vaccine target.</p>","<p id=\"Par5\">In a recent study on transcriptional profiling of <italic>P. vivax</italic> sporozoites, we identified additional <italic>P. vivax</italic> sporozoite antigens including the sporozoite surface protein 3 (SSP3) and sporozoite protein essential for cell traversal (SPECT1), which are upregulated in response to changes in their microenvironment and are associated with host cell infectivity<sup>##REF##30111801##53##</sup>. In the rodent malaria parasite <italic>Plasmodium berghei</italic>, SSP3 is predominantly located on the sporozoite surface and is shown to play a role in gliding motility and/or liver stage development. SPECT1 plays a role in pore formation, which is essential for traversal of the liver sinusoid and Küpffer cells<sup>##REF##14737184##49##,##REF##31251952##54##–##REF##25156733##56##</sup>.</p>","<p id=\"Par6\">In this study, we evaluated the naturally acquired antibodies to <italic>P. vivax</italic> PE antigens CSP (VK210 allele), CelTOS, SSP3 and SPECT1 in plasma samples from <italic>vivax</italic> infected patients from two endemic regions in Thailand. At least 80% of the study subjects had antibodies to all four antigens and these antibodies inhibited sporozoite infection and hepatocytes development in vitro. Understanding the magnitude and quality of naturally acquired antibody responses to these sporozoite antigens in endemic populations will guide target selection for inclusion in a multi-valent PE vaccine aimed at generating protective antibodies against sporozoites infection and development in hepatocytes, preventing blood stage infection and disease.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par2\\\"><italic>Plasmodium vivax</italic> malaria is a global health problem in many tropical and sub-tropical countries of the world, with > 70% of cases occurring in Asia and the Americas<sup>##REF##18165478##1##–##REF##11425182##3##</sup>. An effective vaccine that provides protection and prevents transmission is considered the most cost-effective tool for malaria control and would greatly facilitate <italic>P. vivax</italic> elimination. The best targets for malaria vaccine development are parasite antigens that can induce an effective immune response in natural and experimental infections that is capable of inhibiting host cell invasion and parasite development<sup>##REF##23176656##4##,##REF##25120545##5##</sup>. To this respect, promising candidates are parasite proteins that play an important role in targeting cell infection.</p>\",\n \"<p id=\\\"Par3\\\">Research into <italic>P. vivax</italic> vaccine targets have focused mainly on blood stage antigens, with very few studies on pre-erythrocytic (PE) antigens. Sporozoites, the infective stage of the malarial parasite are considered ideal targets for antimalarial strategies and protective immunity<sup>##REF##7045272##6##</sup>. Sporozoites constitute a bottleneck in the parasite complex life cycle as only a very few sporozoites are injected by an infected mosquito<sup>##REF##15972531##7##</sup>, they have a longer exposure time to the host immune system than antigens of blood-stage invasive stages<sup>##REF##23053392##8##</sup>, and the PE stages are clinically silent. Liver infection is an obligatory step in malarial transmission. Once injected into the skin, sporozoites actively migrate in the dermis, traverse the capillary epithelium into the bloodstream and through the liver sinusoids into the parenchyma where they invade host hepatocytes, proliferate, and develop into exoerythrocytic forms (EEFs) inside a parasitophorous vacuole. Thus, PE vaccines are aimed at targeting the sporozoites and the EEFs, thereby preventing progression of the parasite to the blood stage. In other <italic>Plasmodium</italic> spp., sporozoite antigens are considered good vaccine candidates as antibodies against these antigens represent the first line of defense against infection. Studies have demonstrated that subunit vaccines based on sporozoite surface antigens and attenuated whole sporozoites can induce protection in both animal models and humans<sup>##REF##30001524##9##</sup>. Orthologues of sporozoite antigens from other <italic>Plasmodium</italic> spp. are also present in <italic>P. vivax</italic> and have been shown to play critical roles during hepatocyte infection. Amongst them, the <italic>P. vivax</italic> circumsporozoite surface protein (CSP), the dominant molecule on the sporozoite surface is a leading vaccine candidate and a prime target in irradiated sporozoite immunity<sup>##REF##7045272##6##,##REF##6084251##10##–##REF##6189951##14##</sup>. CSP plays multiple essential functions throughout pre-erythrocytic stage development including motility, cell traversal, and liver stage development (reviewed in<sup>##REF##19575563##15##</sup>). Recent studies have demonstrated that CSP-based vaccines can elicit significant protection after sporozoite challenge<sup>##REF##1517574##16##,##REF##26074888##17##</sup> and attenuate liver-stage (LS) development<sup>##REF##29743474##18##</sup>. This molecule forms the basis of the <italic>Plasmodium falciparum</italic> malaria vaccines (RTS,S/AS01 and R21/MM) currently authorized for use in children in endemic regions<sup>##REF##25913272##19##–##REF##34432975##21##</sup>. However, very limited progress has been achieved towards a <italic>P. vivax</italic> CSP based vaccine<sup>##REF##26919472##22##–##REF##32402755##29##</sup>. Other studies suggested protective anti-PvCSP memory B-cells are short lived due to poor immunogenicity<sup>##REF##33554669##30##,##REF##33481638##31##</sup>. Thus, there is a need to identify other potential candidates to partner with CSP in a multivalent vaccine to protect against infection and disease.</p>\",\n \"<p id=\\\"Par4\\\">The cell-traversal protein for ookinetes and sporozoites (CelTOS) is another PE antigen, which is highly conserved among <italic>Plasmodium</italic> spp.<sup>##REF##21504890##32##</sup> and considered an attractive vaccine candidate. It plays a critical role in sporozoite egress from host cells during traversal<sup>##REF##16468982##33##–##REF##31487334##36##</sup>, which is a necessary part of ookinete infection of the mosquito midgut and sporozoite infection of the liver. <italic>P. vivax</italic> CelTOS is naturally immunogenic<sup>##REF##28223984##37##,##REF##32266169##38##</sup> and immunization of mice with recombinant CelTOS elicits both humoral and cellular immune responses that reduced hepatocyte infection<sup>##REF##27906127##34##,##REF##28223984##37##,##REF##20808868##39##–##REF##27895131##52##</sup>. Antibodies targeting CelTOS can inhibit gliding motility, cell traversal, sporozoite hepatocyte infection, and impaired parasite development in the mosquito<sup>##REF##20808868##39##,##REF##27895131##52##</sup>. Parasites lacking expression of CelTOS are defective in mosquito and liver stage development<sup>##REF##16468982##33##</sup>, suggesting its importance as both a transmission blocking and PE vaccine target.</p>\",\n \"<p id=\\\"Par5\\\">In a recent study on transcriptional profiling of <italic>P. vivax</italic> sporozoites, we identified additional <italic>P. vivax</italic> sporozoite antigens including the sporozoite surface protein 3 (SSP3) and sporozoite protein essential for cell traversal (SPECT1), which are upregulated in response to changes in their microenvironment and are associated with host cell infectivity<sup>##REF##30111801##53##</sup>. In the rodent malaria parasite <italic>Plasmodium berghei</italic>, SSP3 is predominantly located on the sporozoite surface and is shown to play a role in gliding motility and/or liver stage development. SPECT1 plays a role in pore formation, which is essential for traversal of the liver sinusoid and Küpffer cells<sup>##REF##14737184##49##,##REF##31251952##54##–##REF##25156733##56##</sup>.</p>\",\n \"<p id=\\\"Par6\\\">In this study, we evaluated the naturally acquired antibodies to <italic>P. vivax</italic> PE antigens CSP (VK210 allele), CelTOS, SSP3 and SPECT1 in plasma samples from <italic>vivax</italic> infected patients from two endemic regions in Thailand. At least 80% of the study subjects had antibodies to all four antigens and these antibodies inhibited sporozoite infection and hepatocytes development in vitro. Understanding the magnitude and quality of naturally acquired antibody responses to these sporozoite antigens in endemic populations will guide target selection for inclusion in a multi-valent PE vaccine aimed at generating protective antibodies against sporozoites infection and development in hepatocytes, preventing blood stage infection and disease.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Materials and methods</title>","<title>Ethics statement</title>","<p id=\"Par16\">This study was approved by the Committee on Human Rights Related to Human Experimentation, Mahidol University Central Institutional Review Board (MU-IRB 2012/079.2408 and MU-CIRB 2021/281.2505), and the Committee on Use of Human Subjects in Research, University of South Florida Institutional Review Board (IRB-Pro00018143). All the study participants or their legal guardians provided written informed consent.</p>","<title>Study population and sample collection</title>","<p id=\"Par17\">A total of 51 plasma samples were obtained from acute symptomatic <italic>P. vivax-infected</italic> patients in an area of low transmission in the provinces of Ranong (n = 23) and Chumphon (n = 28) in southern Thailand. In these regions, both <italic>P. vivax</italic> and <italic>P. falciparum</italic> infections are common, with an average of 40 vivax malaria cases per year based on 2017–2022 data from the Thai Department of Disease Control, Ministry of Public Health. Samples were collected during the rainy season, between August and December, which coincides with the high transmission season. Self-reporting study participants were screened for vivax malaria by microscopy of Giemsa-stained smears and confirmed by PCR. Only individuals aged 18 and above, and positive for <italic>P. vivax</italic> infection were recruited for this study. Other inclusion criteria included a systolic blood pressure greater than 90 mmHg, body temperature less than 40 °C, and hematocrit greater than 25%. Blood samples were collected from patients in heparinized tubes and the plasma was separated and stored frozen until needed. Plasma samples from six naïve North American volunteers were used as controls.</p>","<title>Recombinant protein production</title>","<p id=\"Par18\">Recombinant proteins were produced from four <italic>P. vivax</italic> pre-erythrocytic (PE) vaccine candidates: SPECT1 (PVP01_1212300), SSP3 (PVX_123155), CelTOS (Sal1, PVX_123510) and CSP (PVP01_0835600.1). For CSP and SPECT1 the signal peptides and transmembrane domains were excluded in expressed recombinant proteins, while for SSP3, only the N-terminal region from residues 19-203 was expressed<sup>##REF##37703302##72##</sup>. The gene coding for CSP was codon optimized for <italic>Escherichia coli</italic> expression and cloned into pET21(a +) expression vector with a C-terminal hexahistidine tag. Recombinant CSP was expressed in <italic>E. coli</italic> BL21 Star (DE3) strain and purified on HisTrap HP by affinity chromatography using the Akta Pure System (GE). The production of rSSP3 and rSPECT1 was previously reported<sup>##REF##37703302##72##</sup>. Recombinant CelTOS was expressed in <italic>E. coli</italic>, purified by Nickel-NTA chromatography and gel filtration as previously reported<sup>##REF##27906127##34##,##REF##35809642##35##,##REF##28932762##73##,##REF##28932762##74##</sup>. The purity of recombinant proteins was evaluated by SDS-PAGE.</p>","<title>Assessment of antibody response to <italic>P. vivax</italic> PE antigens</title>","<p id=\"Par19\">Plasma samples from vivax-infected patients (n = 51) were screened for antibody responses to recombinant <italic>P. vivax</italic> antigens CSP, CelTOS, SSP3, and SPECT1 by indirect ELISA. Briefly, 96-well microtiter plates were coated overnight at 4 °C with 100 µl/well of recombinant proteins at 3 µg/ml in PBS. Coated plates were washed with PBS/0.05% Tween-20 (PBS-T) and blocked with 5% (w/v) skimmed milk in PBS-T for 2 h at room temperature. Plasma samples diluted 1:200 with 2% milk in PBS-T were added to duplicate wells and incubated on a shaker for 2 h at room temperature. Six naïve North American plasma samples were tested on each plate as negative controls, and wells without coated antigens were used for background control. After another PBS-T wash, wells were incubated with a phosphatase-labelled goat anti-human IgG (H + L) antibody (SeraCare) for 90 min at room temperature. Bound antibodies were detected after development with 100 μl of phosphatase substrate solution (SeraCare) and optical density (OD) values were measured at 650 nm. Antibody responses were reported as Reactive Index (RI)<sup>##REF##36417454##75##</sup>. RI was calculated by dividing the OD of test sample by a cut-off value determined for each plate. Cut-off value = Mean OD values + 2 SD of 6 naïve North American control samples. RI = (OD<sub>TS</sub>–OD<sub>BK</sub>)/(Mean OD<sub>CS</sub> + 2 SD), where TS = test sample, BK = blank, CS = control samples, and SD = standard deviation. A sample with RI ≥ 1 (cut-off value) was considered a responder, while RI < 1 is considered a non-responder.</p>","<title>Animal studies</title>","<p id=\"Par20\">Female Swiss webster ND4 mice (6–8 weeks; Envigo, USA) were used in the current study and all animal procedures were performed in compliance with relevant guidelines and regulations in IACUC protocol IS00008179 approved by the Division of Research Integrity and Compliance, University of South Florida. Mice were housed in ventilated cages and maintained at 21 °C with 12:12 h light–dark cycles at a relative humidity of 55 ± 10%. All animal studies are reported following ARRIVE guidelines<sup>##REF##20613859##76##</sup>.</p>","<title>Parasites</title>","<p id=\"Par21\">The following <italic>P</italic>. <italic>berghei</italic> ANKA–<italic>P. vivax</italic> transgenic parasite lines were used: (i) 2321cl3, <italic>Pb-Pv</italic>CelTOS(r) where endogenous <italic>P. berghei</italic> CelTOS is replaced with <italic>P. vivax</italic> CelTOS (RMgm-4111, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.pberghei.eu\">www.pberghei.eu</ext-link>)<sup>##REF##28179403##50##</sup>. (ii) 3378cl1, <italic>Pb-Pv</italic>SPECT1(r) where endogenous <italic>P. berghei</italic> SPECT1 is replaced with <italic>P. vivax</italic> SPECT1, (iii) 3392cl1, <italic>Pb-Pv</italic>SSP3(r) where endogenous <italic>P. berghei</italic> SSP3 is replaced with <italic>P. vivax</italic> SSP3 and (iv) Pb05cl1, <italic>Pb-Pv</italic>CSP P01(r) where endogenous <italic>P. berghei</italic> CSP is replaced with <italic>P. vivax</italic> CSP from P01 reference strain (Kolli SK, et al., manuscript in preparation). <italic>Pb-Pv</italic>CelTOS(r) and <italic>Pb-Pv</italic>SPECT1(r) transgenic lines express <italic>gfp-luciferase</italic> fusion reporter gene under the control of constitutive <italic>Pbeef1α</italic> promoter integrated into the neutral <italic>230p</italic> gene locus<sup>##REF##16242190##77##</sup>. <italic>Pb-Pv</italic>SSP3(r) and <italic>Pb-Pv</italic>CSP P01(r) express mCherry and luciferase reporter genes under the constitutive <italic>Pbhsp70</italic> and <italic>Pbeef1α</italic> promoters, respectively integrated into the neutral <italic>230p</italic> gene locus<sup>##REF##26208778##78##</sup>.</p>","<title>Cell lines</title>","<p id=\"Par22\">Human hepatocyte cell line HC-04 (MRA-975) obtained from BEI resources were cultured in Minimum essential medium (Gibco) and Ham’s F12 nutrient mix (Gibco) supplemented with 10% heat-inactivated fetal bovine serum (GenClone), 30 mM HEPES (Gibco), 2 mM L-Glutamine (Gibco) and 40 µg/ml Gentamicin (Sigma-Aldrich) and maintained at 37 °C with 5% CO2 in a collagen coated flask (Corning). The cell line was tested negative for mycoplasma contamination (Invitrogen) and authenticated via American Type Culture Collection Human STR Profiling Service (ATCC).</p>","<title>Production of transgenic <italic>P. berghei</italic> sporozoites</title>","<p id=\"Par23\">Groups of female Swiss Webster ND4 mice (n = 2), were infected with a cryopreserved stock of respective <italic>P. berghei</italic> transgenic parasites line intraperitoneally. At 2–5% parasitemia and comparable gametocytemia, three to five days old female <italic>Anopheles stephensi</italic> mosquitoes were allowed to feed on the mice that were under anesthesia for 15–20 min. The mice were then euthanized with CO<sub>2</sub> after mosquitos’ infection. Infected mosquitoes were maintained at 21.5 °C and 80% relative humidity and supplied with 5% glucose ad libitum. Infectivity of mosquitoes was assessed by counting the number of oocysts on day 14 post feeding. Sporozoites were isolated by manual dissection of mosquito salivary glands on day 18–21 post feeding and collected in Leibovitz’s L-15 medium (Thermos Fisher Scientific). The glands were centrifuged at 6000 rpm for 1 min and mechanically disrupted using a plastic pestle to release the sporozoites. The crushed sporozoite solution was filtered through a 40 µM cell strainer (Greiner Bio-One) and sporozoites were counted using a hemocytometer.</p>","<title>Inhibition of liver stage development assay (ILSDA)</title>","<p id=\"Par24\">Collagen coated 384 well plate (Greiner Bio-One) was seeded with HC-04 cells at a density of 8000 cells/well for 16 h before infecting with transgenic <italic>P. berghei</italic> sporozoites. Sporozoites (75 spz/µl) were incubated in 1:100 dilution of patient plasma or North American naïve plasma for 20 min at room temperature. After incubation, 3 × 10<sup>3</sup> sporozoites per well were added to triplicate wells. The plate was centrifuged at 200 × g for 5 min and incubated at 37 °C in CO<sub>2</sub> incubator to allow hepatocyte invasion. After 1 h, culture media was changed to remove uninvaded sporozoites and a subsequent media change 24 hpi. At 48 hpi, EEFs of transgenic lines expressing mCherry were imaged using live fluorescence and the nuclei stained with Hoechst 33342 for 30 min at 37 °C. Images were acquired using high content imaging system, Cell Insight CX7, at 20X objective.</p>","<p id=\"Par25\">The EEFs of transgenic lines expressing GFP were fixed with 4% PFA and EEFs were imaged after performing an indirect immunofluorescence assay as previously described with minor changes<sup>##REF##15710439##63##</sup>. Briefly, the PFA from the wells was washed off twice with PBS and the parasites stained by incubation with goat polyclonal <italic>P. berghei</italic> UIS4 antibody (LS Bio) at 1:1000 dilution in blocking buffer (1% BSA and 0.3% Triton X-100) at 4 °C overnight. The wells were washed twice with PBS and counter stained with an anti-goat Alexa Fluor 594 conjugated secondary antibody (Invitrogen) and Hoechst 33342 (Invitrogen) to stain the nuclei for 1 h at 37 °C. The wells were again washed with PBS and incubated in fresh PBS. Images were acquired using high content imaging system, Cell Insight CX7, at 20X objective. The images were exported, and parasites counted using an in-house Python program (Supplementary Methods Text ##SUPPL##0##S1##).</p>","<p id=\"Par26\">Live fluorescence imaging of mCherry expressing EEFs were counted using parasite cytoplasmic (PCP) staining whereas GFP expressing parasites were counted using UIS4 positive staining of parasitophorous vacuolar membrane (PVM), and Hoechst 33342 for host nuclei staining. Two independent assays were performed for each serum.</p>","<p id=\"Par27\">% inhibition of infection = 100–(Mean EEFs in test sample divided by Mean EEFs in 6 naïve North American samples) × 100.</p>","<title>Statistical analysis</title>","<p id=\"Par28\">All data were tested for normality using the Anderson–Darling test before analysis. Statistical differences for ELISA data were determined using the Kruskal–Wallis non-parametric test with a Dunn’s multiple comparison adjustment. Statistical differences for sporozoite invasion inhibition were also determined with the Kruskal–Wallis non-parametric test with a Dunn’s multiple comparison adjustment. A Spearman Correlation was done on the overall percent inhibition for each antigen. Analysis was performed using GraphPad Prism v. 10.0.2 for MacOS.</p>"],"string":"[\n \"<title>Materials and methods</title>\",\n \"<title>Ethics statement</title>\",\n \"<p id=\\\"Par16\\\">This study was approved by the Committee on Human Rights Related to Human Experimentation, Mahidol University Central Institutional Review Board (MU-IRB 2012/079.2408 and MU-CIRB 2021/281.2505), and the Committee on Use of Human Subjects in Research, University of South Florida Institutional Review Board (IRB-Pro00018143). All the study participants or their legal guardians provided written informed consent.</p>\",\n \"<title>Study population and sample collection</title>\",\n \"<p id=\\\"Par17\\\">A total of 51 plasma samples were obtained from acute symptomatic <italic>P. vivax-infected</italic> patients in an area of low transmission in the provinces of Ranong (n = 23) and Chumphon (n = 28) in southern Thailand. In these regions, both <italic>P. vivax</italic> and <italic>P. falciparum</italic> infections are common, with an average of 40 vivax malaria cases per year based on 2017–2022 data from the Thai Department of Disease Control, Ministry of Public Health. Samples were collected during the rainy season, between August and December, which coincides with the high transmission season. Self-reporting study participants were screened for vivax malaria by microscopy of Giemsa-stained smears and confirmed by PCR. Only individuals aged 18 and above, and positive for <italic>P. vivax</italic> infection were recruited for this study. Other inclusion criteria included a systolic blood pressure greater than 90 mmHg, body temperature less than 40 °C, and hematocrit greater than 25%. Blood samples were collected from patients in heparinized tubes and the plasma was separated and stored frozen until needed. Plasma samples from six naïve North American volunteers were used as controls.</p>\",\n \"<title>Recombinant protein production</title>\",\n \"<p id=\\\"Par18\\\">Recombinant proteins were produced from four <italic>P. vivax</italic> pre-erythrocytic (PE) vaccine candidates: SPECT1 (PVP01_1212300), SSP3 (PVX_123155), CelTOS (Sal1, PVX_123510) and CSP (PVP01_0835600.1). For CSP and SPECT1 the signal peptides and transmembrane domains were excluded in expressed recombinant proteins, while for SSP3, only the N-terminal region from residues 19-203 was expressed<sup>##REF##37703302##72##</sup>. The gene coding for CSP was codon optimized for <italic>Escherichia coli</italic> expression and cloned into pET21(a +) expression vector with a C-terminal hexahistidine tag. Recombinant CSP was expressed in <italic>E. coli</italic> BL21 Star (DE3) strain and purified on HisTrap HP by affinity chromatography using the Akta Pure System (GE). The production of rSSP3 and rSPECT1 was previously reported<sup>##REF##37703302##72##</sup>. Recombinant CelTOS was expressed in <italic>E. coli</italic>, purified by Nickel-NTA chromatography and gel filtration as previously reported<sup>##REF##27906127##34##,##REF##35809642##35##,##REF##28932762##73##,##REF##28932762##74##</sup>. The purity of recombinant proteins was evaluated by SDS-PAGE.</p>\",\n \"<title>Assessment of antibody response to <italic>P. vivax</italic> PE antigens</title>\",\n \"<p id=\\\"Par19\\\">Plasma samples from vivax-infected patients (n = 51) were screened for antibody responses to recombinant <italic>P. vivax</italic> antigens CSP, CelTOS, SSP3, and SPECT1 by indirect ELISA. Briefly, 96-well microtiter plates were coated overnight at 4 °C with 100 µl/well of recombinant proteins at 3 µg/ml in PBS. Coated plates were washed with PBS/0.05% Tween-20 (PBS-T) and blocked with 5% (w/v) skimmed milk in PBS-T for 2 h at room temperature. Plasma samples diluted 1:200 with 2% milk in PBS-T were added to duplicate wells and incubated on a shaker for 2 h at room temperature. Six naïve North American plasma samples were tested on each plate as negative controls, and wells without coated antigens were used for background control. After another PBS-T wash, wells were incubated with a phosphatase-labelled goat anti-human IgG (H + L) antibody (SeraCare) for 90 min at room temperature. Bound antibodies were detected after development with 100 μl of phosphatase substrate solution (SeraCare) and optical density (OD) values were measured at 650 nm. Antibody responses were reported as Reactive Index (RI)<sup>##REF##36417454##75##</sup>. RI was calculated by dividing the OD of test sample by a cut-off value determined for each plate. Cut-off value = Mean OD values + 2 SD of 6 naïve North American control samples. RI = (OD<sub>TS</sub>–OD<sub>BK</sub>)/(Mean OD<sub>CS</sub> + 2 SD), where TS = test sample, BK = blank, CS = control samples, and SD = standard deviation. A sample with RI ≥ 1 (cut-off value) was considered a responder, while RI < 1 is considered a non-responder.</p>\",\n \"<title>Animal studies</title>\",\n \"<p id=\\\"Par20\\\">Female Swiss webster ND4 mice (6–8 weeks; Envigo, USA) were used in the current study and all animal procedures were performed in compliance with relevant guidelines and regulations in IACUC protocol IS00008179 approved by the Division of Research Integrity and Compliance, University of South Florida. Mice were housed in ventilated cages and maintained at 21 °C with 12:12 h light–dark cycles at a relative humidity of 55 ± 10%. All animal studies are reported following ARRIVE guidelines<sup>##REF##20613859##76##</sup>.</p>\",\n \"<title>Parasites</title>\",\n \"<p id=\\\"Par21\\\">The following <italic>P</italic>. <italic>berghei</italic> ANKA–<italic>P. vivax</italic> transgenic parasite lines were used: (i) 2321cl3, <italic>Pb-Pv</italic>CelTOS(r) where endogenous <italic>P. berghei</italic> CelTOS is replaced with <italic>P. vivax</italic> CelTOS (RMgm-4111, <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"http://www.pberghei.eu\\\">www.pberghei.eu</ext-link>)<sup>##REF##28179403##50##</sup>. (ii) 3378cl1, <italic>Pb-Pv</italic>SPECT1(r) where endogenous <italic>P. berghei</italic> SPECT1 is replaced with <italic>P. vivax</italic> SPECT1, (iii) 3392cl1, <italic>Pb-Pv</italic>SSP3(r) where endogenous <italic>P. berghei</italic> SSP3 is replaced with <italic>P. vivax</italic> SSP3 and (iv) Pb05cl1, <italic>Pb-Pv</italic>CSP P01(r) where endogenous <italic>P. berghei</italic> CSP is replaced with <italic>P. vivax</italic> CSP from P01 reference strain (Kolli SK, et al., manuscript in preparation). <italic>Pb-Pv</italic>CelTOS(r) and <italic>Pb-Pv</italic>SPECT1(r) transgenic lines express <italic>gfp-luciferase</italic> fusion reporter gene under the control of constitutive <italic>Pbeef1α</italic> promoter integrated into the neutral <italic>230p</italic> gene locus<sup>##REF##16242190##77##</sup>. <italic>Pb-Pv</italic>SSP3(r) and <italic>Pb-Pv</italic>CSP P01(r) express mCherry and luciferase reporter genes under the constitutive <italic>Pbhsp70</italic> and <italic>Pbeef1α</italic> promoters, respectively integrated into the neutral <italic>230p</italic> gene locus<sup>##REF##26208778##78##</sup>.</p>\",\n \"<title>Cell lines</title>\",\n \"<p id=\\\"Par22\\\">Human hepatocyte cell line HC-04 (MRA-975) obtained from BEI resources were cultured in Minimum essential medium (Gibco) and Ham’s F12 nutrient mix (Gibco) supplemented with 10% heat-inactivated fetal bovine serum (GenClone), 30 mM HEPES (Gibco), 2 mM L-Glutamine (Gibco) and 40 µg/ml Gentamicin (Sigma-Aldrich) and maintained at 37 °C with 5% CO2 in a collagen coated flask (Corning). The cell line was tested negative for mycoplasma contamination (Invitrogen) and authenticated via American Type Culture Collection Human STR Profiling Service (ATCC).</p>\",\n \"<title>Production of transgenic <italic>P. berghei</italic> sporozoites</title>\",\n \"<p id=\\\"Par23\\\">Groups of female Swiss Webster ND4 mice (n = 2), were infected with a cryopreserved stock of respective <italic>P. berghei</italic> transgenic parasites line intraperitoneally. At 2–5% parasitemia and comparable gametocytemia, three to five days old female <italic>Anopheles stephensi</italic> mosquitoes were allowed to feed on the mice that were under anesthesia for 15–20 min. The mice were then euthanized with CO<sub>2</sub> after mosquitos’ infection. Infected mosquitoes were maintained at 21.5 °C and 80% relative humidity and supplied with 5% glucose ad libitum. Infectivity of mosquitoes was assessed by counting the number of oocysts on day 14 post feeding. Sporozoites were isolated by manual dissection of mosquito salivary glands on day 18–21 post feeding and collected in Leibovitz’s L-15 medium (Thermos Fisher Scientific). The glands were centrifuged at 6000 rpm for 1 min and mechanically disrupted using a plastic pestle to release the sporozoites. The crushed sporozoite solution was filtered through a 40 µM cell strainer (Greiner Bio-One) and sporozoites were counted using a hemocytometer.</p>\",\n \"<title>Inhibition of liver stage development assay (ILSDA)</title>\",\n \"<p id=\\\"Par24\\\">Collagen coated 384 well plate (Greiner Bio-One) was seeded with HC-04 cells at a density of 8000 cells/well for 16 h before infecting with transgenic <italic>P. berghei</italic> sporozoites. Sporozoites (75 spz/µl) were incubated in 1:100 dilution of patient plasma or North American naïve plasma for 20 min at room temperature. After incubation, 3 × 10<sup>3</sup> sporozoites per well were added to triplicate wells. The plate was centrifuged at 200 × g for 5 min and incubated at 37 °C in CO<sub>2</sub> incubator to allow hepatocyte invasion. After 1 h, culture media was changed to remove uninvaded sporozoites and a subsequent media change 24 hpi. At 48 hpi, EEFs of transgenic lines expressing mCherry were imaged using live fluorescence and the nuclei stained with Hoechst 33342 for 30 min at 37 °C. Images were acquired using high content imaging system, Cell Insight CX7, at 20X objective.</p>\",\n \"<p id=\\\"Par25\\\">The EEFs of transgenic lines expressing GFP were fixed with 4% PFA and EEFs were imaged after performing an indirect immunofluorescence assay as previously described with minor changes<sup>##REF##15710439##63##</sup>. Briefly, the PFA from the wells was washed off twice with PBS and the parasites stained by incubation with goat polyclonal <italic>P. berghei</italic> UIS4 antibody (LS Bio) at 1:1000 dilution in blocking buffer (1% BSA and 0.3% Triton X-100) at 4 °C overnight. The wells were washed twice with PBS and counter stained with an anti-goat Alexa Fluor 594 conjugated secondary antibody (Invitrogen) and Hoechst 33342 (Invitrogen) to stain the nuclei for 1 h at 37 °C. The wells were again washed with PBS and incubated in fresh PBS. Images were acquired using high content imaging system, Cell Insight CX7, at 20X objective. The images were exported, and parasites counted using an in-house Python program (Supplementary Methods Text ##SUPPL##0##S1##).</p>\",\n \"<p id=\\\"Par26\\\">Live fluorescence imaging of mCherry expressing EEFs were counted using parasite cytoplasmic (PCP) staining whereas GFP expressing parasites were counted using UIS4 positive staining of parasitophorous vacuolar membrane (PVM), and Hoechst 33342 for host nuclei staining. Two independent assays were performed for each serum.</p>\",\n \"<p id=\\\"Par27\\\">% inhibition of infection = 100–(Mean EEFs in test sample divided by Mean EEFs in 6 naïve North American samples) × 100.</p>\",\n \"<title>Statistical analysis</title>\",\n \"<p id=\\\"Par28\\\">All data were tested for normality using the Anderson–Darling test before analysis. Statistical differences for ELISA data were determined using the Kruskal–Wallis non-parametric test with a Dunn’s multiple comparison adjustment. Statistical differences for sporozoite invasion inhibition were also determined with the Kruskal–Wallis non-parametric test with a Dunn’s multiple comparison adjustment. A Spearman Correlation was done on the overall percent inhibition for each antigen. Analysis was performed using GraphPad Prism v. 10.0.2 for MacOS.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results</title>","<title>Naturally acquired antibodies to <italic>P. vivax</italic> PE antigens</title>","<p id=\"Par7\">Plasma samples from 51 Thai patients with acute <italic>P. vivax</italic> infections were screened for the presence of antigen-specific antibodies to selected PE antigens including CSP, SSP3, SPECT1 and CelTOS. There was considerable variation in magnitude of IgG antibody responses in individual patient samples against the different antigens, with significant differences in overall response observed between SSP3, CelTOS and SPECT1 (Fig. ##FIG##0##1##a). Based on the reactivity profiles, antigen specific responses could be categorized into three response groups: high responders (HR), defined as samples with antibody reactivity index (RI) greater than the mean reactivity of all samples per antigen, low responders (LR), with RI less than the mean reactivity of all samples but greater than the cut-off value (RI = 1) and non-responders (NR) with RI less than or equal to the cut-off value (Fig. ##FIG##0##1##b). Out of the 51 samples screened, 25 (49%) of them were HR for CSP and 25 (49%) for SPECT1. Similarly, SSP3 and CelTOS had the same number of HR at 19 (37.3%). However, these were not the same samples for each antigen. A total of 17 (33.3%), 30 (58.8%), 22 (43.1%) and 31 (60.8%) of samples were LR for CSP, SSP3, SPECT1 and CelTOS, respectively. Independent of their responder classifications, about 80% plasma samples had antibodies to all four antigens, 12% to three antigens, 6% to two antigens and no sample had antibodies to a single antigen. Only 2% of samples did not have antibodies to any of the four antigens (Fig. ##FIG##0##1##c). The prevalence of antigen specific antibodies was 98% for CelTOS, 96.1% for SSP3, 92.2% for SPECT1 and 82.4% for CSP (Fig. ##FIG##0##1##c).</p>","<title>Antibodies in plasma of Thai patients inhibit sporozoites invasion of hepatocytes</title>","<p id=\"Par8\">The potential protective effect of naturally acquired antibodies to these antigens on sporozoite invasion of hepatocytes was evaluated by an in vitro inhibition of liver stage development assay (ILSDA). This assay is performed in 384-well plate, and makes use of a human hepatoma cell line (HC-04) and transgenic <italic>P. berghei</italic> sporozoites expressing the different <italic>P. vivax</italic> PE antigens to support high level liver infections and parasite development to blood stage breakthrough<sup>##REF##29743474##18##</sup>. Antigen specific inhibitory antibodies were present in the patient samples and inhibited invasion of transgenic sporozoites into hepatocytes. There was a wide variation and significant differences in the levels of antigen specific inhibitory antibody responses of individual plasma samples<bold>,</bold> with antigen inhibition ranging from 0 to 50% at the tested plasma dilution of 1:100 (Fig. ##FIG##1##2##a). The highest inhibitory effect was observed with anti-CelTOS antibodies, with mean percent inhibition ranging from 11 to 46.6% (mean = 27.3%), followed by SPECT1 with 4–44.6% (mean = 20.2%), SSP3 with 0–31.1% (mean = 13.9%) and CSP with 0–48.6% (mean = 14.7%). Spearman pairwise correlation of antibody inhibition between antigens demonstrates a moderately significant correlation between anti-SSP3 and anti-SPECT1 antibodies (r = 0.62 and <italic>p</italic> < 0.0001) as well as anti-SPECT1 and anti-CelTOS antibodies (r = 0.65, <italic>p</italic> < 0.0001), while a relatively weaker correlation albeit statistically significant was found between anti-SSP3 and anti-CelTOS (r = 0.41, <italic>p</italic> = 0.003) (Fig. ##FIG##1##2##b). No correlation of inhibition was observed between anti-CSP antibodies and antibodies against SSP3, SPECT1 or CelTOS. There was no correlation between antibody titer (RI) and inhibition of sporozoite invasion into hepatocytes (Supplementary Fig. ##SUPPL##0##1##).</p>"],"string":"[\n \"<title>Results</title>\",\n \"<title>Naturally acquired antibodies to <italic>P. vivax</italic> PE antigens</title>\",\n \"<p id=\\\"Par7\\\">Plasma samples from 51 Thai patients with acute <italic>P. vivax</italic> infections were screened for the presence of antigen-specific antibodies to selected PE antigens including CSP, SSP3, SPECT1 and CelTOS. There was considerable variation in magnitude of IgG antibody responses in individual patient samples against the different antigens, with significant differences in overall response observed between SSP3, CelTOS and SPECT1 (Fig. ##FIG##0##1##a). Based on the reactivity profiles, antigen specific responses could be categorized into three response groups: high responders (HR), defined as samples with antibody reactivity index (RI) greater than the mean reactivity of all samples per antigen, low responders (LR), with RI less than the mean reactivity of all samples but greater than the cut-off value (RI = 1) and non-responders (NR) with RI less than or equal to the cut-off value (Fig. ##FIG##0##1##b). Out of the 51 samples screened, 25 (49%) of them were HR for CSP and 25 (49%) for SPECT1. Similarly, SSP3 and CelTOS had the same number of HR at 19 (37.3%). However, these were not the same samples for each antigen. A total of 17 (33.3%), 30 (58.8%), 22 (43.1%) and 31 (60.8%) of samples were LR for CSP, SSP3, SPECT1 and CelTOS, respectively. Independent of their responder classifications, about 80% plasma samples had antibodies to all four antigens, 12% to three antigens, 6% to two antigens and no sample had antibodies to a single antigen. Only 2% of samples did not have antibodies to any of the four antigens (Fig. ##FIG##0##1##c). The prevalence of antigen specific antibodies was 98% for CelTOS, 96.1% for SSP3, 92.2% for SPECT1 and 82.4% for CSP (Fig. ##FIG##0##1##c).</p>\",\n \"<title>Antibodies in plasma of Thai patients inhibit sporozoites invasion of hepatocytes</title>\",\n \"<p id=\\\"Par8\\\">The potential protective effect of naturally acquired antibodies to these antigens on sporozoite invasion of hepatocytes was evaluated by an in vitro inhibition of liver stage development assay (ILSDA). This assay is performed in 384-well plate, and makes use of a human hepatoma cell line (HC-04) and transgenic <italic>P. berghei</italic> sporozoites expressing the different <italic>P. vivax</italic> PE antigens to support high level liver infections and parasite development to blood stage breakthrough<sup>##REF##29743474##18##</sup>. Antigen specific inhibitory antibodies were present in the patient samples and inhibited invasion of transgenic sporozoites into hepatocytes. There was a wide variation and significant differences in the levels of antigen specific inhibitory antibody responses of individual plasma samples<bold>,</bold> with antigen inhibition ranging from 0 to 50% at the tested plasma dilution of 1:100 (Fig. ##FIG##1##2##a). The highest inhibitory effect was observed with anti-CelTOS antibodies, with mean percent inhibition ranging from 11 to 46.6% (mean = 27.3%), followed by SPECT1 with 4–44.6% (mean = 20.2%), SSP3 with 0–31.1% (mean = 13.9%) and CSP with 0–48.6% (mean = 14.7%). Spearman pairwise correlation of antibody inhibition between antigens demonstrates a moderately significant correlation between anti-SSP3 and anti-SPECT1 antibodies (r = 0.62 and <italic>p</italic> < 0.0001) as well as anti-SPECT1 and anti-CelTOS antibodies (r = 0.65, <italic>p</italic> < 0.0001), while a relatively weaker correlation albeit statistically significant was found between anti-SSP3 and anti-CelTOS (r = 0.41, <italic>p</italic> = 0.003) (Fig. ##FIG##1##2##b). No correlation of inhibition was observed between anti-CSP antibodies and antibodies against SSP3, SPECT1 or CelTOS. There was no correlation between antibody titer (RI) and inhibition of sporozoite invasion into hepatocytes (Supplementary Fig. ##SUPPL##0##1##).</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Discussion</title>","<p id=\"Par9\">Characterization of naturally acquired antibody responses to <italic>P. vivax</italic> antigens following <italic>vivax</italic> infections is an important step towards target selection and rational vaccine design to protect against vivax malaria. Naturally acquired IgG antibody responses to <italic>P. vivax</italic> antigens has focused mainly on blood-stage antigens rather than pre-erythrocytic antigens<sup>##REF##26656115##57##,##REF##26864134##58##</sup>. <italic>Plasmodium</italic> sporozoites injected by the mosquito migrate to the liver where they infect hepatocytes and develop into liver-stage forms or exoerythrocytic forms (EEFs)<sup>##REF##14737184##49##,##REF##15901208##59##</sup>. Since sporozoite antigens play a critical functional role during this migration process, they are considered potential vaccine targets. To determine if PE antigens are naturally immunogenic and could induce protective antibody responses in residents of endemic regions, quantitative and qualitative analysis of IgG antibody responses to four <italic>P. vivax</italic> PE antigens: CSP, SSP3, SPECT1 and CelTOS were evaluated in 51 plasma samples from acute symptomatic vivax infected patients from a low endemic region of Southern Thailand. Antigen specific antibodies were prevalent in the study subjects, with considerable heterogeneity in magnitude of antibody responses (Fig. ##FIG##0##1##). Over 80% of the patients had antibodies to all four antigens, suggesting that epitopes of these antigens are immunogenic and commonly exposed to the host immune responses during natural infections. However, the lack of correlation between the antibodies’ titer and level of functional inhibition suggest epitope specificity and strain variation may be important variables in development of protective immune antibodies.</p>","<p id=\"Par10\">Anti-CelTOS specific antibodies had the highest frequency, with 98% seropositivity, followed by anti-SSP3, anti-SPECT1 and anti-CSP antibodies with 96%, 92% and 82% respectively. In a low endemic region in Western Thailand where a majority of the study population was not infected, CelTOS was not substantially immunogenic<sup>##REF##26656115##57##</sup>, while in the Brazilian Amazon, only 17.8% (94/528) of study subjects had specific CelTOS IgG antibodies<sup>##REF##28223984##37##</sup>. This later study demonstrated that high antibodies against CelTOS were driven by cytophilic antibodies, suggesting that antibody response to CelTOS might be associated with recent infections<sup>##REF##28223984##37##</sup>, which could explain the high prevalence of anti-CelTOS antibodies in our study. However, Longley et al.<sup>##REF##26656115##57##</sup> demonstrated that anti-CelTOS antibodies could last up to 1 year in the absence of <italic>P. vivax</italic> infection.</p>","<p id=\"Par11\">We also demonstrated the prevalence of naturally acquired anti-CSP antibodies in our study subjects. Other studies also reported the prevalence of anti-CSP antibodies in other endemic regions, some of which could last between 5 and 12 months even in the absence of detectable exposure to <italic>P. vivax</italic> infection<sup>##REF##26656115##57##,##REF##9535000##60##–##REF##32820195##62##</sup>, with most of the antibody response biased towards the immunodominant central repeat region. The other <italic>P. vivax</italic> PE stage antigens, SSP3 and SPECT1, are also known to play important roles in sporozoite migration and infectivity. Most data on SSP3 and SPECT1 are based on studies with the rodent malaria parasite <italic>P. berghei</italic><sup>##REF##25479287##55##,##REF##25156733##56##</sup>. Currently, there are no published data on the acquisition of naturally acquired immunity to <italic>P. vivax</italic> SSP3 and SPECT1 in endemic regions. Here, we showed that these two antigens are naturally immunogenic, with SSP3 showing significantly higher antibody responses than SPECT1 in the study subjects (Fig. ##FIG##0##1##).</p>","<p id=\"Par12\">The association between patient age and the presence of antibodies to these PE antigens was evaluated to determine if there was a correlation between the acquisition of naturally acquired immunity to malaria and age. The Median (interquartile range; IQR) age of participants was 32 (20–44) years old. No correlations were found between antibody levels to these antigens and the age of study participants (Supplementary Fig. ##SUPPL##0##2##), although future studies with a larger study population will be needed to validate this finding. Nonetheless, these data mirror studies with blood-stage antigen PvAMA-1<sup>##REF##15710439##63##</sup> and liver-stage antigen, PvTRAP<sup>##REF##28549910##64##</sup>, which demonstrated no correlations between age and antibody levels in individuals with acute symptomatic <italic>P. vivax</italic> infection.</p>","<p id=\"Par13\">Orthologues of these PE stage antigens have been shown to play important roles in gliding, traversal, and invasion in other <italic>Plasmodium</italic> species<sup>##REF##14737184##49##,##REF##31251952##54##,##REF##15155670##65##–##REF##36499467##67##</sup>. Antibodies to PfCelTOS inhibited sporozoite motility and hepatocyte invasion in mice immunizations and sterile immunity in a heterologous <italic>P. berghei</italic> sporozoite challenge<sup>##REF##20808868##39##,##REF##21722682##41##</sup>. Anti-PfCSP human antibodies are also associated with protective immunity<sup>##REF##2665440##68##</sup>. In <italic>P. vivax,</italic> monoclonal antibodies to CSP not only block sporozoite entry but also could inhibit subsequent development within the hepatocytes in vitro as evidenced by abnormal EEFs<sup>##REF##8732082##69##</sup>. To determine the potential vaccine efficacy of antibodies to the <italic>P. vivax</italic> PE antigens, we evaluated naturally acquired antibodies in patient samples for inhibition of sporozoites invasion of human hepatocytes by ILSDA. This assay platform has been validated for evaluating inhibition of liver stage developmental and to assess the specificity and sensitivity of both <italic>P. falciparum</italic> and <italic>P. vivax</italic> antibodies<sup>##REF##7045272##6##,##REF##29743474##18##,##REF##9806864##70##</sup>. We showed that naturally acquired antibodies to these PE antigens in patient samples inhibited transgenic <italic>P. berghei</italic> sporozoites expressing the different PE stage antigens from invading hepatocytes in vitro (Fig. ##FIG##1##2##), suggesting the presence of functional epitopes of naturally acquired protective antibodies on these antigens. Overall, the highest inhibitory responses were against CelTOS, followed by SPECT1. Although a few individuals had high inhibitory antibodies against CSP and SSP3, most of them had either very low or no inhibitory antibodies at all. It should be noted that of the three naturally occurring <italic>P. vivax</italic> CSP variants, which differ in the immunodominant repeat regions, only the CSP-VK210 variant was assessed in this study, which might account for the high rate of non-responders and low or non-inhibitory antibodies against CSP if the repeat regions are the primary targets of inhibitory antibodies. Thus, there is a possibility that some of the observed non-responders and non-inhibitory samples of CSP could be responders and inhibitory against a different CSP variant. In the case of SSP3, it could be possible that the use of a truncated SSP3 protein (amino acids 19-203) may have resulted in a low estimate for the SSP3 titers.</p>","<p id=\"Par14\">Interestingly, a sample with high inhibitory antibodies (% inhibition > 30%), against one antigen did not necessarily show high inhibition against the other antigens. Only 1 individual had high inhibitory antibodies against all three antigens (CelTOS, SPECT1, SSP3), 2 individuals against two antigens (CelTOS, SPECT1 or SSP3) and no individual with inhibitory antibodies against all four antigens. There was no correlation between high titer anti-CSP inhibitory antibodies and inhibitory antibodies to any of the other three antigens. Although other parasite factors such as the specific strain of the parasite and host immune factors including previous exposure play a role in the development of immunity against these different antigens, our data suggest that the acquisition of functional antibodies against each antigen is independent of the other and support the need for identifying potential PE antigens to partner with CSP in a multivalent vaccine targeting different PE antigens. Thus, identifying parasite antigens that are targets of naturally acquired inhibitory antibodies are ideal candidates for inclusion in such a vaccine to prevent hepatocyte infection and the onset of clinical disease by blocking the progression of the parasite in the liver to blood-stage breakthrough.</p>","<p id=\"Par15\">In summary, we showed that naturally acquired antibodies to the selected <italic>P. vivax</italic> PE antigens were prevalent in the study subjects from Thailand, but individuals had significant quantitative and qualitative differences in their antigen-specific antibody responses. Secondly, we demonstrated that there was no correlation between antibody titer (defined by RI), age of patients, and functional inhibitory activity against each antigen. This finding mirrors results previously reported for the Duffy binding protein (DBPII), which is a leading <italic>P. vivax</italic> blood-stage vaccine candidate<sup>##REF##20008533##71##</sup>. This study is the first to investigate the functional activity of naturally acquired antibodies to these four <italic>P. vivax</italic> PE antigens. Together, our data highlights the potential of these PE antigens as vaccine targets and supports their inclusion in vaccine designs aimed at targeting sporozoite invasion of hepatocytes and liver stage development. Preventing parasites from developing in the liver and progressing to the blood stage will not only prevent the development of clinical disease and morbidity but also prevent transmission. Additional studies are needed to investigate the dynamics involved in the acquisition of antigen-specific antibodies and the frequency of vivax infections in the study subjects. Further studies are required to assess the magnitude, longevity and factors affecting the acquisition of antigen-specific antibodies with a larger cohort of patients.</p>"],"string":"[\n \"<title>Discussion</title>\",\n \"<p id=\\\"Par9\\\">Characterization of naturally acquired antibody responses to <italic>P. vivax</italic> antigens following <italic>vivax</italic> infections is an important step towards target selection and rational vaccine design to protect against vivax malaria. Naturally acquired IgG antibody responses to <italic>P. vivax</italic> antigens has focused mainly on blood-stage antigens rather than pre-erythrocytic antigens<sup>##REF##26656115##57##,##REF##26864134##58##</sup>. <italic>Plasmodium</italic> sporozoites injected by the mosquito migrate to the liver where they infect hepatocytes and develop into liver-stage forms or exoerythrocytic forms (EEFs)<sup>##REF##14737184##49##,##REF##15901208##59##</sup>. Since sporozoite antigens play a critical functional role during this migration process, they are considered potential vaccine targets. To determine if PE antigens are naturally immunogenic and could induce protective antibody responses in residents of endemic regions, quantitative and qualitative analysis of IgG antibody responses to four <italic>P. vivax</italic> PE antigens: CSP, SSP3, SPECT1 and CelTOS were evaluated in 51 plasma samples from acute symptomatic vivax infected patients from a low endemic region of Southern Thailand. Antigen specific antibodies were prevalent in the study subjects, with considerable heterogeneity in magnitude of antibody responses (Fig. ##FIG##0##1##). Over 80% of the patients had antibodies to all four antigens, suggesting that epitopes of these antigens are immunogenic and commonly exposed to the host immune responses during natural infections. However, the lack of correlation between the antibodies’ titer and level of functional inhibition suggest epitope specificity and strain variation may be important variables in development of protective immune antibodies.</p>\",\n \"<p id=\\\"Par10\\\">Anti-CelTOS specific antibodies had the highest frequency, with 98% seropositivity, followed by anti-SSP3, anti-SPECT1 and anti-CSP antibodies with 96%, 92% and 82% respectively. In a low endemic region in Western Thailand where a majority of the study population was not infected, CelTOS was not substantially immunogenic<sup>##REF##26656115##57##</sup>, while in the Brazilian Amazon, only 17.8% (94/528) of study subjects had specific CelTOS IgG antibodies<sup>##REF##28223984##37##</sup>. This later study demonstrated that high antibodies against CelTOS were driven by cytophilic antibodies, suggesting that antibody response to CelTOS might be associated with recent infections<sup>##REF##28223984##37##</sup>, which could explain the high prevalence of anti-CelTOS antibodies in our study. However, Longley et al.<sup>##REF##26656115##57##</sup> demonstrated that anti-CelTOS antibodies could last up to 1 year in the absence of <italic>P. vivax</italic> infection.</p>\",\n \"<p id=\\\"Par11\\\">We also demonstrated the prevalence of naturally acquired anti-CSP antibodies in our study subjects. Other studies also reported the prevalence of anti-CSP antibodies in other endemic regions, some of which could last between 5 and 12 months even in the absence of detectable exposure to <italic>P. vivax</italic> infection<sup>##REF##26656115##57##,##REF##9535000##60##–##REF##32820195##62##</sup>, with most of the antibody response biased towards the immunodominant central repeat region. The other <italic>P. vivax</italic> PE stage antigens, SSP3 and SPECT1, are also known to play important roles in sporozoite migration and infectivity. Most data on SSP3 and SPECT1 are based on studies with the rodent malaria parasite <italic>P. berghei</italic><sup>##REF##25479287##55##,##REF##25156733##56##</sup>. Currently, there are no published data on the acquisition of naturally acquired immunity to <italic>P. vivax</italic> SSP3 and SPECT1 in endemic regions. Here, we showed that these two antigens are naturally immunogenic, with SSP3 showing significantly higher antibody responses than SPECT1 in the study subjects (Fig. ##FIG##0##1##).</p>\",\n \"<p id=\\\"Par12\\\">The association between patient age and the presence of antibodies to these PE antigens was evaluated to determine if there was a correlation between the acquisition of naturally acquired immunity to malaria and age. The Median (interquartile range; IQR) age of participants was 32 (20–44) years old. No correlations were found between antibody levels to these antigens and the age of study participants (Supplementary Fig. ##SUPPL##0##2##), although future studies with a larger study population will be needed to validate this finding. Nonetheless, these data mirror studies with blood-stage antigen PvAMA-1<sup>##REF##15710439##63##</sup> and liver-stage antigen, PvTRAP<sup>##REF##28549910##64##</sup>, which demonstrated no correlations between age and antibody levels in individuals with acute symptomatic <italic>P. vivax</italic> infection.</p>\",\n \"<p id=\\\"Par13\\\">Orthologues of these PE stage antigens have been shown to play important roles in gliding, traversal, and invasion in other <italic>Plasmodium</italic> species<sup>##REF##14737184##49##,##REF##31251952##54##,##REF##15155670##65##–##REF##36499467##67##</sup>. Antibodies to PfCelTOS inhibited sporozoite motility and hepatocyte invasion in mice immunizations and sterile immunity in a heterologous <italic>P. berghei</italic> sporozoite challenge<sup>##REF##20808868##39##,##REF##21722682##41##</sup>. Anti-PfCSP human antibodies are also associated with protective immunity<sup>##REF##2665440##68##</sup>. In <italic>P. vivax,</italic> monoclonal antibodies to CSP not only block sporozoite entry but also could inhibit subsequent development within the hepatocytes in vitro as evidenced by abnormal EEFs<sup>##REF##8732082##69##</sup>. To determine the potential vaccine efficacy of antibodies to the <italic>P. vivax</italic> PE antigens, we evaluated naturally acquired antibodies in patient samples for inhibition of sporozoites invasion of human hepatocytes by ILSDA. This assay platform has been validated for evaluating inhibition of liver stage developmental and to assess the specificity and sensitivity of both <italic>P. falciparum</italic> and <italic>P. vivax</italic> antibodies<sup>##REF##7045272##6##,##REF##29743474##18##,##REF##9806864##70##</sup>. We showed that naturally acquired antibodies to these PE antigens in patient samples inhibited transgenic <italic>P. berghei</italic> sporozoites expressing the different PE stage antigens from invading hepatocytes in vitro (Fig. ##FIG##1##2##), suggesting the presence of functional epitopes of naturally acquired protective antibodies on these antigens. Overall, the highest inhibitory responses were against CelTOS, followed by SPECT1. Although a few individuals had high inhibitory antibodies against CSP and SSP3, most of them had either very low or no inhibitory antibodies at all. It should be noted that of the three naturally occurring <italic>P. vivax</italic> CSP variants, which differ in the immunodominant repeat regions, only the CSP-VK210 variant was assessed in this study, which might account for the high rate of non-responders and low or non-inhibitory antibodies against CSP if the repeat regions are the primary targets of inhibitory antibodies. Thus, there is a possibility that some of the observed non-responders and non-inhibitory samples of CSP could be responders and inhibitory against a different CSP variant. In the case of SSP3, it could be possible that the use of a truncated SSP3 protein (amino acids 19-203) may have resulted in a low estimate for the SSP3 titers.</p>\",\n \"<p id=\\\"Par14\\\">Interestingly, a sample with high inhibitory antibodies (% inhibition > 30%), against one antigen did not necessarily show high inhibition against the other antigens. Only 1 individual had high inhibitory antibodies against all three antigens (CelTOS, SPECT1, SSP3), 2 individuals against two antigens (CelTOS, SPECT1 or SSP3) and no individual with inhibitory antibodies against all four antigens. There was no correlation between high titer anti-CSP inhibitory antibodies and inhibitory antibodies to any of the other three antigens. Although other parasite factors such as the specific strain of the parasite and host immune factors including previous exposure play a role in the development of immunity against these different antigens, our data suggest that the acquisition of functional antibodies against each antigen is independent of the other and support the need for identifying potential PE antigens to partner with CSP in a multivalent vaccine targeting different PE antigens. Thus, identifying parasite antigens that are targets of naturally acquired inhibitory antibodies are ideal candidates for inclusion in such a vaccine to prevent hepatocyte infection and the onset of clinical disease by blocking the progression of the parasite in the liver to blood-stage breakthrough.</p>\",\n \"<p id=\\\"Par15\\\">In summary, we showed that naturally acquired antibodies to the selected <italic>P. vivax</italic> PE antigens were prevalent in the study subjects from Thailand, but individuals had significant quantitative and qualitative differences in their antigen-specific antibody responses. Secondly, we demonstrated that there was no correlation between antibody titer (defined by RI), age of patients, and functional inhibitory activity against each antigen. This finding mirrors results previously reported for the Duffy binding protein (DBPII), which is a leading <italic>P. vivax</italic> blood-stage vaccine candidate<sup>##REF##20008533##71##</sup>. This study is the first to investigate the functional activity of naturally acquired antibodies to these four <italic>P. vivax</italic> PE antigens. Together, our data highlights the potential of these PE antigens as vaccine targets and supports their inclusion in vaccine designs aimed at targeting sporozoite invasion of hepatocytes and liver stage development. Preventing parasites from developing in the liver and progressing to the blood stage will not only prevent the development of clinical disease and morbidity but also prevent transmission. Additional studies are needed to investigate the dynamics involved in the acquisition of antigen-specific antibodies and the frequency of vivax infections in the study subjects. Further studies are required to assess the magnitude, longevity and factors affecting the acquisition of antigen-specific antibodies with a larger cohort of patients.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">In <italic>Plasmodium vivax</italic>, the most studied vaccine antigens are aimed at blocking merozoite invasion of erythrocytes and disease development. Very few studies have evaluated pre-erythrocytic (PE) stage antigens. The <italic>P. vivax</italic> circumsporozoite protein (CSP), is considered the leading PE vaccine candidate, but immunity to CSP is short-lived and variant specific. Thus, there is a need to identify other potential candidates to partner with CSP in a multivalent vaccine to protect against infection and disease. We hypothesize that sporozoite antigens important for host cell infection are considered potential targets. In this study, we evaluated the magnitude and quality of naturally acquired antibody responses to four <italic>P. vivax </italic>PE antigens: sporozoite surface protein 3 (SSP3), sporozoite protein essential for traversal 1 (SPECT1), cell traversal protein of ookinetes and sporozoites (CelTOS) and CSP in plasma of <italic>P. vivax</italic> infected patients from Thailand. Naturally acquired antibodies to these antigens were prevalent in the study subjects, but with significant differences in magnitude of IgG antibody responses. About 80% of study participants had antibodies to all four antigens and only 2% did not have antibodies to any of the antigens. Most importantly, these antibodies inhibited sporozoite infection of hepatocytes in vitro. Significant variations in magnitude of antigen-specific inhibitory antibody responses were observed with individual samples. The highest inhibitory responses were observed with anti-CelTOS antibodies, followed by anti-SPECT1, SSP3 and CSP antibodies respectively. These data highlight the vaccine potential of these antigens in protecting against hepatocyte infection and the need for a multi-valent pre-erythrocytic vaccine to prevent liver stage development of <italic>P. vivax</italic> sporozoites.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">In <italic>Plasmodium vivax</italic>, the most studied vaccine antigens are aimed at blocking merozoite invasion of erythrocytes and disease development. Very few studies have evaluated pre-erythrocytic (PE) stage antigens. The <italic>P. vivax</italic> circumsporozoite protein (CSP), is considered the leading PE vaccine candidate, but immunity to CSP is short-lived and variant specific. Thus, there is a need to identify other potential candidates to partner with CSP in a multivalent vaccine to protect against infection and disease. We hypothesize that sporozoite antigens important for host cell infection are considered potential targets. In this study, we evaluated the magnitude and quality of naturally acquired antibody responses to four <italic>P. vivax </italic>PE antigens: sporozoite surface protein 3 (SSP3), sporozoite protein essential for traversal 1 (SPECT1), cell traversal protein of ookinetes and sporozoites (CelTOS) and CSP in plasma of <italic>P. vivax</italic> infected patients from Thailand. Naturally acquired antibodies to these antigens were prevalent in the study subjects, but with significant differences in magnitude of IgG antibody responses. About 80% of study participants had antibodies to all four antigens and only 2% did not have antibodies to any of the antigens. Most importantly, these antibodies inhibited sporozoite infection of hepatocytes in vitro. Significant variations in magnitude of antigen-specific inhibitory antibody responses were observed with individual samples. The highest inhibitory responses were observed with anti-CelTOS antibodies, followed by anti-SPECT1, SSP3 and CSP antibodies respectively. These data highlight the vaccine potential of these antigens in protecting against hepatocyte infection and the need for a multi-valent pre-erythrocytic vaccine to prevent liver stage development of <italic>P. vivax</italic> sporozoites.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary Information</title>","<p>\n</p>"],"string":"[\n \"<title>Supplementary Information</title>\",\n \"<p>\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary Information</title>","<p>The online version contains supplementary material available at 10.1038/s41598-024-51820-2.</p>","<title>Acknowledgements</title>","<p>We thank the residents of Ranong and Chumphon provinces of Thailand for providing the plasma samples that were used for this study. The following reagent was obtained through BEI Resources, NIAID, NIH: HC-04, Hepatocyte (human), MRA-975, contributed by Jetsumon Sattabongkot Prachumsri.</p>","<title>Author contributions</title>","<p>Study design; J.H.A., manuscript draft; F.B.N., performed experiments; F.B.N., S.K.K., P.A.S., J.N., M.M.O., data analysis; S.J.B., python code; B.B., provided materials; N.H.T., N.D.S., P.T., P.C. All authors reviewed the manuscript.</p>","<title>Funding</title>","<p>This study was supported by the National Institutes of Health grant (UO1: 5U01AI155361-0 (to J.H.A.). N.H.T. and N.D.S are supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p>","<title>Data availability</title>","<p>The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.</p>","<title>Competing interests</title>","<p id=\"Par29\">N.H.T., N.D.S. and J.H.A. are inventors on patent number US-20190276506-A1 related to this work. The remaining authors declare that they have no competing interests.</p>"],"string":"[\n \"<title>Supplementary Information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41598-024-51820-2.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>We thank the residents of Ranong and Chumphon provinces of Thailand for providing the plasma samples that were used for this study. The following reagent was obtained through BEI Resources, NIAID, NIH: HC-04, Hepatocyte (human), MRA-975, contributed by Jetsumon Sattabongkot Prachumsri.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>Study design; J.H.A., manuscript draft; F.B.N., performed experiments; F.B.N., S.K.K., P.A.S., J.N., M.M.O., data analysis; S.J.B., python code; B.B., provided materials; N.H.T., N.D.S., P.T., P.C. All authors reviewed the manuscript.</p>\",\n \"<title>Funding</title>\",\n \"<p>This study was supported by the National Institutes of Health grant (UO1: 5U01AI155361-0 (to J.H.A.). N.H.T. and N.D.S are supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p>\",\n \"<title>Data availability</title>\",\n \"<p>The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par29\\\">N.H.T., N.D.S. and J.H.A. are inventors on patent number US-20190276506-A1 related to this work. The remaining authors declare that they have no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Profile of naturally acquired IgG antibodies against PE antigens. (<bold>a</bold>) Antigen-specific antibody levels in plasma samples from Thai <italic>P. vivax</italic> infected patients (n = 51) were measured by indirect ELISA against recombinant CSP, SSP3, SPECT1, and CelTOS. Data is expressed as reactive index (RI) units, calculated by dividing the mean OD value of each patient sample by a cut-off value. The cut-off value was calculated as the mean OD + 2 SD of 6 naïve North American control samples<sup>##REF##36417454##75##</sup>. Each point on the graph represents individual RI units with median and interquartile range. Plasma samples with RI < 1 (dotted lines) are considered not reactive (<bold>b</bold>) Frequency of IgG antibodies against respective antigens classified into HR (RI ≥ mean reactivity of all samples), LR (RI < mean reactivity of all samples but > cut-off value), and NR (RI < cut-off value). (<bold>c</bold>) Number of individuals with cross-reactive IgG antibodies to the different antigens.</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Inhibition of sporozoite infectivity of human hepatocytes in vitro. (<bold>a</bold>) Sporozoites of <italic>P. berghei</italic> transgenic parasites expressing <italic>P. vivax</italic> CSP, SSP3, SPECT1, CelTOS, were pre-exposed to plasma samples from vivax-infected patients (n = 51) before being added to a monolayer of HC-04 cell line in 384 well plates. Inhibition of hepatocyte infection was expressed as percent inhibition in the formation of EEFs in the presence of patient plasma (test samples) relative to the naïve North American control plasma. Each point on the graph represents the percent inhibition of individual plasma and bars represent the median and interquartile range for two independent experiments tested in triplicate. Statistical analysis was performed by Kruskal–Wallis test Dunn’s multiple comparison analysis. (<bold>b</bold>) Correlation of antibody inhibitory responses between <italic>PE</italic> antigens. Pairwise Spearman’s correlations were determined by percent inhibition in ILSDA between each antigen pair. Correlation was performed to predict inhibitory interactions between PE antigens. Antigen pairs with Spearman r > 0.6 and adjusted <italic>p</italic>-value < 0.0001 suggest a moderately significant correlation. Significance indicated by *<italic>p</italic> < 0.005, ** <italic>p</italic> < 0.0001.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>Profile of naturally acquired IgG antibodies against PE antigens. (<bold>a</bold>) Antigen-specific antibody levels in plasma samples from Thai <italic>P. vivax</italic> infected patients (n = 51) were measured by indirect ELISA against recombinant CSP, SSP3, SPECT1, and CelTOS. Data is expressed as reactive index (RI) units, calculated by dividing the mean OD value of each patient sample by a cut-off value. The cut-off value was calculated as the mean OD + 2 SD of 6 naïve North American control samples<sup>##REF##36417454##75##</sup>. Each point on the graph represents individual RI units with median and interquartile range. Plasma samples with RI < 1 (dotted lines) are considered not reactive (<bold>b</bold>) Frequency of IgG antibodies against respective antigens classified into HR (RI ≥ mean reactivity of all samples), LR (RI < mean reactivity of all samples but > cut-off value), and NR (RI < cut-off value). (<bold>c</bold>) Number of individuals with cross-reactive IgG antibodies to the different antigens.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>Inhibition of sporozoite infectivity of human hepatocytes in vitro. (<bold>a</bold>) Sporozoites of <italic>P. berghei</italic> transgenic parasites expressing <italic>P. vivax</italic> CSP, SSP3, SPECT1, CelTOS, were pre-exposed to plasma samples from vivax-infected patients (n = 51) before being added to a monolayer of HC-04 cell line in 384 well plates. Inhibition of hepatocyte infection was expressed as percent inhibition in the formation of EEFs in the presence of patient plasma (test samples) relative to the naïve North American control plasma. Each point on the graph represents the percent inhibition of individual plasma and bars represent the median and interquartile range for two independent experiments tested in triplicate. Statistical analysis was performed by Kruskal–Wallis test Dunn’s multiple comparison analysis. (<bold>b</bold>) Correlation of antibody inhibitory responses between <italic>PE</italic> antigens. Pairwise Spearman’s correlations were determined by percent inhibition in ILSDA between each antigen pair. Correlation was performed to predict inhibitory interactions between PE antigens. Antigen pairs with Spearman r > 0.6 and adjusted <italic>p</italic>-value < 0.0001 suggest a moderately significant correlation. Significance indicated by *<italic>p</italic> < 0.005, ** <italic>p</italic> < 0.0001.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":[],"string":"[]","isConversational":false},"box":{"kind":"list like","value":[],"string":"[]","isConversational":false},"code":{"kind":"list like","value":[],"string":"[]","isConversational":false},"quote":{"kind":"list like","value":[],"string":"[]","isConversational":false},"chemical":{"kind":"list like","value":[],"string":"[]","isConversational":false},"supplementary":{"kind":"list like","value":["<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"],"string":"[\n \"<supplementary-material content-type=\\\"local-data\\\" id=\\\"MOESM1\\\"></supplementary-material>\"\n]","isConversational":false},"footnote":{"kind":"list like","value":["<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Francis Babila Ntumngia, Surendra Kumar Kolli, Pradeep Annamalai Subramani and Samantha J. Barnes.</p></fn></fn-group>"],"string":"[\n \"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Francis Babila Ntumngia, Surendra Kumar Kolli, Pradeep Annamalai Subramani and Samantha J. Barnes.</p></fn></fn-group>\"\n]","isConversational":false},"graphic":{"kind":"list like","value":["<graphic xlink:href=\"41598_2024_51820_Fig1_HTML\" id=\"MO1\"/>","<graphic xlink:href=\"41598_2024_51820_Fig2_HTML\" id=\"MO2\"/>"],"string":"[\n \"<graphic xlink:href=\\\"41598_2024_51820_Fig1_HTML\\\" id=\\\"MO1\\\"/>\",\n \"<graphic xlink:href=\\\"41598_2024_51820_Fig2_HTML\\\" id=\\\"MO2\\\"/>\"\n]","isConversational":false},"media":{"kind":"list like","value":["<media xlink:href=\"41598_2024_51820_MOESM1_ESM.docx\"><caption><p>Supplementary Information.</p></caption></media>"],"string":"[\n \"<media xlink:href=\\\"41598_2024_51820_MOESM1_ESM.docx\\\"><caption><p>Supplementary Information.</p></caption></media>\"\n]","isConversational":false},"unknown_pub":{"kind":"string","value":"[{\"label\": [\"66.\"], \"surname\": [\"Yang\"], \"given-names\": [\"ASP\"], \"article-title\": [\"AMA1 and MAEBL are important for \"], \"italic\": [\"Plasmodium falciparum\"], \"source\": [\"Cell. Microbiol.\"], \"year\": [\"2017\"], \"volume\": [\"19\"], \"fpage\": [\"e12745\"], \"pub-id\": [\"10.1111/cmi.12745\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":78,"string":"78"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:57"},"citation":{"kind":"string","value":"Sci Rep. 2024 Jan 13; 14:1260"},"package_file":{"kind":"string","value":"oa_package/1b/ad/PMC10787766.tar.gz"}}},{"rowIdx":97,"cells":{"accession_id":{"kind":"string","value":"PMC10787767"},"pmid":{"kind":"string","value":"38218968"},"introduction":{"kind":"list like","value":["<title>Background & Summary</title>","<p id=\"Par2\">Repeat Terrestrial Laser Scanning (TLS) topography measurements were part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, in which researchers aboard <italic>R/V Polarstern</italic><sup>##UREF##0##1##</sup> drifted with and studied the same collection of ice floes in the Central Arctic from October 2019 to May 2020<sup>##UREF##1##2##–##UREF##3##4##</sup>. Arctic sea ice has grown dramatically younger and thinner in recent decades<sup>##UREF##4##5##</sup> and the overall objectives of MOSAiC were to understand the causes and consequences of this ‘new Arctic’. To do so, researchers were divided into teams studying the snow and ice (including on-ice and satellite remote sensing)<sup>##UREF##1##2##</sup>, atmosphere<sup>##UREF##2##3##</sup>, ocean<sup>##UREF##3##4##</sup>, ecosystem, and biogeochemistry. We conducted the TLS measurements as part of the ice team, for the primary purpose of quantifying snow accumulation and redistribution. Other applications of these data include observations of ice dynamics; surface roughness for ice-atmosphere interactions; and providing context for atmospheric observations, remote sensing instruments (e.g., on-ice radars<sup>##UREF##5##6##</sup>), autonomous buoys, snow pit measurements, and more.</p>","<p id=\"Par3\">Snow substantially affects the Arctic sea ice mass balance due to its opposing impacts of insulating in the winter (restraining ice growth) and reflecting shortwave radiation in the summer (protecting against ice melt<sup>##UREF##6##7##</sup>). Three of the four most important uncertainties impacting September sea ice volume in the CICE sea ice model<sup>##UREF##7##8##</sup> are related to the thermal and optical properties of the snow<sup>##UREF##8##9##</sup>. Snow spatial variability due to wind driven snow redistribution impacts these properties<sup>##UREF##9##10##,##UREF##10##11##</sup>. However, redistribution is challenging to measure due to this spatial variability. Repeat observations of snow changes on a substantial area of the same piece of ice are needed to observe these redistribution processes and understand their mechanisms. Furthermore, the magnitude of snow accumulation can be very small. For example, the largest snowfall event on MOSAiC precipitated just 1.6 cm water equivalent<sup>##UREF##11##12##</sup>. Thus, our observations of changes must be highly accurate. Finally, to observe snow accumulation and redistribution throughout the winter, measurements must be feasible in polar night.</p>","<p id=\"Par4\">TLS is routinely used to make highly-accurate measurements of snow accumulation on areas of 40 m<sup>2</sup> to 0.5 km<sup>2</sup>\n<sup>##UREF##12##13##–##UREF##16##17##</sup>. The instrument is a laser scanner that is mounted on a ∼ 2.2 m tall tripod. Due to the generally flat topography of sea ice and shadowing, the instrument collects topographic information up to 100–200 m from itself. To observe a larger area, we relocate the tripod and collect measurements from different locations, which we then co-register into a common reference frame. TLS measurements on Arctic sea ice face unusual challenges and our procedures for the MOSAiC Expedition were informed by prior experiments, including: the Seasonal Ice Zone Observing Network project<sup>##UREF##17##18##</sup>; the Snow, Wind, and Time project<sup>##UREF##16##17##</sup>; and the Sea Ice Dynamics Experiment. First, the typical temperatures of −15 to −35 °C are below the operating range of commercially-available TLS instruments. We addressed this issue with a custom-designed heater case. Second, TLS measurements are typically aligned in a geodetic reference frame via highly-accurate GNSS measurements. However, on drifting sea ice, the relevant reference frame for snow processes is a lagrangian reference frame fixed to the surface of the ice. We developed a custom software package—pydar<sup>##UREF##18##19##</sup>—to align repeat TLS measurements into this lagrangian, ice-fixed reference frame. To verify that our alignment achieved the necessary vertical accuracy for snow accumulation, we statistically validated it through comparison with <italic>in-situ</italic> measurements<sup>##UREF##19##20##</sup>. The quantitative results from our alignment validation are specific to this dataset. Finally, the TLS data contain potentially-useful information that are irrelevant to our needs (e.g., sub-cm snow surface roughness in areas near the scanner, backscatter reflectance, roughness relevant to aerodynamic drag, etc…). We hope that future researchers will use these data for purposes that we have not imagined. To facilitate this future usage, we have designed pydar to preserve the full scope of the data and to make our data processing decisions transparent to future researchers.</p>","<p id=\"Par5\">The primary purpose of this manuscript is to describe the TLS data collected at MOSAiC. However, given the unique challenges of using TLS on drifting sea ice, some discussion of lessons learned and future methodological developments is warranted. Prior experience on sea ice near Utqiaġvik, AK<sup>##UREF##16##17##,##UREF##17##18##</sup> found that spacing scan positions between 150 and 200 m apart generally produced acceptable data. However, the ice at MOSAiC was rougher than those experiments, and the placement of scan positions was also restricted due to not trespassing in sensitive measurement sites. Under these constraints, we found that spacing scan positions around 100 m apart produced better data, although we sometimes prioritized having measurements co-located with complementary measurements sites (e.g., the snow and ice thickness transects) over achieving the best TLS coverage. Additionally, the alignment procedures and validation were developed after the expedition, thus the alignment validation relies on what coincident <italic>in-situ</italic> measurements were available and had not experienced blowing snow events between the <italic>in-situ</italic> measurement and the TLS acquisition. The statistical validation methods presented herein are applicable for future campaigns, but the specific quantitative results depend on the measurements. Therefore, validation should be conducted for any TLS campaign on sea ice. For future campaigns, we strongly recommend including more <italic>in-situ</italic> point measurements of snow surface changes coincident with the TLS measurements. One expeditious approach for this would be to include a small array of snow thickness stakes near each reflector post. This would ensure that the <italic>in-situ</italic> measurement sites were visible from multiple scan positions, and the snow surface measurements could be made quickly while distributing the reflectors at the start of a TLS measurement day.</p>","<p id=\"Par6\">This manuscript describes the repeat TLS data collected on the MOSAiC expedition from October 2019 to May 2020<sup>##UREF##20##21##</sup>. We present the philosophy of the data processing, and it’s implementation in pydar<sup>##UREF##18##19##</sup>. Finally, we validate the vertical alignment and discuss considerations for reuse of these data.</p>"],"string":"[\n \"<title>Background & Summary</title>\",\n \"<p id=\\\"Par2\\\">Repeat Terrestrial Laser Scanning (TLS) topography measurements were part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, in which researchers aboard <italic>R/V Polarstern</italic><sup>##UREF##0##1##</sup> drifted with and studied the same collection of ice floes in the Central Arctic from October 2019 to May 2020<sup>##UREF##1##2##–##UREF##3##4##</sup>. Arctic sea ice has grown dramatically younger and thinner in recent decades<sup>##UREF##4##5##</sup> and the overall objectives of MOSAiC were to understand the causes and consequences of this ‘new Arctic’. To do so, researchers were divided into teams studying the snow and ice (including on-ice and satellite remote sensing)<sup>##UREF##1##2##</sup>, atmosphere<sup>##UREF##2##3##</sup>, ocean<sup>##UREF##3##4##</sup>, ecosystem, and biogeochemistry. We conducted the TLS measurements as part of the ice team, for the primary purpose of quantifying snow accumulation and redistribution. Other applications of these data include observations of ice dynamics; surface roughness for ice-atmosphere interactions; and providing context for atmospheric observations, remote sensing instruments (e.g., on-ice radars<sup>##UREF##5##6##</sup>), autonomous buoys, snow pit measurements, and more.</p>\",\n \"<p id=\\\"Par3\\\">Snow substantially affects the Arctic sea ice mass balance due to its opposing impacts of insulating in the winter (restraining ice growth) and reflecting shortwave radiation in the summer (protecting against ice melt<sup>##UREF##6##7##</sup>). Three of the four most important uncertainties impacting September sea ice volume in the CICE sea ice model<sup>##UREF##7##8##</sup> are related to the thermal and optical properties of the snow<sup>##UREF##8##9##</sup>. Snow spatial variability due to wind driven snow redistribution impacts these properties<sup>##UREF##9##10##,##UREF##10##11##</sup>. However, redistribution is challenging to measure due to this spatial variability. Repeat observations of snow changes on a substantial area of the same piece of ice are needed to observe these redistribution processes and understand their mechanisms. Furthermore, the magnitude of snow accumulation can be very small. For example, the largest snowfall event on MOSAiC precipitated just 1.6 cm water equivalent<sup>##UREF##11##12##</sup>. Thus, our observations of changes must be highly accurate. Finally, to observe snow accumulation and redistribution throughout the winter, measurements must be feasible in polar night.</p>\",\n \"<p id=\\\"Par4\\\">TLS is routinely used to make highly-accurate measurements of snow accumulation on areas of 40 m<sup>2</sup> to 0.5 km<sup>2</sup>\\n<sup>##UREF##12##13##–##UREF##16##17##</sup>. The instrument is a laser scanner that is mounted on a ∼ 2.2 m tall tripod. Due to the generally flat topography of sea ice and shadowing, the instrument collects topographic information up to 100–200 m from itself. To observe a larger area, we relocate the tripod and collect measurements from different locations, which we then co-register into a common reference frame. TLS measurements on Arctic sea ice face unusual challenges and our procedures for the MOSAiC Expedition were informed by prior experiments, including: the Seasonal Ice Zone Observing Network project<sup>##UREF##17##18##</sup>; the Snow, Wind, and Time project<sup>##UREF##16##17##</sup>; and the Sea Ice Dynamics Experiment. First, the typical temperatures of −15 to −35 °C are below the operating range of commercially-available TLS instruments. We addressed this issue with a custom-designed heater case. Second, TLS measurements are typically aligned in a geodetic reference frame via highly-accurate GNSS measurements. However, on drifting sea ice, the relevant reference frame for snow processes is a lagrangian reference frame fixed to the surface of the ice. We developed a custom software package—pydar<sup>##UREF##18##19##</sup>—to align repeat TLS measurements into this lagrangian, ice-fixed reference frame. To verify that our alignment achieved the necessary vertical accuracy for snow accumulation, we statistically validated it through comparison with <italic>in-situ</italic> measurements<sup>##UREF##19##20##</sup>. The quantitative results from our alignment validation are specific to this dataset. Finally, the TLS data contain potentially-useful information that are irrelevant to our needs (e.g., sub-cm snow surface roughness in areas near the scanner, backscatter reflectance, roughness relevant to aerodynamic drag, etc…). We hope that future researchers will use these data for purposes that we have not imagined. To facilitate this future usage, we have designed pydar to preserve the full scope of the data and to make our data processing decisions transparent to future researchers.</p>\",\n \"<p id=\\\"Par5\\\">The primary purpose of this manuscript is to describe the TLS data collected at MOSAiC. However, given the unique challenges of using TLS on drifting sea ice, some discussion of lessons learned and future methodological developments is warranted. Prior experience on sea ice near Utqiaġvik, AK<sup>##UREF##16##17##,##UREF##17##18##</sup> found that spacing scan positions between 150 and 200 m apart generally produced acceptable data. However, the ice at MOSAiC was rougher than those experiments, and the placement of scan positions was also restricted due to not trespassing in sensitive measurement sites. Under these constraints, we found that spacing scan positions around 100 m apart produced better data, although we sometimes prioritized having measurements co-located with complementary measurements sites (e.g., the snow and ice thickness transects) over achieving the best TLS coverage. Additionally, the alignment procedures and validation were developed after the expedition, thus the alignment validation relies on what coincident <italic>in-situ</italic> measurements were available and had not experienced blowing snow events between the <italic>in-situ</italic> measurement and the TLS acquisition. The statistical validation methods presented herein are applicable for future campaigns, but the specific quantitative results depend on the measurements. Therefore, validation should be conducted for any TLS campaign on sea ice. For future campaigns, we strongly recommend including more <italic>in-situ</italic> point measurements of snow surface changes coincident with the TLS measurements. One expeditious approach for this would be to include a small array of snow thickness stakes near each reflector post. This would ensure that the <italic>in-situ</italic> measurement sites were visible from multiple scan positions, and the snow surface measurements could be made quickly while distributing the reflectors at the start of a TLS measurement day.</p>\",\n \"<p id=\\\"Par6\\\">This manuscript describes the repeat TLS data collected on the MOSAiC expedition from October 2019 to May 2020<sup>##UREF##20##21##</sup>. We present the philosophy of the data processing, and it’s implementation in pydar<sup>##UREF##18##19##</sup>. Finally, we validate the vertical alignment and discuss considerations for reuse of these data.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Methods</title>","<title>Terminology</title>","<p id=\"Par7\">We use the following terms throughout this manuscript. They are mostly drawn from their usage in RiSCAN (Riegl’s software for acquiring and processing TLS data):<list list-type=\"bullet\"><list-item><p id=\"Par8\">Scan Position: set-up the tripod at a given location and measure the topography within the scanner’s line of sight.</p></list-item><list-item><p id=\"Par9\">SingleScan: the data collected from a single scan position. We use this term to refer to both the point cloud of topographic measurements from this scan position and ancillary data such as the locations of TLS reflectors within the scanner’s reference frame at this scan position and the rigid transformations that register and align this SingleScan with others (see below).<list list-type=\"bullet\"><list-item><p id=\"Par10\">Project: a collection of SingleScans covering a contiguous area that were collected during a sufficiently short time interval such that no topographic change occurred between scan positions (sometimes ice deformation occurred during a Project, these exceptions are described in the Usage Notes). Typically, the set of SingleScans in a Project were collected in a single day of measurements although on some occasions measurements were collected over two days.</p></list-item><list-item><p id=\"Par11\">Registration: the act of computing the rigid transformations that describe the spatial relationships between the different SingleScans in a Project. Registration places all SingleScans in a Project into a common reference frame (whose origin and unit vectors are typically defined by an arbitrary SingleScan).</p></list-item><list-item><p id=\"Par12\">Scan Area: a region of ice whose topography we measured over time with a succession of Projects.</p></list-item><list-item><p id=\"Par13\">Alignment: the act of computing the rigid transformations such that SingleScans from different Projects in the same Scan Area are in a common reference frame. Alignment is necessary to precisely locate topographic changes (e.g., how much snow accumulation occurred at specific location on the ice from 4 January to 11 March).</p></list-item></list></p></list-item></list></p>","<title>Data collection</title>","<p id=\"Par14\">We used a Riegl VZ1000, which has an eye-safe, near infrared laser (1550 nm). For each scan position, we mounted the scanner on a tripod, and the scanner rotated on vertical and horizontal axes to create a point cloud of its surroundings. The scanner was controlled via WiFi from a field laptop. The angular stepwidths in the azimuthal and vertical directions were each 0.025° and it took approximately 8 minutes to acquire a point cloud with a Laser Pulse Repitition Rate of 300 kHz. The origin and unit vectors of the point cloud are defined relative to the scanner and this reference frame is named the Scanner’s Own Coordinate System (SOCS). Air temperature was typically below the VZ1000’s minimum operating temperature, so we placed the scanner in a custom-designed heater case (Fig. ##FIG##0##1##) to maintain its temperature within acceptable bounds. Due to the overall flat topography, occlusions, and low reflectivity of snow and ice at 1550 nm, the scanner collects useful data up to 100–200 m from its location. To map a larger area, on each measurement day we relocated the scanner to additional scan positions and acquired subsequent measurements (i.e., we collected a SingleScan at each scan position), which we then linked together into a Project. Choosing the locations for scan positions was a trade-off between maximizing the area covered, co-locating with other measurements, minimizing shadows within the measured area, and not trespassing in sensitive measurement sites. In order to make a complete map, we registered the SingleScans measured from each scan position into a common reference frame. We placed Riegl 10 cm cylinder reflectors on posts frozen into the ice (Figs. ##FIG##0##1##, ##FIG##1##2##), and used them to locate and orient each SingleScan in the Project’s common reference frame (described below in Data Processing in RiSCAN). Typically between 4 and 10 reflectors were visible from each scan position. These reflectors also served as the starting point for aligning Projects collected in the same Scan Area on different days into a lagrangian, ice-fixed reference frame.</p>","<title>Scan areas</title>","<p id=\"Par15\">Repeat TLS observations on MOSAiC were focused on three primary Scan Areas: Snow1, Snow2, and ROV (Fig. ##FIG##1##2## and Table ##TAB##0##1##). These Scan Areas were selected to observe a variety of ice topography, to co-locate with other measurements, and to be logistically accessible. In March, ice dynamics caused the Met City atmospheric measurement site<sup>##UREF##2##3##</sup> and the on-ice Remote Sensing measurement site<sup>##UREF##1##2##</sup> to be removed from the Snow1 scan area (where they had been located from October through February). Additional TLS measurements were made at these sites (which we labelled ‘RS’ for Remote Sensing and ‘MET’ for Met City; Table ##TAB##0##1##). Below are descriptions of each Scan Area.</p>","<title>Snow1 Scan Area</title>","<p id=\"Par16\">The Snow1 Scan Area (hereafter we use ‘Snow1’ to refer specifically to the TLS measurement area) was generally off the bow and port side of Polarstern and composed of residual ice of which only the upper 30 cm was solid when we arrived in October<sup>##UREF##21##22##</sup>, refrozen melt ponds, first year ice in refrozen leads, and first year ridges. We first measured Snow1 on 18 October and our final measurement was on 3 May. Ice dynamics caused frequent (multiple times per month) crack and ridge formation in the Scan Area. These dynamics resulted in substantial variation in the area of ice measured and the number of SingleScans collected in each Project. Snow1 was co-located with the SLoop snow and ice thickness transect<sup>##UREF##22##23##</sup>, the Snow1 snow sampling area<sup>##UREF##1##2##</sup>, the BGC3 ice coring area, the Met City atmospheric measurement site<sup>##UREF##2##3##</sup> (18 October to 28 February), the first on-ice Remote Sensing site<sup>##UREF##1##2##</sup> (18 October to 15 November), the second on-ice Remote Sensing site<sup>##UREF##1##2##</sup> (6 December to 28 February), the Ocean City oceanic measurement site<sup>##UREF##3##4##</sup> (18 October to 6 December), the Bow Stakes mass balance site<sup>##UREF##19##20##</sup> (6 December to 28 February), the Met Stakes mass balance site<sup>##UREF##19##20##</sup> (18 January to 3 May), the Stakes3 mass balance site<sup>##UREF##19##20##</sup> (6 December to 26 April), the second Remotely Operated Vehicle site<sup>##UREF##1##2##</sup> (1 November to 6 December), and a number of vibrating wire ice stress gauges<sup>##UREF##1##2##</sup>. The given dates are the first and last dates that the installations were present in the TLS data, not necessarily the installation or decommission dates of the installations.</p>","<title>Snow2 Scan Area</title>","<p id=\"Par17\">The Snow2 Scan Area (hereafter we use ‘Snow2’ to refer specifically to the TLS measurement area) was generally off the bow and starboard side of Polarstern beyond the logistics area<sup>##UREF##1##2##</sup> and was composed of refrozen melt ponds and an approximately 1 m tall (on average) second year ridge. No ice thickness measurements were made in Snow2, but the residual ice was likely thicker than that in Snow1. Similar level ice in the NLoop transect had a modal thickness of approximately 75 cm in early November<sup>##UREF##22##23##</sup>. We first measured Snow2 on 6 November and our final measurement was on 9 May. Snow2 was the most stable region observed. The core of the measurement area was not deformed from 6 November 9 May with the exception of a 1-m-wide crack that formed between 13 November and 6 December. The measurement area progressively shrank due to ice dynamics removing areas in November, March, April, and May. On two occasions—6 December and 6 February—we made measurements on Snow1 and Snow2 as part of the same Project. Snow2 was co-located with the Snow2 snow sampling area<sup>##UREF##1##2##</sup>, an atmospheric flux chamber measurement site, a section of the NLoop snow and ice thickness transect<sup>##UREF##22##23##</sup> (6 November to 6 December), the Stakes1 mass balance site<sup>##UREF##19##20##</sup> (only on 6 December, thereafter this was part of the ROV Scan Area), and the Alli’s ridge measurement site<sup>##UREF##1##2##</sup> (only on 6 December, thereafter this was part of the ROV Scan Area).</p>","<title>ROV Scan Area</title>","<p id=\"Par18\">The ROV Scan Area (hereafter referred to as ‘ROV’, it was named for the Remotely Operated Vehicle installation) was generally off the stern and starboard side of Polarstern beyond the logistics area<sup>##UREF##1##2##</sup> and was composed of deformed second year ice, refrozen melt ponds, level first year ice, and first year ridges. We first measured the ROV Scan Area on 4 January and our final measurement was on 9 May. In early January, the modal ice thickness on both level first year ice<sup>##UREF##19##20##</sup> and level second year ice<sup>##UREF##22##23##</sup> was approximately 1 m. In March, ice dynamics displaced the first year ice region out of the ROV Scan Area and demolished the Ft. Ridge measurement site. These dynamics also produced young ice in a 40-m-wide, refrozen lead. The core region of ROV was connected to Snow2 from 6 December to 9 May. On two occasions—4 April and 9 May—we made measurements on Snow2 and ROV as part of the same project. ROV was co-located with the third Remotely Operated Vehicle site<sup>##UREF##1##2##</sup>, the NLoop snow and ice thickness transect<sup>##UREF##22##23##</sup>, the Ft. Ridge measurement site<sup>##UREF##1##2##</sup> (4 January to 18 March), half of the Alli’s Ridge measurement site<sup>##UREF##1##2##</sup> (4 January to 22 February), the Ridge Ranch mass balance site<sup>##UREF##19##20##</sup> (19 January to 11 March), the Stakes4 mass balance site<sup>##UREF##19##20##</sup> (4 January to 22 February), the David’s Ridge measurement site<sup>##UREF##1##2##</sup> (14 March to 9 May), the ROV3 broadband albedo transect<sup>##UREF##23##24##</sup> (11 April to 9 May), the SYI broadband albedo transect<sup>##UREF##23##24##</sup> (29 April to 9 May), and a number of vibrating wire ice stress gauges<sup>##UREF##1##2##</sup>.</p>","<title>Data Processing in RiSCAN</title>","<p id=\"Par19\">TLS data acquisition and initial post-processing steps were conducted in RiSCAN PRO (Riegl’s software for TLS data acquisition and processing: <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.riegl.com/products/software-packages/riscan-pro/\">http://www.riegl.com/products/software-packages/riscan-pro/</ext-link>) following standard protocols described in the user manual. The following steps were conducted for each Project. First, an arbitrary SingleScan was chosen to be the origin of the Project. This SingleScan was levelled to establish the horizontal axis plane using the VZ1000’s onboard inclination sensors. Next, another SingleScan was registered to the first by computing the rigid transformation that minimizes the sum of the least-squares error in the positions of pairs of reflectors (a.k.a. ‘keypoints’) observed in both SingleScans. We repeated this process until all SingleScans in the Project are registered. Finally, we refined the registration of each SingleScan except for the origin with RiSCAN’s ‘Multi-Station Adjustment’. In this process, meter-scale planar facets were extracted from each SingleScan. Then, pairs of overlapping facets and pairs of reflectors between the SingleScans were all used as keypoints and an optimization procedure adjusted the rigid transformations of each SingleScan (except the origin) in order to minimize the sum of the least-squares error between all keypoints. Unlike in urban environments where walls, roads, and other human-made objects provide large planar facets, planar surfaces at MOSAiC were mostly meter-scale or smaller wind-scoured snow surfaces. When conducting Multi-Station Adjustment, we found that using a search radius (maximum distance between potential keypoints) between 0.3 and 0.7 m produced the best results. Search radii larger than this tended to match planar facets that were not truly co-planar (e.g., different faces of a snow dune), which causes misalignment.</p>","<p id=\"Par20\">The data for each Project was stored in a directory with the same name as the Project. All relevant parameters were exported from RiSCAN into open formats. The rigid transformation for each SingleScan is represented by a 4 × 4 matrix (using homogeneous coordinates<sup>##UREF##24##25##</sup>) that is named the Scanner’s Own Position (SOP) matrix. The SOP matrices for each SingleScan were exported into tab-delimited. DAT files in the Project directory (e.g., ‘ScanPos001.DAT’). We gave each reflector a unique identifier (e.g., ‘r01’) that was consistent across all of the Projects. We exported the reflector positions (in the Project’s reference frame) for each Project into a comma-delimited file (named ‘tiepoints.csv’) in the Project directory. Finally, we exported the point cloud data itself for each SingleScan in LAS 1.4 format into a subdirectory named ‘lasfiles’. LAS 1.4 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/ASPRSorg/LAS\">https://github.com/ASPRSorg/LAS</ext-link>) is an open, community standard for point cloud data that is maintained by the American Society for Photogrammetry and Remote Sensing.</p>","<title>Data processing in pydar</title>","<title>Overview of pydar</title>","<p id=\"Par21\">The overall objective of pydar is to align SingleScans from different Projects in the same Scan Area into a lagrangian, ice-fixed reference frame such that one can observe topographic changes (e.g., snow deposition and erosion) over time. Furthermore, we sought to facilitate re-use of these data by preserving features of the data even when they are unimportant for our use case (e.g., cm-scale point density near the scanner, backscatter reflectance data, etc…) and enabling future researchers to revise our alignment of SingleScans (e.g., if they design a superior alignment procedure). This section provides a brief description of key features of pydar<sup>##UREF##18##19##</sup> and the steps taken to process the repeat TLS data from MOSAiC. Additional functionality and implementation details can be found in the code documentation. To achieve these goals, pydar has an object-oriented design that mimics the hierarchical structure of the TLS data. And, pydar distinguishes between the spatial relationships of TLS points measured from the same scan position (i.e., within a SingleScan) and the spatial relationships of TLS points measured from different scan positions (i.e., different SingleScans in a Project). pydar is implemented primarily in Python<sup>##UREF##25##26##</sup>, with substantial use of the Numpy<sup>##REF##32939066##27##</sup>, Scipy<sup>##REF##32015543##28##</sup>, and VTK<sup>##UREF##26##29##</sup> libraries. Some functionality is implemented in Cython<sup>##UREF##27##30##</sup>.</p>","<p id=\"Par22\">The core of pydar consists of four related classes: SingleScan, Project, ScanArea, and TiePointList (in this manuscript we use teletype font to reference classes and methods in code). SingleScan objects store the point cloud data for that SingleScan in the Scanner’s Own Coordinate System (as a vtkPolyData object: SingleScan.polydata_raw) and the rigid transformation to transform that point cloud into the desired reference frame (as a vtkTransform object: SingleScan.transform). Separating the spatial information for the point cloud of an entire Project into SingleScans enables us to adjust the spatial relationships between points from different SingleScans (as we do below) without altering the spatial relationships within each SingleScan. SingleScan also contains methods for filtering the point cloud data (e.g., FlakeOut<sup>##UREF##28##31##</sup>) and reading and writing the data. When we filter TLS points, we set flags in the ‘Classification’ data field (following LAS 1.4 conventions: <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/ASPRSorg/LAS\">https://github.com/ASPRSorg/LAS</ext-link>), rather than deleting points. A Project object contains the set of SingleScan objects for this Project (as a dictionary: Project.scan_dict) and an object representing the reflector positions (an instance of TiePointList). Project also contains methods for visualizing the data (e.g., Project.display_project), writing data output (e.g., Project.write_las_pdal), and converting the point cloud into a surface representation (e.g., Project.point_to_grid_average_image). Finally, a ScanArea object contains a set of Project objects for this Scan Area (as a dictionary: ScanArea.project_dict) and methods for aligning the SingleScans within those Projects (e.g., ScanArea.z_tilt_alignment_ss, see below).</p>","<title>Filtering of TLS data</title>","<p id=\"Par23\">Wind-blown snow particles were filtered using FlakeOut<sup>##UREF##28##31##</sup> with the standard parameters (z_max = 3, radial_precision = 0.005, z_std_mult = 3.5, and leafsize = 100) and assigned the classification flag ‘65’ (the LAS 1.4 standard prescribes that user-defined classifications be greater than 63). Additionally, we manually filtered the logistics area (classification flag ‘73’) from the Snow2 and ROV data because vehicle traffic there substantially disturbed the snow surface. For TLS data that has been processed by pydar, we store the processed data in a subdirectory of the Project directory named ‘npyfiles_archive’. Within the ‘npyfiles_archive’ subdirectories there are subdirectories for each SingleScan (e.g., ‘ScanPos001’). These subdirectories contain numpy<sup>##REF##32939066##27##</sup> files for the point locations in the Scanner’s Own Coordinate System (‘Points.npy’) and each data attribute (e.g., ‘Reflectance.npy’, ‘Classification.npy’, etc…). We decided to store the data in this manner for three reasons. First, ‘.npy’ is a space-efficient, open-source format (<ext-link ext-link-type=\"uri\" xlink:href=\"https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html\">https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html</ext-link>) which is easy to open with widely-available tools. Second, separating the data attributes allows the user to only load the attributes they need into memory, which is useful because the ‘.las’ file for a SingleScan is approximately 600 MB. Third, reading ‘.npy’ files into memory in Python<sup>##UREF##25##26##</sup> is considerably faster than reading ‘.las’, which speeds up the overall workflow.</p>","<title>Alignment of TLS data</title>","<p id=\"Par24\">For our purposes of observing snow accumulation and redistribution, the greatest source of error is bias due to misalignment of SingleScans from measurements on different dates. The stochastic errors due to measurement uncertainty within a SingleScan are insignificant<sup>##UREF##14##15##</sup>. We developed a three step process to align SingleScans from one Project (Project_1) to another Project (Project_0):<list list-type=\"order\"><list-item><p id=\"Par25\">Coarsely align Project_1 to Project_0 by minimizing the least-squares error between their reflector positions.</p></list-item><list-item><p id=\"Par26\">Align each SingleScan in Project_1 to the nearest SingleScan in Project_0 using local maxima as keypoints, which reduces tilt biases (a tilt bias of 0.0001 radian creates a vertical error of 0.01 m at 100 m distance from the scanner).</p></list-item><list-item><p id=\"Par27\">Perform a fine-scale vertical alignment for each SingleScan in Project_1 by minimizing modal vertical differences between it and Project_0.</p></list-item></list></p>","<p id=\"Par28\">For reflector alignment, we labeled each reflector with a consistent name (e.g., ‘r01’) in RiSCAN. Ice deformation and errors in the VZ1000’s reflector search process can shift a reflector relative to the other reflectors. We manually compared the pairwise distances between reflectors and used only the set of reflectors whose pairwise distances between Projects changed by 0.02 m or less. This aligned the scans horizontally to within 0.02 m. Typically, this set comprised 4 to 8 reflectors. With this set of reflectors as keypoints, we computed the rigid transformation that minimized the least-squares error between the keypoints<sup>##UREF##29##32##</sup> (implemented in TiePointList.calc_transformation). The default version of this rigid transformation calculation (mode = 'LS' in TiePointList.calc_transformation) requires at least 3 pairs of keypoints and has six degrees of freedom: translations in the 3 spatial directions and rotations around the 3 unit vectors (roll, pitch, and yaw). However, sometimes when there were just 3 or 4 pairs of keypoints, small vertical errors in the reflector positions produced unrealistic tilts, assessed manually by looking at the vertical differences between the aligned Projects. For these cases and when there were only 2 pairs of keypoints, we calculated the rigid transformation without permitting tilt changes (mode = 'Yaw' in TiePointList.calc_transformation). Finally, in two, cases ice dynamics caused there to be no reflectors whose pairwise distance changed by less than 0.02 m. In these cases, we used a single reflector to determine the translational components of the rigid transformation and manually adjusted the yaw component such that flag posts frozen into the ice (see Fig. ##FIG##0##1## for example) aligned to within 0.02 m at their bases. Manual inspection of the results indicated that reflector alignment brings vertical biases within 0.05 m and tilt biases within 0.001 radian.</p>","<p id=\"Par29\">Local maxima alignment for each SingleScan followed the same process as reflector alignment, except that it used local maxima as keypoints instead of reflectors. Local maxima are mostly the crests of ridges, hummocks, or human installations (e.g., poles) and are unlikely to erode or accumulate snow. A pair of local maxima from the SingleScans was required to be within a set spatial tolerance of each other to be used as keypoints. We defined the tolerance in cylindrical coordinates centered on the scanner’s location of the SingleScan that was being aligned. We used a tolerance of 0.0008 radian yaw, 0.001 radian tilt, and 0.1 m radial difference. Local maxima were located on 5 × 5 m regions (changing the region size to 2 × 2 m or 10 × 10 m had no discernable impact on the results). These settings produced several hundred keypoints for each pair of SingleScans from the different Projects. The tilt biases after local maxima alignment were less than 0.0001 radian (i.e., a 1 cm vertical offset across a 100 m distance), determined by manual inspection. Local maxima alignment is implemented in ScanArea.max_alignment_ss.</p>","<p id=\"Par30\">Finally, for the fine-scale vertical alignment, we exploited the fact that numerous field observations at MOSAiC suggested that a plurality of the snow surface did not change on weekly, or even monthly, time-frames. These observations included: snowmobile tracks did not typically get covered by snow except for isolated, distinct snow drifts. The indentations made by the feet of the TLS tripod were often visible when we revisited scan position locations. The circular mark left in the snow by the atmospheric flux chamber measurement could be identified months after the measurement was made. Cm-scale micro-relief on the snow surface observed near the TLS scanner (where point density is very high) appears consistent between scans, unless a snow dune happened to form in that location. Certain distinctive snow features (e.g., barchan dunes) remained unchanged for months. If the plurality of the snow surface does not change between Projects, then the modal vertical difference must be zero. For each SingleScan, we computed the distribution of vertical differences between it and the Project we were aligning it to. We used a raster with 1 m horizontal resolution created by averaging the z-components of the TLS points within each grid cell<sup>##UREF##15##16##</sup>. We used only grid cells with at least 25 points per square meter in the SingleScan and the Project it was being aligned to. The vertical component of the transformation for the SingleScan was set such that the modal difference is zero. Fine-scale vertical alignment is implemented in ScanArea.z_alignment. Manual inspection of the results indicated that vertical biases were reduced to within about 0.01 m (see below for Technical Validation).</p>","<p id=\"Par31\">In Snow1, deformation occurred frequently and throughout the Scan Area, such that there was no core ice floe with multiple reflectors on it that did not experience ice deformation for an extended period of time (as there was for Snow2 and ROV). Alignment steps 2 and 3 are predicated on the ice floe itself remaining the same, and hence could not be applied in this dynamic environment. We include Snow1 for completeness and for use by future researchers (e.g., these ice dynamics would not prevent another researcher from using the Snow1 data to compute aerodynamic drag coefficients). However, we did not validate its vertical alignment to cm-scale accuracy nor do we recommend that the data be used for snow accumulation without further work quantifying ice deformation (which is beyond the scope of this data processing).</p>","<p id=\"Par32\">For convenience and to avoid needing to recompute the transformations, the rigid transformation aligning each SingleScan has been written out to a ‘.npy’ file that can be loaded directly in pydar. In the Project’s directory, there is a subdirectory named ‘transforms’, within which there are subdirectories for each SingleScan (e.g., ‘ScanPos001’). The transformation is in this subdirectory and is named ‘current_transform.npy’. Finally, because the ROV and Snow2 Scan Areas were connected, we decided to place them within the same lagrangian, ice-fixed reference frame (for which the origin happens to be near the Remotely Operated Vehicle tent<sup>##UREF##1##2##</sup>).</p>","<title>Surface Reconstruction from Point Clouds</title>","<p id=\"Par33\">Many applications of topographical data—including measurement of snow accumulation—require surfaces or gridded data rather than point clouds (the format of TLS data). To produce gridded surfaces, we used gaussian process regression<sup>##UREF##30##33##</sup>. Also known as kriging, gaussian process regression is an interpolation technique that provides the best linear unbiased estimate (minimizes least-squares error) of a parameter (e.g., surface height) at unsampled locations (e.g., a regular grid of points) given nearby measurements (e.g., TLS points) and the covariance function (also referred to as the kernel)<sup>##UREF##30##33##,##UREF##31##34##</sup>. It has previously been applied to TLS data on Arctic sea ice<sup>##UREF##32##35##</sup>. The vertical uncertainty in an individual TLS data point increases with distance from the scanner due to the divergence of the laser beam (0.3 mrad for the VZ1000) and is represented as gaussian noise<sup>##UREF##14##15##</sup>. Because the TLS collects useful data up to 200 m from the scanner, the vertical uncertainties in individual data points varies by an order of magnitude. One advantage of gaussian process regression, is that it can factor in the vertical uncertainty of each point when interpolating.</p>","<p id=\"Par34\">We chose to use an exponential covariance function because it is the simplest covariance function that can represent continuous, non-differentiable (i.e., not-smooth) surfaces<sup>##UREF##31##34##</sup>. We chose this because wind-driven spatially variable snow deposition and erosion produce rough snow bedforms on horizontal scales of 10 cm to several meters<sup>##UREF##33##36##</sup>. The exponential covariance function contains two hyperparameters: the ‘range’, defined as the distance at which the correlation between two points is less than 5%; and the ‘sill’, defined as the variance between two uncorrelated points (i.e., points further apart than the range). We can estimate appropriate values of the hyperparameters from the data itself, by optimizing the marginal likelihood of the gaussian process<sup>##UREF##31##34##</sup>. On the scale of our scan areas (several hundred meters across) the snow and ice topography varies from rough areas of pressure ridges and rubble to smooth areas on level ice. To account for this spatial variability, we divided the domain into a grid of non-overlapping 1.2 m by 1.2 m subdomains. For each subdomain we estimated the sill from the variance of TLS points within a 5 m radius of the center of the subdomain and the range via marginal likelihood optimization using GPyTorch<sup>##UREF##34##37##</sup> with a KeOps<sup>##UREF##35##38##</sup> kernel on an NVIDIA Quaddro P2000 GPU. The size of the subdomains was chosen such that they were significantly smaller than the spatial scales of the pressure ridges (which were at least 10 s of meters) and balancing GPU memory limitations with computational time. After estimating the hyperparameters, we interpolated the gaussian processes on a regular grid with 10 cm spacing. This surface reconstruction process is implemented in Project.merged_points_to_image.</p>"],"string":"[\n \"<title>Methods</title>\",\n \"<title>Terminology</title>\",\n \"<p id=\\\"Par7\\\">We use the following terms throughout this manuscript. They are mostly drawn from their usage in RiSCAN (Riegl’s software for acquiring and processing TLS data):<list list-type=\\\"bullet\\\"><list-item><p id=\\\"Par8\\\">Scan Position: set-up the tripod at a given location and measure the topography within the scanner’s line of sight.</p></list-item><list-item><p id=\\\"Par9\\\">SingleScan: the data collected from a single scan position. We use this term to refer to both the point cloud of topographic measurements from this scan position and ancillary data such as the locations of TLS reflectors within the scanner’s reference frame at this scan position and the rigid transformations that register and align this SingleScan with others (see below).<list list-type=\\\"bullet\\\"><list-item><p id=\\\"Par10\\\">Project: a collection of SingleScans covering a contiguous area that were collected during a sufficiently short time interval such that no topographic change occurred between scan positions (sometimes ice deformation occurred during a Project, these exceptions are described in the Usage Notes). Typically, the set of SingleScans in a Project were collected in a single day of measurements although on some occasions measurements were collected over two days.</p></list-item><list-item><p id=\\\"Par11\\\">Registration: the act of computing the rigid transformations that describe the spatial relationships between the different SingleScans in a Project. Registration places all SingleScans in a Project into a common reference frame (whose origin and unit vectors are typically defined by an arbitrary SingleScan).</p></list-item><list-item><p id=\\\"Par12\\\">Scan Area: a region of ice whose topography we measured over time with a succession of Projects.</p></list-item><list-item><p id=\\\"Par13\\\">Alignment: the act of computing the rigid transformations such that SingleScans from different Projects in the same Scan Area are in a common reference frame. Alignment is necessary to precisely locate topographic changes (e.g., how much snow accumulation occurred at specific location on the ice from 4 January to 11 March).</p></list-item></list></p></list-item></list></p>\",\n \"<title>Data collection</title>\",\n \"<p id=\\\"Par14\\\">We used a Riegl VZ1000, which has an eye-safe, near infrared laser (1550 nm). For each scan position, we mounted the scanner on a tripod, and the scanner rotated on vertical and horizontal axes to create a point cloud of its surroundings. The scanner was controlled via WiFi from a field laptop. The angular stepwidths in the azimuthal and vertical directions were each 0.025° and it took approximately 8 minutes to acquire a point cloud with a Laser Pulse Repitition Rate of 300 kHz. The origin and unit vectors of the point cloud are defined relative to the scanner and this reference frame is named the Scanner’s Own Coordinate System (SOCS). Air temperature was typically below the VZ1000’s minimum operating temperature, so we placed the scanner in a custom-designed heater case (Fig. ##FIG##0##1##) to maintain its temperature within acceptable bounds. Due to the overall flat topography, occlusions, and low reflectivity of snow and ice at 1550 nm, the scanner collects useful data up to 100–200 m from its location. To map a larger area, on each measurement day we relocated the scanner to additional scan positions and acquired subsequent measurements (i.e., we collected a SingleScan at each scan position), which we then linked together into a Project. Choosing the locations for scan positions was a trade-off between maximizing the area covered, co-locating with other measurements, minimizing shadows within the measured area, and not trespassing in sensitive measurement sites. In order to make a complete map, we registered the SingleScans measured from each scan position into a common reference frame. We placed Riegl 10 cm cylinder reflectors on posts frozen into the ice (Figs. ##FIG##0##1##, ##FIG##1##2##), and used them to locate and orient each SingleScan in the Project’s common reference frame (described below in Data Processing in RiSCAN). Typically between 4 and 10 reflectors were visible from each scan position. These reflectors also served as the starting point for aligning Projects collected in the same Scan Area on different days into a lagrangian, ice-fixed reference frame.</p>\",\n \"<title>Scan areas</title>\",\n \"<p id=\\\"Par15\\\">Repeat TLS observations on MOSAiC were focused on three primary Scan Areas: Snow1, Snow2, and ROV (Fig. ##FIG##1##2## and Table ##TAB##0##1##). These Scan Areas were selected to observe a variety of ice topography, to co-locate with other measurements, and to be logistically accessible. In March, ice dynamics caused the Met City atmospheric measurement site<sup>##UREF##2##3##</sup> and the on-ice Remote Sensing measurement site<sup>##UREF##1##2##</sup> to be removed from the Snow1 scan area (where they had been located from October through February). Additional TLS measurements were made at these sites (which we labelled ‘RS’ for Remote Sensing and ‘MET’ for Met City; Table ##TAB##0##1##). Below are descriptions of each Scan Area.</p>\",\n \"<title>Snow1 Scan Area</title>\",\n \"<p id=\\\"Par16\\\">The Snow1 Scan Area (hereafter we use ‘Snow1’ to refer specifically to the TLS measurement area) was generally off the bow and port side of Polarstern and composed of residual ice of which only the upper 30 cm was solid when we arrived in October<sup>##UREF##21##22##</sup>, refrozen melt ponds, first year ice in refrozen leads, and first year ridges. We first measured Snow1 on 18 October and our final measurement was on 3 May. Ice dynamics caused frequent (multiple times per month) crack and ridge formation in the Scan Area. These dynamics resulted in substantial variation in the area of ice measured and the number of SingleScans collected in each Project. Snow1 was co-located with the SLoop snow and ice thickness transect<sup>##UREF##22##23##</sup>, the Snow1 snow sampling area<sup>##UREF##1##2##</sup>, the BGC3 ice coring area, the Met City atmospheric measurement site<sup>##UREF##2##3##</sup> (18 October to 28 February), the first on-ice Remote Sensing site<sup>##UREF##1##2##</sup> (18 October to 15 November), the second on-ice Remote Sensing site<sup>##UREF##1##2##</sup> (6 December to 28 February), the Ocean City oceanic measurement site<sup>##UREF##3##4##</sup> (18 October to 6 December), the Bow Stakes mass balance site<sup>##UREF##19##20##</sup> (6 December to 28 February), the Met Stakes mass balance site<sup>##UREF##19##20##</sup> (18 January to 3 May), the Stakes3 mass balance site<sup>##UREF##19##20##</sup> (6 December to 26 April), the second Remotely Operated Vehicle site<sup>##UREF##1##2##</sup> (1 November to 6 December), and a number of vibrating wire ice stress gauges<sup>##UREF##1##2##</sup>. The given dates are the first and last dates that the installations were present in the TLS data, not necessarily the installation or decommission dates of the installations.</p>\",\n \"<title>Snow2 Scan Area</title>\",\n \"<p id=\\\"Par17\\\">The Snow2 Scan Area (hereafter we use ‘Snow2’ to refer specifically to the TLS measurement area) was generally off the bow and starboard side of Polarstern beyond the logistics area<sup>##UREF##1##2##</sup> and was composed of refrozen melt ponds and an approximately 1 m tall (on average) second year ridge. No ice thickness measurements were made in Snow2, but the residual ice was likely thicker than that in Snow1. Similar level ice in the NLoop transect had a modal thickness of approximately 75 cm in early November<sup>##UREF##22##23##</sup>. We first measured Snow2 on 6 November and our final measurement was on 9 May. Snow2 was the most stable region observed. The core of the measurement area was not deformed from 6 November 9 May with the exception of a 1-m-wide crack that formed between 13 November and 6 December. The measurement area progressively shrank due to ice dynamics removing areas in November, March, April, and May. On two occasions—6 December and 6 February—we made measurements on Snow1 and Snow2 as part of the same Project. Snow2 was co-located with the Snow2 snow sampling area<sup>##UREF##1##2##</sup>, an atmospheric flux chamber measurement site, a section of the NLoop snow and ice thickness transect<sup>##UREF##22##23##</sup> (6 November to 6 December), the Stakes1 mass balance site<sup>##UREF##19##20##</sup> (only on 6 December, thereafter this was part of the ROV Scan Area), and the Alli’s ridge measurement site<sup>##UREF##1##2##</sup> (only on 6 December, thereafter this was part of the ROV Scan Area).</p>\",\n \"<title>ROV Scan Area</title>\",\n \"<p id=\\\"Par18\\\">The ROV Scan Area (hereafter referred to as ‘ROV’, it was named for the Remotely Operated Vehicle installation) was generally off the stern and starboard side of Polarstern beyond the logistics area<sup>##UREF##1##2##</sup> and was composed of deformed second year ice, refrozen melt ponds, level first year ice, and first year ridges. We first measured the ROV Scan Area on 4 January and our final measurement was on 9 May. In early January, the modal ice thickness on both level first year ice<sup>##UREF##19##20##</sup> and level second year ice<sup>##UREF##22##23##</sup> was approximately 1 m. In March, ice dynamics displaced the first year ice region out of the ROV Scan Area and demolished the Ft. Ridge measurement site. These dynamics also produced young ice in a 40-m-wide, refrozen lead. The core region of ROV was connected to Snow2 from 6 December to 9 May. On two occasions—4 April and 9 May—we made measurements on Snow2 and ROV as part of the same project. ROV was co-located with the third Remotely Operated Vehicle site<sup>##UREF##1##2##</sup>, the NLoop snow and ice thickness transect<sup>##UREF##22##23##</sup>, the Ft. Ridge measurement site<sup>##UREF##1##2##</sup> (4 January to 18 March), half of the Alli’s Ridge measurement site<sup>##UREF##1##2##</sup> (4 January to 22 February), the Ridge Ranch mass balance site<sup>##UREF##19##20##</sup> (19 January to 11 March), the Stakes4 mass balance site<sup>##UREF##19##20##</sup> (4 January to 22 February), the David’s Ridge measurement site<sup>##UREF##1##2##</sup> (14 March to 9 May), the ROV3 broadband albedo transect<sup>##UREF##23##24##</sup> (11 April to 9 May), the SYI broadband albedo transect<sup>##UREF##23##24##</sup> (29 April to 9 May), and a number of vibrating wire ice stress gauges<sup>##UREF##1##2##</sup>.</p>\",\n \"<title>Data Processing in RiSCAN</title>\",\n \"<p id=\\\"Par19\\\">TLS data acquisition and initial post-processing steps were conducted in RiSCAN PRO (Riegl’s software for TLS data acquisition and processing: <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"http://www.riegl.com/products/software-packages/riscan-pro/\\\">http://www.riegl.com/products/software-packages/riscan-pro/</ext-link>) following standard protocols described in the user manual. The following steps were conducted for each Project. First, an arbitrary SingleScan was chosen to be the origin of the Project. This SingleScan was levelled to establish the horizontal axis plane using the VZ1000’s onboard inclination sensors. Next, another SingleScan was registered to the first by computing the rigid transformation that minimizes the sum of the least-squares error in the positions of pairs of reflectors (a.k.a. ‘keypoints’) observed in both SingleScans. We repeated this process until all SingleScans in the Project are registered. Finally, we refined the registration of each SingleScan except for the origin with RiSCAN’s ‘Multi-Station Adjustment’. In this process, meter-scale planar facets were extracted from each SingleScan. Then, pairs of overlapping facets and pairs of reflectors between the SingleScans were all used as keypoints and an optimization procedure adjusted the rigid transformations of each SingleScan (except the origin) in order to minimize the sum of the least-squares error between all keypoints. Unlike in urban environments where walls, roads, and other human-made objects provide large planar facets, planar surfaces at MOSAiC were mostly meter-scale or smaller wind-scoured snow surfaces. When conducting Multi-Station Adjustment, we found that using a search radius (maximum distance between potential keypoints) between 0.3 and 0.7 m produced the best results. Search radii larger than this tended to match planar facets that were not truly co-planar (e.g., different faces of a snow dune), which causes misalignment.</p>\",\n \"<p id=\\\"Par20\\\">The data for each Project was stored in a directory with the same name as the Project. All relevant parameters were exported from RiSCAN into open formats. The rigid transformation for each SingleScan is represented by a 4 × 4 matrix (using homogeneous coordinates<sup>##UREF##24##25##</sup>) that is named the Scanner’s Own Position (SOP) matrix. The SOP matrices for each SingleScan were exported into tab-delimited. DAT files in the Project directory (e.g., ‘ScanPos001.DAT’). We gave each reflector a unique identifier (e.g., ‘r01’) that was consistent across all of the Projects. We exported the reflector positions (in the Project’s reference frame) for each Project into a comma-delimited file (named ‘tiepoints.csv’) in the Project directory. Finally, we exported the point cloud data itself for each SingleScan in LAS 1.4 format into a subdirectory named ‘lasfiles’. LAS 1.4 (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://github.com/ASPRSorg/LAS\\\">https://github.com/ASPRSorg/LAS</ext-link>) is an open, community standard for point cloud data that is maintained by the American Society for Photogrammetry and Remote Sensing.</p>\",\n \"<title>Data processing in pydar</title>\",\n \"<title>Overview of pydar</title>\",\n \"<p id=\\\"Par21\\\">The overall objective of pydar is to align SingleScans from different Projects in the same Scan Area into a lagrangian, ice-fixed reference frame such that one can observe topographic changes (e.g., snow deposition and erosion) over time. Furthermore, we sought to facilitate re-use of these data by preserving features of the data even when they are unimportant for our use case (e.g., cm-scale point density near the scanner, backscatter reflectance data, etc…) and enabling future researchers to revise our alignment of SingleScans (e.g., if they design a superior alignment procedure). This section provides a brief description of key features of pydar<sup>##UREF##18##19##</sup> and the steps taken to process the repeat TLS data from MOSAiC. Additional functionality and implementation details can be found in the code documentation. To achieve these goals, pydar has an object-oriented design that mimics the hierarchical structure of the TLS data. And, pydar distinguishes between the spatial relationships of TLS points measured from the same scan position (i.e., within a SingleScan) and the spatial relationships of TLS points measured from different scan positions (i.e., different SingleScans in a Project). pydar is implemented primarily in Python<sup>##UREF##25##26##</sup>, with substantial use of the Numpy<sup>##REF##32939066##27##</sup>, Scipy<sup>##REF##32015543##28##</sup>, and VTK<sup>##UREF##26##29##</sup> libraries. Some functionality is implemented in Cython<sup>##UREF##27##30##</sup>.</p>\",\n \"<p id=\\\"Par22\\\">The core of pydar consists of four related classes: SingleScan, Project, ScanArea, and TiePointList (in this manuscript we use teletype font to reference classes and methods in code). SingleScan objects store the point cloud data for that SingleScan in the Scanner’s Own Coordinate System (as a vtkPolyData object: SingleScan.polydata_raw) and the rigid transformation to transform that point cloud into the desired reference frame (as a vtkTransform object: SingleScan.transform). Separating the spatial information for the point cloud of an entire Project into SingleScans enables us to adjust the spatial relationships between points from different SingleScans (as we do below) without altering the spatial relationships within each SingleScan. SingleScan also contains methods for filtering the point cloud data (e.g., FlakeOut<sup>##UREF##28##31##</sup>) and reading and writing the data. When we filter TLS points, we set flags in the ‘Classification’ data field (following LAS 1.4 conventions: <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://github.com/ASPRSorg/LAS\\\">https://github.com/ASPRSorg/LAS</ext-link>), rather than deleting points. A Project object contains the set of SingleScan objects for this Project (as a dictionary: Project.scan_dict) and an object representing the reflector positions (an instance of TiePointList). Project also contains methods for visualizing the data (e.g., Project.display_project), writing data output (e.g., Project.write_las_pdal), and converting the point cloud into a surface representation (e.g., Project.point_to_grid_average_image). Finally, a ScanArea object contains a set of Project objects for this Scan Area (as a dictionary: ScanArea.project_dict) and methods for aligning the SingleScans within those Projects (e.g., ScanArea.z_tilt_alignment_ss, see below).</p>\",\n \"<title>Filtering of TLS data</title>\",\n \"<p id=\\\"Par23\\\">Wind-blown snow particles were filtered using FlakeOut<sup>##UREF##28##31##</sup> with the standard parameters (z_max = 3, radial_precision = 0.005, z_std_mult = 3.5, and leafsize = 100) and assigned the classification flag ‘65’ (the LAS 1.4 standard prescribes that user-defined classifications be greater than 63). Additionally, we manually filtered the logistics area (classification flag ‘73’) from the Snow2 and ROV data because vehicle traffic there substantially disturbed the snow surface. For TLS data that has been processed by pydar, we store the processed data in a subdirectory of the Project directory named ‘npyfiles_archive’. Within the ‘npyfiles_archive’ subdirectories there are subdirectories for each SingleScan (e.g., ‘ScanPos001’). These subdirectories contain numpy<sup>##REF##32939066##27##</sup> files for the point locations in the Scanner’s Own Coordinate System (‘Points.npy’) and each data attribute (e.g., ‘Reflectance.npy’, ‘Classification.npy’, etc…). We decided to store the data in this manner for three reasons. First, ‘.npy’ is a space-efficient, open-source format (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html\\\">https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html</ext-link>) which is easy to open with widely-available tools. Second, separating the data attributes allows the user to only load the attributes they need into memory, which is useful because the ‘.las’ file for a SingleScan is approximately 600 MB. Third, reading ‘.npy’ files into memory in Python<sup>##UREF##25##26##</sup> is considerably faster than reading ‘.las’, which speeds up the overall workflow.</p>\",\n \"<title>Alignment of TLS data</title>\",\n \"<p id=\\\"Par24\\\">For our purposes of observing snow accumulation and redistribution, the greatest source of error is bias due to misalignment of SingleScans from measurements on different dates. The stochastic errors due to measurement uncertainty within a SingleScan are insignificant<sup>##UREF##14##15##</sup>. We developed a three step process to align SingleScans from one Project (Project_1) to another Project (Project_0):<list list-type=\\\"order\\\"><list-item><p id=\\\"Par25\\\">Coarsely align Project_1 to Project_0 by minimizing the least-squares error between their reflector positions.</p></list-item><list-item><p id=\\\"Par26\\\">Align each SingleScan in Project_1 to the nearest SingleScan in Project_0 using local maxima as keypoints, which reduces tilt biases (a tilt bias of 0.0001 radian creates a vertical error of 0.01 m at 100 m distance from the scanner).</p></list-item><list-item><p id=\\\"Par27\\\">Perform a fine-scale vertical alignment for each SingleScan in Project_1 by minimizing modal vertical differences between it and Project_0.</p></list-item></list></p>\",\n \"<p id=\\\"Par28\\\">For reflector alignment, we labeled each reflector with a consistent name (e.g., ‘r01’) in RiSCAN. Ice deformation and errors in the VZ1000’s reflector search process can shift a reflector relative to the other reflectors. We manually compared the pairwise distances between reflectors and used only the set of reflectors whose pairwise distances between Projects changed by 0.02 m or less. This aligned the scans horizontally to within 0.02 m. Typically, this set comprised 4 to 8 reflectors. With this set of reflectors as keypoints, we computed the rigid transformation that minimized the least-squares error between the keypoints<sup>##UREF##29##32##</sup> (implemented in TiePointList.calc_transformation). The default version of this rigid transformation calculation (mode = 'LS' in TiePointList.calc_transformation) requires at least 3 pairs of keypoints and has six degrees of freedom: translations in the 3 spatial directions and rotations around the 3 unit vectors (roll, pitch, and yaw). However, sometimes when there were just 3 or 4 pairs of keypoints, small vertical errors in the reflector positions produced unrealistic tilts, assessed manually by looking at the vertical differences between the aligned Projects. For these cases and when there were only 2 pairs of keypoints, we calculated the rigid transformation without permitting tilt changes (mode = 'Yaw' in TiePointList.calc_transformation). Finally, in two, cases ice dynamics caused there to be no reflectors whose pairwise distance changed by less than 0.02 m. In these cases, we used a single reflector to determine the translational components of the rigid transformation and manually adjusted the yaw component such that flag posts frozen into the ice (see Fig. ##FIG##0##1## for example) aligned to within 0.02 m at their bases. Manual inspection of the results indicated that reflector alignment brings vertical biases within 0.05 m and tilt biases within 0.001 radian.</p>\",\n \"<p id=\\\"Par29\\\">Local maxima alignment for each SingleScan followed the same process as reflector alignment, except that it used local maxima as keypoints instead of reflectors. Local maxima are mostly the crests of ridges, hummocks, or human installations (e.g., poles) and are unlikely to erode or accumulate snow. A pair of local maxima from the SingleScans was required to be within a set spatial tolerance of each other to be used as keypoints. We defined the tolerance in cylindrical coordinates centered on the scanner’s location of the SingleScan that was being aligned. We used a tolerance of 0.0008 radian yaw, 0.001 radian tilt, and 0.1 m radial difference. Local maxima were located on 5 × 5 m regions (changing the region size to 2 × 2 m or 10 × 10 m had no discernable impact on the results). These settings produced several hundred keypoints for each pair of SingleScans from the different Projects. The tilt biases after local maxima alignment were less than 0.0001 radian (i.e., a 1 cm vertical offset across a 100 m distance), determined by manual inspection. Local maxima alignment is implemented in ScanArea.max_alignment_ss.</p>\",\n \"<p id=\\\"Par30\\\">Finally, for the fine-scale vertical alignment, we exploited the fact that numerous field observations at MOSAiC suggested that a plurality of the snow surface did not change on weekly, or even monthly, time-frames. These observations included: snowmobile tracks did not typically get covered by snow except for isolated, distinct snow drifts. The indentations made by the feet of the TLS tripod were often visible when we revisited scan position locations. The circular mark left in the snow by the atmospheric flux chamber measurement could be identified months after the measurement was made. Cm-scale micro-relief on the snow surface observed near the TLS scanner (where point density is very high) appears consistent between scans, unless a snow dune happened to form in that location. Certain distinctive snow features (e.g., barchan dunes) remained unchanged for months. If the plurality of the snow surface does not change between Projects, then the modal vertical difference must be zero. For each SingleScan, we computed the distribution of vertical differences between it and the Project we were aligning it to. We used a raster with 1 m horizontal resolution created by averaging the z-components of the TLS points within each grid cell<sup>##UREF##15##16##</sup>. We used only grid cells with at least 25 points per square meter in the SingleScan and the Project it was being aligned to. The vertical component of the transformation for the SingleScan was set such that the modal difference is zero. Fine-scale vertical alignment is implemented in ScanArea.z_alignment. Manual inspection of the results indicated that vertical biases were reduced to within about 0.01 m (see below for Technical Validation).</p>\",\n \"<p id=\\\"Par31\\\">In Snow1, deformation occurred frequently and throughout the Scan Area, such that there was no core ice floe with multiple reflectors on it that did not experience ice deformation for an extended period of time (as there was for Snow2 and ROV). Alignment steps 2 and 3 are predicated on the ice floe itself remaining the same, and hence could not be applied in this dynamic environment. We include Snow1 for completeness and for use by future researchers (e.g., these ice dynamics would not prevent another researcher from using the Snow1 data to compute aerodynamic drag coefficients). However, we did not validate its vertical alignment to cm-scale accuracy nor do we recommend that the data be used for snow accumulation without further work quantifying ice deformation (which is beyond the scope of this data processing).</p>\",\n \"<p id=\\\"Par32\\\">For convenience and to avoid needing to recompute the transformations, the rigid transformation aligning each SingleScan has been written out to a ‘.npy’ file that can be loaded directly in pydar. In the Project’s directory, there is a subdirectory named ‘transforms’, within which there are subdirectories for each SingleScan (e.g., ‘ScanPos001’). The transformation is in this subdirectory and is named ‘current_transform.npy’. Finally, because the ROV and Snow2 Scan Areas were connected, we decided to place them within the same lagrangian, ice-fixed reference frame (for which the origin happens to be near the Remotely Operated Vehicle tent<sup>##UREF##1##2##</sup>).</p>\",\n \"<title>Surface Reconstruction from Point Clouds</title>\",\n \"<p id=\\\"Par33\\\">Many applications of topographical data—including measurement of snow accumulation—require surfaces or gridded data rather than point clouds (the format of TLS data). To produce gridded surfaces, we used gaussian process regression<sup>##UREF##30##33##</sup>. Also known as kriging, gaussian process regression is an interpolation technique that provides the best linear unbiased estimate (minimizes least-squares error) of a parameter (e.g., surface height) at unsampled locations (e.g., a regular grid of points) given nearby measurements (e.g., TLS points) and the covariance function (also referred to as the kernel)<sup>##UREF##30##33##,##UREF##31##34##</sup>. It has previously been applied to TLS data on Arctic sea ice<sup>##UREF##32##35##</sup>. The vertical uncertainty in an individual TLS data point increases with distance from the scanner due to the divergence of the laser beam (0.3 mrad for the VZ1000) and is represented as gaussian noise<sup>##UREF##14##15##</sup>. Because the TLS collects useful data up to 200 m from the scanner, the vertical uncertainties in individual data points varies by an order of magnitude. One advantage of gaussian process regression, is that it can factor in the vertical uncertainty of each point when interpolating.</p>\",\n \"<p id=\\\"Par34\\\">We chose to use an exponential covariance function because it is the simplest covariance function that can represent continuous, non-differentiable (i.e., not-smooth) surfaces<sup>##UREF##31##34##</sup>. We chose this because wind-driven spatially variable snow deposition and erosion produce rough snow bedforms on horizontal scales of 10 cm to several meters<sup>##UREF##33##36##</sup>. The exponential covariance function contains two hyperparameters: the ‘range’, defined as the distance at which the correlation between two points is less than 5%; and the ‘sill’, defined as the variance between two uncorrelated points (i.e., points further apart than the range). We can estimate appropriate values of the hyperparameters from the data itself, by optimizing the marginal likelihood of the gaussian process<sup>##UREF##31##34##</sup>. On the scale of our scan areas (several hundred meters across) the snow and ice topography varies from rough areas of pressure ridges and rubble to smooth areas on level ice. To account for this spatial variability, we divided the domain into a grid of non-overlapping 1.2 m by 1.2 m subdomains. For each subdomain we estimated the sill from the variance of TLS points within a 5 m radius of the center of the subdomain and the range via marginal likelihood optimization using GPyTorch<sup>##UREF##34##37##</sup> with a KeOps<sup>##UREF##35##38##</sup> kernel on an NVIDIA Quaddro P2000 GPU. The size of the subdomains was chosen such that they were significantly smaller than the spatial scales of the pressure ridges (which were at least 10 s of meters) and balancing GPU memory limitations with computational time. After estimating the hyperparameters, we interpolated the gaussian processes on a regular grid with 10 cm spacing. This surface reconstruction process is implemented in Project.merged_points_to_image.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":[],"string":"[]","isConversational":false},"discussion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Snow and ice topography impact and are impacted by fluxes of mass, energy, and momentum in Arctic sea ice. We measured the topography on approximately a 0.5 km<sup>2</sup> drifting parcel of Arctic sea ice on 42 separate days from 18 October 2019 to 9 May 2020 via Terrestrial Laser Scanning (TLS). These data are aligned into an ice-fixed, lagrangian reference frame such that topographic changes (e.g., snow accumulation) can be observed for time periods of up to six months. Using <italic>in-situ</italic> measurements, we have validated the vertical accuracy of the alignment to ± 0.011 m. This data collection and processing workflow is the culmination of several prior measurement campaigns and may be generally applied for repeat TLS measurements on drifting sea ice. We present a description of the data, a software package written to process and align these data, and the philosophy of the data processing. These data can be used to investigate snow accumulation and redistribution, ice dynamics, surface roughness, and they can provide valuable context for co-located measurements.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Snow and ice topography impact and are impacted by fluxes of mass, energy, and momentum in Arctic sea ice. We measured the topography on approximately a 0.5 km<sup>2</sup> drifting parcel of Arctic sea ice on 42 separate days from 18 October 2019 to 9 May 2020 via Terrestrial Laser Scanning (TLS). These data are aligned into an ice-fixed, lagrangian reference frame such that topographic changes (e.g., snow accumulation) can be observed for time periods of up to six months. Using <italic>in-situ</italic> measurements, we have validated the vertical accuracy of the alignment to ± 0.011 m. This data collection and processing workflow is the culmination of several prior measurement campaigns and may be generally applied for repeat TLS measurements on drifting sea ice. We present a description of the data, a software package written to process and align these data, and the philosophy of the data processing. These data can be used to investigate snow accumulation and redistribution, ice dynamics, surface roughness, and they can provide valuable context for co-located measurements.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Data Records</title>","<p id=\"Par35\">Repeat TLS data in Table ##TAB##0##1## are available at the Arctic Data Center<sup>##UREF##20##21##</sup>. The top level of the archive contains a directory for each Scan Area: Snow1, ROV (which includes the Snow2 projects since they are in the same reference frame), RS (April Remote Sensing site), and MET (single Project focused on Met City on 4 May). Within these directories is a subdirectory for each Project that contains all data records for that Project (as described in the Methods section). An illustrative directory tree for a Scan Area is shown below.</p>","<title>Technical Validation</title>","<title>Alignment Validation</title>","<p id=\"Par36\">We qualitatively assessed the alignment results by examining the patterns of snow accumulation and redistribution in comparison with the locations of the scan positions. In general, after the full alignment process we did not find artifacts due to either the distance from the nearest scan position or on regions observed from different scan positions. In contrast such artifacts were readily-apparent when conducting only reflector alignment, indicating that local maxima and fine-scale vertical alignment steps improved the alignment for observing snow processes. To quantitatively assess the uncertainty in our alignment procedure, we used a Bayesian statistical model to compare changes in snow thickness measured by TLS with manual measurements of snow thickness changes at snow thickness stakes in the Ridge Ranch mass balance site<sup>##UREF##36##39##</sup>, while accounting for the uncertainties in the individual measurements. The Ridge Ranch mass balance site was located on level, first-year ice within the ROV Scan Area from 19 January to 18 March (at which point ice deformation relocated Ridge Ranch outside of the TLS measurement area). Ridge Ranch included nine snow thickness stakes, arranged in a cross, with approximately eight meters between stakes. Each stake was frozen into the ice and had a metric length scale marked on its side. Snow thickness was measured by manually observing the location of the snow surface on this metric scale with an accuracy of 0.01 m. Changes in snow thickness at each stake can be determined by comparing repeat measurements. Because the stake was permanently frozen into the ice, these measurements directly recorded changes in the snow surface height. They are unaffected by the changes in snow or snow-ice interface properties that may change the penetration depth of a snow thickness probe<sup>##UREF##37##40##</sup>. Manual measurements at Ridge Ranch were made on 28 January, 5 February, and 7 March. ROV TLS measurements were made on 25 January, 4 February, and 11 March (Table ##TAB##0##1##). Observers in the field did not observe snow accumulation or drifting snow at Ridge Ranch during 25–28 January, 4–5 February, and 7–11 March. From these three TLS measurements and three manual stakes measurements, we defined two evaluation periods (Table ##TAB##2##3##) in which the TLS and the manual measurements should observe the same change in the surface at each stake, if there were no measurement noise or bias due scan misalignment.</p>","<p id=\"Par37\">We define <italic>S</italic><sub><italic>k,i</italic></sub> as the change in the snow surface observed by manual stake measurements at stake <italic>i</italic> over a evaluation period <italic>k</italic>. Mathematically, <italic>S</italic><sub><italic>k,i</italic></sub> is the true change in the snow surface <italic>r</italic><sub><italic>k,i</italic></sub> plus noise due to measurement error <italic>n</italic><sub><italic>k,i</italic></sub> (Eq. ##FORMU##2##1##). We consider the measurement accuracy of an individual stake reading (0.01 m) to represent two standard deviations of the measurement noise. This implies that the standard deviation of the measurement noise for a single stake reading is 0.005 m. Thus, we represent <italic>n</italic><sub><italic>k,i</italic></sub> as an instance of a zero-mean, normally distributed random variable with a variance . Note that each <italic>S</italic><sub><italic>k,i</italic></sub> is the difference of two independent measurements, hence the multiplication by two in the variance. We define <italic>t</italic><sub><italic>k,i</italic></sub> as the change in the snow surface observed by TLS for each stake and evaluation period. <italic>t</italic><sub><italic>k,i</italic></sub> is the true change <italic>r</italic><sub><italic>k,i</italic></sub> plus measurement noise <italic>m</italic><sub><italic>k,i</italic></sub> minus a constant bias for the evaluation period <italic>b</italic><sub><italic>k</italic></sub> due to scan misalignment (Eq. ##FORMU##4##3##). To quantify the change observed in the TLS data at a stake, we looked at the mean vertical distance for all exclusive pairs of horizontally closest points within 10 cm of the stake (excluding the stake itself) in the scans at the beginning and end of the evaluation period. The measurement noise <italic>m</italic><sub><italic>k,i</italic></sub> is represented as an instance of zero-mean, normally distributed random variable with a variance () determined by the vertical uncertainty in each TLS point due to the laser beam spreading with distance<sup>##UREF##14##15##</sup>.</p>","<p id=\"Par38\">To quantify how misaligned our TLS measurements may be, we computed the posterior distribution of the bias, <italic>b</italic><sub><italic>k</italic></sub>, given our measurements at each stake. We define <italic>y</italic><sub><italic>k,i</italic></sub> as the difference between the stake measurement <italic>s</italic><sub><italic>k,i</italic></sub> and the TLS measurement <italic>t</italic><sub><italic>k,i</italic></sub> for each stake and evaluation period (Eq. ##FORMU##6##5##). <italic>y</italic><sub><italic>k,i</italic></sub> is equal to the bias for the evaluation period <italic>b</italic><sub><italic>k</italic></sub> plus the difference in the measurement noise for each measurement, which we denote by <italic>g</italic><sub><italic>k,i</italic></sub>. The measurement noise for the TLS and stake measurements are independent and normally distributed, allowing us to represent their difference as a zero-mean, normally distributed random variable whose variance is the sum of the variance of each measurement noise. Thus, each difference <italic>y</italic><sub><italic>k,i</italic></sub> is an instance of a normally distributed random variable given by:</p>","<p id=\"Par39\">Applying Bayes Rule, the posterior probability density of <italic>b</italic><sub><italic>k</italic></sub> given the data for an evaluation period is proportional to the prior probability density multiplied by the likelihood (Eq. ##FORMU##14##8##). <italic>y</italic><sub><italic>k</italic></sub> denotes the set of all observations for evaluation period <italic>k</italic>: . Equation ##FORMU##6##5## indicates that the likelihood of a single observation is gaussian. Conditioned on the bias, the observations are independent. Hence the likelihood of all observations is their product (Eq. ##FORMU##15##9##). Finally, we chose to represent our prior for the bias as a normal distribution with a mean of zero (we do not expect there to be any bias) and a variance of . This variance was chosen because a bias of greater than 0.04 m (twice the standard deviation of our prior) would be obvious on manual inspection of the data, and was not observed. Moreover, the variance of the uncertainty in the observations is lower than the variance of this prior. So increasing the variance of the prior has little impact on the results (i.e., the information content of the observations is considerably higher than this prior). This yields the following expression for the posterior probability density:</p>","<p id=\"Par40\">Algebraic simplification of Eq. ##FORMU##16##10## yields that the posterior density is a gaussian (Eq. ##FORMU##19##11##) whose mean is the mean of the prior and the observations, each weighted by the inverse of their variance (Eq. ##FORMU##20##12##). The variance of is determined from the sum of the inverse variances of the prior and the observations (Eq. ##FORMU##21##13##).</p>","<p id=\"Par41\">We use the posterior density of the bias to establish a 95% credible interval (the interval between the 2.5th and 97.5th percentiles in the distribution of a parameter) for the bias in our alignment procedure.</p>","<p id=\"Par42\">To validate the TLS alignment, we compared the snow surface change observed by TLS and manual measurements (Fig. ##FIG##2##3##) at the Ridge Ranch mass balance site<sup>##UREF##38##41##</sup> during evaluation periods 1 and 2 (Table ##TAB##2##3##). Each evaluation period included at least one snow accumulation and redistribution event. The evaluation periods collectively span 25 January to 11 March. Manual measurements indicated that most stakes experienced little to no change in the snow surface in either evaluation period. The largest snow accumulation observed manually at any of the stakes occurred during evaluation period 2, when 0.07 m of snow accumulated at two stakes (Fig. ##FIG##2##3##). The TLS measurements, too, observed approximately 0.07 m of change at the same two stakes and little to no change at the others (Fig. ##FIG##2##3##). With these data, we compute the posterior density of the bias (Table ##TAB##3##4##) following Eqs. ##FORMU##2##1##–##FORMU##20##12##. The estimated mean biases for evaluation period 1 and 2 are −0.001 m and −0.004 m, respectively. And the minimum and maximum 95% credible interval bounds are −0.011 m and 0.007 m respectively. Thus, we conclude that the bias due to scan misalignment is less than 0.011 m. We stress that these numerical values are particular to the ROV and Snow2 scan areas at MOSAiC (regular ice deformation in the Snow1 area will require future work to correct for). Future TLS measurement campaigns should conduct <italic>in-situ</italic> validation measurements for their specific measurement sites.</p>","<title>Surface Reconstruction Validation</title>","<p id=\"Par43\">We validated our gaussian process regression approach to surface reconstruction by examining the differences between the vertical components of TLS data points and the reconstructed surface. Figure ##FIG##3##4## shows an example of the distribution of these differences for a 250 m × 65 m region reconstructed on a 10 cm grid containing a large ridge and level ice. The mean difference is 2.5 × 10<sup>−5</sup> m, the median difference is 6.5 × 10<sup>−5</sup> m, the standard deviation of the differences is 0.0054 m and the median absolute deviation, a metric of the variability of data that is robust to extreme values<sup>##UREF##39##42##</sup>, for these data is 0.0017 m. This is similar in magnitude to the median standard deviation of the vertical uncertainty due to laser beam divergence: 0.0026 m. These results combined with manual inspection of the differences suggest that almost all of the differences between the TLS points and the reconstructed surface can be attributed to the divergence of the laser beam with distance from the scanner. A small fraction (less than 5%) of the differences are due to areas with high surface roughness on horizontal length scales of less than the 10 cm grid spacing. These rough areas cause the tails of the distribution of differences to be more extreme than a normal distribution (Fig. ##FIG##3##4##). For the purpose of assessing snow accumulation, the uncertainties in our surface reconstruction approach are insignificant compared to the alignment uncertainties (0.011 m, see above). However, we caution that applications involving surface roughness may want to further develop surface reconstruction techniques.</p>","<title>Usage Notes</title>","<p id=\"Par44\">On 18 March, the ice was deforming within the ROV Scan Area while we were collecting TLS data. On 8 April, Snow1 split in two while we were collecting TLS data. Data collected after the deformation began are in a second project (‘mosaic_01_080420.RiSCAN’). We recommend caution when using data collected during deformation events. The convention in project names was inadvertently switched from ‘MMDDYY’ to ‘DDMMYY’ at the turn of the year (except for ‘mosaic_01b_061219.RiSCAN.RiSCAN.RiSCAN’ on 6 December). A function is provided in pydar to convert a Project’s name to its date (pydar.mosaic_date_parser). Sometimes extra ‘.RiSCAN’s were included in the Project name, these have no significance.</p>","<p id=\"Par45\">Researchers interested in extending the functionality of pydar (e.g., adding feature tracking functionality for ice deformation, point cloud segmentation, etc) are encouraged to contact the corresponding author in case related efforts are underway. We also welcome discussions on potential uses of these data and collaborations with other data products.</p>"],"string":"[\n \"<title>Data Records</title>\",\n \"<p id=\\\"Par35\\\">Repeat TLS data in Table ##TAB##0##1## are available at the Arctic Data Center<sup>##UREF##20##21##</sup>. The top level of the archive contains a directory for each Scan Area: Snow1, ROV (which includes the Snow2 projects since they are in the same reference frame), RS (April Remote Sensing site), and MET (single Project focused on Met City on 4 May). Within these directories is a subdirectory for each Project that contains all data records for that Project (as described in the Methods section). An illustrative directory tree for a Scan Area is shown below.</p>\",\n \"<title>Technical Validation</title>\",\n \"<title>Alignment Validation</title>\",\n \"<p id=\\\"Par36\\\">We qualitatively assessed the alignment results by examining the patterns of snow accumulation and redistribution in comparison with the locations of the scan positions. In general, after the full alignment process we did not find artifacts due to either the distance from the nearest scan position or on regions observed from different scan positions. In contrast such artifacts were readily-apparent when conducting only reflector alignment, indicating that local maxima and fine-scale vertical alignment steps improved the alignment for observing snow processes. To quantitatively assess the uncertainty in our alignment procedure, we used a Bayesian statistical model to compare changes in snow thickness measured by TLS with manual measurements of snow thickness changes at snow thickness stakes in the Ridge Ranch mass balance site<sup>##UREF##36##39##</sup>, while accounting for the uncertainties in the individual measurements. The Ridge Ranch mass balance site was located on level, first-year ice within the ROV Scan Area from 19 January to 18 March (at which point ice deformation relocated Ridge Ranch outside of the TLS measurement area). Ridge Ranch included nine snow thickness stakes, arranged in a cross, with approximately eight meters between stakes. Each stake was frozen into the ice and had a metric length scale marked on its side. Snow thickness was measured by manually observing the location of the snow surface on this metric scale with an accuracy of 0.01 m. Changes in snow thickness at each stake can be determined by comparing repeat measurements. Because the stake was permanently frozen into the ice, these measurements directly recorded changes in the snow surface height. They are unaffected by the changes in snow or snow-ice interface properties that may change the penetration depth of a snow thickness probe<sup>##UREF##37##40##</sup>. Manual measurements at Ridge Ranch were made on 28 January, 5 February, and 7 March. ROV TLS measurements were made on 25 January, 4 February, and 11 March (Table ##TAB##0##1##). Observers in the field did not observe snow accumulation or drifting snow at Ridge Ranch during 25–28 January, 4–5 February, and 7–11 March. From these three TLS measurements and three manual stakes measurements, we defined two evaluation periods (Table ##TAB##2##3##) in which the TLS and the manual measurements should observe the same change in the surface at each stake, if there were no measurement noise or bias due scan misalignment.</p>\",\n \"<p id=\\\"Par37\\\">We define <italic>S</italic><sub><italic>k,i</italic></sub> as the change in the snow surface observed by manual stake measurements at stake <italic>i</italic> over a evaluation period <italic>k</italic>. Mathematically, <italic>S</italic><sub><italic>k,i</italic></sub> is the true change in the snow surface <italic>r</italic><sub><italic>k,i</italic></sub> plus noise due to measurement error <italic>n</italic><sub><italic>k,i</italic></sub> (Eq. ##FORMU##2##1##). We consider the measurement accuracy of an individual stake reading (0.01 m) to represent two standard deviations of the measurement noise. This implies that the standard deviation of the measurement noise for a single stake reading is 0.005 m. Thus, we represent <italic>n</italic><sub><italic>k,i</italic></sub> as an instance of a zero-mean, normally distributed random variable with a variance . Note that each <italic>S</italic><sub><italic>k,i</italic></sub> is the difference of two independent measurements, hence the multiplication by two in the variance. We define <italic>t</italic><sub><italic>k,i</italic></sub> as the change in the snow surface observed by TLS for each stake and evaluation period. <italic>t</italic><sub><italic>k,i</italic></sub> is the true change <italic>r</italic><sub><italic>k,i</italic></sub> plus measurement noise <italic>m</italic><sub><italic>k,i</italic></sub> minus a constant bias for the evaluation period <italic>b</italic><sub><italic>k</italic></sub> due to scan misalignment (Eq. ##FORMU##4##3##). To quantify the change observed in the TLS data at a stake, we looked at the mean vertical distance for all exclusive pairs of horizontally closest points within 10 cm of the stake (excluding the stake itself) in the scans at the beginning and end of the evaluation period. The measurement noise <italic>m</italic><sub><italic>k,i</italic></sub> is represented as an instance of zero-mean, normally distributed random variable with a variance () determined by the vertical uncertainty in each TLS point due to the laser beam spreading with distance<sup>##UREF##14##15##</sup>.</p>\",\n \"<p id=\\\"Par38\\\">To quantify how misaligned our TLS measurements may be, we computed the posterior distribution of the bias, <italic>b</italic><sub><italic>k</italic></sub>, given our measurements at each stake. We define <italic>y</italic><sub><italic>k,i</italic></sub> as the difference between the stake measurement <italic>s</italic><sub><italic>k,i</italic></sub> and the TLS measurement <italic>t</italic><sub><italic>k,i</italic></sub> for each stake and evaluation period (Eq. ##FORMU##6##5##). <italic>y</italic><sub><italic>k,i</italic></sub> is equal to the bias for the evaluation period <italic>b</italic><sub><italic>k</italic></sub> plus the difference in the measurement noise for each measurement, which we denote by <italic>g</italic><sub><italic>k,i</italic></sub>. The measurement noise for the TLS and stake measurements are independent and normally distributed, allowing us to represent their difference as a zero-mean, normally distributed random variable whose variance is the sum of the variance of each measurement noise. Thus, each difference <italic>y</italic><sub><italic>k,i</italic></sub> is an instance of a normally distributed random variable given by:</p>\",\n \"<p id=\\\"Par39\\\">Applying Bayes Rule, the posterior probability density of <italic>b</italic><sub><italic>k</italic></sub> given the data for an evaluation period is proportional to the prior probability density multiplied by the likelihood (Eq. ##FORMU##14##8##). <italic>y</italic><sub><italic>k</italic></sub> denotes the set of all observations for evaluation period <italic>k</italic>: . Equation ##FORMU##6##5## indicates that the likelihood of a single observation is gaussian. Conditioned on the bias, the observations are independent. Hence the likelihood of all observations is their product (Eq. ##FORMU##15##9##). Finally, we chose to represent our prior for the bias as a normal distribution with a mean of zero (we do not expect there to be any bias) and a variance of . This variance was chosen because a bias of greater than 0.04 m (twice the standard deviation of our prior) would be obvious on manual inspection of the data, and was not observed. Moreover, the variance of the uncertainty in the observations is lower than the variance of this prior. So increasing the variance of the prior has little impact on the results (i.e., the information content of the observations is considerably higher than this prior). This yields the following expression for the posterior probability density:</p>\",\n \"<p id=\\\"Par40\\\">Algebraic simplification of Eq. ##FORMU##16##10## yields that the posterior density is a gaussian (Eq. ##FORMU##19##11##) whose mean is the mean of the prior and the observations, each weighted by the inverse of their variance (Eq. ##FORMU##20##12##). The variance of is determined from the sum of the inverse variances of the prior and the observations (Eq. ##FORMU##21##13##).</p>\",\n \"<p id=\\\"Par41\\\">We use the posterior density of the bias to establish a 95% credible interval (the interval between the 2.5th and 97.5th percentiles in the distribution of a parameter) for the bias in our alignment procedure.</p>\",\n \"<p id=\\\"Par42\\\">To validate the TLS alignment, we compared the snow surface change observed by TLS and manual measurements (Fig. ##FIG##2##3##) at the Ridge Ranch mass balance site<sup>##UREF##38##41##</sup> during evaluation periods 1 and 2 (Table ##TAB##2##3##). Each evaluation period included at least one snow accumulation and redistribution event. The evaluation periods collectively span 25 January to 11 March. Manual measurements indicated that most stakes experienced little to no change in the snow surface in either evaluation period. The largest snow accumulation observed manually at any of the stakes occurred during evaluation period 2, when 0.07 m of snow accumulated at two stakes (Fig. ##FIG##2##3##). The TLS measurements, too, observed approximately 0.07 m of change at the same two stakes and little to no change at the others (Fig. ##FIG##2##3##). With these data, we compute the posterior density of the bias (Table ##TAB##3##4##) following Eqs. ##FORMU##2##1##–##FORMU##20##12##. The estimated mean biases for evaluation period 1 and 2 are −0.001 m and −0.004 m, respectively. And the minimum and maximum 95% credible interval bounds are −0.011 m and 0.007 m respectively. Thus, we conclude that the bias due to scan misalignment is less than 0.011 m. We stress that these numerical values are particular to the ROV and Snow2 scan areas at MOSAiC (regular ice deformation in the Snow1 area will require future work to correct for). Future TLS measurement campaigns should conduct <italic>in-situ</italic> validation measurements for their specific measurement sites.</p>\",\n \"<title>Surface Reconstruction Validation</title>\",\n \"<p id=\\\"Par43\\\">We validated our gaussian process regression approach to surface reconstruction by examining the differences between the vertical components of TLS data points and the reconstructed surface. Figure ##FIG##3##4## shows an example of the distribution of these differences for a 250 m × 65 m region reconstructed on a 10 cm grid containing a large ridge and level ice. The mean difference is 2.5 × 10<sup>−5</sup> m, the median difference is 6.5 × 10<sup>−5</sup> m, the standard deviation of the differences is 0.0054 m and the median absolute deviation, a metric of the variability of data that is robust to extreme values<sup>##UREF##39##42##</sup>, for these data is 0.0017 m. This is similar in magnitude to the median standard deviation of the vertical uncertainty due to laser beam divergence: 0.0026 m. These results combined with manual inspection of the differences suggest that almost all of the differences between the TLS points and the reconstructed surface can be attributed to the divergence of the laser beam with distance from the scanner. A small fraction (less than 5%) of the differences are due to areas with high surface roughness on horizontal length scales of less than the 10 cm grid spacing. These rough areas cause the tails of the distribution of differences to be more extreme than a normal distribution (Fig. ##FIG##3##4##). For the purpose of assessing snow accumulation, the uncertainties in our surface reconstruction approach are insignificant compared to the alignment uncertainties (0.011 m, see above). However, we caution that applications involving surface roughness may want to further develop surface reconstruction techniques.</p>\",\n \"<title>Usage Notes</title>\",\n \"<p id=\\\"Par44\\\">On 18 March, the ice was deforming within the ROV Scan Area while we were collecting TLS data. On 8 April, Snow1 split in two while we were collecting TLS data. Data collected after the deformation began are in a second project (‘mosaic_01_080420.RiSCAN’). We recommend caution when using data collected during deformation events. The convention in project names was inadvertently switched from ‘MMDDYY’ to ‘DDMMYY’ at the turn of the year (except for ‘mosaic_01b_061219.RiSCAN.RiSCAN.RiSCAN’ on 6 December). A function is provided in pydar to convert a Project’s name to its date (pydar.mosaic_date_parser). Sometimes extra ‘.RiSCAN’s were included in the Project name, these have no significance.</p>\",\n \"<p id=\\\"Par45\\\">Researchers interested in extending the functionality of pydar (e.g., adding feature tracking functionality for ice deformation, point cloud segmentation, etc) are encouraged to contact the corresponding author in case related efforts are underway. We also welcome discussions on potential uses of these data and collaborations with other data products.</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Acknowledgements</title>","<p>Data used in this manuscript were produced as part of the international MOSAiC project with the tag MOSAiC20192020 and the Project_ID: AWI_PS122_00. We thank all people involved in the expedition of the research vessel Polarstern<sup>##UREF##0##1##</sup> during MOSAiC in 2019–2020 as listed in the MOSAiC Extended Acknowledgement<sup>##UREF##40##43##</sup>. Thank you to Jon Holmgren for fabricating the heater cover for the TLS. Thank you to Eric Brossier, Steven Fons, Jesper Hansen, Thomas Olufson, Martin Radenz, Carl Martin Schonning, Saga Svavarsdóttir and Monica Votvik for their capable assistance in the field. D.C.S. was supported by NSF OPP-1724540, NSF OPP-1724424 and NSF OPP-2138788. I.A.R., and D.P. were supported by NSF OPP-1724540, NSF OPP-1724424 and NSF OPP-2138785. C.P. was supported by NSF OPP-1724540 and NSF OPP-2138785. MO was supported by NSF OPP-1735862. A.R.M. was supported by WSL_201812N1678 and SPI DIRCR-2018-003. I.S.O.M. supported by BMBF Grant 03F0810A, jointly funded by BMBF (DE) and NERC (UK). D.N.W. was supported by SNSF-200020_179130 and SPI DIRCR-2018-003. M.J. was supported by EU-Arice DEARice. P.I. was supported by NSF #1820927 and RCN #287871.</p>","<title>Author contributions</title>","<p>The concept of TLS snow redistribution and accumulation measurements on MOSAiC was conceived, facilitated, and supervised by C.P. D.C.-S. led the data collection from March to May 2020, developed the data processing methods, and wrote the initial draft. I.A.R. led the data collection from October 2019 to February 2020. M.P. provided guidance and advice on the technical validation and surface reconstruction. C.P. designed the heater cover for the TLS. D.P. supervised this project. P.I., M.J., A.J., A.R.M., I.M., M.O., D.W., and R.V. contributed to data acquisition. All authors reviewed the manuscript.</p>","<title>Code availability</title>","<p>pydar is available at Zenodo<sup>##UREF##18##19##</sup> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://zenodo.org/record/8120858\">https://zenodo.org/record/8120858</ext-link>).</p>","<title>Competing interests</title>","<p id=\"Par46\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Acknowledgements</title>\",\n \"<p>Data used in this manuscript were produced as part of the international MOSAiC project with the tag MOSAiC20192020 and the Project_ID: AWI_PS122_00. We thank all people involved in the expedition of the research vessel Polarstern<sup>##UREF##0##1##</sup> during MOSAiC in 2019–2020 as listed in the MOSAiC Extended Acknowledgement<sup>##UREF##40##43##</sup>. Thank you to Jon Holmgren for fabricating the heater cover for the TLS. Thank you to Eric Brossier, Steven Fons, Jesper Hansen, Thomas Olufson, Martin Radenz, Carl Martin Schonning, Saga Svavarsdóttir and Monica Votvik for their capable assistance in the field. D.C.S. was supported by NSF OPP-1724540, NSF OPP-1724424 and NSF OPP-2138788. I.A.R., and D.P. were supported by NSF OPP-1724540, NSF OPP-1724424 and NSF OPP-2138785. C.P. was supported by NSF OPP-1724540 and NSF OPP-2138785. MO was supported by NSF OPP-1735862. A.R.M. was supported by WSL_201812N1678 and SPI DIRCR-2018-003. I.S.O.M. supported by BMBF Grant 03F0810A, jointly funded by BMBF (DE) and NERC (UK). D.N.W. was supported by SNSF-200020_179130 and SPI DIRCR-2018-003. M.J. was supported by EU-Arice DEARice. P.I. was supported by NSF #1820927 and RCN #287871.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>The concept of TLS snow redistribution and accumulation measurements on MOSAiC was conceived, facilitated, and supervised by C.P. D.C.-S. led the data collection from March to May 2020, developed the data processing methods, and wrote the initial draft. I.A.R. led the data collection from October 2019 to February 2020. M.P. provided guidance and advice on the technical validation and surface reconstruction. C.P. designed the heater cover for the TLS. D.P. supervised this project. P.I., M.J., A.J., A.R.M., I.M., M.O., D.W., and R.V. contributed to data acquisition. All authors reviewed the manuscript.</p>\",\n \"<title>Code availability</title>\",\n \"<p>pydar is available at Zenodo<sup>##UREF##18##19##</sup> (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://zenodo.org/record/8120858\\\">https://zenodo.org/record/8120858</ext-link>).</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par46\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Photo showing data acquisition of a typical scan position. The Riegl VZ1000 terrestrial laser scanner is inside the custom heater case (metal cylinder) on top of the tripod. The researcher controls the scanner from the laptop via WiFi. To the right of the tripod is a Riegl 10 cm cylinder reflector mounted on a post frozen into the ice. The Polarstern can be seen in the background along with other installations of the ice camp including Balloon Town<sup>##UREF##2##3##</sup> (orange tents) and Ocean City<sup>##UREF##3##4##</sup> (blue and white tent). Photo credit: S. Svavarsdóttir.</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Map of the three Scan Areas in the MOSAiC Central Observatory<sup>##UREF##1##2##</sup> in early February. The Scan Areas are labelled and roughly outlined in white (the exact extent of the measured areas changed due to ice deformation and the number of SingleScans conducted). Notable installations are indicated in green (see MOSAiC snow and ice overview publication<sup>##UREF##1##2##</sup> for more installations). Reflector locations are also shown in white. Reflector heights are provided in Table ##TAB##1##2##. This map is a rendering of the combined point cloud data from Projects mosaic_rov_040220.RiSCAN (collected on 4 February) and mosaic_01_060220.RiSCAN (collected on 6–7 February). The origin for topographic height is the surface of recently frozen lead (so the values approximately correspond to height above the sea surface).</p></caption></fig>","<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Comparison of snow surface changes measured manually at snow thickness stakes with the snow surface changes at the same stakes measured by TLS during two evaluation periods (Table ##TAB##2##3##). Error bars show the 95% confidence intervals of the measurement uncertainty for the manual measurement (Eq. ##FORMU##3##2##) and the TLS measurement (Eq. ##FORMU##5##4##, note that each TLS measurement has an individual uncertainty which depends on the distance of the stake from the scanner and the number of TLS points within 10 cm of the stake). The black, dashed line is the 1:1 line (on which the manual measurement is equal to the TLS measurement). The grey shading above and below line represents the ± 0.011 m uncertainty in our posterior estimate of the bias. No bias between the manual and TLS measurements is visually apparent.</p></caption></fig>","<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Histogram of vertical differences between TLS data points and reconstructed surface for a 250 m x 65 m region of sea ice containing a large ridge and level ice measured on 26 March 2020. The overlain curve shows a normal distribution with the same median absolute deviation as the data. The 2.5th percentile to the 97.5th percentile of the differences roughly matches a normal distribution but the tails are more extreme.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><p>Photo showing data acquisition of a typical scan position. The Riegl VZ1000 terrestrial laser scanner is inside the custom heater case (metal cylinder) on top of the tripod. The researcher controls the scanner from the laptop via WiFi. To the right of the tripod is a Riegl 10 cm cylinder reflector mounted on a post frozen into the ice. The Polarstern can be seen in the background along with other installations of the ice camp including Balloon Town<sup>##UREF##2##3##</sup> (orange tents) and Ocean City<sup>##UREF##3##4##</sup> (blue and white tent). Photo credit: S. Svavarsdóttir.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><p>Map of the three Scan Areas in the MOSAiC Central Observatory<sup>##UREF##1##2##</sup> in early February. The Scan Areas are labelled and roughly outlined in white (the exact extent of the measured areas changed due to ice deformation and the number of SingleScans conducted). Notable installations are indicated in green (see MOSAiC snow and ice overview publication<sup>##UREF##1##2##</sup> for more installations). Reflector locations are also shown in white. Reflector heights are provided in Table ##TAB##1##2##. This map is a rendering of the combined point cloud data from Projects mosaic_rov_040220.RiSCAN (collected on 4 February) and mosaic_01_060220.RiSCAN (collected on 6–7 February). The origin for topographic height is the surface of recently frozen lead (so the values approximately correspond to height above the sea surface).</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Fig. 3</label><caption><p>Comparison of snow surface changes measured manually at snow thickness stakes with the snow surface changes at the same stakes measured by TLS during two evaluation periods (Table ##TAB##2##3##). Error bars show the 95% confidence intervals of the measurement uncertainty for the manual measurement (Eq. ##FORMU##3##2##) and the TLS measurement (Eq. ##FORMU##5##4##, note that each TLS measurement has an individual uncertainty which depends on the distance of the stake from the scanner and the number of TLS points within 10 cm of the stake). The black, dashed line is the 1:1 line (on which the manual measurement is equal to the TLS measurement). The grey shading above and below line represents the ± 0.011 m uncertainty in our posterior estimate of the bias. No bias between the manual and TLS measurements is visually apparent.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Fig. 4</label><caption><p>Histogram of vertical differences between TLS data points and reconstructed surface for a 250 m x 65 m region of sea ice containing a large ridge and level ice measured on 26 March 2020. The overlain curve shows a normal distribution with the same median absolute deviation as the data. The 2.5th percentile to the 97.5th percentile of the differences roughly matches a normal distribution but the tails are more extreme.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":["<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Summary of repeat TLS measurements from October 2019 to May 2020 on MOSAiC.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Project Name</th><th>Date</th><th>Scan Area(s)</th><th># SingleScans</th><th>Location</th></tr></thead><tbody><tr><td>mosaic_01_101819.RiSCAN</td><td>2019-10-18</td><td>Snow1</td><td>2</td><td>84.79°N, 133.09°E</td></tr><tr><td>mosaic_01_102019.RiSCAN</td><td>2019-10-20</td><td>Snow1</td><td>5</td><td>84.97°N, 132.76°E</td></tr><tr><td>mosaic_01_102519.RiSCAN</td><td>2019-10-25</td><td>Snow1</td><td>5</td><td>85.44°N, 128.15°E</td></tr><tr><td>mosaic_01_110119.RiSCAN</td><td>2019-11-01</td><td>Snow1</td><td>7</td><td>85.80°N, 122.97 ° E</td></tr><tr><td>mosaic_02_110619.RiSCAN</td><td>2019-11-06</td><td>Snow2</td><td>7</td><td>85.93°N, 117.74°E</td></tr><tr><td>mosaic_01_110819.RiSCAN</td><td>2019-11-08</td><td>Snow1</td><td>3</td><td>85.92°N, 116.53°E</td></tr><tr><td>mosaic_02_111319.RiSCAN</td><td>2019-11-13</td><td>Snow2</td><td>8</td><td>86.10°N, 117.84°E</td></tr><tr><td>mosaic_01_111519.RiSCAN</td><td>2019-11-15</td><td>Snow1</td><td>8</td><td>86.16°N, 118.20°E</td></tr><tr><td>mosaic_01b_061219.RiSCAN.RiSCAN.RiSCAN</td><td>2019-12-06<sup>a</sup></td><td>Snow1, Snow2</td><td>12</td><td>86.14°N, 122.25°E</td></tr><tr><td>mosaic_01_122719.RiSCAN</td><td>2019-12-27</td><td>Snow1</td><td>5</td><td>86.68°N, 115.95°E</td></tr><tr><td>mosaic_rov_040120.RiSCAN</td><td>2020-01-04</td><td>ROV</td><td>6</td><td>86.99°N, 115.44°E</td></tr><tr><td>mosaic_01_040120.RiSCAN</td><td>2020-01-04<sup>a</sup></td><td>Snow1</td><td>7</td><td>87.01°N, 115.35°E</td></tr><tr><td>mosaic_rov_110120.RiSCAN</td><td>2020-01-11</td><td>ROV</td><td>5</td><td>87.21°N, 111.40°E</td></tr><tr><td>mosaic_01_180120.RiSCAN</td><td>2020-01-18</td><td>Snow1</td><td>8</td><td>87.41°N, 98.27°E</td></tr><tr><td>mosaic_rov_190120.RiSCAN</td><td>2020-01-19</td><td>ROV</td><td>8</td><td>87.40°N, 98.36°E</td></tr><tr><td>mosaic_rov_250120.RiSCAN</td><td>2020-01-25</td><td>ROV</td><td>11</td><td>87.41°N, 92.79°E</td></tr><tr><td>mosaic_01_290120.RiSCAN</td><td>2020-01-29</td><td>Snow1</td><td>8</td><td>87.47°N, 95.18°E</td></tr><tr><td>mosaic_rov_040220.RiSCAN</td><td>2020-02-04</td><td>ROV</td><td>9</td><td>87.48°N, 95.30°E</td></tr><tr><td>mosaic_01_060220.RiSCAN</td><td>2020-02-06<sup>a</sup></td><td>Snow1, Snow2</td><td>16</td><td>87.60°N, 94.04°E</td></tr><tr><td>mosaic_01_150220.RiSCAN.RiSCAN</td><td>2020-02-15</td><td>Snow1</td><td>8</td><td>88.07°N, 79.87°E</td></tr><tr><td>mosaic_rov_220220.RiSCAN.RiSCAN</td><td>2020-02-22</td><td>ROV</td><td>9</td><td>88.58°N, 64.54°E</td></tr><tr><td>mosaic_01_280220.RiSCAN</td><td>2020-02-28</td><td>Snow1</td><td>7</td><td>88.34°N, 33.99°E</td></tr><tr><td>mosaic_rov_110320.RiSCAN</td><td>2020-03-11</td><td>ROV</td><td>1</td><td>88.27°N, 31.95°E</td></tr><tr><td>mosaic_rov_140320.RiSCAN.RiSCAN</td><td>2020-03-14</td><td>ROV</td><td>4</td><td>87.67°N, 24.59°E</td></tr><tr><td>mosaic_rov_180320.RiSCAN</td><td>2020-03-18</td><td>ROV</td><td>4</td><td>87.14°N, 17.28°E</td></tr><tr><td>mosaic_01_220320.RiSCAN</td><td>2020-03-22</td><td>Snow1</td><td>6</td><td>86.23°N, 15.70°E</td></tr><tr><td>mosaic_02_260320.RiSCAN</td><td>2020-03-26</td><td>Snow2</td><td>5</td><td>86.00°N, 13.09°E</td></tr><tr><td>mosaic_02_300320.RiSCAN.RiSCAN</td><td>2020-03-30</td><td>Snow2</td><td>5</td><td>85.38°N, 13.20°E</td></tr><tr><td>mosaic_02_040420.RiSCAN</td><td>2020-04-04<sup>a</sup></td><td>Snow2, ROV</td><td>11</td><td>84.67°N, 12.89°E</td></tr><tr><td>mosaic_01_080420.RiSCAN</td><td>2020-04-08</td><td>Snow1</td><td>4</td><td>84.48°N, 14.64°E</td></tr><tr><td>mosaic_01_080420b.RiSCAN</td><td>2020-04-08</td><td>Snow1</td><td>3</td><td>84.48°N, 14.64°E</td></tr><tr><td>mosaic_02_110420_rov.RiSCAN</td><td>2020-04-11</td><td>ROV</td><td>6</td><td>84.33°N, 14.75°E</td></tr><tr><td>mosaic_02_130420_2.RiSCAN</td><td>2020-04-13</td><td>Snow2</td><td>6</td><td>84.29°N, 14.96°E</td></tr><tr><td>mosaic_rov_170420.RiSCAN</td><td>2020-04-17</td><td>ROV</td><td>6</td><td>84.41°N, 13.67°E</td></tr><tr><td>mosaic_rs_170420.RiSCAN</td><td>2020-04-17</td><td>RS</td><td>1</td><td>84.41°N, 13.67°E</td></tr><tr><td>mosaic_rov_220420.RiSCAN</td><td>2020-04-22</td><td>ROV</td><td>6</td><td>84.13°N, 15.89°E</td></tr><tr><td>mosaic_rs_220420.RiSCAN</td><td>2020-04-22</td><td>RS</td><td>1</td><td>84.13°N, 15.89°E</td></tr><tr><td>mosaic_02_230420.RiSCAN</td><td>2020-04-23</td><td>Snow2</td><td>6</td><td>84.08°N, 16.05°E</td></tr><tr><td>mosaic_01_250420.RiSCAN.RiSCAN</td><td>2020-04-25</td><td>Snow1</td><td>4</td><td>84.00°N, 15.59°E</td></tr><tr><td>mosaic_01_260420.RiSCAN</td><td>2020-04-26</td><td>Snow1</td><td>3</td><td>83.93°N, 15.51°E</td></tr><tr><td>mosaic_rov_290420.RiSCAN</td><td>2020-04-29</td><td>ROV</td><td>7</td><td>84.02°N, 17.24°E</td></tr><tr><td>mosaic_02_300420.RiSCAN</td><td>2020-04-30</td><td>Snow2</td><td>6</td><td>83.94°N, 17.42°E</td></tr><tr><td>mosaic_rs_300420.RiSCAN</td><td>2020-04-30</td><td>RS</td><td>1</td><td>83.94°N, 17.42°E</td></tr><tr><td>mosaic_01_030520.RiSCAN</td><td>2020-05-03</td><td>Snow1</td><td>4</td><td>83.89°N, 17.96°E</td></tr><tr><td>mosaic_met_040520.RiSCAN</td><td>2020-05-04</td><td>MET</td><td>2</td><td>83.91°N, 18.35°E</td></tr><tr><td>mosaic_rov_02_090520.RiSCAN</td><td>2020-05-09</td><td>Snow2, ROV</td><td>12</td><td>83.79°N, 14.09°E</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Reflector heights both relative to the local snow surface (Terrain Relative Height) and relative to the approximately the sea surface (Pole Height).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Reflector</th><th>Pole Height (m)</th><th>Terrain Relative Height (m)</th></tr></thead><tbody><tr><td>r01</td><td>2.10</td><td>1.38</td></tr><tr><td>r03</td><td>2.03</td><td>0.99</td></tr><tr><td>r05</td><td>2.01</td><td>1.10</td></tr><tr><td>r09</td><td>1.72</td><td>1.05</td></tr><tr><td>r10</td><td>1.71</td><td>1.13</td></tr><tr><td>r11</td><td>1.81</td><td>1.51</td></tr><tr><td>r12</td><td>1.79</td><td>1.26</td></tr><tr><td>r13</td><td>2.89</td><td>1.67</td></tr><tr><td>r14</td><td>2.06</td><td>1.60</td></tr><tr><td>r15</td><td>2.47</td><td>1.73</td></tr><tr><td>r16</td><td>1.96</td><td>1.57</td></tr><tr><td>r17</td><td>1.96</td><td>1.54</td></tr><tr><td>r18</td><td>1.93</td><td>1.16</td></tr><tr><td>r19</td><td>2.06</td><td>1.56</td></tr><tr><td>r20</td><td>1.90</td><td>1.44</td></tr><tr><td>r21</td><td>1.73</td><td>1.38</td></tr><tr><td>r23</td><td>1.99</td><td>1.46</td></tr><tr><td>r24</td><td>1.85</td><td>1.55</td></tr><tr><td>r28</td><td>1.80</td><td>1.24</td></tr><tr><td>r29</td><td>1.77</td><td>1.30</td></tr><tr><td>r30</td><td>1.63</td><td>1.21</td></tr><tr><td>r31</td><td>2.36</td><td>1.48</td></tr><tr><td>r32</td><td>1.98</td><td>1.53</td></tr><tr><td>r33</td><td>2.86</td><td>1.49</td></tr><tr><td>r34</td><td>2.84</td><td>1.44</td></tr><tr><td>r35</td><td>2.09</td><td>1.53</td></tr><tr><td>r36</td><td>2.30</td><td>1.47</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Evaluation periods for validating TLS alignment.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Evaluation Period</th><th>First TLS Measurement</th><th>Second TLS Measurement</th><th>First Manual Measurement</th><th>Second Manual Measurement</th></tr></thead><tbody><tr><td>1</td><td>2020-01-25</td><td>2020-02-04</td><td>2020-01-28</td><td>2020-02-05</td></tr><tr><td>2</td><td>2020-02-04</td><td>2020-03-11</td><td>2020-02-05</td><td>2020-03-07</td></tr></tbody></table></table-wrap>","<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Scan misalignment bias.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Evaluation Period</th><th>Expectation of Posterior Density of Bias</th><th>95% Credible Interval</th></tr></thead><tbody><tr><td>1</td><td>−0.001 m</td><td>[−0.009 m, 0.007 m]</td></tr><tr><td>2</td><td>−0.004 m</td><td>[−0.011 m, 0.004 m]</td></tr></tbody></table></table-wrap>"],"string":"[\n \"<table-wrap id=\\\"Tab1\\\"><label>Table 1</label><caption><p>Summary of repeat TLS measurements from October 2019 to May 2020 on MOSAiC.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th>Project Name</th><th>Date</th><th>Scan Area(s)</th><th># SingleScans</th><th>Location</th></tr></thead><tbody><tr><td>mosaic_01_101819.RiSCAN</td><td>2019-10-18</td><td>Snow1</td><td>2</td><td>84.79°N, 133.09°E</td></tr><tr><td>mosaic_01_102019.RiSCAN</td><td>2019-10-20</td><td>Snow1</td><td>5</td><td>84.97°N, 132.76°E</td></tr><tr><td>mosaic_01_102519.RiSCAN</td><td>2019-10-25</td><td>Snow1</td><td>5</td><td>85.44°N, 128.15°E</td></tr><tr><td>mosaic_01_110119.RiSCAN</td><td>2019-11-01</td><td>Snow1</td><td>7</td><td>85.80°N, 122.97 ° E</td></tr><tr><td>mosaic_02_110619.RiSCAN</td><td>2019-11-06</td><td>Snow2</td><td>7</td><td>85.93°N, 117.74°E</td></tr><tr><td>mosaic_01_110819.RiSCAN</td><td>2019-11-08</td><td>Snow1</td><td>3</td><td>85.92°N, 116.53°E</td></tr><tr><td>mosaic_02_111319.RiSCAN</td><td>2019-11-13</td><td>Snow2</td><td>8</td><td>86.10°N, 117.84°E</td></tr><tr><td>mosaic_01_111519.RiSCAN</td><td>2019-11-15</td><td>Snow1</td><td>8</td><td>86.16°N, 118.20°E</td></tr><tr><td>mosaic_01b_061219.RiSCAN.RiSCAN.RiSCAN</td><td>2019-12-06<sup>a</sup></td><td>Snow1, Snow2</td><td>12</td><td>86.14°N, 122.25°E</td></tr><tr><td>mosaic_01_122719.RiSCAN</td><td>2019-12-27</td><td>Snow1</td><td>5</td><td>86.68°N, 115.95°E</td></tr><tr><td>mosaic_rov_040120.RiSCAN</td><td>2020-01-04</td><td>ROV</td><td>6</td><td>86.99°N, 115.44°E</td></tr><tr><td>mosaic_01_040120.RiSCAN</td><td>2020-01-04<sup>a</sup></td><td>Snow1</td><td>7</td><td>87.01°N, 115.35°E</td></tr><tr><td>mosaic_rov_110120.RiSCAN</td><td>2020-01-11</td><td>ROV</td><td>5</td><td>87.21°N, 111.40°E</td></tr><tr><td>mosaic_01_180120.RiSCAN</td><td>2020-01-18</td><td>Snow1</td><td>8</td><td>87.41°N, 98.27°E</td></tr><tr><td>mosaic_rov_190120.RiSCAN</td><td>2020-01-19</td><td>ROV</td><td>8</td><td>87.40°N, 98.36°E</td></tr><tr><td>mosaic_rov_250120.RiSCAN</td><td>2020-01-25</td><td>ROV</td><td>11</td><td>87.41°N, 92.79°E</td></tr><tr><td>mosaic_01_290120.RiSCAN</td><td>2020-01-29</td><td>Snow1</td><td>8</td><td>87.47°N, 95.18°E</td></tr><tr><td>mosaic_rov_040220.RiSCAN</td><td>2020-02-04</td><td>ROV</td><td>9</td><td>87.48°N, 95.30°E</td></tr><tr><td>mosaic_01_060220.RiSCAN</td><td>2020-02-06<sup>a</sup></td><td>Snow1, Snow2</td><td>16</td><td>87.60°N, 94.04°E</td></tr><tr><td>mosaic_01_150220.RiSCAN.RiSCAN</td><td>2020-02-15</td><td>Snow1</td><td>8</td><td>88.07°N, 79.87°E</td></tr><tr><td>mosaic_rov_220220.RiSCAN.RiSCAN</td><td>2020-02-22</td><td>ROV</td><td>9</td><td>88.58°N, 64.54°E</td></tr><tr><td>mosaic_01_280220.RiSCAN</td><td>2020-02-28</td><td>Snow1</td><td>7</td><td>88.34°N, 33.99°E</td></tr><tr><td>mosaic_rov_110320.RiSCAN</td><td>2020-03-11</td><td>ROV</td><td>1</td><td>88.27°N, 31.95°E</td></tr><tr><td>mosaic_rov_140320.RiSCAN.RiSCAN</td><td>2020-03-14</td><td>ROV</td><td>4</td><td>87.67°N, 24.59°E</td></tr><tr><td>mosaic_rov_180320.RiSCAN</td><td>2020-03-18</td><td>ROV</td><td>4</td><td>87.14°N, 17.28°E</td></tr><tr><td>mosaic_01_220320.RiSCAN</td><td>2020-03-22</td><td>Snow1</td><td>6</td><td>86.23°N, 15.70°E</td></tr><tr><td>mosaic_02_260320.RiSCAN</td><td>2020-03-26</td><td>Snow2</td><td>5</td><td>86.00°N, 13.09°E</td></tr><tr><td>mosaic_02_300320.RiSCAN.RiSCAN</td><td>2020-03-30</td><td>Snow2</td><td>5</td><td>85.38°N, 13.20°E</td></tr><tr><td>mosaic_02_040420.RiSCAN</td><td>2020-04-04<sup>a</sup></td><td>Snow2, ROV</td><td>11</td><td>84.67°N, 12.89°E</td></tr><tr><td>mosaic_01_080420.RiSCAN</td><td>2020-04-08</td><td>Snow1</td><td>4</td><td>84.48°N, 14.64°E</td></tr><tr><td>mosaic_01_080420b.RiSCAN</td><td>2020-04-08</td><td>Snow1</td><td>3</td><td>84.48°N, 14.64°E</td></tr><tr><td>mosaic_02_110420_rov.RiSCAN</td><td>2020-04-11</td><td>ROV</td><td>6</td><td>84.33°N, 14.75°E</td></tr><tr><td>mosaic_02_130420_2.RiSCAN</td><td>2020-04-13</td><td>Snow2</td><td>6</td><td>84.29°N, 14.96°E</td></tr><tr><td>mosaic_rov_170420.RiSCAN</td><td>2020-04-17</td><td>ROV</td><td>6</td><td>84.41°N, 13.67°E</td></tr><tr><td>mosaic_rs_170420.RiSCAN</td><td>2020-04-17</td><td>RS</td><td>1</td><td>84.41°N, 13.67°E</td></tr><tr><td>mosaic_rov_220420.RiSCAN</td><td>2020-04-22</td><td>ROV</td><td>6</td><td>84.13°N, 15.89°E</td></tr><tr><td>mosaic_rs_220420.RiSCAN</td><td>2020-04-22</td><td>RS</td><td>1</td><td>84.13°N, 15.89°E</td></tr><tr><td>mosaic_02_230420.RiSCAN</td><td>2020-04-23</td><td>Snow2</td><td>6</td><td>84.08°N, 16.05°E</td></tr><tr><td>mosaic_01_250420.RiSCAN.RiSCAN</td><td>2020-04-25</td><td>Snow1</td><td>4</td><td>84.00°N, 15.59°E</td></tr><tr><td>mosaic_01_260420.RiSCAN</td><td>2020-04-26</td><td>Snow1</td><td>3</td><td>83.93°N, 15.51°E</td></tr><tr><td>mosaic_rov_290420.RiSCAN</td><td>2020-04-29</td><td>ROV</td><td>7</td><td>84.02°N, 17.24°E</td></tr><tr><td>mosaic_02_300420.RiSCAN</td><td>2020-04-30</td><td>Snow2</td><td>6</td><td>83.94°N, 17.42°E</td></tr><tr><td>mosaic_rs_300420.RiSCAN</td><td>2020-04-30</td><td>RS</td><td>1</td><td>83.94°N, 17.42°E</td></tr><tr><td>mosaic_01_030520.RiSCAN</td><td>2020-05-03</td><td>Snow1</td><td>4</td><td>83.89°N, 17.96°E</td></tr><tr><td>mosaic_met_040520.RiSCAN</td><td>2020-05-04</td><td>MET</td><td>2</td><td>83.91°N, 18.35°E</td></tr><tr><td>mosaic_rov_02_090520.RiSCAN</td><td>2020-05-09</td><td>Snow2, ROV</td><td>12</td><td>83.79°N, 14.09°E</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab2\\\"><label>Table 2</label><caption><p>Reflector heights both relative to the local snow surface (Terrain Relative Height) and relative to the approximately the sea surface (Pole Height).</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th>Reflector</th><th>Pole Height (m)</th><th>Terrain Relative Height (m)</th></tr></thead><tbody><tr><td>r01</td><td>2.10</td><td>1.38</td></tr><tr><td>r03</td><td>2.03</td><td>0.99</td></tr><tr><td>r05</td><td>2.01</td><td>1.10</td></tr><tr><td>r09</td><td>1.72</td><td>1.05</td></tr><tr><td>r10</td><td>1.71</td><td>1.13</td></tr><tr><td>r11</td><td>1.81</td><td>1.51</td></tr><tr><td>r12</td><td>1.79</td><td>1.26</td></tr><tr><td>r13</td><td>2.89</td><td>1.67</td></tr><tr><td>r14</td><td>2.06</td><td>1.60</td></tr><tr><td>r15</td><td>2.47</td><td>1.73</td></tr><tr><td>r16</td><td>1.96</td><td>1.57</td></tr><tr><td>r17</td><td>1.96</td><td>1.54</td></tr><tr><td>r18</td><td>1.93</td><td>1.16</td></tr><tr><td>r19</td><td>2.06</td><td>1.56</td></tr><tr><td>r20</td><td>1.90</td><td>1.44</td></tr><tr><td>r21</td><td>1.73</td><td>1.38</td></tr><tr><td>r23</td><td>1.99</td><td>1.46</td></tr><tr><td>r24</td><td>1.85</td><td>1.55</td></tr><tr><td>r28</td><td>1.80</td><td>1.24</td></tr><tr><td>r29</td><td>1.77</td><td>1.30</td></tr><tr><td>r30</td><td>1.63</td><td>1.21</td></tr><tr><td>r31</td><td>2.36</td><td>1.48</td></tr><tr><td>r32</td><td>1.98</td><td>1.53</td></tr><tr><td>r33</td><td>2.86</td><td>1.49</td></tr><tr><td>r34</td><td>2.84</td><td>1.44</td></tr><tr><td>r35</td><td>2.09</td><td>1.53</td></tr><tr><td>r36</td><td>2.30</td><td>1.47</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab3\\\"><label>Table 3</label><caption><p>Evaluation periods for validating TLS alignment.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th>Evaluation Period</th><th>First TLS Measurement</th><th>Second TLS Measurement</th><th>First Manual Measurement</th><th>Second Manual Measurement</th></tr></thead><tbody><tr><td>1</td><td>2020-01-25</td><td>2020-02-04</td><td>2020-01-28</td><td>2020-02-05</td></tr><tr><td>2</td><td>2020-02-04</td><td>2020-03-11</td><td>2020-02-05</td><td>2020-03-07</td></tr></tbody></table></table-wrap>\",\n \"<table-wrap id=\\\"Tab4\\\"><label>Table 4</label><caption><p>Scan misalignment bias.</p></caption><table frame=\\\"hsides\\\" rules=\\\"groups\\\"><thead><tr><th>Evaluation Period</th><th>Expectation of Posterior Density of Bias</th><th>95% Credible Interval</th></tr></thead><tbody><tr><td>1</td><td>−0.001 m</td><td>[−0.009 m, 0.007 m]</td></tr><tr><td>2</td><td>−0.004 m</td><td>[−0.011 m, 0.004 m]</td></tr></tbody></table></table-wrap>\"\n]","isConversational":false},"formula":{"kind":"list like","value":["<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\sigma }_{s}^{2}=2* {(0.005m)}^{2}$$\\end{document}</tex-math><mml:math 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\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${m}_{k,i} \\sim N\\left(0,{\\sigma }_{t,k,i}^{2}\\right)$$\\end{document}</tex-math><mml:math id=\"M12\" display=\"block\"><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mi>N</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ5\"><label>5</label><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${y}_{k,i}={s}_{k,i}-{t}_{k,i}={b}_{k}+{g}_{k,i} \\sim N({b}_{k},{\\sigma }_{g,k,i}^{2})$$\\end{document}</tex-math><mml:math id=\"M14\" display=\"block\"><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>g</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mi>N</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>g</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ6\"><label>6</label><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${g}_{k,i}={n}_{k,i}-{m}_{k,i} \\sim N(0,{\\sigma }_{g,k,i}^{2})$$\\end{document}</tex-math><mml:math id=\"M16\" display=\"block\"><mml:msub><mml:mrow><mml:mi>g</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mi>N</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>g</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ7\"><label>7</label><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\sigma }_{g,k,i}^{2}={\\sigma }_{s}^{2}+{\\sigma }_{t,k,i}^{2}$$\\end{document}</tex-math><mml:math id=\"M18\" display=\"block\"><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>g</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>+</mml:mo><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p({b}_{k}|{y}_{k})$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p({b}_{k})$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p({b}_{k}|{y}_{k})$$\\end{document}</tex-math><mml:math id=\"M24\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${y}_{k}=\\{{y}_{k,1},{y}_{k,2},...,{y}_{k,N}\\}$$\\end{document}</tex-math><mml:math id=\"M26\"><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo>{</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mo>.</mml:mo><mml:mspace width=\"-0.25em\"/><mml:mo>.</mml:mo><mml:mspace width=\"-0.25em\"/><mml:mo>.</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\tau }_{0}^{2}={(0.02m)}^{2}$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:msubsup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>0.02</mml:mn><mml:mi>m</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ8\"><label>8</label><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p({b}_{k}|{y}_{k})\\propto p({b}_{k})p({y}_{k}|{b}_{k})$$\\end{document}</tex-math><mml:math id=\"M30\" display=\"block\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>∝</mml:mo><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ9\"><label>9</label><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\propto \\quad p({b}_{k})\\mathop{\\prod }\\limits_{i=1}^{N}p({b}_{k}|{y}_{k})$$\\end{document}</tex-math><mml:math id=\"M32\" display=\"block\"><mml:mo>∝</mml:mo><mml:mspace width=\"1.0em\"/><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo>∏</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:munderover><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ10\"><label>10</label><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\propto \\quad exp\\left(-\\frac{{b}_{k}^{2}}{2{\\tau }_{0}^{2}}\\right)\\mathop{\\prod }\\limits_{i=1}^{N}exp\\left(-\\frac{1}{{\\sigma }_{g,k,i}^{2}}{({y}_{k,i}-{b}_{k})}^{2}\\right)$$\\end{document}</tex-math><mml:math id=\"M34\" display=\"block\"><mml:mo>∝</mml:mo><mml:mspace width=\"1.0em\"/><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>p</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:msubsup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo>∏</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:munderover><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>p</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>g</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfrac><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>","<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p({b}_{k}|{y}_{k})$$\\end{document}</tex-math><mml:math id=\"M36\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p({b}_{k}|{y}_{k})$$\\end{document}</tex-math><mml:math id=\"M38\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>","<disp-formula id=\"Equ11\"><label>11</label><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p({b}_{k}|{y}_{k})=N({\\mu }_{k},{\\tau }_{k}^{2})$$\\end{document}</tex-math><mml:math id=\"M40\" display=\"block\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mi>N</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>μ</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msubsup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ12\"><label>12</label><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mu }_{k}={\\tau }_{k}^{2}\\mathop{\\sum }\\limits_{i=1}^{N}\\frac{{y}_{k,i}}{{\\sigma }_{g,k,i}^{2}}$$\\end{document}</tex-math><mml:math id=\"M42\" display=\"block\"><mml:msub><mml:mrow><mml:mi>μ</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:munderover><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>g</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfrac></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equ13\"><label>13</label><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{1}{{\\tau }_{k}^{2}}=\\frac{1}{{\\tau }_{0}^{2}}+\\mathop{\\sum }\\limits_{i=0}^{N}\\frac{1}{{\\sigma }_{g,k,i}^{2}}$$\\end{document}</tex-math><mml:math id=\"M44\" display=\"block\"><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msubsup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msubsup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:munderover><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>g</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfrac></mml:math></alternatives></disp-formula>"],"string":"[\n \"<inline-formula id=\\\"IEq1\\\"><alternatives><tex-math id=\\\"M1\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\sigma }_{s}^{2}=2* {(0.005m)}^{2}$$\\\\end{document}</tex-math><mml:math id=\\\"M2\\\"><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mn>2</mml:mn><mml:mo>*</mml:mo><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>0.005</mml:mn><mml:mi>m</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq2\\\"><alternatives><tex-math id=\\\"M3\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\sigma }_{t,k,i}^{2}$$\\\\end{document}</tex-math><mml:math id=\\\"M4\\\"><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ1\\\"><label>1</label><alternatives><tex-math id=\\\"M5\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${s}_{k,i}={r}_{k,i}+{n}_{k,i}$$\\\\end{document}</tex-math><mml:math id=\\\"M6\\\" display=\\\"block\\\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ2\\\"><label>2</label><alternatives><tex-math id=\\\"M7\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${n}_{k,i} \\\\sim N\\\\left(0,{\\\\sigma }_{s}^{2}\\\\right)$$\\\\end{document}</tex-math><mml:math id=\\\"M8\\\" display=\\\"block\\\"><mml:msub><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mi>N</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ3\\\"><label>3</label><alternatives><tex-math id=\\\"M9\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${t}_{k,i}={r}_{k,i}+{m}_{k,i}-{b}_{k}$$\\\\end{document}</tex-math><mml:math id=\\\"M10\\\" display=\\\"block\\\"><mml:msub><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ4\\\"><label>4</label><alternatives><tex-math id=\\\"M11\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${m}_{k,i} \\\\sim N\\\\left(0,{\\\\sigma }_{t,k,i}^{2}\\\\right)$$\\\\end{document}</tex-math><mml:math id=\\\"M12\\\" display=\\\"block\\\"><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mi>N</mml:mi><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ5\\\"><label>5</label><alternatives><tex-math id=\\\"M13\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${y}_{k,i}={s}_{k,i}-{t}_{k,i}={b}_{k}+{g}_{k,i} \\\\sim N({b}_{k},{\\\\sigma }_{g,k,i}^{2})$$\\\\end{document}</tex-math><mml:math id=\\\"M14\\\" display=\\\"block\\\"><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>g</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mi>N</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>g</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ6\\\"><label>6</label><alternatives><tex-math id=\\\"M15\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${g}_{k,i}={n}_{k,i}-{m}_{k,i} \\\\sim N(0,{\\\\sigma }_{g,k,i}^{2})$$\\\\end{document}</tex-math><mml:math id=\\\"M16\\\" display=\\\"block\\\"><mml:msub><mml:mrow><mml:mi>g</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mi>N</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>g</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ7\\\"><label>7</label><alternatives><tex-math id=\\\"M17\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\sigma }_{g,k,i}^{2}={\\\\sigma }_{s}^{2}+{\\\\sigma }_{t,k,i}^{2}$$\\\\end{document}</tex-math><mml:math id=\\\"M18\\\" display=\\\"block\\\"><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>g</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>+</mml:mo><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq3\\\"><alternatives><tex-math id=\\\"M19\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$p({b}_{k}|{y}_{k})$$\\\\end{document}</tex-math><mml:math id=\\\"M20\\\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq4\\\"><alternatives><tex-math id=\\\"M21\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$p({b}_{k})$$\\\\end{document}</tex-math><mml:math id=\\\"M22\\\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq5\\\"><alternatives><tex-math id=\\\"M23\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$p({b}_{k}|{y}_{k})$$\\\\end{document}</tex-math><mml:math id=\\\"M24\\\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq6\\\"><alternatives><tex-math id=\\\"M25\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${y}_{k}=\\\\{{y}_{k,1},{y}_{k,2},...,{y}_{k,N}\\\\}$$\\\\end{document}</tex-math><mml:math id=\\\"M26\\\"><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mo>{</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mo>.</mml:mo><mml:mspace width=\\\"-0.25em\\\"/><mml:mo>.</mml:mo><mml:mspace width=\\\"-0.25em\\\"/><mml:mo>.</mml:mo><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq7\\\"><alternatives><tex-math id=\\\"M27\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\tau }_{0}^{2}={(0.02m)}^{2}$$\\\\end{document}</tex-math><mml:math id=\\\"M28\\\"><mml:msubsup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>0.02</mml:mn><mml:mi>m</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ8\\\"><label>8</label><alternatives><tex-math id=\\\"M29\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$p({b}_{k}|{y}_{k})\\\\propto p({b}_{k})p({y}_{k}|{b}_{k})$$\\\\end{document}</tex-math><mml:math id=\\\"M30\\\" display=\\\"block\\\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>∝</mml:mo><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ9\\\"><label>9</label><alternatives><tex-math id=\\\"M31\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\propto \\\\quad p({b}_{k})\\\\mathop{\\\\prod }\\\\limits_{i=1}^{N}p({b}_{k}|{y}_{k})$$\\\\end{document}</tex-math><mml:math id=\\\"M32\\\" display=\\\"block\\\"><mml:mo>∝</mml:mo><mml:mspace width=\\\"1.0em\\\"/><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:munderover accent=\\\"false\\\" accentunder=\\\"false\\\"><mml:mrow><mml:mo>∏</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:munderover><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equ10\\\"><label>10</label><alternatives><tex-math id=\\\"M33\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$\\\\propto \\\\quad exp\\\\left(-\\\\frac{{b}_{k}^{2}}{2{\\\\tau }_{0}^{2}}\\\\right)\\\\mathop{\\\\prod }\\\\limits_{i=1}^{N}exp\\\\left(-\\\\frac{1}{{\\\\sigma }_{g,k,i}^{2}}{({y}_{k,i}-{b}_{k})}^{2}\\\\right)$$\\\\end{document}</tex-math><mml:math id=\\\"M34\\\" display=\\\"block\\\"><mml:mo>∝</mml:mo><mml:mspace width=\\\"1.0em\\\"/><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>p</mml:mi><mml:mrow><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:msubsup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow><mml:munderover accent=\\\"false\\\" accentunder=\\\"false\\\"><mml:mrow><mml:mo>∏</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:munderover><mml:mi>e</mml:mi><mml:mi>x</mml:mi><mml:mi>p</mml:mi><mml:mrow><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msubsup><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>g</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfrac><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<inline-formula id=\\\"IEq8\\\"><alternatives><tex-math id=\\\"M35\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$p({b}_{k}|{y}_{k})$$\\\\end{document}</tex-math><mml:math id=\\\"M36\\\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<inline-formula id=\\\"IEq9\\\"><alternatives><tex-math id=\\\"M37\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$p({b}_{k}|{y}_{k})$$\\\\end{document}</tex-math><mml:math id=\\\"M38\\\"><mml:mi>p</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>b</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>|</mml:mo><mml:msub><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>\",\n \"<disp-formula id=\\\"Equ11\\\"><label>11</label><alternatives><tex-math id=\\\"M39\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$$p({b}_{k}|{y}_{k})=N({\\\\mu }_{k},{\\\\tau }_{k}^{2})$$\\\\end{document}</tex-math><mml:math id=\\\"M40\\\" 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\\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\mu }_{k}={\\\\tau }_{k}^{2}\\\\mathop{\\\\sum }\\\\limits_{i=1}^{N}\\\\frac{{y}_{k,i}}{{\\\\sigma }_{g,k,i}^{2}}$$\\\\end{document}</tex-math><mml:math id=\\\"M42\\\" display=\\\"block\\\"><mml:msub><mml:mrow><mml:mi>μ</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mi>τ</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:munderover accent=\\\"false\\\" 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Least-Squares Fitting of Two 3-D Point Sets. \"], \"italic\": [\"IEEE Transactions on Pattern Analysis and Machine Intelligence\"], \"bold\": [\"PAMI-9\"]}, {\"label\": [\"33.\"], \"surname\": [\"Matheron\"], \"given-names\": [\"G\"], \"article-title\": [\"Principles of geostatistics\"], \"source\": [\"Economic Geology\"], \"year\": [\"1963\"], \"volume\": [\"58\"], \"fpage\": [\"1246\"], \"lpage\": [\"1266\"], \"pub-id\": [\"10.2113/gsecongeo.58.8.1246\"]}, {\"label\": [\"34.\"], \"mixed-citation\": [\"Rasmussen, C. E. & Williams, C. K. \"], \"italic\": [\"Gaussian Processes for Machine Learning\"]}, {\"label\": [\"35.\"], \"surname\": [\"Landy\", \"Ehn\", \"Shields\", \"Barber\"], \"given-names\": [\"J\", \"J\", \"M\", \"D\"], \"article-title\": [\"Surface and melt pond evolution on landfast first-year sea ice in the Canadian Arctic Archipelago\"], \"source\": [\"Journal of Geophysical Research: Oceans\"], \"year\": [\"2014\"], \"volume\": [\"119\"], \"fpage\": [\"3054\"], \"lpage\": [\"3075\"], \"pub-id\": [\"10.1002/2013JC009617\"]}, {\"label\": [\"36.\"], \"surname\": [\"Filhol\", \"Sturm\"], \"given-names\": [\"S\", \"M\"], \"article-title\": [\"Snow bedforms: A review, new data, and a formation model\"], \"source\": [\"Journal of Geophysical Research: Earth Surface\"], \"year\": [\"2015\"], \"volume\": [\"120\"], \"fpage\": [\"1645\"], \"lpage\": [\"1669\"], \"pub-id\": [\"10.1002/2015JF003529\"]}, {\"label\": [\"37.\"], \"mixed-citation\": [\"Gardner, J. R., Pleiss, G., Bindel, D., Weinberger, K. Q. & Wilson, A. G. GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration. \"], \"italic\": [\"arXiv:1809.11165 [cs, stat]\"]}, {\"label\": [\"38.\"], \"surname\": [\"Charlier\", \"Feydy\", \"Glaun\\u2019es\", \"Collin\", \"Durif\"], \"given-names\": [\"B\", \"J\", \"JA\", \"F-D\", \"G\"], \"article-title\": [\"Kernel Operations on the GPU, with Autodiff, without Memory Overflows\"], \"source\": [\"Journal of Machine Learning Research\"], \"year\": [\"2021\"], \"volume\": [\"22\"], \"fpage\": [\"1\"], \"lpage\": [\"6\"]}, {\"label\": [\"39.\"], \"mixed-citation\": [\"Raphael, I., Perovich, D. K., Polashenski, C. M., Clemens-Sewall, D. & Planck, C. An autonomous sea ice mass balance observation network. In \"], \"italic\": [\"AGU Fall Meeting 2021\"]}, {\"label\": [\"40.\"], \"mixed-citation\": [\"Sturm, M. & Holmgren, J. An Automatic Snow Depth Probe for Field Validation Campaigns. \"], \"italic\": [\"Water Resources Research\"], \"bold\": [\"54\"]}, {\"label\": [\"41.\"], \"mixed-citation\": [\"Raphael, I. \"], \"italic\": [\"et al\", \"EGU General Assembly Conference Abstracts\"]}, {\"label\": [\"42.\"], \"mixed-citation\": [\"Rousseeuw, P. J. & Croux, C. Alternatives to the Median Absolute Deviation. \"], \"italic\": [\"Journal of the American Statistical Association\"], \"bold\": [\"88\"]}, {\"label\": [\"43.\"], \"surname\": [\"Nixdorf\"], \"given-names\": [\"U\"], \"year\": [\"2021\"], \"data-title\": [\"MOSAiC Extended Acknowledgement\"], \"source\": [\"Zenodo\"], \"pub-id\": [\"10.5281/zenodo.5541624\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":43,"string":"43"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:58"},"citation":{"kind":"string","value":"Sci Data. 2024 Jan 13; 11:70"},"package_file":{"kind":"string","value":"oa_package/2b/ae/PMC10787767.tar.gz"}}},{"rowIdx":98,"cells":{"accession_id":{"kind":"string","value":"PMC10787768"},"pmid":{"kind":"string","value":"38218967"},"introduction":{"kind":"list like","value":["<title>Introduction</title>","<p id=\"Par3\">Skin provides convenient access for delivering most biotherapeutics and vaccines, typically via a hypodermic needle. However, ensuring patient compliance with long-term, repetitive pharmaceutical treatments remains a significant challenge. Hypodermic injection provides a low-cost and rapid method of drug delivery, but concerns about safe disposal, potential transmission of bloodborne pathogens, and the need for trained personnel hinder its widespread implementation. Recently, advancements in transdermal drug delivery strategies, such as sonophoresis<sup>##REF##30694397##1##,##UREF##0##2##</sup>, iontophoresis<sup>##REF##33486687##3##,##REF##35019362##4##</sup>, electroporation<sup>##REF##20158490##5##,##REF##32505795##6##</sup>, photomechanical waves<sup>##REF##24490743##7##,##REF##10571277##8##</sup>, heat<sup>##REF##26506371##9##,##REF##32696221##10##</sup>, microneedles (MN), and others<sup>##REF##34321111##11##</sup>, have improved drug permeation through skin, providing safe and painless operations<sup>##REF##33340568##12##</sup> with easy-to-use procedures<sup>##UREF##1##13##,##UREF##2##14##</sup>. However, the lack of an automated mechanism for active, precise, and coordinated drug administration over extended periods hinders their applicability for chronic pharmaceutical management. This problem becomes particularly critical for chronic diseases<sup>##REF##32882162##15##,##UREF##3##16##</sup>, including diabetes<sup>##UREF##4##17##</sup>, hyperlipidemia<sup>##REF##26149914##18##</sup>, asthma<sup>##REF##32557846##19##</sup>, depression<sup>##REF##33946703##20##</sup>, and others, where repetitive drug administrations are required, and a dynamically personalized delivery schedule could improve drug efficacy and decrease drug toxicity<sup>##REF##30415656##21##</sup>. However, most existing drug delivery devices have limited capability to automate delivery digitally, especially outside hospital settings and in a comfortable, long-lasting fashion<sup>##REF##34481032##22##</sup>.</p>","<p id=\"Par4\">Microneedles have shown great promise in facilitating the delivery of various types of drugs, including small molecules<sup>##REF##30948808##23##,##REF##31723599##24##</sup>, peptides<sup>##REF##26100900##25##</sup>, nucleic acids<sup>##REF##24291361##26##,##REF##26122168##27##</sup>, and nano composites<sup>##REF##19768415##28##–##UREF##5##33##</sup>. Furthermore, modulating the structural integration and chemical functionalization of MNs enables a broad range of release profiles. For example, MNs with a core-shell structure that hosts a drug reservoir inside each needle can exhibit a pre-programmed, multi-step release profile with tunability via designing the degradation time of MN shell layers<sup>##REF##33230304##34##</sup>. This method also allows the integration of multiple drugs to enable sequential release as a combined therapy<sup>##REF##32832606##35##</sup>. However, the complexity involved in fabrication poses challenges for manufacturing scalability, and once deployed, it becomes difficult to modify the pre-programmed release time of the microneedles. Chemical functionalization on MNs provides a solution to introduce self-sensing and self-responsiveness capabilities. For insulin delivery, researchers use specific chemical groups to functionalize the material of MNs, such as phenylboronic acid or aminoimidazole<sup>##REF##26100900##25##,##REF##32015407##36##</sup>, which react with glucose in body biofluids and induce structural changes in the polymer network of MNs<sup>##REF##29176623##32##</sup>. This triggers the release of embedded drugs in response to glucose levels in the surrounding environment, enabling convenient and self-regulated long-term drug release to control chronic diseases. However, the complexity of synthesis and the reliance on local microenvironments rather than global body physiology limit its practical applicability.</p>","<p id=\"Par5\">In addition to passive drug release, the potential of active control to deliver drugs on demand through external triggers has paved the way for closed-loop therapeutic systems when integrated with associated health monitors, enhancing treatment precision and dynamics. The heat-triggered release represents a typical example where MNs with drug loaded inside a thermally responsive material (e.g., Expancel microspheres and paraffin C<sub>23</sub><sup>##REF##18334398##37##,##UREF##6##38##</sup>, which exhibit significant expansion in pore or volumetric size upon heating at, typically, 47 °C, to secrete the drug accordingly. Mechanisms such as these allow drug release to be controlled with thermal triggers via an electrical heater or optical illumination<sup>##REF##28345030##39##,##REF##25422476##40##</sup>. However, the inability to effectively focus thermal energy in a confined space of biological tissue precludes its delivery precision and safety. Thin membranes based on biocompatible metals serving as a gate for drug reservoirs can enable actively controlled drug delivery<sup>##UREF##7##41##,##UREF##8##42##</sup>. The metallic gates can disintegrate or dissolve upon an electrical trigger<sup>##REF##32923633##43##</sup>. Demonstrated devices exploit various biocompatible metals, including Mg (~30 μm), Mo (~10 μm), and Au (~300 nm), as the metal gates to form electronic implants that enable on-demand drug delivery<sup>##REF##28458955##44##,##REF##22344516##45##</sup>. Opening of the metallic gates by either anodic oxidation (typically for Mg and Mo) or corrosion-induced crevices (typically for gold) via electrical triggers can effectively initiate the active release behavior. The compatible integration via microfabrication technologies enables such drug-releasing mechanisms with high spatiotemporal controllability via small electrical signals (typically, +1.04 V vs. SCE)<sup>##REF##32923633##43##,##REF##9988626##46##</sup>, which could potentially realize pharmaceutical automation in space and time.</p>","<p id=\"Par6\">Here, we present a skin-interfaced drug delivery system that utilizes electrically triggered, gated MNs to realize on-demand drug delivery with high spatiotemporal controllability. The drug delivery system, named spatiotemporal on-demand patch (SOP), uses a thin gold layer (~150 nm) coated onto MNs to enable drug encapsulation and protection at the standby stage. Small electrical triggers (~2.5 V, DC) for 30 s effectively disintegrate the gold coating, which exposes the drug to initiate delivery. Microfabrication processes enable circuitry designs of the gold layer to realize release triggering of individual MNs or subsections through a wireless communication module (e.g., Near-field communication, Bluetooth Low Energy). Direct deposition of the gold layer onto MNs overcomes limitations in the fabrication complexity and device robustness associated with the reservoir systems with free-standing metallic gates, as reported previously in implantable devices<sup>##REF##32923633##43##</sup>. Ultrafine spatial control (<1 mm<sup>2</sup>) of drug release of single MN, active management with high temporal (less than 30 s) resolution of drug release, wireless operation, and comfort wearability highlight the enabling capabilities of the SOP. Both benchtop experiments using a fluorescent dye and in vivo study through intracranial injection demonstrate the high potential of SOP as a general, fully wireless, wearable platform for personalized, chronic drug delivery to improve pharmaceutical efficacy and user adherence. Moreover, in vivo demonstration via intracranial injection of SOP reveals its potential utility for neural therapy and modulation. The high spatiotemporal resolution and SOP’s on-demand drug release feature make it an advanced tool for brain research with model animals, especially in studying neural circuits mapping and cause-and-effect relationships between neural activity and behavior<sup>##REF##33479237##47##</sup>. Capabilities offered by SOP to deliver therapeutic agents sequentially and proactively to specific brain regions associated with neurological disorders may deepen our understanding of the underlying mechanisms of their pathologies. This insight can lead to the development of more targeted treatments for disorders like Parkinson’s disease, epilepsy, depression, and Alzheimer’s disease<sup>##REF##18485316##48##–##REF##27188934##52##</sup>.</p>"],"string":"[\n \"<title>Introduction</title>\",\n \"<p id=\\\"Par3\\\">Skin provides convenient access for delivering most biotherapeutics and vaccines, typically via a hypodermic needle. However, ensuring patient compliance with long-term, repetitive pharmaceutical treatments remains a significant challenge. Hypodermic injection provides a low-cost and rapid method of drug delivery, but concerns about safe disposal, potential transmission of bloodborne pathogens, and the need for trained personnel hinder its widespread implementation. Recently, advancements in transdermal drug delivery strategies, such as sonophoresis<sup>##REF##30694397##1##,##UREF##0##2##</sup>, iontophoresis<sup>##REF##33486687##3##,##REF##35019362##4##</sup>, electroporation<sup>##REF##20158490##5##,##REF##32505795##6##</sup>, photomechanical waves<sup>##REF##24490743##7##,##REF##10571277##8##</sup>, heat<sup>##REF##26506371##9##,##REF##32696221##10##</sup>, microneedles (MN), and others<sup>##REF##34321111##11##</sup>, have improved drug permeation through skin, providing safe and painless operations<sup>##REF##33340568##12##</sup> with easy-to-use procedures<sup>##UREF##1##13##,##UREF##2##14##</sup>. However, the lack of an automated mechanism for active, precise, and coordinated drug administration over extended periods hinders their applicability for chronic pharmaceutical management. This problem becomes particularly critical for chronic diseases<sup>##REF##32882162##15##,##UREF##3##16##</sup>, including diabetes<sup>##UREF##4##17##</sup>, hyperlipidemia<sup>##REF##26149914##18##</sup>, asthma<sup>##REF##32557846##19##</sup>, depression<sup>##REF##33946703##20##</sup>, and others, where repetitive drug administrations are required, and a dynamically personalized delivery schedule could improve drug efficacy and decrease drug toxicity<sup>##REF##30415656##21##</sup>. However, most existing drug delivery devices have limited capability to automate delivery digitally, especially outside hospital settings and in a comfortable, long-lasting fashion<sup>##REF##34481032##22##</sup>.</p>\",\n \"<p id=\\\"Par4\\\">Microneedles have shown great promise in facilitating the delivery of various types of drugs, including small molecules<sup>##REF##30948808##23##,##REF##31723599##24##</sup>, peptides<sup>##REF##26100900##25##</sup>, nucleic acids<sup>##REF##24291361##26##,##REF##26122168##27##</sup>, and nano composites<sup>##REF##19768415##28##–##UREF##5##33##</sup>. Furthermore, modulating the structural integration and chemical functionalization of MNs enables a broad range of release profiles. For example, MNs with a core-shell structure that hosts a drug reservoir inside each needle can exhibit a pre-programmed, multi-step release profile with tunability via designing the degradation time of MN shell layers<sup>##REF##33230304##34##</sup>. This method also allows the integration of multiple drugs to enable sequential release as a combined therapy<sup>##REF##32832606##35##</sup>. However, the complexity involved in fabrication poses challenges for manufacturing scalability, and once deployed, it becomes difficult to modify the pre-programmed release time of the microneedles. Chemical functionalization on MNs provides a solution to introduce self-sensing and self-responsiveness capabilities. For insulin delivery, researchers use specific chemical groups to functionalize the material of MNs, such as phenylboronic acid or aminoimidazole<sup>##REF##26100900##25##,##REF##32015407##36##</sup>, which react with glucose in body biofluids and induce structural changes in the polymer network of MNs<sup>##REF##29176623##32##</sup>. This triggers the release of embedded drugs in response to glucose levels in the surrounding environment, enabling convenient and self-regulated long-term drug release to control chronic diseases. However, the complexity of synthesis and the reliance on local microenvironments rather than global body physiology limit its practical applicability.</p>\",\n \"<p id=\\\"Par5\\\">In addition to passive drug release, the potential of active control to deliver drugs on demand through external triggers has paved the way for closed-loop therapeutic systems when integrated with associated health monitors, enhancing treatment precision and dynamics. The heat-triggered release represents a typical example where MNs with drug loaded inside a thermally responsive material (e.g., Expancel microspheres and paraffin C<sub>23</sub><sup>##REF##18334398##37##,##UREF##6##38##</sup>, which exhibit significant expansion in pore or volumetric size upon heating at, typically, 47 °C, to secrete the drug accordingly. Mechanisms such as these allow drug release to be controlled with thermal triggers via an electrical heater or optical illumination<sup>##REF##28345030##39##,##REF##25422476##40##</sup>. However, the inability to effectively focus thermal energy in a confined space of biological tissue precludes its delivery precision and safety. Thin membranes based on biocompatible metals serving as a gate for drug reservoirs can enable actively controlled drug delivery<sup>##UREF##7##41##,##UREF##8##42##</sup>. The metallic gates can disintegrate or dissolve upon an electrical trigger<sup>##REF##32923633##43##</sup>. Demonstrated devices exploit various biocompatible metals, including Mg (~30 μm), Mo (~10 μm), and Au (~300 nm), as the metal gates to form electronic implants that enable on-demand drug delivery<sup>##REF##28458955##44##,##REF##22344516##45##</sup>. Opening of the metallic gates by either anodic oxidation (typically for Mg and Mo) or corrosion-induced crevices (typically for gold) via electrical triggers can effectively initiate the active release behavior. The compatible integration via microfabrication technologies enables such drug-releasing mechanisms with high spatiotemporal controllability via small electrical signals (typically, +1.04 V vs. SCE)<sup>##REF##32923633##43##,##REF##9988626##46##</sup>, which could potentially realize pharmaceutical automation in space and time.</p>\",\n \"<p id=\\\"Par6\\\">Here, we present a skin-interfaced drug delivery system that utilizes electrically triggered, gated MNs to realize on-demand drug delivery with high spatiotemporal controllability. The drug delivery system, named spatiotemporal on-demand patch (SOP), uses a thin gold layer (~150 nm) coated onto MNs to enable drug encapsulation and protection at the standby stage. Small electrical triggers (~2.5 V, DC) for 30 s effectively disintegrate the gold coating, which exposes the drug to initiate delivery. Microfabrication processes enable circuitry designs of the gold layer to realize release triggering of individual MNs or subsections through a wireless communication module (e.g., Near-field communication, Bluetooth Low Energy). Direct deposition of the gold layer onto MNs overcomes limitations in the fabrication complexity and device robustness associated with the reservoir systems with free-standing metallic gates, as reported previously in implantable devices<sup>##REF##32923633##43##</sup>. Ultrafine spatial control (<1 mm<sup>2</sup>) of drug release of single MN, active management with high temporal (less than 30 s) resolution of drug release, wireless operation, and comfort wearability highlight the enabling capabilities of the SOP. Both benchtop experiments using a fluorescent dye and in vivo study through intracranial injection demonstrate the high potential of SOP as a general, fully wireless, wearable platform for personalized, chronic drug delivery to improve pharmaceutical efficacy and user adherence. Moreover, in vivo demonstration via intracranial injection of SOP reveals its potential utility for neural therapy and modulation. The high spatiotemporal resolution and SOP’s on-demand drug release feature make it an advanced tool for brain research with model animals, especially in studying neural circuits mapping and cause-and-effect relationships between neural activity and behavior<sup>##REF##33479237##47##</sup>. Capabilities offered by SOP to deliver therapeutic agents sequentially and proactively to specific brain regions associated with neurological disorders may deepen our understanding of the underlying mechanisms of their pathologies. This insight can lead to the development of more targeted treatments for disorders like Parkinson’s disease, epilepsy, depression, and Alzheimer’s disease<sup>##REF##18485316##48##–##REF##27188934##52##</sup>.</p>\"\n]","isConversational":false},"methods":{"kind":"list like","value":["<title>Methods</title>","<p id=\"Par34\">The sample size used in this study is based on the expected variations between animals and is comparable to many previous reports using similar techniques (cited in the corresponding sections). Sample size of each experiments can be found in the Figure legends. Data from animals were excluded based on histological criteria that included injection sites, virus expression and optical fiber placement. Only animals with injection sites/virus expression/optical fiber placement in the region of interest were included, based on our previous reports. The experiments were not randomized. Animal were allocated into experimental groups by matched gender, age, weight, etc. Investigators were blinded to the experimental groups until all data had been collected and analyzed.</p>","<p id=\"Par35\">C57BL/6 mice (8–10 weeks, females) were used for all animal experiments. Animals were group-housed at constant temperature (22–24 °C) and humidity (40–60%), and bred in a dedicated husbandry facility with 12/12-h light-dark cycles with food and water ad libitum and under veterinary supervision. Animals subjected to surgical procedures were moved to a satellite housing facility for recovery with the same light-dark cycle. All procedures were conducted in accordance with the NIH Guide for the Care and Use of Laboratory Animals and with the approval of the Institutional Animal Care and Use Committee at the University of North Carolina at Chapel Hill (UNC), under the protocol # 22–146<sup>##REF##37061718##84##</sup>.</p>","<title>Fabrication of the SOP</title>","<title>Normal MN patch</title>","<p id=\"Par36\">The general procedure of MN fabrication was demonstrated in Supplementary Fig. ##SUPPL##0##2##. First, 50 g of PDMS (Sylgard 184, Dow Corning) was fully cured in a glass petri dish at 60 °C for 2 h with 5 g of its corresponding curing agent. Then, a negative MN mold was patterned on the cured PDMS by a UV laser ablation system (SFX-5UV, Luoyang Xincheng Precision Machinery). The MN molds were fabricated with different depths from 0.5 to 3.5 mm, a base diameter of around 0.25 mm, and an inter-needle spacing of 1 mm. The depth of the MN mold can be controlled by tuning the loops and power of UV laser ablation. The UV ablation was followed by acetone sonication for at least 5 min to clean up the surface of the PDMS negative mold. Then, a PLGA (PLGA, Mw = 50 ~ 75 kDa, ester terminated, Sigma Aldrich) solution (10 wt% in acetone, VWR) was drop cast on the PDMS negative mold in the petri dish. The PLGA-covered mold was heated at 45 °C and 60–160 mmHg for around 2 min to let the PLGA solution fill in the mold and evaporate. The entire PDMS mold was capped by another petri dish to slow down the evaporation of acetone. The evaporation process was followed by a refill of PLGA solution. The evaporation-refilling cycle was conducted 10–20 times to provide enough PLGA for the MN patch, with a thickness from 0.6 to 1.2 mm. After that, the PLGA-covered mold was kept in the oven at 45 °C and 1 atm for at least 8 h to dry the surface. The mold was then frozen at −20 °C for at least 30 min to harden the PLGA patch, which was subsequently extracted from the mold. The free-standing PLGA patch was allowed to further dry up on both sides at 45 °C and 1 atm for another 24 h, then trimmed by UV laser ablation. The hardened and dry PLGA patch was eventually deposited with a layer of gold (usually 150 nm in thickness) by sputter coating (PVD 75 sputterer, Kurt J. Lesker). The Gold traces were patterned by an IR laser ablation system (SFX-50GS, Luoyang Xincheng Precision Machinery).</p>","<title>Melatonin-loaded MN</title>","<p id=\"Par37\">A melatonin loaded PLGA solution was prepared in advance. The melatonin (Sigma Aldrich) was mixed with PLGA (Sigma Aldrich) at a ratio by weight from 1:10 to 1:2. Then, the mixture was dissolved in acetone (VWR) at a 1:10 ratio by weight. The solution is stored in refrigerator at around 4 °C no longer than 12 h, and wrapped with aluminum foil to avoid light.</p>","<p id=\"Par38\">First, 50 g of PDMS (Sylgard 184, Dow Corning) was fully cured in a glass petri dish at 60 °C for 2 h with 5 g of its corresponding curing agent. Then, a negative MN mold was patterned on the cured PDMS by a UV laser ablation system (SFX-5UV, Luoyang Xincheng Precision Machinery). The MN molds were fabricated with a depth around 3 mm, a base diameter of around 0.35–0.5 mm, and an inter-needle spacing of at least 5 mm. The depth of the MN mold can be controlled by tuning the loops and power of UV laser ablation. The UV ablation was followed by acetone sonication for at least 5 min to clean up the surface of the PDMS negative mold. Then, the melatonin-PLGA solution (10 wt% in acetone) was drop cast on the PDMS negative mold in the petri dish. The PLGA-covered mold was heated at 30 °C and 60–160 mmHg for around 2 min to let the PLGA solution fill in the mold and evaporate. The entire PDMS mold was capped by another petri dish to slow down the evaporation of acetone. The evaporation process was followed by a refill of PLGA solution. The evaporation-refilling cycle was conducted 10–20 times to provide enough PLGA for the MN patch, with a thickness from 0.6 to 1.2 mm. After that, the PLGA-covered mold was kept in the oven at 30 °C and 1 atm for at least 8 h to dry the surface. The mold was then frozen at −20 °C for at least 30 min to harden the PLGA patch, which was subsequently extracted from the mold. The free-standing PLGA patch was allowed to further dry up on both sides at 30 °C and 1 atm for another 24–48 h, then trimmed by UV laser ablation. The hardened and dry PLGA patch was eventually deposited with a layer of gold (usually 150–200 nm in thickness) by sputter coating (PVD 75 sputterer, Kurt J. Lesker). The Gold traces were patterned by an IR laser ablation system (SFX-50GS, Luoyang Xincheng Precision Machinery).</p>","<title>Rhodamine B-loaded MN</title>","<p id=\"Par39\">A Rhodamine B (Thermo scientific) loaded PLGA solution was prepared in advance. Rhodamine B was dissolved in the acetone (VWR) solution of PLGA (usually at 1/300 ratio by weight, Sigma Aldrich).</p>","<p id=\"Par40\">First, 50 g of PDMS (Sylgard 184, Dow Corning) was fully cured in a glass petri dish at 60 °C for 2 h with 5 g of its corresponding curing agent. Then, a negative MN mold was patterned on the cured PDMS by a UV laser ablation system (SFX-5UV, Luoyang Xincheng Precision Machinery). The MN molds were fabricated with different depths from 0.5 to 3.5 mm, a base diameter of around 0.25 mm, and an inter-needle spacing of 1 mm. The depth of the MN mold can be controlled by tuning the loops and power of UV laser ablation. The UV ablation was followed by acetone sonication for at least 5 min to clean up the surface of the PDMS negative mold. Then, the Rhodamine B-PLGA solution (10 wt% in acetone) was drop cast on the PDMS negative mold in the petri dish. The PLGA-covered mold was heated at 45 °C and 60–160 mmHg for around 2 min to let the PLGA solution fill in the mold and evaporate. The entire PDMS mold was capped by another petri dish to slow down the evaporation of acetone. The evaporation process was followed by a refill of PLGA solution. The evaporation-refilling cycle was conducted 10–20 times to provide enough PLGA for the MN patch, with a thickness from 0.6 to 1.2 mm. After that, the PLGA-covered mold was kept in the oven at 45 °C and 1 atm for at least 8 h to dry the surface. The mold was then frozen at −20 °C for at least 30 min to harden the PLGA patch, which was subsequently extracted from the mold. The free-standing PLGA patch was allowed to further dry up on both sides at 45 °C and 1 atm for another 24 h, then trimmed by UV laser ablation. The hardened and dry PLGA patch was eventually deposited with a layer of gold (usually 150 nm in thickness) by sputter coating (PVD 75 sputterer, Kurt J. Lesker). The Gold traces were patterned by an IR laser ablation system (SFX-50GS, Luoyang Xincheng Precision Machinery). For controlled dye release model, a layer of PDMS (1:10, ~ 15 µm) was carefully, manually coated and cured on specific positions that should be covered.</p>","<title>Wireless on-demand patch</title>","<p id=\"Par41\">A commercially available Pyralux Kapton soft PCB material made of polyimide sandwiched between copper was sprayed on and coated with masking paint (Krylon). The paint mask was then partially removed using an infrared laser cutter to expose unwanted copper, which was removed by etching in ferric chloride solution (MG Chemicals 415) for 15 min. The soft PCB was then rinsed with water and acetone to remove the remaining etchant and paint mask. Surface mount electrical components, including diodes and power regulators, were soldered using solder paste and hot air guns. An MN array (1.2-mm, 150 nm gold coated) was integrated into the wireless patch using PI-based adhesives. A small jumper wire was attached to the MN array using silver-based conductive adhesives to ensure conductivity. Another MN array was integrated into the wireless patch in the same way to serve as the counter electrode of crevice corrosion.</p>","<title>Kinetic characterization of electrochemical corrosion</title>","<p id=\"Par42\">An MN patch coated with gold was connected to a piece of graphene tape. The peripheral area of the MN patch (except for needles) and the graphene tape (5113SFT, 3 M) were protected by PDMS from corrosion, with an opening area of 0.5 * 0.5 cm<sup>2</sup>. An amperemeter (NI-USB 4065, National Instruments) was applied to monitor the current density versus time. The experiment was carried out in a two-electrode system, where the counter electrode was graphene tape. A power source (SPD3303X-E, Siglent) was used to provide a constant voltage between the cathode and anode, while the anode was connected to the gold-coated MN patch to provide oxidative potential. The MN patch with graphene tape was coated with PDMS (~10 μm in thickness) to protect the exposed surface except for the MN regions. The electrochemical corrosion was carried out in a standard environment (1X DPBS, Cornings) to mimic the body fluid. I-t curves were obtained at 2.2, 2.4, 2.6, 2.8, 3.0 V.</p>","<p id=\"Par43\">Kinetic characterizations were also carried out for Mo-coated MN arrays in the same way (Supplementary Fig. ##SUPPL##0##12##).</p>","<title>Study of dye release</title>","<title>Free release without encapsulation</title>","<p id=\"Par44\">Rhodamine B (Thermo scientific, mass ratio versus PLGA 1:300) was mixed with PLGA (Sigma Aldrich) and dissolved in an acetone (VWR) solution. The fabricated MN patch was immersed in a petri dish containing 20 mL of deionized water (HAVENLAB) at 45 °C constant temperature. Samples were taken for UV-Vis spectrometry since the MN patch is immersed every 1 min from 0 to 10 min, every 5 min from 10 to 30 min, and at 60 min. The UV-Vis absorbance was characterized by a UV-Vis spectrophotometer (VWR-10037, VWR) from 800 to 300 nm, with an interval of 1 nm. The samples were returned to the petri dish immediately after characterization to maintain a constant volume. The absorption data was obtained by VWR UV software and visualized by Origin Pro 2022.</p>","<title>Encapsulated release with encapsulation</title>","<p id=\"Par45\">A Rhodamine B-loaded MN patch was deposited with a 100-nm gold layer on the side with needles to encapsulate the PLGA and dye. The back side of the MN patch was fixed and encapsulated into PDMS to prevent exposure to water. The patch was immersed in a petri dish with 20 mL of 1X DPBS (Cornings) at 45 °C. The UV-Vis absorbance was characterized by a UV-Vis spectrophotometer (VWR-10037, VWR) from 800 to 300 nm, with an interval of 1 nm. The samples were returned to the petri dish immediately after characterization to maintain a constant volume. The absorption data was obtained by VWR UV software and visualized by Origin Pro 2022.</p>","<title>On-demand stepwise release</title>","<p id=\"Par46\">A 150-nm gold layer was deposited to a Rhodamine B-loaded MN patch by sputter coating on the side with needles, then patterned by IR laser ablation to generate gold traces. The gold electrodes were connected by silver paste (8331D, MG Chemicals) to the constant voltage power source. Then, the entire PLGA patch was encapsulated with PDMS except for the needle region. The patch was immersed in a petri dish with 20 mL of DI water at 60 °C. A 2.5 V constant voltage was applied to trigger the electrochemical corrosion of gold within 20 s. The dye release of the four MN arrays was subsequently triggered every 30 min. The UV-Vis absorbance was characterized by a UV-Vis spectrophotometer (VWR-10037, VWR) from 800 to 300 nm, with an interval of 1 nm. Samples were taken at certain intervals for UV-Vis absorbance characterization and returned to keep the volume constant. The absorption data was obtained by VWR UV software and visualized by Origin Pro 2022.</p>","<title>Surface profilometry of corrosion</title>","<p id=\"Par47\">A silicon wafer ({100} facet, UniversityWafer) was deposited with a 150-nm gold layer by sputtering and divided into 4 cm<sup>2</sup> squares. The gold-coated side of the wafer was connected to the DC power source by graphene tape. The peripheral area of the wafer square was encapsulated by PDMS with a 1 cm<sup>2</sup> window exposed in the center, as illustrated in Supplementary Fig. ##SUPPL##0##7##. The wafer square was immersed in 10 mL 1X DPBS (Cornings), and the electrochemical corrosion was triggered by a 2.5 V constant voltage. Optical images of the wafer square were captured by a microscope (S9i, Leica) starting from the beginning. The time interval of images was 0.5 min from 0 to 5 min and 1 min from 5 to 9 min. The obtained images were cropped to leave only the exposed window in the center and further analyzed by FIJI ImageJ (Java 1.8.0_172). Arithmetic mean roughness (Ra) and root mean square roughness (Rq) were calculated for each image by the roughness analysis module.</p>","<title>Study of thermal effect</title>","<p id=\"Par48\">A 150-nm gold-coated MN array was placed in a Petri dish and immersed in 10 mL 1X DPBS (Cornings) at room temperature (25 °C). The MN array was soldered with a copper wire and connected to the DC power source. The infrared radiation image was recorded by a thermal camera (ETS320, FLIR). For the first 1 min, the temperature of the MN array was recorded without any voltages applied. For the following 1 min, the MN array was applied with a 2.5 V constant voltage. Temperature data points were taken every 5 s from the videos.</p>","<title>Elemental analysis of corrosion</title>","<p id=\"Par49\">The electrochemical corrosion of MN arrays (1.2 mm in length, 150-nm gold coated) was conducted at 2.4 V in 1X DPBS (Cornings). The triggering time was 0.5 min and 2 min for two MN arrays, representing the “transitioning” and “release” stages of the corrosion procedure. Optical and SEM images were captured, as shown in Fig. ##FIG##1##2a##.</p>","<p id=\"Par50\">The EDXS element mapping was conducted by the Scanning Electron Microscope (SEM, Hitachi S-4700). 20 kV was applied under analysis mode at a selected 100 * 120 μm<sup>2</sup> area, and the signal was collected for 400 s. For MN samples at the releasing stage, which have little gold coating left, a 5-nm Pd layer was sputter coated before characterization to increase the surface conductivity. The data analysis was automatically done by the INCA software (Oxford Instruments).</p>","<title>Mechanical strength measurement</title>","<p id=\"Par51\">A force measurement system (Mark 10, ESM 303) was used to study the mechanical strength of MNs. An MN array (9 or 25-needle, 1.2-mm, w/ or w/o 150-nm gold coated) was attached to the top sample holder by glue and double-sided tapes (Supplementary Fig. ##SUPPL##0##15g##). The bottom sample holder was placed with a piece of glass to serve as a hard object. Both the glass and the MN array were placed as horizontally as possible. The sampling rate of the force gauge was set as “as high as possible”, and the moving speed of the sample holder was set as 13 mm/min, which was the lowest value. The sample holder will gradually descend to a point where the MNs were in contact with the glass and stopped manually when the MN array is fully crushed (Supplementary Fig. ##SUPPL##0##15a, c##). Another soft contact experiment was also conducted under the same parameters except for the glass, which was replaced by 0.5% agar to mimic the brain (Supplementary Fig. ##SUPPL##0##15e–f##).</p>","<title>Wireless power transfer analysis</title>","<p id=\"Par52\">A signal generator (SDG2042, Siglent) was used to provide a radio frequency (RF) signal to the inductive coil. Performance at several different frequencies (30–40 MHz) is examined to find the optimal transmission efficiency. The high-frequency alternating current is then fed into the inductive coil closely coupled with the receiving coil, which is connected to a rectifier and a power regulator to provide a DC voltage for MNs (1.2 mm, 150 nm gold coated) immersed in saline solution. The voltage is measured using a benchtop oscilloscope (SDS1204, Siglent). The impedance of the MNs is measured using a portable network analyzer (NanoVNA). The power dissipated by the MNs during the electrochemical process, (), can be calculated using both the voltage measurement (peak-to-peak amplitude, ) and the real part of the impedance under the frequency at which measurements () are performed using the following equation:</p>","<title>Finite element analysis of crevice corrosion</title>","<p id=\"Par53\">The finite element analysis (FEA) of gold layer crevice corrosion on MNs was simulated by COMSOL 6.1. A model MN pair (1.2 mm, 3.5 mm in the distance) was set up to simulate the exposed surface of cathode and anode in the fluid. The tip of the MN was treated as a hemisphere (100 μm in diameter) to facilitate convergence, while the bottom face was set as a circle (270 μm in diameter). The surface of one MN was set as the cathode boundary, and the other was set as the anode boundary, as shown in Supplementary Fig. ##SUPPL##0##6b–d##. The entire simulation domain was set as a cubic (5 mm in length) space, including the two MNs. To simulate the physical and chemical properties of 1X DPBS, water (Water, liquid (mat1)) from the built-in database was chosen as the material of the cubic domain (not including MNs). The electroconductivity was set to be 1.6 S/m, which was a typical value provided by the manufacturer of PBS. Other surfaces of the model (except for the MN surfaces) were set as insulation in the boundary conditions. The stationary and transient simulations were performed based on secondary current distribution, which considered overpotentials while assuming homogeneous electrolytes. The anode equilibrium potential, according to literatures<sup>##UREF##17##85##</sup>, was set as 1.83 V (Au<sup>+</sup>|Au) to describe the electrochemical force needed to trigger gold oxidation. The cathode equilibrium potential was set as 0 V, and the electromotive force, which was the voltage of the external power source, was set as 2.5 V unless specified. The model was automatically meshed at the ultrafine level under physics-controlled mode. The current density distribution (Supplementary Fig. ##SUPPL##0##6a##) and iso-potential surface (Supplementary Fig. ##SUPPL##0##6b–d##) were simulated at the starting point of the crevice corrosion, which was stationary. The anodic polarization curve applying different electromotive forces (2.0–3.2 V) is simulated, as shown in Supplementary Fig. ##SUPPL##0##6e##. Transient simulation is conducted for the corrosion depth versus time, assuming the gold layer on the MN is thick enough. The corrosion depths from the starting time (0 s) to 60 s are monitored every 10 s (Supplementary Fig. ##SUPPL##0##6f##) and visualized (Supplementary Fig. ##SUPPL##0##6g##).</p>","<title>Immunohistochemical analysis</title>","<p id=\"Par54\">Mice were given a lethal dose of pentobarbital sodium (Sigma Aldrich), followed by intracardial perfusion with 4% paraformaldehyde (Sigma Aldrich) in PBS, as reported previously<sup>##REF##35524139##86##–##REF##31068780##88##</sup>. Then, the brains were dissected, post-fixed for 24 h at 4 °C, and cryoprotected with a solution of 30% sucrose (Fisher Scientific) in 0.1 M phosphate buffer (pH 7.4) (Sigma Aldrich) at 4 °C for at least 24 h, fully submerged. This was followed by cutting into 40-μm sections, washing three times in PBS, three 5-min incubations in 1 mg/ml<sup>–1</sup> sodium borohydride (Honeywell Fluka) in PBS, then 1-h incubations in 1% Triton-X-100 (Sigma Aldrich) in PBS. A blocking step was then performed using 5% donkey serum (Sigma Aldrich) in 0.3% PBST (phosphate-buffered saline with Triton X-100) for 1 h. Brain sections were then incubated for ∼16 h at 4 °C in a blocking buffer containing goat anti-GFAP of Santa Cruz Biotechnology (1:1000, Cat# sc-6170) and rabbit anti-Iba1 of Fujifilm Wako (1:500, Cat# 019-19741). Sections were then transferred to a secondary antibody solution containing Fisher Scientific Alexa Fluor 647 donkey anti-rabbit IgG (1:1000, Cat# A32795), Alexa Fluor 568 donkey anti-goat IgG (1:1000, Cat# A11057) and Neurotrace 435/455 Blue Fluorescent Nissl stain (1:100, Cat# N21479) in 0.1% PBST for 1 h at 24 °C, with intermittent brief periods of shaking. Sections were washed three times for 30 min each in 0.1% PBT, with 1 μM DAPI (Invitrogen) solution included on the third wash step. After rinsing, slices were dried on a piece of slide glass and coverslipped. All brain slices were imaged with an Olympus FV3000 microscope and obtained by FV31s-VW software. All images were processed with the same settings using the Fiji software by ImageJ (Java 1.8.0_172).</p>","<title>Stereotaxic surgery</title>","<p id=\"Par55\">Mice were anesthetized under 1.5–2% isoflurane in oxygen (Baxter Healthcare) at 0.8 LPM flow rate. Probes covering melatonin or vehicle were implanted unilaterally into the medial prefrontal cortex (anteroposterior (AP): −1.5 mm, mediolateral (ML): ±0.3 mm, dorsoventral (DV): −2.1 mm). Then, mice were chronically implanted with EEG/EMG electrodes for polysomnographic recordings. The electrodes consisted of two stainless steel screws connected to EEG Teflon-coated wires, which were inserted through the skull, and two EMG Teflon-coated wires that were bilaterally placed into both trapezius muscles. All of the electrodes were fixed to the skull with dental cement and attached to a microconnector<sup>##REF##20335474##89##</sup>. The scalp wound was closed with surgical sutures, and each mouse was kept in a warm environment until it resumed normal activity as previously described<sup>##REF##36280664##90##,##REF##35021048##91##</sup>.</p>","<title>Polygraphic recordings and vigilance state analysis</title>","<p id=\"Par56\">All mice were recorded by an EEG/EMG polysomnographic system of Pinnacle. The recordings of EEG and EMG were performed by means of a specially designed slip ring so that the behavioral movement of the mice would not be restricted. First, cortical EEG and EMG signals were amplified and filtered (EEG, 0.5–30 Hz; EMG, 20–200 Hz) and then digitized at a sampling rate of 200 Hz and recorded by using Sirenia Acquisition (Pinnacle). When completed, polygraphic recordings were automatically scored off-line by 4 s epochs as wakefulness, REM, and NREM sleep by SLEEPSIGN (Kissei Comtec, Nagano, Japan) according to standard criteria<sup>##REF##15965471##92##</sup>. As a final step, defined sleep-wake stages were examined visually and corrected, if necessary. The data related to EEG/EMG were analyzed by GraphPad Prism 8 and Matlab R2022b.</p>","<title>Gold layer thickness analysis</title>","<p id=\"Par57\">To analyze the uniformity of the gold layer, we prepare two MN samples: (1) a 1.5 mm MN with 150 nm gold layer is broken in half after freezing (Supplementary Fig. ##SUPPL##0##10a–c##); (2) a 1.2 mm MN with 100 nm gold layer undergoes partial Au film exfoliation (Supplementary Fig. ##SUPPL##0##10d–f##). By SEM, the cross-section images of the MN reveal the detailed structure of the outer gold layer. Supplementary Fig. 10c shows the cross-section of the gold layer (colored orange), which is approximately estimated at 160 nm in width. Considering the existence of a diffusion layer (where gold atoms mix with polymer), the error in thickness is acceptable. Namely, Supplementary Fig. ##SUPPL##0##10f## shows the thickness of the exfoliated gold film, roughly calculated to be ~ 95 nm, after perspective correction. Both samples show good uniformity of the thickness of the gold layer, which provides good encapsulation performance. Furthermore, we utilize AFM to characterize the surface roughness of the coated gold layer on the polymer substrate, as presented in Supplementary Fig. ##SUPPL##0##10##. The average and root-mean-square (RMS) roughness is calculated to be less than 3 nm, which meets the requirement of thickness uniformity.</p>","<title>Immunohistochemical analysis (additional)</title>","<p id=\"Par58\">Brain sections were then incubated for ∼16 h at 4 °C in a blocking buffer containing goat anti-GFAP of Santa Cruz Biotechnology (1:1000, Cat# sc-6170), rabbit anti-Iba1 of Fujifilm Wako (1:500, Cat# 019-19741), and Chichen anti-Neuron Specific Enolase (1:100, NSE Millipore Cat# AB9698).</p>","<p id=\"Par59\">All data values were presented as the mean ± standard deviation (SD). For analyzing fluorescence density and mean of peak value in the region of interest (ROI), we define the ROI as the areas within 200 μm lateral to the edge of the probes. One-way ANOVA was used to compare the fluorescence density and mean of peak value between different experimental groups.</p>","<p id=\"Par60\">As is well known, GFAP and IBA-1, representing astrocytes and microglia, respectively, reflect the inflammation in the brain tissues<sup>##REF##32800705##93##,##REF##34630027##94##</sup>. Neuron-specific enolase (NSE), an acidic protease unique to neurons and neuroendocrine cells and a sensitive indicator for assessing the severity of nerve cell damage and prognosis, is widely used to reflect neural injuries and pathological processing<sup>##REF##27604350##95##</sup>.</p>","<title>Stereotaxic surgery (additional)</title>","<p id=\"Par61\">For in vivo photometry recordings, mice were unilaterally injected with 250 nl of AAV5-CaMKIIG-CaMP6f mixed with 5 nl AAV5-CaMKII-tdTomato (from UNC Vector Core) into the mPFC at these coordinates AP: −1.5 mm, ML: +0.3 mm, DV: -2.1 mm. Optical fibers (Newdoon Inc, O.D.: 1.25 mm, core: 200 mm, NA: 0.37) were implanted 0.2 mm above the mPFC (AP: -1.5 mm, ML: +1.4 mm, DV: -1.9 mm). At the same time, MN probes were implanted ipsilaterally into the medial prefrontal cortex at 30 degrees lateral to the optical fiber.</p>","<title>Fiber photometry system</title>","<p id=\"Par62\">The multi-fiber photometry system was used as previously described<sup>##REF##29731250##96##,##REF##32167473##97##</sup>. Briefly, the system consisted of a 488 nm excitation laser, a fluorescence cube, and a spectrometer. The 488 nm laser beams first launched into the fluorescence cube and then into the optical fibers. The GCaMP and tdTomato emission fluorescence collected from the fiber probe traveled back to the spectrometer. Only animals with strong GCaMP and tdTomato expression were included in the study (Supplementary Fig. ##SUPPL##0##22a##). Spectral data was acquired by OceanView software (Ocean Optics, Inc) at 10 Hz and was synchronized to a 20 Hz video recording system to acquire the animal behavior.</p>","<p id=\"Par63\">The in vivo recordings were carried out in an open-top home cage (21.6 “ 17.8 “ 12.7 cm) in the 30 Lux red light environment. Laser power was adjusted to a final optical fiber output of 30 mW. Photometry data were exported to MATLAB R2014b for analysis. Coefficients of GCaMP6f and tdTomato were unmixed by a customized script by fitting spectrum signals to standard emission curves. GCaMP6f signals were normalized by tdTomato signals for motion correction. (Supplementary Fig. ##SUPPL##0##22a##). A 0.1 Hz high-pass filter corrected the fluorescence bleaching. Photometry signals (ΔF/F) were derived by calculating (F–F0)/F0, where F0 is the median of the fluorescence signal (Supplementary Fig. ##SUPPL##0##22b##). For the home cage analysis, we recorded data for 5 min per mouse and calculated the ΔF/F to further analyze the correlation of SuM and DG signals in raw data (Supplementary Fig. ##SUPPL##0##22c##). Cumulative activity = ΔF/F х Time. The average of the peak of ΔF/F above 2 SD during 5 mins in the home cage was calculated.</p>","<title>PLGA MN accelerated degradation</title>","<p id=\"Par64\">We conducted an accelerated degradation experiment of PLGA MN (3 mm, without drug payload) by soaking it in 65 °C 1X DPBS (Cornings). We observed changes in the shape and morphology during the immersion (Supplementary Fig. ##SUPPL##0##23##). The MN was taken out of the solution for observation after 30 min, 1 h, 6 h, and 24 h. It is clear that, after 24-h degradation, the main body of PLGA MN collapsed. This experiment simulates the chronic degradation of PLGA in the biofluidic environment under ambient conditions, showing significant degradation of PLGA MN.</p>","<title>Reporting summary</title>","<p id=\"Par65\">Further information on research design is available in the ##SUPPL##4##Nature Portfolio Reporting Summary## linked to this article.</p>"],"string":"[\n \"<title>Methods</title>\",\n \"<p id=\\\"Par34\\\">The sample size used in this study is based on the expected variations between animals and is comparable to many previous reports using similar techniques (cited in the corresponding sections). Sample size of each experiments can be found in the Figure legends. Data from animals were excluded based on histological criteria that included injection sites, virus expression and optical fiber placement. Only animals with injection sites/virus expression/optical fiber placement in the region of interest were included, based on our previous reports. The experiments were not randomized. Animal were allocated into experimental groups by matched gender, age, weight, etc. Investigators were blinded to the experimental groups until all data had been collected and analyzed.</p>\",\n \"<p id=\\\"Par35\\\">C57BL/6 mice (8–10 weeks, females) were used for all animal experiments. Animals were group-housed at constant temperature (22–24 °C) and humidity (40–60%), and bred in a dedicated husbandry facility with 12/12-h light-dark cycles with food and water ad libitum and under veterinary supervision. Animals subjected to surgical procedures were moved to a satellite housing facility for recovery with the same light-dark cycle. All procedures were conducted in accordance with the NIH Guide for the Care and Use of Laboratory Animals and with the approval of the Institutional Animal Care and Use Committee at the University of North Carolina at Chapel Hill (UNC), under the protocol # 22–146<sup>##REF##37061718##84##</sup>.</p>\",\n \"<title>Fabrication of the SOP</title>\",\n \"<title>Normal MN patch</title>\",\n \"<p id=\\\"Par36\\\">The general procedure of MN fabrication was demonstrated in Supplementary Fig. ##SUPPL##0##2##. First, 50 g of PDMS (Sylgard 184, Dow Corning) was fully cured in a glass petri dish at 60 °C for 2 h with 5 g of its corresponding curing agent. Then, a negative MN mold was patterned on the cured PDMS by a UV laser ablation system (SFX-5UV, Luoyang Xincheng Precision Machinery). The MN molds were fabricated with different depths from 0.5 to 3.5 mm, a base diameter of around 0.25 mm, and an inter-needle spacing of 1 mm. The depth of the MN mold can be controlled by tuning the loops and power of UV laser ablation. The UV ablation was followed by acetone sonication for at least 5 min to clean up the surface of the PDMS negative mold. Then, a PLGA (PLGA, Mw = 50 ~ 75 kDa, ester terminated, Sigma Aldrich) solution (10 wt% in acetone, VWR) was drop cast on the PDMS negative mold in the petri dish. The PLGA-covered mold was heated at 45 °C and 60–160 mmHg for around 2 min to let the PLGA solution fill in the mold and evaporate. The entire PDMS mold was capped by another petri dish to slow down the evaporation of acetone. The evaporation process was followed by a refill of PLGA solution. The evaporation-refilling cycle was conducted 10–20 times to provide enough PLGA for the MN patch, with a thickness from 0.6 to 1.2 mm. After that, the PLGA-covered mold was kept in the oven at 45 °C and 1 atm for at least 8 h to dry the surface. The mold was then frozen at −20 °C for at least 30 min to harden the PLGA patch, which was subsequently extracted from the mold. The free-standing PLGA patch was allowed to further dry up on both sides at 45 °C and 1 atm for another 24 h, then trimmed by UV laser ablation. The hardened and dry PLGA patch was eventually deposited with a layer of gold (usually 150 nm in thickness) by sputter coating (PVD 75 sputterer, Kurt J. Lesker). The Gold traces were patterned by an IR laser ablation system (SFX-50GS, Luoyang Xincheng Precision Machinery).</p>\",\n \"<title>Melatonin-loaded MN</title>\",\n \"<p id=\\\"Par37\\\">A melatonin loaded PLGA solution was prepared in advance. The melatonin (Sigma Aldrich) was mixed with PLGA (Sigma Aldrich) at a ratio by weight from 1:10 to 1:2. Then, the mixture was dissolved in acetone (VWR) at a 1:10 ratio by weight. The solution is stored in refrigerator at around 4 °C no longer than 12 h, and wrapped with aluminum foil to avoid light.</p>\",\n \"<p id=\\\"Par38\\\">First, 50 g of PDMS (Sylgard 184, Dow Corning) was fully cured in a glass petri dish at 60 °C for 2 h with 5 g of its corresponding curing agent. Then, a negative MN mold was patterned on the cured PDMS by a UV laser ablation system (SFX-5UV, Luoyang Xincheng Precision Machinery). The MN molds were fabricated with a depth around 3 mm, a base diameter of around 0.35–0.5 mm, and an inter-needle spacing of at least 5 mm. The depth of the MN mold can be controlled by tuning the loops and power of UV laser ablation. The UV ablation was followed by acetone sonication for at least 5 min to clean up the surface of the PDMS negative mold. Then, the melatonin-PLGA solution (10 wt% in acetone) was drop cast on the PDMS negative mold in the petri dish. The PLGA-covered mold was heated at 30 °C and 60–160 mmHg for around 2 min to let the PLGA solution fill in the mold and evaporate. The entire PDMS mold was capped by another petri dish to slow down the evaporation of acetone. The evaporation process was followed by a refill of PLGA solution. The evaporation-refilling cycle was conducted 10–20 times to provide enough PLGA for the MN patch, with a thickness from 0.6 to 1.2 mm. After that, the PLGA-covered mold was kept in the oven at 30 °C and 1 atm for at least 8 h to dry the surface. The mold was then frozen at −20 °C for at least 30 min to harden the PLGA patch, which was subsequently extracted from the mold. The free-standing PLGA patch was allowed to further dry up on both sides at 30 °C and 1 atm for another 24–48 h, then trimmed by UV laser ablation. The hardened and dry PLGA patch was eventually deposited with a layer of gold (usually 150–200 nm in thickness) by sputter coating (PVD 75 sputterer, Kurt J. Lesker). The Gold traces were patterned by an IR laser ablation system (SFX-50GS, Luoyang Xincheng Precision Machinery).</p>\",\n \"<title>Rhodamine B-loaded MN</title>\",\n \"<p id=\\\"Par39\\\">A Rhodamine B (Thermo scientific) loaded PLGA solution was prepared in advance. Rhodamine B was dissolved in the acetone (VWR) solution of PLGA (usually at 1/300 ratio by weight, Sigma Aldrich).</p>\",\n \"<p id=\\\"Par40\\\">First, 50 g of PDMS (Sylgard 184, Dow Corning) was fully cured in a glass petri dish at 60 °C for 2 h with 5 g of its corresponding curing agent. Then, a negative MN mold was patterned on the cured PDMS by a UV laser ablation system (SFX-5UV, Luoyang Xincheng Precision Machinery). The MN molds were fabricated with different depths from 0.5 to 3.5 mm, a base diameter of around 0.25 mm, and an inter-needle spacing of 1 mm. The depth of the MN mold can be controlled by tuning the loops and power of UV laser ablation. The UV ablation was followed by acetone sonication for at least 5 min to clean up the surface of the PDMS negative mold. Then, the Rhodamine B-PLGA solution (10 wt% in acetone) was drop cast on the PDMS negative mold in the petri dish. The PLGA-covered mold was heated at 45 °C and 60–160 mmHg for around 2 min to let the PLGA solution fill in the mold and evaporate. The entire PDMS mold was capped by another petri dish to slow down the evaporation of acetone. The evaporation process was followed by a refill of PLGA solution. The evaporation-refilling cycle was conducted 10–20 times to provide enough PLGA for the MN patch, with a thickness from 0.6 to 1.2 mm. After that, the PLGA-covered mold was kept in the oven at 45 °C and 1 atm for at least 8 h to dry the surface. The mold was then frozen at −20 °C for at least 30 min to harden the PLGA patch, which was subsequently extracted from the mold. The free-standing PLGA patch was allowed to further dry up on both sides at 45 °C and 1 atm for another 24 h, then trimmed by UV laser ablation. The hardened and dry PLGA patch was eventually deposited with a layer of gold (usually 150 nm in thickness) by sputter coating (PVD 75 sputterer, Kurt J. Lesker). The Gold traces were patterned by an IR laser ablation system (SFX-50GS, Luoyang Xincheng Precision Machinery). For controlled dye release model, a layer of PDMS (1:10, ~ 15 µm) was carefully, manually coated and cured on specific positions that should be covered.</p>\",\n \"<title>Wireless on-demand patch</title>\",\n \"<p id=\\\"Par41\\\">A commercially available Pyralux Kapton soft PCB material made of polyimide sandwiched between copper was sprayed on and coated with masking paint (Krylon). The paint mask was then partially removed using an infrared laser cutter to expose unwanted copper, which was removed by etching in ferric chloride solution (MG Chemicals 415) for 15 min. The soft PCB was then rinsed with water and acetone to remove the remaining etchant and paint mask. Surface mount electrical components, including diodes and power regulators, were soldered using solder paste and hot air guns. An MN array (1.2-mm, 150 nm gold coated) was integrated into the wireless patch using PI-based adhesives. A small jumper wire was attached to the MN array using silver-based conductive adhesives to ensure conductivity. Another MN array was integrated into the wireless patch in the same way to serve as the counter electrode of crevice corrosion.</p>\",\n \"<title>Kinetic characterization of electrochemical corrosion</title>\",\n \"<p id=\\\"Par42\\\">An MN patch coated with gold was connected to a piece of graphene tape. The peripheral area of the MN patch (except for needles) and the graphene tape (5113SFT, 3 M) were protected by PDMS from corrosion, with an opening area of 0.5 * 0.5 cm<sup>2</sup>. An amperemeter (NI-USB 4065, National Instruments) was applied to monitor the current density versus time. The experiment was carried out in a two-electrode system, where the counter electrode was graphene tape. A power source (SPD3303X-E, Siglent) was used to provide a constant voltage between the cathode and anode, while the anode was connected to the gold-coated MN patch to provide oxidative potential. The MN patch with graphene tape was coated with PDMS (~10 μm in thickness) to protect the exposed surface except for the MN regions. The electrochemical corrosion was carried out in a standard environment (1X DPBS, Cornings) to mimic the body fluid. I-t curves were obtained at 2.2, 2.4, 2.6, 2.8, 3.0 V.</p>\",\n \"<p id=\\\"Par43\\\">Kinetic characterizations were also carried out for Mo-coated MN arrays in the same way (Supplementary Fig. ##SUPPL##0##12##).</p>\",\n \"<title>Study of dye release</title>\",\n \"<title>Free release without encapsulation</title>\",\n \"<p id=\\\"Par44\\\">Rhodamine B (Thermo scientific, mass ratio versus PLGA 1:300) was mixed with PLGA (Sigma Aldrich) and dissolved in an acetone (VWR) solution. The fabricated MN patch was immersed in a petri dish containing 20 mL of deionized water (HAVENLAB) at 45 °C constant temperature. Samples were taken for UV-Vis spectrometry since the MN patch is immersed every 1 min from 0 to 10 min, every 5 min from 10 to 30 min, and at 60 min. The UV-Vis absorbance was characterized by a UV-Vis spectrophotometer (VWR-10037, VWR) from 800 to 300 nm, with an interval of 1 nm. The samples were returned to the petri dish immediately after characterization to maintain a constant volume. The absorption data was obtained by VWR UV software and visualized by Origin Pro 2022.</p>\",\n \"<title>Encapsulated release with encapsulation</title>\",\n \"<p id=\\\"Par45\\\">A Rhodamine B-loaded MN patch was deposited with a 100-nm gold layer on the side with needles to encapsulate the PLGA and dye. The back side of the MN patch was fixed and encapsulated into PDMS to prevent exposure to water. The patch was immersed in a petri dish with 20 mL of 1X DPBS (Cornings) at 45 °C. The UV-Vis absorbance was characterized by a UV-Vis spectrophotometer (VWR-10037, VWR) from 800 to 300 nm, with an interval of 1 nm. The samples were returned to the petri dish immediately after characterization to maintain a constant volume. The absorption data was obtained by VWR UV software and visualized by Origin Pro 2022.</p>\",\n \"<title>On-demand stepwise release</title>\",\n \"<p id=\\\"Par46\\\">A 150-nm gold layer was deposited to a Rhodamine B-loaded MN patch by sputter coating on the side with needles, then patterned by IR laser ablation to generate gold traces. The gold electrodes were connected by silver paste (8331D, MG Chemicals) to the constant voltage power source. Then, the entire PLGA patch was encapsulated with PDMS except for the needle region. The patch was immersed in a petri dish with 20 mL of DI water at 60 °C. A 2.5 V constant voltage was applied to trigger the electrochemical corrosion of gold within 20 s. The dye release of the four MN arrays was subsequently triggered every 30 min. The UV-Vis absorbance was characterized by a UV-Vis spectrophotometer (VWR-10037, VWR) from 800 to 300 nm, with an interval of 1 nm. Samples were taken at certain intervals for UV-Vis absorbance characterization and returned to keep the volume constant. The absorption data was obtained by VWR UV software and visualized by Origin Pro 2022.</p>\",\n \"<title>Surface profilometry of corrosion</title>\",\n \"<p id=\\\"Par47\\\">A silicon wafer ({100} facet, UniversityWafer) was deposited with a 150-nm gold layer by sputtering and divided into 4 cm<sup>2</sup> squares. The gold-coated side of the wafer was connected to the DC power source by graphene tape. The peripheral area of the wafer square was encapsulated by PDMS with a 1 cm<sup>2</sup> window exposed in the center, as illustrated in Supplementary Fig. ##SUPPL##0##7##. The wafer square was immersed in 10 mL 1X DPBS (Cornings), and the electrochemical corrosion was triggered by a 2.5 V constant voltage. Optical images of the wafer square were captured by a microscope (S9i, Leica) starting from the beginning. The time interval of images was 0.5 min from 0 to 5 min and 1 min from 5 to 9 min. The obtained images were cropped to leave only the exposed window in the center and further analyzed by FIJI ImageJ (Java 1.8.0_172). Arithmetic mean roughness (Ra) and root mean square roughness (Rq) were calculated for each image by the roughness analysis module.</p>\",\n \"<title>Study of thermal effect</title>\",\n \"<p id=\\\"Par48\\\">A 150-nm gold-coated MN array was placed in a Petri dish and immersed in 10 mL 1X DPBS (Cornings) at room temperature (25 °C). The MN array was soldered with a copper wire and connected to the DC power source. The infrared radiation image was recorded by a thermal camera (ETS320, FLIR). For the first 1 min, the temperature of the MN array was recorded without any voltages applied. For the following 1 min, the MN array was applied with a 2.5 V constant voltage. Temperature data points were taken every 5 s from the videos.</p>\",\n \"<title>Elemental analysis of corrosion</title>\",\n \"<p id=\\\"Par49\\\">The electrochemical corrosion of MN arrays (1.2 mm in length, 150-nm gold coated) was conducted at 2.4 V in 1X DPBS (Cornings). The triggering time was 0.5 min and 2 min for two MN arrays, representing the “transitioning” and “release” stages of the corrosion procedure. Optical and SEM images were captured, as shown in Fig. ##FIG##1##2a##.</p>\",\n \"<p id=\\\"Par50\\\">The EDXS element mapping was conducted by the Scanning Electron Microscope (SEM, Hitachi S-4700). 20 kV was applied under analysis mode at a selected 100 * 120 μm<sup>2</sup> area, and the signal was collected for 400 s. For MN samples at the releasing stage, which have little gold coating left, a 5-nm Pd layer was sputter coated before characterization to increase the surface conductivity. The data analysis was automatically done by the INCA software (Oxford Instruments).</p>\",\n \"<title>Mechanical strength measurement</title>\",\n \"<p id=\\\"Par51\\\">A force measurement system (Mark 10, ESM 303) was used to study the mechanical strength of MNs. An MN array (9 or 25-needle, 1.2-mm, w/ or w/o 150-nm gold coated) was attached to the top sample holder by glue and double-sided tapes (Supplementary Fig. ##SUPPL##0##15g##). The bottom sample holder was placed with a piece of glass to serve as a hard object. Both the glass and the MN array were placed as horizontally as possible. The sampling rate of the force gauge was set as “as high as possible”, and the moving speed of the sample holder was set as 13 mm/min, which was the lowest value. The sample holder will gradually descend to a point where the MNs were in contact with the glass and stopped manually when the MN array is fully crushed (Supplementary Fig. ##SUPPL##0##15a, c##). Another soft contact experiment was also conducted under the same parameters except for the glass, which was replaced by 0.5% agar to mimic the brain (Supplementary Fig. ##SUPPL##0##15e–f##).</p>\",\n \"<title>Wireless power transfer analysis</title>\",\n \"<p id=\\\"Par52\\\">A signal generator (SDG2042, Siglent) was used to provide a radio frequency (RF) signal to the inductive coil. Performance at several different frequencies (30–40 MHz) is examined to find the optimal transmission efficiency. The high-frequency alternating current is then fed into the inductive coil closely coupled with the receiving coil, which is connected to a rectifier and a power regulator to provide a DC voltage for MNs (1.2 mm, 150 nm gold coated) immersed in saline solution. The voltage is measured using a benchtop oscilloscope (SDS1204, Siglent). The impedance of the MNs is measured using a portable network analyzer (NanoVNA). The power dissipated by the MNs during the electrochemical process, (), can be calculated using both the voltage measurement (peak-to-peak amplitude, ) and the real part of the impedance under the frequency at which measurements () are performed using the following equation:</p>\",\n \"<title>Finite element analysis of crevice corrosion</title>\",\n \"<p id=\\\"Par53\\\">The finite element analysis (FEA) of gold layer crevice corrosion on MNs was simulated by COMSOL 6.1. A model MN pair (1.2 mm, 3.5 mm in the distance) was set up to simulate the exposed surface of cathode and anode in the fluid. The tip of the MN was treated as a hemisphere (100 μm in diameter) to facilitate convergence, while the bottom face was set as a circle (270 μm in diameter). The surface of one MN was set as the cathode boundary, and the other was set as the anode boundary, as shown in Supplementary Fig. ##SUPPL##0##6b–d##. The entire simulation domain was set as a cubic (5 mm in length) space, including the two MNs. To simulate the physical and chemical properties of 1X DPBS, water (Water, liquid (mat1)) from the built-in database was chosen as the material of the cubic domain (not including MNs). The electroconductivity was set to be 1.6 S/m, which was a typical value provided by the manufacturer of PBS. Other surfaces of the model (except for the MN surfaces) were set as insulation in the boundary conditions. The stationary and transient simulations were performed based on secondary current distribution, which considered overpotentials while assuming homogeneous electrolytes. The anode equilibrium potential, according to literatures<sup>##UREF##17##85##</sup>, was set as 1.83 V (Au<sup>+</sup>|Au) to describe the electrochemical force needed to trigger gold oxidation. The cathode equilibrium potential was set as 0 V, and the electromotive force, which was the voltage of the external power source, was set as 2.5 V unless specified. The model was automatically meshed at the ultrafine level under physics-controlled mode. The current density distribution (Supplementary Fig. ##SUPPL##0##6a##) and iso-potential surface (Supplementary Fig. ##SUPPL##0##6b–d##) were simulated at the starting point of the crevice corrosion, which was stationary. The anodic polarization curve applying different electromotive forces (2.0–3.2 V) is simulated, as shown in Supplementary Fig. ##SUPPL##0##6e##. Transient simulation is conducted for the corrosion depth versus time, assuming the gold layer on the MN is thick enough. The corrosion depths from the starting time (0 s) to 60 s are monitored every 10 s (Supplementary Fig. ##SUPPL##0##6f##) and visualized (Supplementary Fig. ##SUPPL##0##6g##).</p>\",\n \"<title>Immunohistochemical analysis</title>\",\n \"<p id=\\\"Par54\\\">Mice were given a lethal dose of pentobarbital sodium (Sigma Aldrich), followed by intracardial perfusion with 4% paraformaldehyde (Sigma Aldrich) in PBS, as reported previously<sup>##REF##35524139##86##–##REF##31068780##88##</sup>. Then, the brains were dissected, post-fixed for 24 h at 4 °C, and cryoprotected with a solution of 30% sucrose (Fisher Scientific) in 0.1 M phosphate buffer (pH 7.4) (Sigma Aldrich) at 4 °C for at least 24 h, fully submerged. This was followed by cutting into 40-μm sections, washing three times in PBS, three 5-min incubations in 1 mg/ml<sup>–1</sup> sodium borohydride (Honeywell Fluka) in PBS, then 1-h incubations in 1% Triton-X-100 (Sigma Aldrich) in PBS. A blocking step was then performed using 5% donkey serum (Sigma Aldrich) in 0.3% PBST (phosphate-buffered saline with Triton X-100) for 1 h. Brain sections were then incubated for ∼16 h at 4 °C in a blocking buffer containing goat anti-GFAP of Santa Cruz Biotechnology (1:1000, Cat# sc-6170) and rabbit anti-Iba1 of Fujifilm Wako (1:500, Cat# 019-19741). Sections were then transferred to a secondary antibody solution containing Fisher Scientific Alexa Fluor 647 donkey anti-rabbit IgG (1:1000, Cat# A32795), Alexa Fluor 568 donkey anti-goat IgG (1:1000, Cat# A11057) and Neurotrace 435/455 Blue Fluorescent Nissl stain (1:100, Cat# N21479) in 0.1% PBST for 1 h at 24 °C, with intermittent brief periods of shaking. Sections were washed three times for 30 min each in 0.1% PBT, with 1 μM DAPI (Invitrogen) solution included on the third wash step. After rinsing, slices were dried on a piece of slide glass and coverslipped. All brain slices were imaged with an Olympus FV3000 microscope and obtained by FV31s-VW software. All images were processed with the same settings using the Fiji software by ImageJ (Java 1.8.0_172).</p>\",\n \"<title>Stereotaxic surgery</title>\",\n \"<p id=\\\"Par55\\\">Mice were anesthetized under 1.5–2% isoflurane in oxygen (Baxter Healthcare) at 0.8 LPM flow rate. Probes covering melatonin or vehicle were implanted unilaterally into the medial prefrontal cortex (anteroposterior (AP): −1.5 mm, mediolateral (ML): ±0.3 mm, dorsoventral (DV): −2.1 mm). Then, mice were chronically implanted with EEG/EMG electrodes for polysomnographic recordings. The electrodes consisted of two stainless steel screws connected to EEG Teflon-coated wires, which were inserted through the skull, and two EMG Teflon-coated wires that were bilaterally placed into both trapezius muscles. All of the electrodes were fixed to the skull with dental cement and attached to a microconnector<sup>##REF##20335474##89##</sup>. The scalp wound was closed with surgical sutures, and each mouse was kept in a warm environment until it resumed normal activity as previously described<sup>##REF##36280664##90##,##REF##35021048##91##</sup>.</p>\",\n \"<title>Polygraphic recordings and vigilance state analysis</title>\",\n \"<p id=\\\"Par56\\\">All mice were recorded by an EEG/EMG polysomnographic system of Pinnacle. The recordings of EEG and EMG were performed by means of a specially designed slip ring so that the behavioral movement of the mice would not be restricted. First, cortical EEG and EMG signals were amplified and filtered (EEG, 0.5–30 Hz; EMG, 20–200 Hz) and then digitized at a sampling rate of 200 Hz and recorded by using Sirenia Acquisition (Pinnacle). When completed, polygraphic recordings were automatically scored off-line by 4 s epochs as wakefulness, REM, and NREM sleep by SLEEPSIGN (Kissei Comtec, Nagano, Japan) according to standard criteria<sup>##REF##15965471##92##</sup>. As a final step, defined sleep-wake stages were examined visually and corrected, if necessary. The data related to EEG/EMG were analyzed by GraphPad Prism 8 and Matlab R2022b.</p>\",\n \"<title>Gold layer thickness analysis</title>\",\n \"<p id=\\\"Par57\\\">To analyze the uniformity of the gold layer, we prepare two MN samples: (1) a 1.5 mm MN with 150 nm gold layer is broken in half after freezing (Supplementary Fig. ##SUPPL##0##10a–c##); (2) a 1.2 mm MN with 100 nm gold layer undergoes partial Au film exfoliation (Supplementary Fig. ##SUPPL##0##10d–f##). By SEM, the cross-section images of the MN reveal the detailed structure of the outer gold layer. Supplementary Fig. 10c shows the cross-section of the gold layer (colored orange), which is approximately estimated at 160 nm in width. Considering the existence of a diffusion layer (where gold atoms mix with polymer), the error in thickness is acceptable. Namely, Supplementary Fig. ##SUPPL##0##10f## shows the thickness of the exfoliated gold film, roughly calculated to be ~ 95 nm, after perspective correction. Both samples show good uniformity of the thickness of the gold layer, which provides good encapsulation performance. Furthermore, we utilize AFM to characterize the surface roughness of the coated gold layer on the polymer substrate, as presented in Supplementary Fig. ##SUPPL##0##10##. The average and root-mean-square (RMS) roughness is calculated to be less than 3 nm, which meets the requirement of thickness uniformity.</p>\",\n \"<title>Immunohistochemical analysis (additional)</title>\",\n \"<p id=\\\"Par58\\\">Brain sections were then incubated for ∼16 h at 4 °C in a blocking buffer containing goat anti-GFAP of Santa Cruz Biotechnology (1:1000, Cat# sc-6170), rabbit anti-Iba1 of Fujifilm Wako (1:500, Cat# 019-19741), and Chichen anti-Neuron Specific Enolase (1:100, NSE Millipore Cat# AB9698).</p>\",\n \"<p id=\\\"Par59\\\">All data values were presented as the mean ± standard deviation (SD). For analyzing fluorescence density and mean of peak value in the region of interest (ROI), we define the ROI as the areas within 200 μm lateral to the edge of the probes. One-way ANOVA was used to compare the fluorescence density and mean of peak value between different experimental groups.</p>\",\n \"<p id=\\\"Par60\\\">As is well known, GFAP and IBA-1, representing astrocytes and microglia, respectively, reflect the inflammation in the brain tissues<sup>##REF##32800705##93##,##REF##34630027##94##</sup>. Neuron-specific enolase (NSE), an acidic protease unique to neurons and neuroendocrine cells and a sensitive indicator for assessing the severity of nerve cell damage and prognosis, is widely used to reflect neural injuries and pathological processing<sup>##REF##27604350##95##</sup>.</p>\",\n \"<title>Stereotaxic surgery (additional)</title>\",\n \"<p id=\\\"Par61\\\">For in vivo photometry recordings, mice were unilaterally injected with 250 nl of AAV5-CaMKIIG-CaMP6f mixed with 5 nl AAV5-CaMKII-tdTomato (from UNC Vector Core) into the mPFC at these coordinates AP: −1.5 mm, ML: +0.3 mm, DV: -2.1 mm. Optical fibers (Newdoon Inc, O.D.: 1.25 mm, core: 200 mm, NA: 0.37) were implanted 0.2 mm above the mPFC (AP: -1.5 mm, ML: +1.4 mm, DV: -1.9 mm). At the same time, MN probes were implanted ipsilaterally into the medial prefrontal cortex at 30 degrees lateral to the optical fiber.</p>\",\n \"<title>Fiber photometry system</title>\",\n \"<p id=\\\"Par62\\\">The multi-fiber photometry system was used as previously described<sup>##REF##29731250##96##,##REF##32167473##97##</sup>. Briefly, the system consisted of a 488 nm excitation laser, a fluorescence cube, and a spectrometer. The 488 nm laser beams first launched into the fluorescence cube and then into the optical fibers. The GCaMP and tdTomato emission fluorescence collected from the fiber probe traveled back to the spectrometer. Only animals with strong GCaMP and tdTomato expression were included in the study (Supplementary Fig. ##SUPPL##0##22a##). Spectral data was acquired by OceanView software (Ocean Optics, Inc) at 10 Hz and was synchronized to a 20 Hz video recording system to acquire the animal behavior.</p>\",\n \"<p id=\\\"Par63\\\">The in vivo recordings were carried out in an open-top home cage (21.6 “ 17.8 “ 12.7 cm) in the 30 Lux red light environment. Laser power was adjusted to a final optical fiber output of 30 mW. Photometry data were exported to MATLAB R2014b for analysis. Coefficients of GCaMP6f and tdTomato were unmixed by a customized script by fitting spectrum signals to standard emission curves. GCaMP6f signals were normalized by tdTomato signals for motion correction. (Supplementary Fig. ##SUPPL##0##22a##). A 0.1 Hz high-pass filter corrected the fluorescence bleaching. Photometry signals (ΔF/F) were derived by calculating (F–F0)/F0, where F0 is the median of the fluorescence signal (Supplementary Fig. ##SUPPL##0##22b##). For the home cage analysis, we recorded data for 5 min per mouse and calculated the ΔF/F to further analyze the correlation of SuM and DG signals in raw data (Supplementary Fig. ##SUPPL##0##22c##). Cumulative activity = ΔF/F х Time. The average of the peak of ΔF/F above 2 SD during 5 mins in the home cage was calculated.</p>\",\n \"<title>PLGA MN accelerated degradation</title>\",\n \"<p id=\\\"Par64\\\">We conducted an accelerated degradation experiment of PLGA MN (3 mm, without drug payload) by soaking it in 65 °C 1X DPBS (Cornings). We observed changes in the shape and morphology during the immersion (Supplementary Fig. ##SUPPL##0##23##). The MN was taken out of the solution for observation after 30 min, 1 h, 6 h, and 24 h. It is clear that, after 24-h degradation, the main body of PLGA MN collapsed. This experiment simulates the chronic degradation of PLGA in the biofluidic environment under ambient conditions, showing significant degradation of PLGA MN.</p>\",\n \"<title>Reporting summary</title>\",\n \"<p id=\\\"Par65\\\">Further information on research design is available in the ##SUPPL##4##Nature Portfolio Reporting Summary## linked to this article.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results and discussion</title>","<title>Configuration and characterization of the spatiotemporal on-demand patch (SOP)</title>","<p id=\"Par7\">Figure ##FIG##0##1a## highlights the design of an SOP, which consists of two primary parts: i) a drug-loaded microneedle (MN) patch protected with an electrochemically triggerable metal layer (gold) as the drug delivery interface; ii) a near-field communication (NFC) module for the wireless control of triggering signals in controlling location and schedule of drug release. A flexible printed circuit board (PCB) provides interconnected traces that integrate the two parts to form a fully wireless, wearable drug delivery system. The main body of the drug delivery interface (MN arrays) uses poly(D, L-lactide-co-glycolide) (PLGA, Mw = 50 ~ 75 kDa, ester terminated, Sigma Aldrich) as the matrix materials that can undergo bulk erosion upon contact with biofluids to generate biological benign byproducts (lactic acid and glycolic acid)<sup>##UREF##9##53##</sup>. As shown in Fig. ##FIG##0##1b##, the fabrication relies on sputter deposition to coat a thin layer of gold (thickness 150 nm) onto the drug-loaded MN arrays supported by a PLGA base. The gold encapsulation layer is supported on the surface of solid MNs, which enables sufficient stability with a thinner thickness (150 nm) compared with those used in the reservoir designs (thickness of gold layer more than 300 nm) as reported previously (as illustrated in Supplementary Fig. ##SUPPL##0##1##). Thus, the thickness to realize effective drug encapsulation can be smaller. Ablation using a laser beam defines the gold traces that connect with separated MN domains to realize spatial control of drug release. A thin polydimethylsiloxane (PDMS) (thickness 10 µm) covers the gold layer at the circuity base regions. It exposes only the MN regions to allow physical contact of the gold layer on the MN with biofluids.</p>","<p id=\"Par8\">Fabrication of the SOP relies on a low-cost solution-molding procedure (Supplementary Fig. ##SUPPL##0##2##). The process starts with UV laser ablation to define an MN mold from a PDMS pad. Drop casting drug-loaded PLGA solution into the PDMS mold and setting under vacuum allow the entrance of PLGA into the negative MN molds. Sufficient solidification over 8 h allows easy extraction of the PLGA MNs. Then, the PLGA MNs undergo sputtering deposition to coat a 150 nm-thick gold layer that conformally covers the top surface, followed by patterning with a laser ablation system to define control circuits. Drop casting a thin PDMS layer (thickness 10 μm) onto gold layers protects the control circuits and exposes the MNs for drug release. The MN patch is then attached to a flexible PCB and connected with a current regulator, a wireless energy harvester, and a microcontroller to complete the fabrication process. The solution-molding method can produce SOPs with: (1) tunable MN lengths from 600 μm to 3 mm and aspect ratio from 3 to 8; (2) high dimension uniformity in both length and base diameter (Supplementary Fig. ##SUPPL##0##3##); (3) arbitrary MN array configurations (square, hexagonal, single-needle, etc.). Figure ##FIG##0##1e##–##FIG##0##h## shows that the morphology and shape of the PLGA MNs remain stable during both gold deposition and drug loading procedures.</p>","<p id=\"Par9\">Figure ##FIG##0##1c##, ##FIG##0##d## illustrates the overall working mechanism to realize high-precision drug delivery by our SOP. The electrically triggered crevice corrosion of the gold protective layer is the switch for initiating drug release of specific collections of SOP MNs. Upon SOP deployment on the skin, the MNs stay at a standby stage with the drug fully protected by the gold layer. Once a direct current (DC) electrical trigger (2.2–3 V) is applied, the electrochemical crevice corrosion starts to occur on triggered MNs, transitioning them from standby mode to releasing mode. After a short period (<30 s when 2.5 V is applied), the gold protective layer on the MN is fully dissolved, and MNs are exposed to the bio-environment to enable drug release. The compatible integration of microcontrollers allows the electrical triggers to be manipulated at a precise time point for precision delivery. By patterning gold layers via microfabrication, the SOP can realize the spatial profile of drug release at a high spatial resolution (~1 mm<sup>2</sup>). A built-in anode is integrated into the SOP to complete the circuitry for the in vivo electrochemical crevice corrosion.</p>","<title>Characterization of electrically triggered crevice corrosion</title>","<p id=\"Par10\">Figure ##FIG##1##2## demonstrates the characterization of SOP on its active control of drug release (based on MNs, 1.2 mm in height, 150-nm gold coated). The operation of drug-release control includes three stages: (1) standby stage when the MNs are fully coated with gold (labeled as 0 min); (2) transitioning stage when the electrical trigger is activated, and the gold layer is partially dissolved (labeled as 0.5 min); (3) releasing stage when the gold is fully dissolved, and MNs are exposed to the biofluids (labeled as 2 min). 1X Dulbecco’s phosphate-buffered saline (DPBS, Corning) is used here to simulate the body biofluid. The experiment is conducted at room temperature and triggered by a 2.5-V DC. Figure ##FIG##1##2a## shows optical and SEM images of the MN arrays at different stages, which indicates a noticeable change in surface color and roughness associated with the electrically triggered crevice corrosion. The results demonstrate that the main structure of the MN stays stable during the transitioning stage, and the electrical triggers effectively dissolve gold into biofluids and sufficiently expose the core MNs. More characterizations from different perspectives appear in Supplementary Fig. ##SUPPL##0##4##.</p>","<p id=\"Par11\">The electrochemical crevice corrosion of the gold layer can be triggered by a direct current potential within 2 min, which is of clinical relevance for a timely response in drug administrations. A quantitative amperometry study of current density under different potentials is conducted to understand the electrical triggering behavior. Figure ##FIG##1##2e## shows I-V measurements of SOP triggering with potentials bias ranging from 2.0 V to 2.8 V applied to the MN arrays (1.2 mm in length, 150-nm gold coated). A steep drop in current density appears at 15 s of application for 2.8 V bias, indicating the endpoint of the electrochemical crevice corrosion. The time of the current-drop appearance increases as the potential bias decreases. The time consumption from the beginning to the end of the current is defined as the effective corrosion time plotted against the potential in Fig. ##FIG##1##2f##. The fitting is based on the Butler-Volmer equationwhere is the exchange current density, is the Faraday constant, is the overpotential, is the ideal gas constant, is the thermodynamic temperature, and is a coefficient with values ranging from 0 to 1, is the number of electrons in the anodic half reaction.</p>","<p id=\"Par12\">This equation describes the relationship between potential difference and reaction rate<sup>##UREF##10##54##</sup>. At an ambient potential of 2.5 V, the crevice corrosion on MN is triggered within 30 s. This parameter is applied in the following experiments. We hypothesize that the crevice corrosion driven by constant voltage includes two parts: anodic oxidation and mechanical crevices. The 2.5-V potential between the anode and cathode is sufficient for triggering gold oxidation coupled with hydrogen emission reaction (HER) at ambient conditions (1X DPBS, 25 °C). It is reported that an anodic potential larger than 1.95 V (vs standard hydrogen electrode, SHE) is high enough to drive the below-surface oxidation of gold film<sup>##UREF##11##55##</sup>. This corresponds with our observations, as the crevice corrosion is triggered at a potential above 2.0 V, even though the HER on the counter electrode is not under the standard condition. The oxidated gold species in neutral and alkaline environments may include oxides, hydroxides, and free elements, mostly in the form of nanoparticles (NPs)<sup>##UREF##12##56##,##UREF##13##57##</sup>, that collectively in the amount used here are benign to the human body<sup>##UREF##14##58##</sup>. Supplementary Fig. ##SUPPL##0##5## provides a schematic illustration of the two-electrode system of SOP (Supplementary Fig. ##SUPPL##0##5a##) and the atomic scale illustration of the anodic oxidation (Supplementary Fig. ##SUPPL##0##5b, c##) on Au (111) facet in a neutral environment, with reaction products, Au<sub>2</sub>O<sub>3</sub> in the form of nanoparticles. The anodic behavior is also studied by finite element analysis (FEA), including reaction potential, reaction rate, current, and potential distribution (Supplementary Fig. ##SUPPL##0##6a–d##). The simulation results show that the reaction rate at 2.5 V is 281 nm/min (Fig. ##FIG##1##2h, i##), which is consistent with the experimental observations (150 nm in ~ 30 s). The anodic polarization curve under the condition of standard potentials and primary current distribution also shows a minimal triggering potential at around 2.0 V (Supplementary Fig. ##SUPPL##0##6e##). However, the anodic oxidation may not account for the entire loss of the gold coating. Based on the 2.6-V amperometry experiment (Fig. ##FIG##1##2e##), the theoretical weight of the oxidated gold is calculated as 122 μg. The actual value should be lower because water splitting (oxygen generation), as the side reaction, may also contribute to the current density. This amount of gold NPs does not constitute a health hazard since it is much lower than the safe exposure threshold, which is reported as 5 mg/ml<sup>##UREF##14##58##</sup>. Meanwhile, the total weight of the diminished gold film on the MN array is calculated as 290 μg, which is significantly higher than the oxidated amount. This is because the major part of the gold layer is not oxidated but exfoliated from the surface due to crevices and cracks. These defects come from the weakening effect on gold membranes as they become thinner during corrosion. This kind of crevice on metal film is generated by an electrical current, which is reported in some previous studies<sup>##REF##32923633##43##,##REF##22344516##45##</sup>.</p>","<p id=\"Par13\">A gold-on-wafer experiment validates the presence of mechanical crevices and exfoliations. The surface roughness of the crevice corrosion of the gold layer is also studied with the gold-on-wafer ({100} facet, 150-nm, 1 cm<sup>2</sup> square) model (Supplementary Fig. ##SUPPL##0##7b##, ##SUPPL##0##c##). The gold layer is connected to a power source, and crevice corrosion is triggered at 2.5 V in 1X DPBS. The surface roughness of gold is calculated with FIJI ImageJ on optical images collected at various stages of corrosion. As shown in Supplementary Fig. ##SUPPL##0##7d##, the surface roughness is monitored every 0.5 min for 5 min from the initial stage, then every 1 min from the 5-min stage to the 9-min stage. A sharp increase in surface roughness is observed in the first 1 min right after the crevice corrosion is initiated (Supplementary Fig. ##SUPPL##0##7a##). This is consistent with the amperometry study that most electrochemical corrosion happens within 1 min under this potential. The roughness increase indicates the gold layer’s exfoliation from the wafer, which is also observed in the MN corrosion from an SOP. It can be concluded that a significant part of the gold layer is not directly oxidized but exfoliated during the electrical triggers. This corresponds with the previous study using gold film as the control gate for implanted drug reservoirs<sup>##REF##28458955##44##</sup>.</p>","<p id=\"Par14\">To characterize the electrical trigger process, we select two different areas on an SOP MN: the tip area and the waist area, as shown in Fig. ##FIG##1##2d##. The structural difference between the two areas is the surface curvature, with the former being 1.37 mm<sup>−2</sup> and the latter close to 0. The waist area stands for the MN’s major surface, where most drugs are released. The energy-dispersive X-ray spectroscopy (EDXS) mapping is carried out on three stages of MNs from both areas as a semi-quantified surface analysis (SI Figs. ##SUPPL##0##8## and ##SUPPL##0##9##). Oxygen, carbon, and gold are selected as elements of interest, where oxygen and carbon show the exposed polymer body while gold shows the encapsulated surface of the MN. The ratio quantification is based on the relative weight percentage of only these three elements despite the existence of other elements. As shown in Fig. ##FIG##1##2b##, ##FIG##1##c##, both the tip and the waist areas show similar trends in element-relative ratios. An increase in oxygen, carbon, and a decrease in gold (all by weight%) are observed. The waist area, which stands for the main body of an MN, shows a more remarkable change in element constituents, indicating a complete corrosion of triggered crevice for the gold layer. The tip part of MN is still partially capped by a small amount of gold at the releasing stage. This part of the gold layer is isolated during the crevice corrosion process and accounts for the rather high remaining gold shown in Fig. ##FIG##1##2b##. The left gold covering the tip area is too small to hinder the overall drug release from the entire MN. Silicon and copper are also observed in EDXS mapping, which comes from PDMS residuals and conductive wires used during the experiment. More detailed information appears in the Supplementary Information on the EDXS element mapping.</p>","<p id=\"Par15\">Figure ##FIG##1##2g## shows the thermal characterization of SOP during the electrochemical corrosion to validate the thermal safety. The experiment uses an MN array (1.2 mm in length, 150-nm gold coated) connected to a 2.5-V DC power to undergo crevice corrosion in 1X DPBS. A FLIR thermometer records the temperature of the MN array with and without current in a 25 °C environment. The results show no noticeable change in temperature during the electrochemical corrosion, which indicates a low possibility of tissue damage from heat effects.</p>","<p id=\"Par16\">Figure ##FIG##1##2j## and Supplementary Fig. ##SUPPL##0##10## show the uniformity of the gold layer on MN prior to crevice corrosion. We also use atomic force microscopy (AFM) to characterize the surface roughness of gold layer deposited on polymer to illustrate the uniformity of deposition thickness by sputter coating, as shown in Supplementary Fig. ##SUPPL##0##11##.</p>","<title>Release control and electrical triggering of microneedle patch</title>","<p id=\"Par17\">Figure ##FIG##2##3a## demonstrates the encapsulation performance of gold coating on MNs. Here, Rhodamine B (Thermo scientific), a fluorescent dye, is loaded into the microneedle patch (0.3% in weight, ~90 ng per MN) to simulate small-molecule drugs, which can be subsequently quantified by UV-Vis spectroscopy. Figure ##FIG##2##3a, b## show the release profile of Rhodamine B from a bare MN patch (1.2 mm in length) and an MN patch (1.2 mm in length) with a 100-nm gold coating. The release study is carried out in 45 °C 1X DPBS. Significant color fading on the MN patch without gold coating, as shown in Fig. ##FIG##2##3a##, and the apparent increase in absorbance (blue, without gold coating, Fig. ##FIG##2##3b##) indicate a successful release of dye into the biofluids. The average release rate of Rhodamine B in an hour is calculated as ~ 415 ng/min. In contrast, the controlled experiment (black, with gold coating) shows no significant change in absorbance (Fig. ##FIG##2##3b##), suggesting excellent protection of encapsulated drugs from release. The calculation of the accumulative release amount is based on a calibration curve of Rhodamine B solutions and Beer-Lambert Law. Detailed explanation is provided in Supplementary Fig. ##SUPPL##0##13## and SI Table ##SUPPL##0##1##. We further analyze the stability of gold coating in a 0.5% agar model to better simulate the mechanical properties of animal tissue. The MN array (1.2 mm in length, 150-nm gold coated) remains stable during a 2-week soaking test in the agar model without significant changes in shape or surface morphology (Fig. ##FIG##2##3a##). SI Figs. ##SUPPL##0##14## and ##SUPPL##0##15e##, together with Fig. ##FIG##2##3a##, show the stability of gold film on MNs against soaking in biofluids and mechanical friction in tissues. This indicates that the bonding between gold and PLGA is strong enough to meet the need for implantation, which corresponds with previous research on Au-polymer adhesion<sup>##UREF##15##59##–##REF##34936437##61##</sup>.</p>","<p id=\"Par18\">Figure ##FIG##2##3c## demonstrates the wireless design of a SOP. An external power source—in this case, a signal generator—is connected to an inductive coil to provide a high-frequency (~MHz level) alternative current (AC). The inductive coil is paired with the receiving coil on the device to achieve wireless power transfer via magnetic resonance coupling. The AC is then converted to direct current using a full-bridge rectifier. A 2.5-V regulator then regulates the DC to provide a stable potential that facilitates the electrochemical crevice corrosion of the gold layer on MNs. The configuration of the wireless SOP is illustrated in Fig. ##FIG##2##3d##. The wireless SOP consists of an energy harvesting module for wireless power, a power amplification module, a System-on-Chip (SoC) module for remote control, and an MN array coupled with counter electrodes (CE). Within the energy harvesting module, a receiver coil, a full-bridge rectifier, and a regulator are used to provide stable direct current (DC) output. The output is coupled with a 0.1-μF capacitor to constitute a low-pass filter, improving output stability. An equivalent circuit diagram is provided in Fig. ##FIG##2##3g##, which emphasizes the energy harvesting module while simplifying the power amplification and SoC. A complete circuit diagram can be found in Supplementary Fig. ##SUPPL##0##16##. Supplementary Fig. ##SUPPL##0##17## shows the performance and impedance analyses of the energy harvesting module. The optimal signal input is determined as around 15 V peak-to-peak at 15 MHz. The measurements shown in Fig. ##FIG##2##3e## validate the output stability of wireless SOP with the regulator. The final output power signal is stabilized at around 2.5 V with a standard deviation of ~0.1 V, ensuring precise crevice corrosion control for initiating drug release. The input signal’s optimal frequency is around 36 MHz, as shown in Fig. ##FIG##2##3f##.</p>","<title>Temporal and spatial control of SOP triggering</title>","<p id=\"Par19\">To further investigate the temporal and spatial controllability of electrical triggering on the MN patch (Fig. ##FIG##3##4a##), we design a multi-domain SOP to realize the stepwise, on-demand release. The multi-domain SOP consists of 7 domains of MN arrays (1.2-mm in length, 150-nm gold coated). The gold layer on the PLGA patch is patterned by laser ablation to enable separate triggering of individual MN domains. A layer of PDMS (~10 μm) is then applied onto the patch except for the hexagonal MN regions to protect gold interconnects from dissolving during electrical triggering. Figure ##FIG##3##4c## shows the electrical triggering schedule for four of the seven MN domains for the SOP loaded with Rhodamine B (0.3% by weight) during its immersion in 65 °C 1X DPBS as an accelerated study. The electrical triggers use 2.5-V DC bias for 30 s at every 30-min interval of immersion. Following each interval, immediate sampling of the environmental fluids allows quantitative estimation of drug-release dosage using UV-Vis spectroscopy, as shown in Fig. ##FIG##3##4b##. The measurements show a multi-step increase in spectral absorbance, indicating the stepwise increase of drug dosage and confirming the on-demand drug release at the desired time (0, 30, 60, 90 min, Fig. ##FIG##3##4b##). Figure ##FIG##3##4d## demonstrates the staged release of MN domains by selectively dissolving the gold encapsulation layer with an electrical trigger (Supplementary Movie ##SUPPL##3##1##). The red dash line circles the specific MN array triggered from each stage. The results confirm that our SOP realizes both temporal and spatial control of drug release using digital electrical triggers.</p>","<p id=\"Par20\">Supplementary Fig. ##SUPPL##0##18## demonstrates the capability of ultrafine spatial control of drug release. Here, we design a miniaturized SOP with a single domain consisting of 8 MNs (1.1 mm in length, coated with 150 nm-thick gold) with 1–3 mm in spatial separation. With a specific design of gold circuits, each MN in the domain can be triggered individually (as illustrated in Supplementary Fig. ##SUPPL##0##18a##). Supplementary Fig. ##SUPPL##0##18b## shows that each MN follows sequential release via electrical triggering (2.2-V DC) within 15 s without interfering with adjacent MNs. Patterning techniques on the gold layer primarily dictate the spatial resolution of release control for SOP.</p>","<title>Demonstration of SOP in intracranial drug delivery</title>","<p id=\"Par21\">Beyond the clinical applicability for transdermal drug delivery, the SOP could show impactful utility in facilitating animal behavior studies. Here, we demonstrate intracranial delivery of melatonin using SOP for an animal sleep study. Melatonin, a hormone naturally produced in the brain by the pineal gland, plays a crucial role in regulating the sleep-wake cycle while also participating in other regulations like blood pressure and body temperature<sup>##REF##23333399##62##–##REF##15649735##64##</sup>. However, people found many strains of nocturnal laboratory mice are deficient in the release of melatonin, including C57/B6 mice<sup>##REF##20308563##65##,##REF##11792644##66##</sup>, and the effects of melatonin are still controversial<sup>##REF##22171046##67##–##REF##7326588##69##</sup>. Here, we use C57/B6 mice and implant the SOP to test how exogenous spatiotemporally controlled release of melatonin in the deep locations of the cortex, which is mainly distributed by melatonin receptor 1<sup>##REF##15893586##70##</sup>, would affect the sleep-wake cycle. The high spatiotemporal controllability of melatonin release offered by the SOP may open up new opportunities to understand regional brain responses to melatonin and study the pathology of sleep-wake disorders. Loading melatonin into MNs of the SOP follows the solution fabrication method described in Supplementary Fig. ##SUPPL##0##2##. Melatonin that dissolves in acetone can be mixed with the precursor PLGA solution, which ensures the loading dosage (10–35 wt%) for each MN. The 3-mm MNs are fabricated with the PLGA-melatonin ratio from 10:1 to 2:1 (Fig. ##FIG##4##5a##). In addition, the drug can be concentrated around the tip of the MN, which ensures deep-brain delivery for mice, as shown by the dash-red line in Fig. ##FIG##4##5a##. Based on the mixing ratio, the payload of melatonin per MN is estimated to be 22.2 to 81.7 μg, comparable to recommended dosages for mice (4–20 mg/kg)<sup>##REF##6655558##71##,##REF##30081472##72##</sup>. The drug payload is modulated by loading different PLGA solutions during the mold casting procedure. Furthermore, the mechanical property of the 3 mm-tall MNs used for intracranial delivery is characterized by a fracture test (Supplementary Fig. ##SUPPL##0##15##). The ultimate strength of PLGA MN (1.2 mm in length) is determined by the first fracture point in the force-displacement graph, as labeled by the red frame (Fig. ##FIG##4##5b##). The fracture point, recognized by a sudden drop in measured force, corresponds to the initial fracture of the MNs, followed by multiple subsequent fractures appearing in different locations of the MNs (Supplementary Fig. ##SUPPL##0##15b##, ##SUPPL##0##d##). The maximum mechanical strength is derived to be 118 MPa, which is sufficiently rigid for human skin penetration<sup>##REF##1757138##73##</sup>. The calculation considers the pressure measured at the first fracture point, while the contact area of the MN is approximated based on the tip diameter (around 30 μm, Supplementary Fig. ##SUPPL##0##3f##). Another concern is the stability of the gold layer during implantation. Supplementary Fig. ##SUPPL##0##15e## shows the PLGA MN (150 nm gold coated, 1.2 mm) before and after the penetration test. No significant changes can be observed based on the comparison of optical images. Figure ##FIG##2##3a## also proves the stability of the gold layer during the soaking test. In addition, we use the MN array (150 nm gold coated, 1.2 mm) to penetrate the tissue of the chicken thigh for multiple cycles, as presented in Supplementary Fig. ##SUPPL##0##14##. The gold layer remains stable after 20 cycles of penetration.</p>","<p id=\"Par22\">Deployment of the melatonin-loaded SOP in live animal models as they move in a caged environment shows possibilities for actively controlling melatonin release to the deep-brain regions of the parietal lobe (Fig. ##FIG##4##5g##). To ensure device stability, the SOP is coupled with a custom headstage that can be firmly mounted onto mice heads (Supplementary Fig. ##SUPPL##0##19##). Figure ##FIG##4##5h## shows an immunohistochemistry analysis of brain tissues from the mice at various recovery stages following SOP implantation. On day 1 of post-implantation, the brain tissues in close proximity to the SOP MN show structural damage resulting from mechanical forces during intracranial surgery, which is typical for general brain implantation<sup>##REF##25546652##74##</sup>. As the mice recover from the implantation, the levels of GFAP (red) and IBA (white) show a significant decrease in concentration and staining range, indicating excellent biocompatibility of SOP. The tissue in contact with the MN becomes smoother, with fewer rough edges. An obvious increase in neuron regeneration (neurotrace, green) can be observed, indicating good recovery from implantation surgery.</p>","<p id=\"Par23\">Supplementary Fig. ##SUPPL##0##20## shows an in vivo study to validate the safety and biocompatibility of SOP in intracranial deployment. Here, the study compares four groups of MNs (bare MN (MN), gold-coated MN (Au-MN), melatonin-loaded MN (Mel-MN), and melatonin-loaded MN with gold coating (Au-Mel-MN)) in their effects on the inflammation and degree of neural damage after one-month implantation. Supplementary Fig. ##SUPPL##0##21a## shows typical images of MN implanting sites with GFAP/IBA/NSE/DAPI multiple staining. As a result, the number of astrocytes and microglia in the region of interest (ROI) is significantly lower in the Au-MN or Au-Mel-MN group compared to the bare MN group, which indicates that gold-coated MNs have less damage to neural tissues and cause less inflammation compared to bare PLGA MNs. In addition, the fluorescence density of the markers has no difference between bare and gold-coated MNs, which indicates that the Au encapsulation on MNs does not affect the expressing strength of the GFAP or IBA signals (Supplementary Fig. ##SUPPL##0##21c##).</p>","<p id=\"Par24\">Furthermore, we used a fiber photometry system to investigate the neural activity in the medial prefrontal cortex (mPFC) after the implantation of MNs (bare MN, Au-MN, control (no MN), respectively, Supplementary Fig. ##SUPPL##0##22##). As shown in Supplementary Fig. ##SUPPL##0##22c##, there is no observable difference in Ca<sup>2+</sup> dynamics of mPFC excitatory neurons between the control group, bare MN, and Au-MN groups, which indicates the MNs have no significant damage to the brain tissue and introduce little influence on the neuronal activity.</p>","<p id=\"Par25\">Besides, we study the bioresorbability of the MN. Though the degradation of PLGA is much slower than gold<sup>##REF##22577513##75##</sup>, we show this process of PLGA in an accelerated degradation experiment in 65 °C phosphate-buffered saline (PBS), as illustrated in Supplementary Fig. ##SUPPL##0##23##.</p>","<p id=\"Par26\">We further use the in vivo animal models to characterize the SOP functional performance. Here, an additional two recording electrodes are inserted adjacent to the location of SOP implantation, as shown in Fig. ##FIG##4##5c##. First, a set of DC electrical triggers (5 s duration, 2.5 V) is delivered and recorded (5 mm from the stimulation electrode), as shown in Fig. ##FIG##4##5e##. The crevice corrosion of the gold layer (thickness 150 nm) on the MN can be completed by applying 5–7 times the 5-s triggers to allow melatonin release. Then, a series of short pulse stimulation experiments are examined (Supplementary Fig. ##SUPPL##0##24##). The pulse signals vary from 10 mV to 50 mV in amplitude and 1 Hz (duration 10 ms) or 10 Hz (duration 1 ms) in frequency. The signal amplitude-recording distance relationship is also studied based on 10-ms pulses of 50 mV (Fig. ##FIG##4##5f##) and proved to affect a 5-mm area mainly. No significant changes are observed on the gold layer of MN after short pulses, as demonstrated in Fig. ##FIG##4##5d##, indicating electrical signals generated from neuron cells induce negligible damage to the gold protection layer of the SOP. Furthermore, the results demonstrate the gold-coated MNs can also be used as stimulation electrodes for the delivery of low-amplitude (10–50 mV), pulsatile signals (Fig. ##FIG##4##5f##), which may serve as a strategy for neuronal regeneration<sup>##REF##33239608##76##,##REF##30297910##77##</sup>.</p>","<title>Assessment of in-brain drug delivery by microneedles</title>","<p id=\"Par27\">To investigate the gold encapsulation’s performance in controlled drug release from the MN, we fabricated two groups of melatonin-loaded (25 wt%) MN with the exact specification shown in Fig. ##FIG##4##5a##. For effective comparison, the first batch of MNs (Mel-MNs) has no gold encapsulation layer, while the second batch of MNs (Au-Mel-MNs) is coated with 200 nm of gold on the MN surface.</p>","<p id=\"Par28\">First, we conduct the in vivo drug release experiment with the Mel-MNs and the blank control (MNs without loading melatonin) by stereotaxically implanting the MN into the medial prefrontal cortex (mPFC) of mice (one MN in each, Fig. ##FIG##5##6a##). After one week of recovery, we use the Pinnacle sleep system (Sirenia Acquisition) to record EEG and EMG analyses. From 19:00 (active period) to 22:00, the power density of the Mel-MN group is compared with those of the control group (Fig. ##FIG##5##6b##). The total amount of NREM sleep increased by 42.0%, and the amount of wakefulness decreased by 27.9% over the 3-h period compared to these parameters in the control group (Fig. ##FIG##5##6c, d##). The sleep status is calculated from the EEG/EMG traces and the corresponding hypnograms (Supplementary Figs. ##SUPPL##0##25##–##SUPPL##0##29##). The experiment confirms the effective release of Melatonin from Mel-MNs, which can successfully modulate the sleep behavior of mice over a week.</p>","<p id=\"Par29\">Second, we conduct similar in vivo experiments to investigate the feasibility of release control with gold encapsulation from the Au-Mel-MN. Here, we apply the Au-Mel-MNs by stereotaxically implanting them into the medial prefrontal cortex (mPFC) of mice (one MN in each, Fig. ##FIG##5##6e##). For effective comparison, the EEG and EMG recording starts one week after implantation, following the same way as described above. Figure ##FIG##5##6f## shows the power density of the Au-Mel-MN group and control group recorded from 19:00 to 01:00 am (active period), indicating that the delta power density of the Au-Mel-MN group was significantly higher (<italic>P</italic> < 0.01) than that of the control group. The significantly enhanced delta band EEG power indicates a deeper sleeping degree induced by melatonin, compared with the Mel-MNs experiment (Fig. ##FIG##5##6c, d##). The total amount of NREM sleep increased significantly by 36.7%, REM sleep increased by 57.8%, and that of wakefulness significantly decreased by 26.0% over the 6-h period, compared to these parameters in the control group (Fig. ##FIG##5##6g, h##). Essentially, melatonin release in the mPFC induces NREM sleep amount increases in active periods and enhances delta power density during NREM sleep even more significantly than that shown in the Mel-MN group. The delayed release effect mainly explains this due to the existence of gold encapsulation. As a result, the releasing peak of melatonin appeared after the controlled degradation of the gold protection layer. In contrast, the Mel-MN group releases melatonin right after implantation, leading to a weakened effect at the time point of recording (one week post to recovery). Collectively, these in vivo experiments further demonstrate the programmed drug release performance of SOPs in freely moving animals and suggest its potential utility in animal behavior studies. We revealed that programmed drug release of exogenous melatonin in the mPFC would improve NREM sleep, REM sleep, and delta power density, which may represent a novel target for treating sleep disorders.</p>","<p id=\"Par30\">In this study, we present an on-demand drug-delivery patch that can be digitally controlled to enable delivery precision in both space and time. The spatiotemporal on-demand patch (SOP) uses bioresorbable microneedles with high-aspect ratios (3–8) as the drug-loading vehicle and a thin layer of gold (thickness 150 nm) as a release gate that can be digitally controlled with a small electrical trigger (2.5 V for 30 s). This design allows fully active control of drug release at a single-microneedle level with spatial resolution, demonstrated here, less than 1 mm<sup>2</sup>, highlighting the enabling capabilities over most existing drug-delivery devices. This spatial resolution allows more than 20 doses of the drug to be housed within a thin, wearable patch of 1 cm<sup>2</sup>, ensuring comfortable and convenient user adherence for repetitive pharmaceutical treatment over extended periods. The fabrication is compatible with the microfabrication process, which could further decrease the spatial resolution to micrometer scales. The on-demand, rapid-response drug release can be enabled within 30 s following an active electrical trigger.</p>","<p id=\"Par31\">The fabrication procedure of our SOP uses a simple solution-molding method, offering a low-cost approach with high dimensional customizability. Using laser ablation, microneedles, ranging from 0.6–3 mm, can be manufactured efficiently and in high quality to fit a wide range of drugs (melatonin with controllable payloads<sup>##REF##34959479##78##</sup>. Moreover, the solution-based mold process allows drug-load PLGA MNs to be produced at scale with similarly high quality. The process allows convenient compatibility for integrating electronic modules to enable digital automation in drug delivery.</p>","<p id=\"Par32\">The multifunctionality of drug delivery and stimulation therapy could synergistically create advanced therapy as potential future avenues of neuroscience research. The SOP presented in this work exhibits continuous sustained drug release feature that meets the need to treat various chronic neural diseases apart from sleep disorder, especially Alzheimer’s disease (AD)<sup>##REF##28872215##50##</sup>. As a multiphase neural disease that affects most areas of the brain<sup>##REF##22229116##51##,##REF##27188934##52##</sup>, the treatment of AD requires different drugs at different stages. For example, acetylcholinesterase inhibitors (AChEIs) are currently the mainstay of treatment for mild-to-moderate AD. In contrast, memantine is approved for mild-to-moderate dementia as a non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist that prevents the overactivation of neuronal NMDA receptors. At the same time, the combination of these two drugs also significantly improves cognitive function in moderate-to-severe AD<sup>##REF##23657927##79##</sup>. The SOP can enable precise joint delivery of multiple drugs in desired brain regions to achieve enhanced treatment. Combined with a burst release design (like hollow microneedles), the SOP also fits for rapid drug delivery as a timely response towards acute neural diseases, such as cataplexy and epilepsy<sup>##REF##12557281##80##–##REF##23359095##82##</sup>. The remotely powered SOP allows for active drug delivery at a particular time and space coupled with real-time behavioral study, such as sleep-quality characterization, which provides a convenient method for drug-performance analysis. The high-resolution and controllability features of SOP make it suitable for such kind of regional and dosage-sensitive treatment. In addition, many drugs for brain disorders may have detrimental effects on other organs if administered systemically<sup>##REF##20497904##83##</sup>. The regional drug release feature of SOP enables targeted drug delivery that minimizes systemic exposure, reducing the risk of unwanted side effects in non-brain tissues.</p>","<p id=\"Par33\">Moreover, our SOP is capable of wireless operation via near-field communication or, potentially, Bluetooth Low Energy. The agar-soaking test, fracture test, and in vivo intracranial delivery experiments validate the SOP’s practical functionality and biocompatibility. Furthermore, gold-coated MNs can extend beyond drug-releasing control, as the SOP can offer in vivo electrical stimulation. These concepts establish unique approaches in high-precision drug-delivery technologies with additional utilities in advancing fundamental studies of disease pathology (such as cancer metastasis) and neuroscience research, as demonstrated by both benchtop experiments and in vivo studies. Future efforts on fully digital automation of drug delivery will pave the way for the next generation of precision medicine.</p>"],"string":"[\n \"<title>Results and discussion</title>\",\n \"<title>Configuration and characterization of the spatiotemporal on-demand patch (SOP)</title>\",\n \"<p id=\\\"Par7\\\">Figure ##FIG##0##1a## highlights the design of an SOP, which consists of two primary parts: i) a drug-loaded microneedle (MN) patch protected with an electrochemically triggerable metal layer (gold) as the drug delivery interface; ii) a near-field communication (NFC) module for the wireless control of triggering signals in controlling location and schedule of drug release. A flexible printed circuit board (PCB) provides interconnected traces that integrate the two parts to form a fully wireless, wearable drug delivery system. The main body of the drug delivery interface (MN arrays) uses poly(D, L-lactide-co-glycolide) (PLGA, Mw = 50 ~ 75 kDa, ester terminated, Sigma Aldrich) as the matrix materials that can undergo bulk erosion upon contact with biofluids to generate biological benign byproducts (lactic acid and glycolic acid)<sup>##UREF##9##53##</sup>. As shown in Fig. ##FIG##0##1b##, the fabrication relies on sputter deposition to coat a thin layer of gold (thickness 150 nm) onto the drug-loaded MN arrays supported by a PLGA base. The gold encapsulation layer is supported on the surface of solid MNs, which enables sufficient stability with a thinner thickness (150 nm) compared with those used in the reservoir designs (thickness of gold layer more than 300 nm) as reported previously (as illustrated in Supplementary Fig. ##SUPPL##0##1##). Thus, the thickness to realize effective drug encapsulation can be smaller. Ablation using a laser beam defines the gold traces that connect with separated MN domains to realize spatial control of drug release. A thin polydimethylsiloxane (PDMS) (thickness 10 µm) covers the gold layer at the circuity base regions. It exposes only the MN regions to allow physical contact of the gold layer on the MN with biofluids.</p>\",\n \"<p id=\\\"Par8\\\">Fabrication of the SOP relies on a low-cost solution-molding procedure (Supplementary Fig. ##SUPPL##0##2##). The process starts with UV laser ablation to define an MN mold from a PDMS pad. Drop casting drug-loaded PLGA solution into the PDMS mold and setting under vacuum allow the entrance of PLGA into the negative MN molds. Sufficient solidification over 8 h allows easy extraction of the PLGA MNs. Then, the PLGA MNs undergo sputtering deposition to coat a 150 nm-thick gold layer that conformally covers the top surface, followed by patterning with a laser ablation system to define control circuits. Drop casting a thin PDMS layer (thickness 10 μm) onto gold layers protects the control circuits and exposes the MNs for drug release. The MN patch is then attached to a flexible PCB and connected with a current regulator, a wireless energy harvester, and a microcontroller to complete the fabrication process. The solution-molding method can produce SOPs with: (1) tunable MN lengths from 600 μm to 3 mm and aspect ratio from 3 to 8; (2) high dimension uniformity in both length and base diameter (Supplementary Fig. ##SUPPL##0##3##); (3) arbitrary MN array configurations (square, hexagonal, single-needle, etc.). Figure ##FIG##0##1e##–##FIG##0##h## shows that the morphology and shape of the PLGA MNs remain stable during both gold deposition and drug loading procedures.</p>\",\n \"<p id=\\\"Par9\\\">Figure ##FIG##0##1c##, ##FIG##0##d## illustrates the overall working mechanism to realize high-precision drug delivery by our SOP. The electrically triggered crevice corrosion of the gold protective layer is the switch for initiating drug release of specific collections of SOP MNs. Upon SOP deployment on the skin, the MNs stay at a standby stage with the drug fully protected by the gold layer. Once a direct current (DC) electrical trigger (2.2–3 V) is applied, the electrochemical crevice corrosion starts to occur on triggered MNs, transitioning them from standby mode to releasing mode. After a short period (<30 s when 2.5 V is applied), the gold protective layer on the MN is fully dissolved, and MNs are exposed to the bio-environment to enable drug release. The compatible integration of microcontrollers allows the electrical triggers to be manipulated at a precise time point for precision delivery. By patterning gold layers via microfabrication, the SOP can realize the spatial profile of drug release at a high spatial resolution (~1 mm<sup>2</sup>). A built-in anode is integrated into the SOP to complete the circuitry for the in vivo electrochemical crevice corrosion.</p>\",\n \"<title>Characterization of electrically triggered crevice corrosion</title>\",\n \"<p id=\\\"Par10\\\">Figure ##FIG##1##2## demonstrates the characterization of SOP on its active control of drug release (based on MNs, 1.2 mm in height, 150-nm gold coated). The operation of drug-release control includes three stages: (1) standby stage when the MNs are fully coated with gold (labeled as 0 min); (2) transitioning stage when the electrical trigger is activated, and the gold layer is partially dissolved (labeled as 0.5 min); (3) releasing stage when the gold is fully dissolved, and MNs are exposed to the biofluids (labeled as 2 min). 1X Dulbecco’s phosphate-buffered saline (DPBS, Corning) is used here to simulate the body biofluid. The experiment is conducted at room temperature and triggered by a 2.5-V DC. Figure ##FIG##1##2a## shows optical and SEM images of the MN arrays at different stages, which indicates a noticeable change in surface color and roughness associated with the electrically triggered crevice corrosion. The results demonstrate that the main structure of the MN stays stable during the transitioning stage, and the electrical triggers effectively dissolve gold into biofluids and sufficiently expose the core MNs. More characterizations from different perspectives appear in Supplementary Fig. ##SUPPL##0##4##.</p>\",\n \"<p id=\\\"Par11\\\">The electrochemical crevice corrosion of the gold layer can be triggered by a direct current potential within 2 min, which is of clinical relevance for a timely response in drug administrations. A quantitative amperometry study of current density under different potentials is conducted to understand the electrical triggering behavior. Figure ##FIG##1##2e## shows I-V measurements of SOP triggering with potentials bias ranging from 2.0 V to 2.8 V applied to the MN arrays (1.2 mm in length, 150-nm gold coated). A steep drop in current density appears at 15 s of application for 2.8 V bias, indicating the endpoint of the electrochemical crevice corrosion. The time of the current-drop appearance increases as the potential bias decreases. The time consumption from the beginning to the end of the current is defined as the effective corrosion time plotted against the potential in Fig. ##FIG##1##2f##. The fitting is based on the Butler-Volmer equationwhere is the exchange current density, is the Faraday constant, is the overpotential, is the ideal gas constant, is the thermodynamic temperature, and is a coefficient with values ranging from 0 to 1, is the number of electrons in the anodic half reaction.</p>\",\n \"<p id=\\\"Par12\\\">This equation describes the relationship between potential difference and reaction rate<sup>##UREF##10##54##</sup>. At an ambient potential of 2.5 V, the crevice corrosion on MN is triggered within 30 s. This parameter is applied in the following experiments. We hypothesize that the crevice corrosion driven by constant voltage includes two parts: anodic oxidation and mechanical crevices. The 2.5-V potential between the anode and cathode is sufficient for triggering gold oxidation coupled with hydrogen emission reaction (HER) at ambient conditions (1X DPBS, 25 °C). It is reported that an anodic potential larger than 1.95 V (vs standard hydrogen electrode, SHE) is high enough to drive the below-surface oxidation of gold film<sup>##UREF##11##55##</sup>. This corresponds with our observations, as the crevice corrosion is triggered at a potential above 2.0 V, even though the HER on the counter electrode is not under the standard condition. The oxidated gold species in neutral and alkaline environments may include oxides, hydroxides, and free elements, mostly in the form of nanoparticles (NPs)<sup>##UREF##12##56##,##UREF##13##57##</sup>, that collectively in the amount used here are benign to the human body<sup>##UREF##14##58##</sup>. Supplementary Fig. ##SUPPL##0##5## provides a schematic illustration of the two-electrode system of SOP (Supplementary Fig. ##SUPPL##0##5a##) and the atomic scale illustration of the anodic oxidation (Supplementary Fig. ##SUPPL##0##5b, c##) on Au (111) facet in a neutral environment, with reaction products, Au<sub>2</sub>O<sub>3</sub> in the form of nanoparticles. The anodic behavior is also studied by finite element analysis (FEA), including reaction potential, reaction rate, current, and potential distribution (Supplementary Fig. ##SUPPL##0##6a–d##). The simulation results show that the reaction rate at 2.5 V is 281 nm/min (Fig. ##FIG##1##2h, i##), which is consistent with the experimental observations (150 nm in ~ 30 s). The anodic polarization curve under the condition of standard potentials and primary current distribution also shows a minimal triggering potential at around 2.0 V (Supplementary Fig. ##SUPPL##0##6e##). However, the anodic oxidation may not account for the entire loss of the gold coating. Based on the 2.6-V amperometry experiment (Fig. ##FIG##1##2e##), the theoretical weight of the oxidated gold is calculated as 122 μg. The actual value should be lower because water splitting (oxygen generation), as the side reaction, may also contribute to the current density. This amount of gold NPs does not constitute a health hazard since it is much lower than the safe exposure threshold, which is reported as 5 mg/ml<sup>##UREF##14##58##</sup>. Meanwhile, the total weight of the diminished gold film on the MN array is calculated as 290 μg, which is significantly higher than the oxidated amount. This is because the major part of the gold layer is not oxidated but exfoliated from the surface due to crevices and cracks. These defects come from the weakening effect on gold membranes as they become thinner during corrosion. This kind of crevice on metal film is generated by an electrical current, which is reported in some previous studies<sup>##REF##32923633##43##,##REF##22344516##45##</sup>.</p>\",\n \"<p id=\\\"Par13\\\">A gold-on-wafer experiment validates the presence of mechanical crevices and exfoliations. The surface roughness of the crevice corrosion of the gold layer is also studied with the gold-on-wafer ({100} facet, 150-nm, 1 cm<sup>2</sup> square) model (Supplementary Fig. ##SUPPL##0##7b##, ##SUPPL##0##c##). The gold layer is connected to a power source, and crevice corrosion is triggered at 2.5 V in 1X DPBS. The surface roughness of gold is calculated with FIJI ImageJ on optical images collected at various stages of corrosion. As shown in Supplementary Fig. ##SUPPL##0##7d##, the surface roughness is monitored every 0.5 min for 5 min from the initial stage, then every 1 min from the 5-min stage to the 9-min stage. A sharp increase in surface roughness is observed in the first 1 min right after the crevice corrosion is initiated (Supplementary Fig. ##SUPPL##0##7a##). This is consistent with the amperometry study that most electrochemical corrosion happens within 1 min under this potential. The roughness increase indicates the gold layer’s exfoliation from the wafer, which is also observed in the MN corrosion from an SOP. It can be concluded that a significant part of the gold layer is not directly oxidized but exfoliated during the electrical triggers. This corresponds with the previous study using gold film as the control gate for implanted drug reservoirs<sup>##REF##28458955##44##</sup>.</p>\",\n \"<p id=\\\"Par14\\\">To characterize the electrical trigger process, we select two different areas on an SOP MN: the tip area and the waist area, as shown in Fig. ##FIG##1##2d##. The structural difference between the two areas is the surface curvature, with the former being 1.37 mm<sup>−2</sup> and the latter close to 0. The waist area stands for the MN’s major surface, where most drugs are released. The energy-dispersive X-ray spectroscopy (EDXS) mapping is carried out on three stages of MNs from both areas as a semi-quantified surface analysis (SI Figs. ##SUPPL##0##8## and ##SUPPL##0##9##). Oxygen, carbon, and gold are selected as elements of interest, where oxygen and carbon show the exposed polymer body while gold shows the encapsulated surface of the MN. The ratio quantification is based on the relative weight percentage of only these three elements despite the existence of other elements. As shown in Fig. ##FIG##1##2b##, ##FIG##1##c##, both the tip and the waist areas show similar trends in element-relative ratios. An increase in oxygen, carbon, and a decrease in gold (all by weight%) are observed. The waist area, which stands for the main body of an MN, shows a more remarkable change in element constituents, indicating a complete corrosion of triggered crevice for the gold layer. The tip part of MN is still partially capped by a small amount of gold at the releasing stage. This part of the gold layer is isolated during the crevice corrosion process and accounts for the rather high remaining gold shown in Fig. ##FIG##1##2b##. The left gold covering the tip area is too small to hinder the overall drug release from the entire MN. Silicon and copper are also observed in EDXS mapping, which comes from PDMS residuals and conductive wires used during the experiment. More detailed information appears in the Supplementary Information on the EDXS element mapping.</p>\",\n \"<p id=\\\"Par15\\\">Figure ##FIG##1##2g## shows the thermal characterization of SOP during the electrochemical corrosion to validate the thermal safety. The experiment uses an MN array (1.2 mm in length, 150-nm gold coated) connected to a 2.5-V DC power to undergo crevice corrosion in 1X DPBS. A FLIR thermometer records the temperature of the MN array with and without current in a 25 °C environment. The results show no noticeable change in temperature during the electrochemical corrosion, which indicates a low possibility of tissue damage from heat effects.</p>\",\n \"<p id=\\\"Par16\\\">Figure ##FIG##1##2j## and Supplementary Fig. ##SUPPL##0##10## show the uniformity of the gold layer on MN prior to crevice corrosion. We also use atomic force microscopy (AFM) to characterize the surface roughness of gold layer deposited on polymer to illustrate the uniformity of deposition thickness by sputter coating, as shown in Supplementary Fig. ##SUPPL##0##11##.</p>\",\n \"<title>Release control and electrical triggering of microneedle patch</title>\",\n \"<p id=\\\"Par17\\\">Figure ##FIG##2##3a## demonstrates the encapsulation performance of gold coating on MNs. Here, Rhodamine B (Thermo scientific), a fluorescent dye, is loaded into the microneedle patch (0.3% in weight, ~90 ng per MN) to simulate small-molecule drugs, which can be subsequently quantified by UV-Vis spectroscopy. Figure ##FIG##2##3a, b## show the release profile of Rhodamine B from a bare MN patch (1.2 mm in length) and an MN patch (1.2 mm in length) with a 100-nm gold coating. The release study is carried out in 45 °C 1X DPBS. Significant color fading on the MN patch without gold coating, as shown in Fig. ##FIG##2##3a##, and the apparent increase in absorbance (blue, without gold coating, Fig. ##FIG##2##3b##) indicate a successful release of dye into the biofluids. The average release rate of Rhodamine B in an hour is calculated as ~ 415 ng/min. In contrast, the controlled experiment (black, with gold coating) shows no significant change in absorbance (Fig. ##FIG##2##3b##), suggesting excellent protection of encapsulated drugs from release. The calculation of the accumulative release amount is based on a calibration curve of Rhodamine B solutions and Beer-Lambert Law. Detailed explanation is provided in Supplementary Fig. ##SUPPL##0##13## and SI Table ##SUPPL##0##1##. We further analyze the stability of gold coating in a 0.5% agar model to better simulate the mechanical properties of animal tissue. The MN array (1.2 mm in length, 150-nm gold coated) remains stable during a 2-week soaking test in the agar model without significant changes in shape or surface morphology (Fig. ##FIG##2##3a##). SI Figs. ##SUPPL##0##14## and ##SUPPL##0##15e##, together with Fig. ##FIG##2##3a##, show the stability of gold film on MNs against soaking in biofluids and mechanical friction in tissues. This indicates that the bonding between gold and PLGA is strong enough to meet the need for implantation, which corresponds with previous research on Au-polymer adhesion<sup>##UREF##15##59##–##REF##34936437##61##</sup>.</p>\",\n \"<p id=\\\"Par18\\\">Figure ##FIG##2##3c## demonstrates the wireless design of a SOP. An external power source—in this case, a signal generator—is connected to an inductive coil to provide a high-frequency (~MHz level) alternative current (AC). The inductive coil is paired with the receiving coil on the device to achieve wireless power transfer via magnetic resonance coupling. The AC is then converted to direct current using a full-bridge rectifier. A 2.5-V regulator then regulates the DC to provide a stable potential that facilitates the electrochemical crevice corrosion of the gold layer on MNs. The configuration of the wireless SOP is illustrated in Fig. ##FIG##2##3d##. The wireless SOP consists of an energy harvesting module for wireless power, a power amplification module, a System-on-Chip (SoC) module for remote control, and an MN array coupled with counter electrodes (CE). Within the energy harvesting module, a receiver coil, a full-bridge rectifier, and a regulator are used to provide stable direct current (DC) output. The output is coupled with a 0.1-μF capacitor to constitute a low-pass filter, improving output stability. An equivalent circuit diagram is provided in Fig. ##FIG##2##3g##, which emphasizes the energy harvesting module while simplifying the power amplification and SoC. A complete circuit diagram can be found in Supplementary Fig. ##SUPPL##0##16##. Supplementary Fig. ##SUPPL##0##17## shows the performance and impedance analyses of the energy harvesting module. The optimal signal input is determined as around 15 V peak-to-peak at 15 MHz. The measurements shown in Fig. ##FIG##2##3e## validate the output stability of wireless SOP with the regulator. The final output power signal is stabilized at around 2.5 V with a standard deviation of ~0.1 V, ensuring precise crevice corrosion control for initiating drug release. The input signal’s optimal frequency is around 36 MHz, as shown in Fig. ##FIG##2##3f##.</p>\",\n \"<title>Temporal and spatial control of SOP triggering</title>\",\n \"<p id=\\\"Par19\\\">To further investigate the temporal and spatial controllability of electrical triggering on the MN patch (Fig. ##FIG##3##4a##), we design a multi-domain SOP to realize the stepwise, on-demand release. The multi-domain SOP consists of 7 domains of MN arrays (1.2-mm in length, 150-nm gold coated). The gold layer on the PLGA patch is patterned by laser ablation to enable separate triggering of individual MN domains. A layer of PDMS (~10 μm) is then applied onto the patch except for the hexagonal MN regions to protect gold interconnects from dissolving during electrical triggering. Figure ##FIG##3##4c## shows the electrical triggering schedule for four of the seven MN domains for the SOP loaded with Rhodamine B (0.3% by weight) during its immersion in 65 °C 1X DPBS as an accelerated study. The electrical triggers use 2.5-V DC bias for 30 s at every 30-min interval of immersion. Following each interval, immediate sampling of the environmental fluids allows quantitative estimation of drug-release dosage using UV-Vis spectroscopy, as shown in Fig. ##FIG##3##4b##. The measurements show a multi-step increase in spectral absorbance, indicating the stepwise increase of drug dosage and confirming the on-demand drug release at the desired time (0, 30, 60, 90 min, Fig. ##FIG##3##4b##). Figure ##FIG##3##4d## demonstrates the staged release of MN domains by selectively dissolving the gold encapsulation layer with an electrical trigger (Supplementary Movie ##SUPPL##3##1##). The red dash line circles the specific MN array triggered from each stage. The results confirm that our SOP realizes both temporal and spatial control of drug release using digital electrical triggers.</p>\",\n \"<p id=\\\"Par20\\\">Supplementary Fig. ##SUPPL##0##18## demonstrates the capability of ultrafine spatial control of drug release. Here, we design a miniaturized SOP with a single domain consisting of 8 MNs (1.1 mm in length, coated with 150 nm-thick gold) with 1–3 mm in spatial separation. With a specific design of gold circuits, each MN in the domain can be triggered individually (as illustrated in Supplementary Fig. ##SUPPL##0##18a##). Supplementary Fig. ##SUPPL##0##18b## shows that each MN follows sequential release via electrical triggering (2.2-V DC) within 15 s without interfering with adjacent MNs. Patterning techniques on the gold layer primarily dictate the spatial resolution of release control for SOP.</p>\",\n \"<title>Demonstration of SOP in intracranial drug delivery</title>\",\n \"<p id=\\\"Par21\\\">Beyond the clinical applicability for transdermal drug delivery, the SOP could show impactful utility in facilitating animal behavior studies. Here, we demonstrate intracranial delivery of melatonin using SOP for an animal sleep study. Melatonin, a hormone naturally produced in the brain by the pineal gland, plays a crucial role in regulating the sleep-wake cycle while also participating in other regulations like blood pressure and body temperature<sup>##REF##23333399##62##–##REF##15649735##64##</sup>. However, people found many strains of nocturnal laboratory mice are deficient in the release of melatonin, including C57/B6 mice<sup>##REF##20308563##65##,##REF##11792644##66##</sup>, and the effects of melatonin are still controversial<sup>##REF##22171046##67##–##REF##7326588##69##</sup>. Here, we use C57/B6 mice and implant the SOP to test how exogenous spatiotemporally controlled release of melatonin in the deep locations of the cortex, which is mainly distributed by melatonin receptor 1<sup>##REF##15893586##70##</sup>, would affect the sleep-wake cycle. The high spatiotemporal controllability of melatonin release offered by the SOP may open up new opportunities to understand regional brain responses to melatonin and study the pathology of sleep-wake disorders. Loading melatonin into MNs of the SOP follows the solution fabrication method described in Supplementary Fig. ##SUPPL##0##2##. Melatonin that dissolves in acetone can be mixed with the precursor PLGA solution, which ensures the loading dosage (10–35 wt%) for each MN. The 3-mm MNs are fabricated with the PLGA-melatonin ratio from 10:1 to 2:1 (Fig. ##FIG##4##5a##). In addition, the drug can be concentrated around the tip of the MN, which ensures deep-brain delivery for mice, as shown by the dash-red line in Fig. ##FIG##4##5a##. Based on the mixing ratio, the payload of melatonin per MN is estimated to be 22.2 to 81.7 μg, comparable to recommended dosages for mice (4–20 mg/kg)<sup>##REF##6655558##71##,##REF##30081472##72##</sup>. The drug payload is modulated by loading different PLGA solutions during the mold casting procedure. Furthermore, the mechanical property of the 3 mm-tall MNs used for intracranial delivery is characterized by a fracture test (Supplementary Fig. ##SUPPL##0##15##). The ultimate strength of PLGA MN (1.2 mm in length) is determined by the first fracture point in the force-displacement graph, as labeled by the red frame (Fig. ##FIG##4##5b##). The fracture point, recognized by a sudden drop in measured force, corresponds to the initial fracture of the MNs, followed by multiple subsequent fractures appearing in different locations of the MNs (Supplementary Fig. ##SUPPL##0##15b##, ##SUPPL##0##d##). The maximum mechanical strength is derived to be 118 MPa, which is sufficiently rigid for human skin penetration<sup>##REF##1757138##73##</sup>. The calculation considers the pressure measured at the first fracture point, while the contact area of the MN is approximated based on the tip diameter (around 30 μm, Supplementary Fig. ##SUPPL##0##3f##). Another concern is the stability of the gold layer during implantation. Supplementary Fig. ##SUPPL##0##15e## shows the PLGA MN (150 nm gold coated, 1.2 mm) before and after the penetration test. No significant changes can be observed based on the comparison of optical images. Figure ##FIG##2##3a## also proves the stability of the gold layer during the soaking test. In addition, we use the MN array (150 nm gold coated, 1.2 mm) to penetrate the tissue of the chicken thigh for multiple cycles, as presented in Supplementary Fig. ##SUPPL##0##14##. The gold layer remains stable after 20 cycles of penetration.</p>\",\n \"<p id=\\\"Par22\\\">Deployment of the melatonin-loaded SOP in live animal models as they move in a caged environment shows possibilities for actively controlling melatonin release to the deep-brain regions of the parietal lobe (Fig. ##FIG##4##5g##). To ensure device stability, the SOP is coupled with a custom headstage that can be firmly mounted onto mice heads (Supplementary Fig. ##SUPPL##0##19##). Figure ##FIG##4##5h## shows an immunohistochemistry analysis of brain tissues from the mice at various recovery stages following SOP implantation. On day 1 of post-implantation, the brain tissues in close proximity to the SOP MN show structural damage resulting from mechanical forces during intracranial surgery, which is typical for general brain implantation<sup>##REF##25546652##74##</sup>. As the mice recover from the implantation, the levels of GFAP (red) and IBA (white) show a significant decrease in concentration and staining range, indicating excellent biocompatibility of SOP. The tissue in contact with the MN becomes smoother, with fewer rough edges. An obvious increase in neuron regeneration (neurotrace, green) can be observed, indicating good recovery from implantation surgery.</p>\",\n \"<p id=\\\"Par23\\\">Supplementary Fig. ##SUPPL##0##20## shows an in vivo study to validate the safety and biocompatibility of SOP in intracranial deployment. Here, the study compares four groups of MNs (bare MN (MN), gold-coated MN (Au-MN), melatonin-loaded MN (Mel-MN), and melatonin-loaded MN with gold coating (Au-Mel-MN)) in their effects on the inflammation and degree of neural damage after one-month implantation. Supplementary Fig. ##SUPPL##0##21a## shows typical images of MN implanting sites with GFAP/IBA/NSE/DAPI multiple staining. As a result, the number of astrocytes and microglia in the region of interest (ROI) is significantly lower in the Au-MN or Au-Mel-MN group compared to the bare MN group, which indicates that gold-coated MNs have less damage to neural tissues and cause less inflammation compared to bare PLGA MNs. In addition, the fluorescence density of the markers has no difference between bare and gold-coated MNs, which indicates that the Au encapsulation on MNs does not affect the expressing strength of the GFAP or IBA signals (Supplementary Fig. ##SUPPL##0##21c##).</p>\",\n \"<p id=\\\"Par24\\\">Furthermore, we used a fiber photometry system to investigate the neural activity in the medial prefrontal cortex (mPFC) after the implantation of MNs (bare MN, Au-MN, control (no MN), respectively, Supplementary Fig. ##SUPPL##0##22##). As shown in Supplementary Fig. ##SUPPL##0##22c##, there is no observable difference in Ca<sup>2+</sup> dynamics of mPFC excitatory neurons between the control group, bare MN, and Au-MN groups, which indicates the MNs have no significant damage to the brain tissue and introduce little influence on the neuronal activity.</p>\",\n \"<p id=\\\"Par25\\\">Besides, we study the bioresorbability of the MN. Though the degradation of PLGA is much slower than gold<sup>##REF##22577513##75##</sup>, we show this process of PLGA in an accelerated degradation experiment in 65 °C phosphate-buffered saline (PBS), as illustrated in Supplementary Fig. ##SUPPL##0##23##.</p>\",\n \"<p id=\\\"Par26\\\">We further use the in vivo animal models to characterize the SOP functional performance. Here, an additional two recording electrodes are inserted adjacent to the location of SOP implantation, as shown in Fig. ##FIG##4##5c##. First, a set of DC electrical triggers (5 s duration, 2.5 V) is delivered and recorded (5 mm from the stimulation electrode), as shown in Fig. ##FIG##4##5e##. The crevice corrosion of the gold layer (thickness 150 nm) on the MN can be completed by applying 5–7 times the 5-s triggers to allow melatonin release. Then, a series of short pulse stimulation experiments are examined (Supplementary Fig. ##SUPPL##0##24##). The pulse signals vary from 10 mV to 50 mV in amplitude and 1 Hz (duration 10 ms) or 10 Hz (duration 1 ms) in frequency. The signal amplitude-recording distance relationship is also studied based on 10-ms pulses of 50 mV (Fig. ##FIG##4##5f##) and proved to affect a 5-mm area mainly. No significant changes are observed on the gold layer of MN after short pulses, as demonstrated in Fig. ##FIG##4##5d##, indicating electrical signals generated from neuron cells induce negligible damage to the gold protection layer of the SOP. Furthermore, the results demonstrate the gold-coated MNs can also be used as stimulation electrodes for the delivery of low-amplitude (10–50 mV), pulsatile signals (Fig. ##FIG##4##5f##), which may serve as a strategy for neuronal regeneration<sup>##REF##33239608##76##,##REF##30297910##77##</sup>.</p>\",\n \"<title>Assessment of in-brain drug delivery by microneedles</title>\",\n \"<p id=\\\"Par27\\\">To investigate the gold encapsulation’s performance in controlled drug release from the MN, we fabricated two groups of melatonin-loaded (25 wt%) MN with the exact specification shown in Fig. ##FIG##4##5a##. For effective comparison, the first batch of MNs (Mel-MNs) has no gold encapsulation layer, while the second batch of MNs (Au-Mel-MNs) is coated with 200 nm of gold on the MN surface.</p>\",\n \"<p id=\\\"Par28\\\">First, we conduct the in vivo drug release experiment with the Mel-MNs and the blank control (MNs without loading melatonin) by stereotaxically implanting the MN into the medial prefrontal cortex (mPFC) of mice (one MN in each, Fig. ##FIG##5##6a##). After one week of recovery, we use the Pinnacle sleep system (Sirenia Acquisition) to record EEG and EMG analyses. From 19:00 (active period) to 22:00, the power density of the Mel-MN group is compared with those of the control group (Fig. ##FIG##5##6b##). The total amount of NREM sleep increased by 42.0%, and the amount of wakefulness decreased by 27.9% over the 3-h period compared to these parameters in the control group (Fig. ##FIG##5##6c, d##). The sleep status is calculated from the EEG/EMG traces and the corresponding hypnograms (Supplementary Figs. ##SUPPL##0##25##–##SUPPL##0##29##). The experiment confirms the effective release of Melatonin from Mel-MNs, which can successfully modulate the sleep behavior of mice over a week.</p>\",\n \"<p id=\\\"Par29\\\">Second, we conduct similar in vivo experiments to investigate the feasibility of release control with gold encapsulation from the Au-Mel-MN. Here, we apply the Au-Mel-MNs by stereotaxically implanting them into the medial prefrontal cortex (mPFC) of mice (one MN in each, Fig. ##FIG##5##6e##). For effective comparison, the EEG and EMG recording starts one week after implantation, following the same way as described above. Figure ##FIG##5##6f## shows the power density of the Au-Mel-MN group and control group recorded from 19:00 to 01:00 am (active period), indicating that the delta power density of the Au-Mel-MN group was significantly higher (<italic>P</italic> < 0.01) than that of the control group. The significantly enhanced delta band EEG power indicates a deeper sleeping degree induced by melatonin, compared with the Mel-MNs experiment (Fig. ##FIG##5##6c, d##). The total amount of NREM sleep increased significantly by 36.7%, REM sleep increased by 57.8%, and that of wakefulness significantly decreased by 26.0% over the 6-h period, compared to these parameters in the control group (Fig. ##FIG##5##6g, h##). Essentially, melatonin release in the mPFC induces NREM sleep amount increases in active periods and enhances delta power density during NREM sleep even more significantly than that shown in the Mel-MN group. The delayed release effect mainly explains this due to the existence of gold encapsulation. As a result, the releasing peak of melatonin appeared after the controlled degradation of the gold protection layer. In contrast, the Mel-MN group releases melatonin right after implantation, leading to a weakened effect at the time point of recording (one week post to recovery). Collectively, these in vivo experiments further demonstrate the programmed drug release performance of SOPs in freely moving animals and suggest its potential utility in animal behavior studies. We revealed that programmed drug release of exogenous melatonin in the mPFC would improve NREM sleep, REM sleep, and delta power density, which may represent a novel target for treating sleep disorders.</p>\",\n \"<p id=\\\"Par30\\\">In this study, we present an on-demand drug-delivery patch that can be digitally controlled to enable delivery precision in both space and time. The spatiotemporal on-demand patch (SOP) uses bioresorbable microneedles with high-aspect ratios (3–8) as the drug-loading vehicle and a thin layer of gold (thickness 150 nm) as a release gate that can be digitally controlled with a small electrical trigger (2.5 V for 30 s). This design allows fully active control of drug release at a single-microneedle level with spatial resolution, demonstrated here, less than 1 mm<sup>2</sup>, highlighting the enabling capabilities over most existing drug-delivery devices. This spatial resolution allows more than 20 doses of the drug to be housed within a thin, wearable patch of 1 cm<sup>2</sup>, ensuring comfortable and convenient user adherence for repetitive pharmaceutical treatment over extended periods. The fabrication is compatible with the microfabrication process, which could further decrease the spatial resolution to micrometer scales. The on-demand, rapid-response drug release can be enabled within 30 s following an active electrical trigger.</p>\",\n \"<p id=\\\"Par31\\\">The fabrication procedure of our SOP uses a simple solution-molding method, offering a low-cost approach with high dimensional customizability. Using laser ablation, microneedles, ranging from 0.6–3 mm, can be manufactured efficiently and in high quality to fit a wide range of drugs (melatonin with controllable payloads<sup>##REF##34959479##78##</sup>. Moreover, the solution-based mold process allows drug-load PLGA MNs to be produced at scale with similarly high quality. The process allows convenient compatibility for integrating electronic modules to enable digital automation in drug delivery.</p>\",\n \"<p id=\\\"Par32\\\">The multifunctionality of drug delivery and stimulation therapy could synergistically create advanced therapy as potential future avenues of neuroscience research. The SOP presented in this work exhibits continuous sustained drug release feature that meets the need to treat various chronic neural diseases apart from sleep disorder, especially Alzheimer’s disease (AD)<sup>##REF##28872215##50##</sup>. As a multiphase neural disease that affects most areas of the brain<sup>##REF##22229116##51##,##REF##27188934##52##</sup>, the treatment of AD requires different drugs at different stages. For example, acetylcholinesterase inhibitors (AChEIs) are currently the mainstay of treatment for mild-to-moderate AD. In contrast, memantine is approved for mild-to-moderate dementia as a non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist that prevents the overactivation of neuronal NMDA receptors. At the same time, the combination of these two drugs also significantly improves cognitive function in moderate-to-severe AD<sup>##REF##23657927##79##</sup>. The SOP can enable precise joint delivery of multiple drugs in desired brain regions to achieve enhanced treatment. Combined with a burst release design (like hollow microneedles), the SOP also fits for rapid drug delivery as a timely response towards acute neural diseases, such as cataplexy and epilepsy<sup>##REF##12557281##80##–##REF##23359095##82##</sup>. The remotely powered SOP allows for active drug delivery at a particular time and space coupled with real-time behavioral study, such as sleep-quality characterization, which provides a convenient method for drug-performance analysis. The high-resolution and controllability features of SOP make it suitable for such kind of regional and dosage-sensitive treatment. In addition, many drugs for brain disorders may have detrimental effects on other organs if administered systemically<sup>##REF##20497904##83##</sup>. The regional drug release feature of SOP enables targeted drug delivery that minimizes systemic exposure, reducing the risk of unwanted side effects in non-brain tissues.</p>\",\n \"<p id=\\\"Par33\\\">Moreover, our SOP is capable of wireless operation via near-field communication or, potentially, Bluetooth Low Energy. The agar-soaking test, fracture test, and in vivo intracranial delivery experiments validate the SOP’s practical functionality and biocompatibility. Furthermore, gold-coated MNs can extend beyond drug-releasing control, as the SOP can offer in vivo electrical stimulation. These concepts establish unique approaches in high-precision drug-delivery technologies with additional utilities in advancing fundamental studies of disease pathology (such as cancer metastasis) and neuroscience research, as demonstrated by both benchtop experiments and in vivo studies. Future efforts on fully digital automation of drug delivery will pave the way for the next generation of precision medicine.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Results and discussion</title>","<title>Configuration and characterization of the spatiotemporal on-demand patch (SOP)</title>","<p id=\"Par7\">Figure ##FIG##0##1a## highlights the design of an SOP, which consists of two primary parts: i) a drug-loaded microneedle (MN) patch protected with an electrochemically triggerable metal layer (gold) as the drug delivery interface; ii) a near-field communication (NFC) module for the wireless control of triggering signals in controlling location and schedule of drug release. A flexible printed circuit board (PCB) provides interconnected traces that integrate the two parts to form a fully wireless, wearable drug delivery system. The main body of the drug delivery interface (MN arrays) uses poly(D, L-lactide-co-glycolide) (PLGA, Mw = 50 ~ 75 kDa, ester terminated, Sigma Aldrich) as the matrix materials that can undergo bulk erosion upon contact with biofluids to generate biological benign byproducts (lactic acid and glycolic acid)<sup>##UREF##9##53##</sup>. As shown in Fig. ##FIG##0##1b##, the fabrication relies on sputter deposition to coat a thin layer of gold (thickness 150 nm) onto the drug-loaded MN arrays supported by a PLGA base. The gold encapsulation layer is supported on the surface of solid MNs, which enables sufficient stability with a thinner thickness (150 nm) compared with those used in the reservoir designs (thickness of gold layer more than 300 nm) as reported previously (as illustrated in Supplementary Fig. ##SUPPL##0##1##). Thus, the thickness to realize effective drug encapsulation can be smaller. Ablation using a laser beam defines the gold traces that connect with separated MN domains to realize spatial control of drug release. A thin polydimethylsiloxane (PDMS) (thickness 10 µm) covers the gold layer at the circuity base regions. It exposes only the MN regions to allow physical contact of the gold layer on the MN with biofluids.</p>","<p id=\"Par8\">Fabrication of the SOP relies on a low-cost solution-molding procedure (Supplementary Fig. ##SUPPL##0##2##). The process starts with UV laser ablation to define an MN mold from a PDMS pad. Drop casting drug-loaded PLGA solution into the PDMS mold and setting under vacuum allow the entrance of PLGA into the negative MN molds. Sufficient solidification over 8 h allows easy extraction of the PLGA MNs. Then, the PLGA MNs undergo sputtering deposition to coat a 150 nm-thick gold layer that conformally covers the top surface, followed by patterning with a laser ablation system to define control circuits. Drop casting a thin PDMS layer (thickness 10 μm) onto gold layers protects the control circuits and exposes the MNs for drug release. The MN patch is then attached to a flexible PCB and connected with a current regulator, a wireless energy harvester, and a microcontroller to complete the fabrication process. The solution-molding method can produce SOPs with: (1) tunable MN lengths from 600 μm to 3 mm and aspect ratio from 3 to 8; (2) high dimension uniformity in both length and base diameter (Supplementary Fig. ##SUPPL##0##3##); (3) arbitrary MN array configurations (square, hexagonal, single-needle, etc.). Figure ##FIG##0##1e##–##FIG##0##h## shows that the morphology and shape of the PLGA MNs remain stable during both gold deposition and drug loading procedures.</p>","<p id=\"Par9\">Figure ##FIG##0##1c##, ##FIG##0##d## illustrates the overall working mechanism to realize high-precision drug delivery by our SOP. The electrically triggered crevice corrosion of the gold protective layer is the switch for initiating drug release of specific collections of SOP MNs. Upon SOP deployment on the skin, the MNs stay at a standby stage with the drug fully protected by the gold layer. Once a direct current (DC) electrical trigger (2.2–3 V) is applied, the electrochemical crevice corrosion starts to occur on triggered MNs, transitioning them from standby mode to releasing mode. After a short period (<30 s when 2.5 V is applied), the gold protective layer on the MN is fully dissolved, and MNs are exposed to the bio-environment to enable drug release. The compatible integration of microcontrollers allows the electrical triggers to be manipulated at a precise time point for precision delivery. By patterning gold layers via microfabrication, the SOP can realize the spatial profile of drug release at a high spatial resolution (~1 mm<sup>2</sup>). A built-in anode is integrated into the SOP to complete the circuitry for the in vivo electrochemical crevice corrosion.</p>","<title>Characterization of electrically triggered crevice corrosion</title>","<p id=\"Par10\">Figure ##FIG##1##2## demonstrates the characterization of SOP on its active control of drug release (based on MNs, 1.2 mm in height, 150-nm gold coated). The operation of drug-release control includes three stages: (1) standby stage when the MNs are fully coated with gold (labeled as 0 min); (2) transitioning stage when the electrical trigger is activated, and the gold layer is partially dissolved (labeled as 0.5 min); (3) releasing stage when the gold is fully dissolved, and MNs are exposed to the biofluids (labeled as 2 min). 1X Dulbecco’s phosphate-buffered saline (DPBS, Corning) is used here to simulate the body biofluid. The experiment is conducted at room temperature and triggered by a 2.5-V DC. Figure ##FIG##1##2a## shows optical and SEM images of the MN arrays at different stages, which indicates a noticeable change in surface color and roughness associated with the electrically triggered crevice corrosion. The results demonstrate that the main structure of the MN stays stable during the transitioning stage, and the electrical triggers effectively dissolve gold into biofluids and sufficiently expose the core MNs. More characterizations from different perspectives appear in Supplementary Fig. ##SUPPL##0##4##.</p>","<p id=\"Par11\">The electrochemical crevice corrosion of the gold layer can be triggered by a direct current potential within 2 min, which is of clinical relevance for a timely response in drug administrations. A quantitative amperometry study of current density under different potentials is conducted to understand the electrical triggering behavior. Figure ##FIG##1##2e## shows I-V measurements of SOP triggering with potentials bias ranging from 2.0 V to 2.8 V applied to the MN arrays (1.2 mm in length, 150-nm gold coated). A steep drop in current density appears at 15 s of application for 2.8 V bias, indicating the endpoint of the electrochemical crevice corrosion. The time of the current-drop appearance increases as the potential bias decreases. The time consumption from the beginning to the end of the current is defined as the effective corrosion time plotted against the potential in Fig. ##FIG##1##2f##. The fitting is based on the Butler-Volmer equationwhere is the exchange current density, is the Faraday constant, is the overpotential, is the ideal gas constant, is the thermodynamic temperature, and is a coefficient with values ranging from 0 to 1, is the number of electrons in the anodic half reaction.</p>","<p id=\"Par12\">This equation describes the relationship between potential difference and reaction rate<sup>##UREF##10##54##</sup>. At an ambient potential of 2.5 V, the crevice corrosion on MN is triggered within 30 s. This parameter is applied in the following experiments. We hypothesize that the crevice corrosion driven by constant voltage includes two parts: anodic oxidation and mechanical crevices. The 2.5-V potential between the anode and cathode is sufficient for triggering gold oxidation coupled with hydrogen emission reaction (HER) at ambient conditions (1X DPBS, 25 °C). It is reported that an anodic potential larger than 1.95 V (vs standard hydrogen electrode, SHE) is high enough to drive the below-surface oxidation of gold film<sup>##UREF##11##55##</sup>. This corresponds with our observations, as the crevice corrosion is triggered at a potential above 2.0 V, even though the HER on the counter electrode is not under the standard condition. The oxidated gold species in neutral and alkaline environments may include oxides, hydroxides, and free elements, mostly in the form of nanoparticles (NPs)<sup>##UREF##12##56##,##UREF##13##57##</sup>, that collectively in the amount used here are benign to the human body<sup>##UREF##14##58##</sup>. Supplementary Fig. ##SUPPL##0##5## provides a schematic illustration of the two-electrode system of SOP (Supplementary Fig. ##SUPPL##0##5a##) and the atomic scale illustration of the anodic oxidation (Supplementary Fig. ##SUPPL##0##5b, c##) on Au (111) facet in a neutral environment, with reaction products, Au<sub>2</sub>O<sub>3</sub> in the form of nanoparticles. The anodic behavior is also studied by finite element analysis (FEA), including reaction potential, reaction rate, current, and potential distribution (Supplementary Fig. ##SUPPL##0##6a–d##). The simulation results show that the reaction rate at 2.5 V is 281 nm/min (Fig. ##FIG##1##2h, i##), which is consistent with the experimental observations (150 nm in ~ 30 s). The anodic polarization curve under the condition of standard potentials and primary current distribution also shows a minimal triggering potential at around 2.0 V (Supplementary Fig. ##SUPPL##0##6e##). However, the anodic oxidation may not account for the entire loss of the gold coating. Based on the 2.6-V amperometry experiment (Fig. ##FIG##1##2e##), the theoretical weight of the oxidated gold is calculated as 122 μg. The actual value should be lower because water splitting (oxygen generation), as the side reaction, may also contribute to the current density. This amount of gold NPs does not constitute a health hazard since it is much lower than the safe exposure threshold, which is reported as 5 mg/ml<sup>##UREF##14##58##</sup>. Meanwhile, the total weight of the diminished gold film on the MN array is calculated as 290 μg, which is significantly higher than the oxidated amount. This is because the major part of the gold layer is not oxidated but exfoliated from the surface due to crevices and cracks. These defects come from the weakening effect on gold membranes as they become thinner during corrosion. This kind of crevice on metal film is generated by an electrical current, which is reported in some previous studies<sup>##REF##32923633##43##,##REF##22344516##45##</sup>.</p>","<p id=\"Par13\">A gold-on-wafer experiment validates the presence of mechanical crevices and exfoliations. The surface roughness of the crevice corrosion of the gold layer is also studied with the gold-on-wafer ({100} facet, 150-nm, 1 cm<sup>2</sup> square) model (Supplementary Fig. ##SUPPL##0##7b##, ##SUPPL##0##c##). The gold layer is connected to a power source, and crevice corrosion is triggered at 2.5 V in 1X DPBS. The surface roughness of gold is calculated with FIJI ImageJ on optical images collected at various stages of corrosion. As shown in Supplementary Fig. ##SUPPL##0##7d##, the surface roughness is monitored every 0.5 min for 5 min from the initial stage, then every 1 min from the 5-min stage to the 9-min stage. A sharp increase in surface roughness is observed in the first 1 min right after the crevice corrosion is initiated (Supplementary Fig. ##SUPPL##0##7a##). This is consistent with the amperometry study that most electrochemical corrosion happens within 1 min under this potential. The roughness increase indicates the gold layer’s exfoliation from the wafer, which is also observed in the MN corrosion from an SOP. It can be concluded that a significant part of the gold layer is not directly oxidized but exfoliated during the electrical triggers. This corresponds with the previous study using gold film as the control gate for implanted drug reservoirs<sup>##REF##28458955##44##</sup>.</p>","<p id=\"Par14\">To characterize the electrical trigger process, we select two different areas on an SOP MN: the tip area and the waist area, as shown in Fig. ##FIG##1##2d##. The structural difference between the two areas is the surface curvature, with the former being 1.37 mm<sup>−2</sup> and the latter close to 0. The waist area stands for the MN’s major surface, where most drugs are released. The energy-dispersive X-ray spectroscopy (EDXS) mapping is carried out on three stages of MNs from both areas as a semi-quantified surface analysis (SI Figs. ##SUPPL##0##8## and ##SUPPL##0##9##). Oxygen, carbon, and gold are selected as elements of interest, where oxygen and carbon show the exposed polymer body while gold shows the encapsulated surface of the MN. The ratio quantification is based on the relative weight percentage of only these three elements despite the existence of other elements. As shown in Fig. ##FIG##1##2b##, ##FIG##1##c##, both the tip and the waist areas show similar trends in element-relative ratios. An increase in oxygen, carbon, and a decrease in gold (all by weight%) are observed. The waist area, which stands for the main body of an MN, shows a more remarkable change in element constituents, indicating a complete corrosion of triggered crevice for the gold layer. The tip part of MN is still partially capped by a small amount of gold at the releasing stage. This part of the gold layer is isolated during the crevice corrosion process and accounts for the rather high remaining gold shown in Fig. ##FIG##1##2b##. The left gold covering the tip area is too small to hinder the overall drug release from the entire MN. Silicon and copper are also observed in EDXS mapping, which comes from PDMS residuals and conductive wires used during the experiment. More detailed information appears in the Supplementary Information on the EDXS element mapping.</p>","<p id=\"Par15\">Figure ##FIG##1##2g## shows the thermal characterization of SOP during the electrochemical corrosion to validate the thermal safety. The experiment uses an MN array (1.2 mm in length, 150-nm gold coated) connected to a 2.5-V DC power to undergo crevice corrosion in 1X DPBS. A FLIR thermometer records the temperature of the MN array with and without current in a 25 °C environment. The results show no noticeable change in temperature during the electrochemical corrosion, which indicates a low possibility of tissue damage from heat effects.</p>","<p id=\"Par16\">Figure ##FIG##1##2j## and Supplementary Fig. ##SUPPL##0##10## show the uniformity of the gold layer on MN prior to crevice corrosion. We also use atomic force microscopy (AFM) to characterize the surface roughness of gold layer deposited on polymer to illustrate the uniformity of deposition thickness by sputter coating, as shown in Supplementary Fig. ##SUPPL##0##11##.</p>","<title>Release control and electrical triggering of microneedle patch</title>","<p id=\"Par17\">Figure ##FIG##2##3a## demonstrates the encapsulation performance of gold coating on MNs. Here, Rhodamine B (Thermo scientific), a fluorescent dye, is loaded into the microneedle patch (0.3% in weight, ~90 ng per MN) to simulate small-molecule drugs, which can be subsequently quantified by UV-Vis spectroscopy. Figure ##FIG##2##3a, b## show the release profile of Rhodamine B from a bare MN patch (1.2 mm in length) and an MN patch (1.2 mm in length) with a 100-nm gold coating. The release study is carried out in 45 °C 1X DPBS. Significant color fading on the MN patch without gold coating, as shown in Fig. ##FIG##2##3a##, and the apparent increase in absorbance (blue, without gold coating, Fig. ##FIG##2##3b##) indicate a successful release of dye into the biofluids. The average release rate of Rhodamine B in an hour is calculated as ~ 415 ng/min. In contrast, the controlled experiment (black, with gold coating) shows no significant change in absorbance (Fig. ##FIG##2##3b##), suggesting excellent protection of encapsulated drugs from release. The calculation of the accumulative release amount is based on a calibration curve of Rhodamine B solutions and Beer-Lambert Law. Detailed explanation is provided in Supplementary Fig. ##SUPPL##0##13## and SI Table ##SUPPL##0##1##. We further analyze the stability of gold coating in a 0.5% agar model to better simulate the mechanical properties of animal tissue. The MN array (1.2 mm in length, 150-nm gold coated) remains stable during a 2-week soaking test in the agar model without significant changes in shape or surface morphology (Fig. ##FIG##2##3a##). SI Figs. ##SUPPL##0##14## and ##SUPPL##0##15e##, together with Fig. ##FIG##2##3a##, show the stability of gold film on MNs against soaking in biofluids and mechanical friction in tissues. This indicates that the bonding between gold and PLGA is strong enough to meet the need for implantation, which corresponds with previous research on Au-polymer adhesion<sup>##UREF##15##59##–##REF##34936437##61##</sup>.</p>","<p id=\"Par18\">Figure ##FIG##2##3c## demonstrates the wireless design of a SOP. An external power source—in this case, a signal generator—is connected to an inductive coil to provide a high-frequency (~MHz level) alternative current (AC). The inductive coil is paired with the receiving coil on the device to achieve wireless power transfer via magnetic resonance coupling. The AC is then converted to direct current using a full-bridge rectifier. A 2.5-V regulator then regulates the DC to provide a stable potential that facilitates the electrochemical crevice corrosion of the gold layer on MNs. The configuration of the wireless SOP is illustrated in Fig. ##FIG##2##3d##. The wireless SOP consists of an energy harvesting module for wireless power, a power amplification module, a System-on-Chip (SoC) module for remote control, and an MN array coupled with counter electrodes (CE). Within the energy harvesting module, a receiver coil, a full-bridge rectifier, and a regulator are used to provide stable direct current (DC) output. The output is coupled with a 0.1-μF capacitor to constitute a low-pass filter, improving output stability. An equivalent circuit diagram is provided in Fig. ##FIG##2##3g##, which emphasizes the energy harvesting module while simplifying the power amplification and SoC. A complete circuit diagram can be found in Supplementary Fig. ##SUPPL##0##16##. Supplementary Fig. ##SUPPL##0##17## shows the performance and impedance analyses of the energy harvesting module. The optimal signal input is determined as around 15 V peak-to-peak at 15 MHz. The measurements shown in Fig. ##FIG##2##3e## validate the output stability of wireless SOP with the regulator. The final output power signal is stabilized at around 2.5 V with a standard deviation of ~0.1 V, ensuring precise crevice corrosion control for initiating drug release. The input signal’s optimal frequency is around 36 MHz, as shown in Fig. ##FIG##2##3f##.</p>","<title>Temporal and spatial control of SOP triggering</title>","<p id=\"Par19\">To further investigate the temporal and spatial controllability of electrical triggering on the MN patch (Fig. ##FIG##3##4a##), we design a multi-domain SOP to realize the stepwise, on-demand release. The multi-domain SOP consists of 7 domains of MN arrays (1.2-mm in length, 150-nm gold coated). The gold layer on the PLGA patch is patterned by laser ablation to enable separate triggering of individual MN domains. A layer of PDMS (~10 μm) is then applied onto the patch except for the hexagonal MN regions to protect gold interconnects from dissolving during electrical triggering. Figure ##FIG##3##4c## shows the electrical triggering schedule for four of the seven MN domains for the SOP loaded with Rhodamine B (0.3% by weight) during its immersion in 65 °C 1X DPBS as an accelerated study. The electrical triggers use 2.5-V DC bias for 30 s at every 30-min interval of immersion. Following each interval, immediate sampling of the environmental fluids allows quantitative estimation of drug-release dosage using UV-Vis spectroscopy, as shown in Fig. ##FIG##3##4b##. The measurements show a multi-step increase in spectral absorbance, indicating the stepwise increase of drug dosage and confirming the on-demand drug release at the desired time (0, 30, 60, 90 min, Fig. ##FIG##3##4b##). Figure ##FIG##3##4d## demonstrates the staged release of MN domains by selectively dissolving the gold encapsulation layer with an electrical trigger (Supplementary Movie ##SUPPL##3##1##). The red dash line circles the specific MN array triggered from each stage. The results confirm that our SOP realizes both temporal and spatial control of drug release using digital electrical triggers.</p>","<p id=\"Par20\">Supplementary Fig. ##SUPPL##0##18## demonstrates the capability of ultrafine spatial control of drug release. Here, we design a miniaturized SOP with a single domain consisting of 8 MNs (1.1 mm in length, coated with 150 nm-thick gold) with 1–3 mm in spatial separation. With a specific design of gold circuits, each MN in the domain can be triggered individually (as illustrated in Supplementary Fig. ##SUPPL##0##18a##). Supplementary Fig. ##SUPPL##0##18b## shows that each MN follows sequential release via electrical triggering (2.2-V DC) within 15 s without interfering with adjacent MNs. Patterning techniques on the gold layer primarily dictate the spatial resolution of release control for SOP.</p>","<title>Demonstration of SOP in intracranial drug delivery</title>","<p id=\"Par21\">Beyond the clinical applicability for transdermal drug delivery, the SOP could show impactful utility in facilitating animal behavior studies. Here, we demonstrate intracranial delivery of melatonin using SOP for an animal sleep study. Melatonin, a hormone naturally produced in the brain by the pineal gland, plays a crucial role in regulating the sleep-wake cycle while also participating in other regulations like blood pressure and body temperature<sup>##REF##23333399##62##–##REF##15649735##64##</sup>. However, people found many strains of nocturnal laboratory mice are deficient in the release of melatonin, including C57/B6 mice<sup>##REF##20308563##65##,##REF##11792644##66##</sup>, and the effects of melatonin are still controversial<sup>##REF##22171046##67##–##REF##7326588##69##</sup>. Here, we use C57/B6 mice and implant the SOP to test how exogenous spatiotemporally controlled release of melatonin in the deep locations of the cortex, which is mainly distributed by melatonin receptor 1<sup>##REF##15893586##70##</sup>, would affect the sleep-wake cycle. The high spatiotemporal controllability of melatonin release offered by the SOP may open up new opportunities to understand regional brain responses to melatonin and study the pathology of sleep-wake disorders. Loading melatonin into MNs of the SOP follows the solution fabrication method described in Supplementary Fig. ##SUPPL##0##2##. Melatonin that dissolves in acetone can be mixed with the precursor PLGA solution, which ensures the loading dosage (10–35 wt%) for each MN. The 3-mm MNs are fabricated with the PLGA-melatonin ratio from 10:1 to 2:1 (Fig. ##FIG##4##5a##). In addition, the drug can be concentrated around the tip of the MN, which ensures deep-brain delivery for mice, as shown by the dash-red line in Fig. ##FIG##4##5a##. Based on the mixing ratio, the payload of melatonin per MN is estimated to be 22.2 to 81.7 μg, comparable to recommended dosages for mice (4–20 mg/kg)<sup>##REF##6655558##71##,##REF##30081472##72##</sup>. The drug payload is modulated by loading different PLGA solutions during the mold casting procedure. Furthermore, the mechanical property of the 3 mm-tall MNs used for intracranial delivery is characterized by a fracture test (Supplementary Fig. ##SUPPL##0##15##). The ultimate strength of PLGA MN (1.2 mm in length) is determined by the first fracture point in the force-displacement graph, as labeled by the red frame (Fig. ##FIG##4##5b##). The fracture point, recognized by a sudden drop in measured force, corresponds to the initial fracture of the MNs, followed by multiple subsequent fractures appearing in different locations of the MNs (Supplementary Fig. ##SUPPL##0##15b##, ##SUPPL##0##d##). The maximum mechanical strength is derived to be 118 MPa, which is sufficiently rigid for human skin penetration<sup>##REF##1757138##73##</sup>. The calculation considers the pressure measured at the first fracture point, while the contact area of the MN is approximated based on the tip diameter (around 30 μm, Supplementary Fig. ##SUPPL##0##3f##). Another concern is the stability of the gold layer during implantation. Supplementary Fig. ##SUPPL##0##15e## shows the PLGA MN (150 nm gold coated, 1.2 mm) before and after the penetration test. No significant changes can be observed based on the comparison of optical images. Figure ##FIG##2##3a## also proves the stability of the gold layer during the soaking test. In addition, we use the MN array (150 nm gold coated, 1.2 mm) to penetrate the tissue of the chicken thigh for multiple cycles, as presented in Supplementary Fig. ##SUPPL##0##14##. The gold layer remains stable after 20 cycles of penetration.</p>","<p id=\"Par22\">Deployment of the melatonin-loaded SOP in live animal models as they move in a caged environment shows possibilities for actively controlling melatonin release to the deep-brain regions of the parietal lobe (Fig. ##FIG##4##5g##). To ensure device stability, the SOP is coupled with a custom headstage that can be firmly mounted onto mice heads (Supplementary Fig. ##SUPPL##0##19##). Figure ##FIG##4##5h## shows an immunohistochemistry analysis of brain tissues from the mice at various recovery stages following SOP implantation. On day 1 of post-implantation, the brain tissues in close proximity to the SOP MN show structural damage resulting from mechanical forces during intracranial surgery, which is typical for general brain implantation<sup>##REF##25546652##74##</sup>. As the mice recover from the implantation, the levels of GFAP (red) and IBA (white) show a significant decrease in concentration and staining range, indicating excellent biocompatibility of SOP. The tissue in contact with the MN becomes smoother, with fewer rough edges. An obvious increase in neuron regeneration (neurotrace, green) can be observed, indicating good recovery from implantation surgery.</p>","<p id=\"Par23\">Supplementary Fig. ##SUPPL##0##20## shows an in vivo study to validate the safety and biocompatibility of SOP in intracranial deployment. Here, the study compares four groups of MNs (bare MN (MN), gold-coated MN (Au-MN), melatonin-loaded MN (Mel-MN), and melatonin-loaded MN with gold coating (Au-Mel-MN)) in their effects on the inflammation and degree of neural damage after one-month implantation. Supplementary Fig. ##SUPPL##0##21a## shows typical images of MN implanting sites with GFAP/IBA/NSE/DAPI multiple staining. As a result, the number of astrocytes and microglia in the region of interest (ROI) is significantly lower in the Au-MN or Au-Mel-MN group compared to the bare MN group, which indicates that gold-coated MNs have less damage to neural tissues and cause less inflammation compared to bare PLGA MNs. In addition, the fluorescence density of the markers has no difference between bare and gold-coated MNs, which indicates that the Au encapsulation on MNs does not affect the expressing strength of the GFAP or IBA signals (Supplementary Fig. ##SUPPL##0##21c##).</p>","<p id=\"Par24\">Furthermore, we used a fiber photometry system to investigate the neural activity in the medial prefrontal cortex (mPFC) after the implantation of MNs (bare MN, Au-MN, control (no MN), respectively, Supplementary Fig. ##SUPPL##0##22##). As shown in Supplementary Fig. ##SUPPL##0##22c##, there is no observable difference in Ca<sup>2+</sup> dynamics of mPFC excitatory neurons between the control group, bare MN, and Au-MN groups, which indicates the MNs have no significant damage to the brain tissue and introduce little influence on the neuronal activity.</p>","<p id=\"Par25\">Besides, we study the bioresorbability of the MN. Though the degradation of PLGA is much slower than gold<sup>##REF##22577513##75##</sup>, we show this process of PLGA in an accelerated degradation experiment in 65 °C phosphate-buffered saline (PBS), as illustrated in Supplementary Fig. ##SUPPL##0##23##.</p>","<p id=\"Par26\">We further use the in vivo animal models to characterize the SOP functional performance. Here, an additional two recording electrodes are inserted adjacent to the location of SOP implantation, as shown in Fig. ##FIG##4##5c##. First, a set of DC electrical triggers (5 s duration, 2.5 V) is delivered and recorded (5 mm from the stimulation electrode), as shown in Fig. ##FIG##4##5e##. The crevice corrosion of the gold layer (thickness 150 nm) on the MN can be completed by applying 5–7 times the 5-s triggers to allow melatonin release. Then, a series of short pulse stimulation experiments are examined (Supplementary Fig. ##SUPPL##0##24##). The pulse signals vary from 10 mV to 50 mV in amplitude and 1 Hz (duration 10 ms) or 10 Hz (duration 1 ms) in frequency. The signal amplitude-recording distance relationship is also studied based on 10-ms pulses of 50 mV (Fig. ##FIG##4##5f##) and proved to affect a 5-mm area mainly. No significant changes are observed on the gold layer of MN after short pulses, as demonstrated in Fig. ##FIG##4##5d##, indicating electrical signals generated from neuron cells induce negligible damage to the gold protection layer of the SOP. Furthermore, the results demonstrate the gold-coated MNs can also be used as stimulation electrodes for the delivery of low-amplitude (10–50 mV), pulsatile signals (Fig. ##FIG##4##5f##), which may serve as a strategy for neuronal regeneration<sup>##REF##33239608##76##,##REF##30297910##77##</sup>.</p>","<title>Assessment of in-brain drug delivery by microneedles</title>","<p id=\"Par27\">To investigate the gold encapsulation’s performance in controlled drug release from the MN, we fabricated two groups of melatonin-loaded (25 wt%) MN with the exact specification shown in Fig. ##FIG##4##5a##. For effective comparison, the first batch of MNs (Mel-MNs) has no gold encapsulation layer, while the second batch of MNs (Au-Mel-MNs) is coated with 200 nm of gold on the MN surface.</p>","<p id=\"Par28\">First, we conduct the in vivo drug release experiment with the Mel-MNs and the blank control (MNs without loading melatonin) by stereotaxically implanting the MN into the medial prefrontal cortex (mPFC) of mice (one MN in each, Fig. ##FIG##5##6a##). After one week of recovery, we use the Pinnacle sleep system (Sirenia Acquisition) to record EEG and EMG analyses. From 19:00 (active period) to 22:00, the power density of the Mel-MN group is compared with those of the control group (Fig. ##FIG##5##6b##). The total amount of NREM sleep increased by 42.0%, and the amount of wakefulness decreased by 27.9% over the 3-h period compared to these parameters in the control group (Fig. ##FIG##5##6c, d##). The sleep status is calculated from the EEG/EMG traces and the corresponding hypnograms (Supplementary Figs. ##SUPPL##0##25##–##SUPPL##0##29##). The experiment confirms the effective release of Melatonin from Mel-MNs, which can successfully modulate the sleep behavior of mice over a week.</p>","<p id=\"Par29\">Second, we conduct similar in vivo experiments to investigate the feasibility of release control with gold encapsulation from the Au-Mel-MN. Here, we apply the Au-Mel-MNs by stereotaxically implanting them into the medial prefrontal cortex (mPFC) of mice (one MN in each, Fig. ##FIG##5##6e##). For effective comparison, the EEG and EMG recording starts one week after implantation, following the same way as described above. Figure ##FIG##5##6f## shows the power density of the Au-Mel-MN group and control group recorded from 19:00 to 01:00 am (active period), indicating that the delta power density of the Au-Mel-MN group was significantly higher (<italic>P</italic> < 0.01) than that of the control group. The significantly enhanced delta band EEG power indicates a deeper sleeping degree induced by melatonin, compared with the Mel-MNs experiment (Fig. ##FIG##5##6c, d##). The total amount of NREM sleep increased significantly by 36.7%, REM sleep increased by 57.8%, and that of wakefulness significantly decreased by 26.0% over the 6-h period, compared to these parameters in the control group (Fig. ##FIG##5##6g, h##). Essentially, melatonin release in the mPFC induces NREM sleep amount increases in active periods and enhances delta power density during NREM sleep even more significantly than that shown in the Mel-MN group. The delayed release effect mainly explains this due to the existence of gold encapsulation. As a result, the releasing peak of melatonin appeared after the controlled degradation of the gold protection layer. In contrast, the Mel-MN group releases melatonin right after implantation, leading to a weakened effect at the time point of recording (one week post to recovery). Collectively, these in vivo experiments further demonstrate the programmed drug release performance of SOPs in freely moving animals and suggest its potential utility in animal behavior studies. We revealed that programmed drug release of exogenous melatonin in the mPFC would improve NREM sleep, REM sleep, and delta power density, which may represent a novel target for treating sleep disorders.</p>","<p id=\"Par30\">In this study, we present an on-demand drug-delivery patch that can be digitally controlled to enable delivery precision in both space and time. The spatiotemporal on-demand patch (SOP) uses bioresorbable microneedles with high-aspect ratios (3–8) as the drug-loading vehicle and a thin layer of gold (thickness 150 nm) as a release gate that can be digitally controlled with a small electrical trigger (2.5 V for 30 s). This design allows fully active control of drug release at a single-microneedle level with spatial resolution, demonstrated here, less than 1 mm<sup>2</sup>, highlighting the enabling capabilities over most existing drug-delivery devices. This spatial resolution allows more than 20 doses of the drug to be housed within a thin, wearable patch of 1 cm<sup>2</sup>, ensuring comfortable and convenient user adherence for repetitive pharmaceutical treatment over extended periods. The fabrication is compatible with the microfabrication process, which could further decrease the spatial resolution to micrometer scales. The on-demand, rapid-response drug release can be enabled within 30 s following an active electrical trigger.</p>","<p id=\"Par31\">The fabrication procedure of our SOP uses a simple solution-molding method, offering a low-cost approach with high dimensional customizability. Using laser ablation, microneedles, ranging from 0.6–3 mm, can be manufactured efficiently and in high quality to fit a wide range of drugs (melatonin with controllable payloads<sup>##REF##34959479##78##</sup>. Moreover, the solution-based mold process allows drug-load PLGA MNs to be produced at scale with similarly high quality. The process allows convenient compatibility for integrating electronic modules to enable digital automation in drug delivery.</p>","<p id=\"Par32\">The multifunctionality of drug delivery and stimulation therapy could synergistically create advanced therapy as potential future avenues of neuroscience research. The SOP presented in this work exhibits continuous sustained drug release feature that meets the need to treat various chronic neural diseases apart from sleep disorder, especially Alzheimer’s disease (AD)<sup>##REF##28872215##50##</sup>. As a multiphase neural disease that affects most areas of the brain<sup>##REF##22229116##51##,##REF##27188934##52##</sup>, the treatment of AD requires different drugs at different stages. For example, acetylcholinesterase inhibitors (AChEIs) are currently the mainstay of treatment for mild-to-moderate AD. In contrast, memantine is approved for mild-to-moderate dementia as a non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist that prevents the overactivation of neuronal NMDA receptors. At the same time, the combination of these two drugs also significantly improves cognitive function in moderate-to-severe AD<sup>##REF##23657927##79##</sup>. The SOP can enable precise joint delivery of multiple drugs in desired brain regions to achieve enhanced treatment. Combined with a burst release design (like hollow microneedles), the SOP also fits for rapid drug delivery as a timely response towards acute neural diseases, such as cataplexy and epilepsy<sup>##REF##12557281##80##–##REF##23359095##82##</sup>. The remotely powered SOP allows for active drug delivery at a particular time and space coupled with real-time behavioral study, such as sleep-quality characterization, which provides a convenient method for drug-performance analysis. The high-resolution and controllability features of SOP make it suitable for such kind of regional and dosage-sensitive treatment. In addition, many drugs for brain disorders may have detrimental effects on other organs if administered systemically<sup>##REF##20497904##83##</sup>. The regional drug release feature of SOP enables targeted drug delivery that minimizes systemic exposure, reducing the risk of unwanted side effects in non-brain tissues.</p>","<p id=\"Par33\">Moreover, our SOP is capable of wireless operation via near-field communication or, potentially, Bluetooth Low Energy. The agar-soaking test, fracture test, and in vivo intracranial delivery experiments validate the SOP’s practical functionality and biocompatibility. Furthermore, gold-coated MNs can extend beyond drug-releasing control, as the SOP can offer in vivo electrical stimulation. These concepts establish unique approaches in high-precision drug-delivery technologies with additional utilities in advancing fundamental studies of disease pathology (such as cancer metastasis) and neuroscience research, as demonstrated by both benchtop experiments and in vivo studies. Future efforts on fully digital automation of drug delivery will pave the way for the next generation of precision medicine.</p>"],"string":"[\n \"<title>Results and discussion</title>\",\n \"<title>Configuration and characterization of the spatiotemporal on-demand patch (SOP)</title>\",\n \"<p id=\\\"Par7\\\">Figure ##FIG##0##1a## highlights the design of an SOP, which consists of two primary parts: i) a drug-loaded microneedle (MN) patch protected with an electrochemically triggerable metal layer (gold) as the drug delivery interface; ii) a near-field communication (NFC) module for the wireless control of triggering signals in controlling location and schedule of drug release. A flexible printed circuit board (PCB) provides interconnected traces that integrate the two parts to form a fully wireless, wearable drug delivery system. The main body of the drug delivery interface (MN arrays) uses poly(D, L-lactide-co-glycolide) (PLGA, Mw = 50 ~ 75 kDa, ester terminated, Sigma Aldrich) as the matrix materials that can undergo bulk erosion upon contact with biofluids to generate biological benign byproducts (lactic acid and glycolic acid)<sup>##UREF##9##53##</sup>. As shown in Fig. ##FIG##0##1b##, the fabrication relies on sputter deposition to coat a thin layer of gold (thickness 150 nm) onto the drug-loaded MN arrays supported by a PLGA base. The gold encapsulation layer is supported on the surface of solid MNs, which enables sufficient stability with a thinner thickness (150 nm) compared with those used in the reservoir designs (thickness of gold layer more than 300 nm) as reported previously (as illustrated in Supplementary Fig. ##SUPPL##0##1##). Thus, the thickness to realize effective drug encapsulation can be smaller. Ablation using a laser beam defines the gold traces that connect with separated MN domains to realize spatial control of drug release. A thin polydimethylsiloxane (PDMS) (thickness 10 µm) covers the gold layer at the circuity base regions. It exposes only the MN regions to allow physical contact of the gold layer on the MN with biofluids.</p>\",\n \"<p id=\\\"Par8\\\">Fabrication of the SOP relies on a low-cost solution-molding procedure (Supplementary Fig. ##SUPPL##0##2##). The process starts with UV laser ablation to define an MN mold from a PDMS pad. Drop casting drug-loaded PLGA solution into the PDMS mold and setting under vacuum allow the entrance of PLGA into the negative MN molds. Sufficient solidification over 8 h allows easy extraction of the PLGA MNs. Then, the PLGA MNs undergo sputtering deposition to coat a 150 nm-thick gold layer that conformally covers the top surface, followed by patterning with a laser ablation system to define control circuits. Drop casting a thin PDMS layer (thickness 10 μm) onto gold layers protects the control circuits and exposes the MNs for drug release. The MN patch is then attached to a flexible PCB and connected with a current regulator, a wireless energy harvester, and a microcontroller to complete the fabrication process. The solution-molding method can produce SOPs with: (1) tunable MN lengths from 600 μm to 3 mm and aspect ratio from 3 to 8; (2) high dimension uniformity in both length and base diameter (Supplementary Fig. ##SUPPL##0##3##); (3) arbitrary MN array configurations (square, hexagonal, single-needle, etc.). Figure ##FIG##0##1e##–##FIG##0##h## shows that the morphology and shape of the PLGA MNs remain stable during both gold deposition and drug loading procedures.</p>\",\n \"<p id=\\\"Par9\\\">Figure ##FIG##0##1c##, ##FIG##0##d## illustrates the overall working mechanism to realize high-precision drug delivery by our SOP. The electrically triggered crevice corrosion of the gold protective layer is the switch for initiating drug release of specific collections of SOP MNs. Upon SOP deployment on the skin, the MNs stay at a standby stage with the drug fully protected by the gold layer. Once a direct current (DC) electrical trigger (2.2–3 V) is applied, the electrochemical crevice corrosion starts to occur on triggered MNs, transitioning them from standby mode to releasing mode. After a short period (<30 s when 2.5 V is applied), the gold protective layer on the MN is fully dissolved, and MNs are exposed to the bio-environment to enable drug release. The compatible integration of microcontrollers allows the electrical triggers to be manipulated at a precise time point for precision delivery. By patterning gold layers via microfabrication, the SOP can realize the spatial profile of drug release at a high spatial resolution (~1 mm<sup>2</sup>). A built-in anode is integrated into the SOP to complete the circuitry for the in vivo electrochemical crevice corrosion.</p>\",\n \"<title>Characterization of electrically triggered crevice corrosion</title>\",\n \"<p id=\\\"Par10\\\">Figure ##FIG##1##2## demonstrates the characterization of SOP on its active control of drug release (based on MNs, 1.2 mm in height, 150-nm gold coated). The operation of drug-release control includes three stages: (1) standby stage when the MNs are fully coated with gold (labeled as 0 min); (2) transitioning stage when the electrical trigger is activated, and the gold layer is partially dissolved (labeled as 0.5 min); (3) releasing stage when the gold is fully dissolved, and MNs are exposed to the biofluids (labeled as 2 min). 1X Dulbecco’s phosphate-buffered saline (DPBS, Corning) is used here to simulate the body biofluid. The experiment is conducted at room temperature and triggered by a 2.5-V DC. Figure ##FIG##1##2a## shows optical and SEM images of the MN arrays at different stages, which indicates a noticeable change in surface color and roughness associated with the electrically triggered crevice corrosion. The results demonstrate that the main structure of the MN stays stable during the transitioning stage, and the electrical triggers effectively dissolve gold into biofluids and sufficiently expose the core MNs. More characterizations from different perspectives appear in Supplementary Fig. ##SUPPL##0##4##.</p>\",\n \"<p id=\\\"Par11\\\">The electrochemical crevice corrosion of the gold layer can be triggered by a direct current potential within 2 min, which is of clinical relevance for a timely response in drug administrations. A quantitative amperometry study of current density under different potentials is conducted to understand the electrical triggering behavior. Figure ##FIG##1##2e## shows I-V measurements of SOP triggering with potentials bias ranging from 2.0 V to 2.8 V applied to the MN arrays (1.2 mm in length, 150-nm gold coated). A steep drop in current density appears at 15 s of application for 2.8 V bias, indicating the endpoint of the electrochemical crevice corrosion. The time of the current-drop appearance increases as the potential bias decreases. The time consumption from the beginning to the end of the current is defined as the effective corrosion time plotted against the potential in Fig. ##FIG##1##2f##. The fitting is based on the Butler-Volmer equationwhere is the exchange current density, is the Faraday constant, is the overpotential, is the ideal gas constant, is the thermodynamic temperature, and is a coefficient with values ranging from 0 to 1, is the number of electrons in the anodic half reaction.</p>\",\n \"<p id=\\\"Par12\\\">This equation describes the relationship between potential difference and reaction rate<sup>##UREF##10##54##</sup>. At an ambient potential of 2.5 V, the crevice corrosion on MN is triggered within 30 s. This parameter is applied in the following experiments. We hypothesize that the crevice corrosion driven by constant voltage includes two parts: anodic oxidation and mechanical crevices. The 2.5-V potential between the anode and cathode is sufficient for triggering gold oxidation coupled with hydrogen emission reaction (HER) at ambient conditions (1X DPBS, 25 °C). It is reported that an anodic potential larger than 1.95 V (vs standard hydrogen electrode, SHE) is high enough to drive the below-surface oxidation of gold film<sup>##UREF##11##55##</sup>. This corresponds with our observations, as the crevice corrosion is triggered at a potential above 2.0 V, even though the HER on the counter electrode is not under the standard condition. The oxidated gold species in neutral and alkaline environments may include oxides, hydroxides, and free elements, mostly in the form of nanoparticles (NPs)<sup>##UREF##12##56##,##UREF##13##57##</sup>, that collectively in the amount used here are benign to the human body<sup>##UREF##14##58##</sup>. Supplementary Fig. ##SUPPL##0##5## provides a schematic illustration of the two-electrode system of SOP (Supplementary Fig. ##SUPPL##0##5a##) and the atomic scale illustration of the anodic oxidation (Supplementary Fig. ##SUPPL##0##5b, c##) on Au (111) facet in a neutral environment, with reaction products, Au<sub>2</sub>O<sub>3</sub> in the form of nanoparticles. The anodic behavior is also studied by finite element analysis (FEA), including reaction potential, reaction rate, current, and potential distribution (Supplementary Fig. ##SUPPL##0##6a–d##). The simulation results show that the reaction rate at 2.5 V is 281 nm/min (Fig. ##FIG##1##2h, i##), which is consistent with the experimental observations (150 nm in ~ 30 s). The anodic polarization curve under the condition of standard potentials and primary current distribution also shows a minimal triggering potential at around 2.0 V (Supplementary Fig. ##SUPPL##0##6e##). However, the anodic oxidation may not account for the entire loss of the gold coating. Based on the 2.6-V amperometry experiment (Fig. ##FIG##1##2e##), the theoretical weight of the oxidated gold is calculated as 122 μg. The actual value should be lower because water splitting (oxygen generation), as the side reaction, may also contribute to the current density. This amount of gold NPs does not constitute a health hazard since it is much lower than the safe exposure threshold, which is reported as 5 mg/ml<sup>##UREF##14##58##</sup>. Meanwhile, the total weight of the diminished gold film on the MN array is calculated as 290 μg, which is significantly higher than the oxidated amount. This is because the major part of the gold layer is not oxidated but exfoliated from the surface due to crevices and cracks. These defects come from the weakening effect on gold membranes as they become thinner during corrosion. This kind of crevice on metal film is generated by an electrical current, which is reported in some previous studies<sup>##REF##32923633##43##,##REF##22344516##45##</sup>.</p>\",\n \"<p id=\\\"Par13\\\">A gold-on-wafer experiment validates the presence of mechanical crevices and exfoliations. The surface roughness of the crevice corrosion of the gold layer is also studied with the gold-on-wafer ({100} facet, 150-nm, 1 cm<sup>2</sup> square) model (Supplementary Fig. ##SUPPL##0##7b##, ##SUPPL##0##c##). The gold layer is connected to a power source, and crevice corrosion is triggered at 2.5 V in 1X DPBS. The surface roughness of gold is calculated with FIJI ImageJ on optical images collected at various stages of corrosion. As shown in Supplementary Fig. ##SUPPL##0##7d##, the surface roughness is monitored every 0.5 min for 5 min from the initial stage, then every 1 min from the 5-min stage to the 9-min stage. A sharp increase in surface roughness is observed in the first 1 min right after the crevice corrosion is initiated (Supplementary Fig. ##SUPPL##0##7a##). This is consistent with the amperometry study that most electrochemical corrosion happens within 1 min under this potential. The roughness increase indicates the gold layer’s exfoliation from the wafer, which is also observed in the MN corrosion from an SOP. It can be concluded that a significant part of the gold layer is not directly oxidized but exfoliated during the electrical triggers. This corresponds with the previous study using gold film as the control gate for implanted drug reservoirs<sup>##REF##28458955##44##</sup>.</p>\",\n \"<p id=\\\"Par14\\\">To characterize the electrical trigger process, we select two different areas on an SOP MN: the tip area and the waist area, as shown in Fig. ##FIG##1##2d##. The structural difference between the two areas is the surface curvature, with the former being 1.37 mm<sup>−2</sup> and the latter close to 0. The waist area stands for the MN’s major surface, where most drugs are released. The energy-dispersive X-ray spectroscopy (EDXS) mapping is carried out on three stages of MNs from both areas as a semi-quantified surface analysis (SI Figs. ##SUPPL##0##8## and ##SUPPL##0##9##). Oxygen, carbon, and gold are selected as elements of interest, where oxygen and carbon show the exposed polymer body while gold shows the encapsulated surface of the MN. The ratio quantification is based on the relative weight percentage of only these three elements despite the existence of other elements. As shown in Fig. ##FIG##1##2b##, ##FIG##1##c##, both the tip and the waist areas show similar trends in element-relative ratios. An increase in oxygen, carbon, and a decrease in gold (all by weight%) are observed. The waist area, which stands for the main body of an MN, shows a more remarkable change in element constituents, indicating a complete corrosion of triggered crevice for the gold layer. The tip part of MN is still partially capped by a small amount of gold at the releasing stage. This part of the gold layer is isolated during the crevice corrosion process and accounts for the rather high remaining gold shown in Fig. ##FIG##1##2b##. The left gold covering the tip area is too small to hinder the overall drug release from the entire MN. Silicon and copper are also observed in EDXS mapping, which comes from PDMS residuals and conductive wires used during the experiment. More detailed information appears in the Supplementary Information on the EDXS element mapping.</p>\",\n \"<p id=\\\"Par15\\\">Figure ##FIG##1##2g## shows the thermal characterization of SOP during the electrochemical corrosion to validate the thermal safety. The experiment uses an MN array (1.2 mm in length, 150-nm gold coated) connected to a 2.5-V DC power to undergo crevice corrosion in 1X DPBS. A FLIR thermometer records the temperature of the MN array with and without current in a 25 °C environment. The results show no noticeable change in temperature during the electrochemical corrosion, which indicates a low possibility of tissue damage from heat effects.</p>\",\n \"<p id=\\\"Par16\\\">Figure ##FIG##1##2j## and Supplementary Fig. ##SUPPL##0##10## show the uniformity of the gold layer on MN prior to crevice corrosion. We also use atomic force microscopy (AFM) to characterize the surface roughness of gold layer deposited on polymer to illustrate the uniformity of deposition thickness by sputter coating, as shown in Supplementary Fig. ##SUPPL##0##11##.</p>\",\n \"<title>Release control and electrical triggering of microneedle patch</title>\",\n \"<p id=\\\"Par17\\\">Figure ##FIG##2##3a## demonstrates the encapsulation performance of gold coating on MNs. Here, Rhodamine B (Thermo scientific), a fluorescent dye, is loaded into the microneedle patch (0.3% in weight, ~90 ng per MN) to simulate small-molecule drugs, which can be subsequently quantified by UV-Vis spectroscopy. Figure ##FIG##2##3a, b## show the release profile of Rhodamine B from a bare MN patch (1.2 mm in length) and an MN patch (1.2 mm in length) with a 100-nm gold coating. The release study is carried out in 45 °C 1X DPBS. Significant color fading on the MN patch without gold coating, as shown in Fig. ##FIG##2##3a##, and the apparent increase in absorbance (blue, without gold coating, Fig. ##FIG##2##3b##) indicate a successful release of dye into the biofluids. The average release rate of Rhodamine B in an hour is calculated as ~ 415 ng/min. In contrast, the controlled experiment (black, with gold coating) shows no significant change in absorbance (Fig. ##FIG##2##3b##), suggesting excellent protection of encapsulated drugs from release. The calculation of the accumulative release amount is based on a calibration curve of Rhodamine B solutions and Beer-Lambert Law. Detailed explanation is provided in Supplementary Fig. ##SUPPL##0##13## and SI Table ##SUPPL##0##1##. We further analyze the stability of gold coating in a 0.5% agar model to better simulate the mechanical properties of animal tissue. The MN array (1.2 mm in length, 150-nm gold coated) remains stable during a 2-week soaking test in the agar model without significant changes in shape or surface morphology (Fig. ##FIG##2##3a##). SI Figs. ##SUPPL##0##14## and ##SUPPL##0##15e##, together with Fig. ##FIG##2##3a##, show the stability of gold film on MNs against soaking in biofluids and mechanical friction in tissues. This indicates that the bonding between gold and PLGA is strong enough to meet the need for implantation, which corresponds with previous research on Au-polymer adhesion<sup>##UREF##15##59##–##REF##34936437##61##</sup>.</p>\",\n \"<p id=\\\"Par18\\\">Figure ##FIG##2##3c## demonstrates the wireless design of a SOP. An external power source—in this case, a signal generator—is connected to an inductive coil to provide a high-frequency (~MHz level) alternative current (AC). The inductive coil is paired with the receiving coil on the device to achieve wireless power transfer via magnetic resonance coupling. The AC is then converted to direct current using a full-bridge rectifier. A 2.5-V regulator then regulates the DC to provide a stable potential that facilitates the electrochemical crevice corrosion of the gold layer on MNs. The configuration of the wireless SOP is illustrated in Fig. ##FIG##2##3d##. The wireless SOP consists of an energy harvesting module for wireless power, a power amplification module, a System-on-Chip (SoC) module for remote control, and an MN array coupled with counter electrodes (CE). Within the energy harvesting module, a receiver coil, a full-bridge rectifier, and a regulator are used to provide stable direct current (DC) output. The output is coupled with a 0.1-μF capacitor to constitute a low-pass filter, improving output stability. An equivalent circuit diagram is provided in Fig. ##FIG##2##3g##, which emphasizes the energy harvesting module while simplifying the power amplification and SoC. A complete circuit diagram can be found in Supplementary Fig. ##SUPPL##0##16##. Supplementary Fig. ##SUPPL##0##17## shows the performance and impedance analyses of the energy harvesting module. The optimal signal input is determined as around 15 V peak-to-peak at 15 MHz. The measurements shown in Fig. ##FIG##2##3e## validate the output stability of wireless SOP with the regulator. The final output power signal is stabilized at around 2.5 V with a standard deviation of ~0.1 V, ensuring precise crevice corrosion control for initiating drug release. The input signal’s optimal frequency is around 36 MHz, as shown in Fig. ##FIG##2##3f##.</p>\",\n \"<title>Temporal and spatial control of SOP triggering</title>\",\n \"<p id=\\\"Par19\\\">To further investigate the temporal and spatial controllability of electrical triggering on the MN patch (Fig. ##FIG##3##4a##), we design a multi-domain SOP to realize the stepwise, on-demand release. The multi-domain SOP consists of 7 domains of MN arrays (1.2-mm in length, 150-nm gold coated). The gold layer on the PLGA patch is patterned by laser ablation to enable separate triggering of individual MN domains. A layer of PDMS (~10 μm) is then applied onto the patch except for the hexagonal MN regions to protect gold interconnects from dissolving during electrical triggering. Figure ##FIG##3##4c## shows the electrical triggering schedule for four of the seven MN domains for the SOP loaded with Rhodamine B (0.3% by weight) during its immersion in 65 °C 1X DPBS as an accelerated study. The electrical triggers use 2.5-V DC bias for 30 s at every 30-min interval of immersion. Following each interval, immediate sampling of the environmental fluids allows quantitative estimation of drug-release dosage using UV-Vis spectroscopy, as shown in Fig. ##FIG##3##4b##. The measurements show a multi-step increase in spectral absorbance, indicating the stepwise increase of drug dosage and confirming the on-demand drug release at the desired time (0, 30, 60, 90 min, Fig. ##FIG##3##4b##). Figure ##FIG##3##4d## demonstrates the staged release of MN domains by selectively dissolving the gold encapsulation layer with an electrical trigger (Supplementary Movie ##SUPPL##3##1##). The red dash line circles the specific MN array triggered from each stage. The results confirm that our SOP realizes both temporal and spatial control of drug release using digital electrical triggers.</p>\",\n \"<p id=\\\"Par20\\\">Supplementary Fig. ##SUPPL##0##18## demonstrates the capability of ultrafine spatial control of drug release. Here, we design a miniaturized SOP with a single domain consisting of 8 MNs (1.1 mm in length, coated with 150 nm-thick gold) with 1–3 mm in spatial separation. With a specific design of gold circuits, each MN in the domain can be triggered individually (as illustrated in Supplementary Fig. ##SUPPL##0##18a##). Supplementary Fig. ##SUPPL##0##18b## shows that each MN follows sequential release via electrical triggering (2.2-V DC) within 15 s without interfering with adjacent MNs. Patterning techniques on the gold layer primarily dictate the spatial resolution of release control for SOP.</p>\",\n \"<title>Demonstration of SOP in intracranial drug delivery</title>\",\n \"<p id=\\\"Par21\\\">Beyond the clinical applicability for transdermal drug delivery, the SOP could show impactful utility in facilitating animal behavior studies. Here, we demonstrate intracranial delivery of melatonin using SOP for an animal sleep study. Melatonin, a hormone naturally produced in the brain by the pineal gland, plays a crucial role in regulating the sleep-wake cycle while also participating in other regulations like blood pressure and body temperature<sup>##REF##23333399##62##–##REF##15649735##64##</sup>. However, people found many strains of nocturnal laboratory mice are deficient in the release of melatonin, including C57/B6 mice<sup>##REF##20308563##65##,##REF##11792644##66##</sup>, and the effects of melatonin are still controversial<sup>##REF##22171046##67##–##REF##7326588##69##</sup>. Here, we use C57/B6 mice and implant the SOP to test how exogenous spatiotemporally controlled release of melatonin in the deep locations of the cortex, which is mainly distributed by melatonin receptor 1<sup>##REF##15893586##70##</sup>, would affect the sleep-wake cycle. The high spatiotemporal controllability of melatonin release offered by the SOP may open up new opportunities to understand regional brain responses to melatonin and study the pathology of sleep-wake disorders. Loading melatonin into MNs of the SOP follows the solution fabrication method described in Supplementary Fig. ##SUPPL##0##2##. Melatonin that dissolves in acetone can be mixed with the precursor PLGA solution, which ensures the loading dosage (10–35 wt%) for each MN. The 3-mm MNs are fabricated with the PLGA-melatonin ratio from 10:1 to 2:1 (Fig. ##FIG##4##5a##). In addition, the drug can be concentrated around the tip of the MN, which ensures deep-brain delivery for mice, as shown by the dash-red line in Fig. ##FIG##4##5a##. Based on the mixing ratio, the payload of melatonin per MN is estimated to be 22.2 to 81.7 μg, comparable to recommended dosages for mice (4–20 mg/kg)<sup>##REF##6655558##71##,##REF##30081472##72##</sup>. The drug payload is modulated by loading different PLGA solutions during the mold casting procedure. Furthermore, the mechanical property of the 3 mm-tall MNs used for intracranial delivery is characterized by a fracture test (Supplementary Fig. ##SUPPL##0##15##). The ultimate strength of PLGA MN (1.2 mm in length) is determined by the first fracture point in the force-displacement graph, as labeled by the red frame (Fig. ##FIG##4##5b##). The fracture point, recognized by a sudden drop in measured force, corresponds to the initial fracture of the MNs, followed by multiple subsequent fractures appearing in different locations of the MNs (Supplementary Fig. ##SUPPL##0##15b##, ##SUPPL##0##d##). The maximum mechanical strength is derived to be 118 MPa, which is sufficiently rigid for human skin penetration<sup>##REF##1757138##73##</sup>. The calculation considers the pressure measured at the first fracture point, while the contact area of the MN is approximated based on the tip diameter (around 30 μm, Supplementary Fig. ##SUPPL##0##3f##). Another concern is the stability of the gold layer during implantation. Supplementary Fig. ##SUPPL##0##15e## shows the PLGA MN (150 nm gold coated, 1.2 mm) before and after the penetration test. No significant changes can be observed based on the comparison of optical images. Figure ##FIG##2##3a## also proves the stability of the gold layer during the soaking test. In addition, we use the MN array (150 nm gold coated, 1.2 mm) to penetrate the tissue of the chicken thigh for multiple cycles, as presented in Supplementary Fig. ##SUPPL##0##14##. The gold layer remains stable after 20 cycles of penetration.</p>\",\n \"<p id=\\\"Par22\\\">Deployment of the melatonin-loaded SOP in live animal models as they move in a caged environment shows possibilities for actively controlling melatonin release to the deep-brain regions of the parietal lobe (Fig. ##FIG##4##5g##). To ensure device stability, the SOP is coupled with a custom headstage that can be firmly mounted onto mice heads (Supplementary Fig. ##SUPPL##0##19##). Figure ##FIG##4##5h## shows an immunohistochemistry analysis of brain tissues from the mice at various recovery stages following SOP implantation. On day 1 of post-implantation, the brain tissues in close proximity to the SOP MN show structural damage resulting from mechanical forces during intracranial surgery, which is typical for general brain implantation<sup>##REF##25546652##74##</sup>. As the mice recover from the implantation, the levels of GFAP (red) and IBA (white) show a significant decrease in concentration and staining range, indicating excellent biocompatibility of SOP. The tissue in contact with the MN becomes smoother, with fewer rough edges. An obvious increase in neuron regeneration (neurotrace, green) can be observed, indicating good recovery from implantation surgery.</p>\",\n \"<p id=\\\"Par23\\\">Supplementary Fig. ##SUPPL##0##20## shows an in vivo study to validate the safety and biocompatibility of SOP in intracranial deployment. Here, the study compares four groups of MNs (bare MN (MN), gold-coated MN (Au-MN), melatonin-loaded MN (Mel-MN), and melatonin-loaded MN with gold coating (Au-Mel-MN)) in their effects on the inflammation and degree of neural damage after one-month implantation. Supplementary Fig. ##SUPPL##0##21a## shows typical images of MN implanting sites with GFAP/IBA/NSE/DAPI multiple staining. As a result, the number of astrocytes and microglia in the region of interest (ROI) is significantly lower in the Au-MN or Au-Mel-MN group compared to the bare MN group, which indicates that gold-coated MNs have less damage to neural tissues and cause less inflammation compared to bare PLGA MNs. In addition, the fluorescence density of the markers has no difference between bare and gold-coated MNs, which indicates that the Au encapsulation on MNs does not affect the expressing strength of the GFAP or IBA signals (Supplementary Fig. ##SUPPL##0##21c##).</p>\",\n \"<p id=\\\"Par24\\\">Furthermore, we used a fiber photometry system to investigate the neural activity in the medial prefrontal cortex (mPFC) after the implantation of MNs (bare MN, Au-MN, control (no MN), respectively, Supplementary Fig. ##SUPPL##0##22##). As shown in Supplementary Fig. ##SUPPL##0##22c##, there is no observable difference in Ca<sup>2+</sup> dynamics of mPFC excitatory neurons between the control group, bare MN, and Au-MN groups, which indicates the MNs have no significant damage to the brain tissue and introduce little influence on the neuronal activity.</p>\",\n \"<p id=\\\"Par25\\\">Besides, we study the bioresorbability of the MN. Though the degradation of PLGA is much slower than gold<sup>##REF##22577513##75##</sup>, we show this process of PLGA in an accelerated degradation experiment in 65 °C phosphate-buffered saline (PBS), as illustrated in Supplementary Fig. ##SUPPL##0##23##.</p>\",\n \"<p id=\\\"Par26\\\">We further use the in vivo animal models to characterize the SOP functional performance. Here, an additional two recording electrodes are inserted adjacent to the location of SOP implantation, as shown in Fig. ##FIG##4##5c##. First, a set of DC electrical triggers (5 s duration, 2.5 V) is delivered and recorded (5 mm from the stimulation electrode), as shown in Fig. ##FIG##4##5e##. The crevice corrosion of the gold layer (thickness 150 nm) on the MN can be completed by applying 5–7 times the 5-s triggers to allow melatonin release. Then, a series of short pulse stimulation experiments are examined (Supplementary Fig. ##SUPPL##0##24##). The pulse signals vary from 10 mV to 50 mV in amplitude and 1 Hz (duration 10 ms) or 10 Hz (duration 1 ms) in frequency. The signal amplitude-recording distance relationship is also studied based on 10-ms pulses of 50 mV (Fig. ##FIG##4##5f##) and proved to affect a 5-mm area mainly. No significant changes are observed on the gold layer of MN after short pulses, as demonstrated in Fig. ##FIG##4##5d##, indicating electrical signals generated from neuron cells induce negligible damage to the gold protection layer of the SOP. Furthermore, the results demonstrate the gold-coated MNs can also be used as stimulation electrodes for the delivery of low-amplitude (10–50 mV), pulsatile signals (Fig. ##FIG##4##5f##), which may serve as a strategy for neuronal regeneration<sup>##REF##33239608##76##,##REF##30297910##77##</sup>.</p>\",\n \"<title>Assessment of in-brain drug delivery by microneedles</title>\",\n \"<p id=\\\"Par27\\\">To investigate the gold encapsulation’s performance in controlled drug release from the MN, we fabricated two groups of melatonin-loaded (25 wt%) MN with the exact specification shown in Fig. ##FIG##4##5a##. For effective comparison, the first batch of MNs (Mel-MNs) has no gold encapsulation layer, while the second batch of MNs (Au-Mel-MNs) is coated with 200 nm of gold on the MN surface.</p>\",\n \"<p id=\\\"Par28\\\">First, we conduct the in vivo drug release experiment with the Mel-MNs and the blank control (MNs without loading melatonin) by stereotaxically implanting the MN into the medial prefrontal cortex (mPFC) of mice (one MN in each, Fig. ##FIG##5##6a##). After one week of recovery, we use the Pinnacle sleep system (Sirenia Acquisition) to record EEG and EMG analyses. From 19:00 (active period) to 22:00, the power density of the Mel-MN group is compared with those of the control group (Fig. ##FIG##5##6b##). The total amount of NREM sleep increased by 42.0%, and the amount of wakefulness decreased by 27.9% over the 3-h period compared to these parameters in the control group (Fig. ##FIG##5##6c, d##). The sleep status is calculated from the EEG/EMG traces and the corresponding hypnograms (Supplementary Figs. ##SUPPL##0##25##–##SUPPL##0##29##). The experiment confirms the effective release of Melatonin from Mel-MNs, which can successfully modulate the sleep behavior of mice over a week.</p>\",\n \"<p id=\\\"Par29\\\">Second, we conduct similar in vivo experiments to investigate the feasibility of release control with gold encapsulation from the Au-Mel-MN. Here, we apply the Au-Mel-MNs by stereotaxically implanting them into the medial prefrontal cortex (mPFC) of mice (one MN in each, Fig. ##FIG##5##6e##). For effective comparison, the EEG and EMG recording starts one week after implantation, following the same way as described above. Figure ##FIG##5##6f## shows the power density of the Au-Mel-MN group and control group recorded from 19:00 to 01:00 am (active period), indicating that the delta power density of the Au-Mel-MN group was significantly higher (<italic>P</italic> < 0.01) than that of the control group. The significantly enhanced delta band EEG power indicates a deeper sleeping degree induced by melatonin, compared with the Mel-MNs experiment (Fig. ##FIG##5##6c, d##). The total amount of NREM sleep increased significantly by 36.7%, REM sleep increased by 57.8%, and that of wakefulness significantly decreased by 26.0% over the 6-h period, compared to these parameters in the control group (Fig. ##FIG##5##6g, h##). Essentially, melatonin release in the mPFC induces NREM sleep amount increases in active periods and enhances delta power density during NREM sleep even more significantly than that shown in the Mel-MN group. The delayed release effect mainly explains this due to the existence of gold encapsulation. As a result, the releasing peak of melatonin appeared after the controlled degradation of the gold protection layer. In contrast, the Mel-MN group releases melatonin right after implantation, leading to a weakened effect at the time point of recording (one week post to recovery). Collectively, these in vivo experiments further demonstrate the programmed drug release performance of SOPs in freely moving animals and suggest its potential utility in animal behavior studies. We revealed that programmed drug release of exogenous melatonin in the mPFC would improve NREM sleep, REM sleep, and delta power density, which may represent a novel target for treating sleep disorders.</p>\",\n \"<p id=\\\"Par30\\\">In this study, we present an on-demand drug-delivery patch that can be digitally controlled to enable delivery precision in both space and time. The spatiotemporal on-demand patch (SOP) uses bioresorbable microneedles with high-aspect ratios (3–8) as the drug-loading vehicle and a thin layer of gold (thickness 150 nm) as a release gate that can be digitally controlled with a small electrical trigger (2.5 V for 30 s). This design allows fully active control of drug release at a single-microneedle level with spatial resolution, demonstrated here, less than 1 mm<sup>2</sup>, highlighting the enabling capabilities over most existing drug-delivery devices. This spatial resolution allows more than 20 doses of the drug to be housed within a thin, wearable patch of 1 cm<sup>2</sup>, ensuring comfortable and convenient user adherence for repetitive pharmaceutical treatment over extended periods. The fabrication is compatible with the microfabrication process, which could further decrease the spatial resolution to micrometer scales. The on-demand, rapid-response drug release can be enabled within 30 s following an active electrical trigger.</p>\",\n \"<p id=\\\"Par31\\\">The fabrication procedure of our SOP uses a simple solution-molding method, offering a low-cost approach with high dimensional customizability. Using laser ablation, microneedles, ranging from 0.6–3 mm, can be manufactured efficiently and in high quality to fit a wide range of drugs (melatonin with controllable payloads<sup>##REF##34959479##78##</sup>. Moreover, the solution-based mold process allows drug-load PLGA MNs to be produced at scale with similarly high quality. The process allows convenient compatibility for integrating electronic modules to enable digital automation in drug delivery.</p>\",\n \"<p id=\\\"Par32\\\">The multifunctionality of drug delivery and stimulation therapy could synergistically create advanced therapy as potential future avenues of neuroscience research. The SOP presented in this work exhibits continuous sustained drug release feature that meets the need to treat various chronic neural diseases apart from sleep disorder, especially Alzheimer’s disease (AD)<sup>##REF##28872215##50##</sup>. As a multiphase neural disease that affects most areas of the brain<sup>##REF##22229116##51##,##REF##27188934##52##</sup>, the treatment of AD requires different drugs at different stages. For example, acetylcholinesterase inhibitors (AChEIs) are currently the mainstay of treatment for mild-to-moderate AD. In contrast, memantine is approved for mild-to-moderate dementia as a non-competitive N-methyl-D-aspartate (NMDA) receptor antagonist that prevents the overactivation of neuronal NMDA receptors. At the same time, the combination of these two drugs also significantly improves cognitive function in moderate-to-severe AD<sup>##REF##23657927##79##</sup>. The SOP can enable precise joint delivery of multiple drugs in desired brain regions to achieve enhanced treatment. Combined with a burst release design (like hollow microneedles), the SOP also fits for rapid drug delivery as a timely response towards acute neural diseases, such as cataplexy and epilepsy<sup>##REF##12557281##80##–##REF##23359095##82##</sup>. The remotely powered SOP allows for active drug delivery at a particular time and space coupled with real-time behavioral study, such as sleep-quality characterization, which provides a convenient method for drug-performance analysis. The high-resolution and controllability features of SOP make it suitable for such kind of regional and dosage-sensitive treatment. In addition, many drugs for brain disorders may have detrimental effects on other organs if administered systemically<sup>##REF##20497904##83##</sup>. The regional drug release feature of SOP enables targeted drug delivery that minimizes systemic exposure, reducing the risk of unwanted side effects in non-brain tissues.</p>\",\n \"<p id=\\\"Par33\\\">Moreover, our SOP is capable of wireless operation via near-field communication or, potentially, Bluetooth Low Energy. The agar-soaking test, fracture test, and in vivo intracranial delivery experiments validate the SOP’s practical functionality and biocompatibility. Furthermore, gold-coated MNs can extend beyond drug-releasing control, as the SOP can offer in vivo electrical stimulation. These concepts establish unique approaches in high-precision drug-delivery technologies with additional utilities in advancing fundamental studies of disease pathology (such as cancer metastasis) and neuroscience research, as demonstrated by both benchtop experiments and in vivo studies. Future efforts on fully digital automation of drug delivery will pave the way for the next generation of precision medicine.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":[],"string":"[]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Transdermal drug delivery is of vital importance for medical treatments. However, user adherence to long-term repetitive drug delivery poses a grand challenge. Furthermore, the dynamic and unpredictable disease progression demands a pharmaceutical treatment that can be actively controlled in real-time to ensure medical precision and personalization. Here, we report a spatiotemporal on-demand patch (SOP) that integrates drug-loaded microneedles with biocompatible metallic membranes to enable electrically triggered active control of drug release. Precise control of drug release to targeted locations (<1 mm<sup>2</sup>), rapid drug release response to electrical triggers (<30 s), and multi-modal operation involving both drug release and electrical stimulation highlight the novelty. Solution-based fabrication ensures high customizability and scalability to tailor the SOP for various pharmaceutical needs. The wireless-powered and digital-controlled SOP demonstrates great promise in achieving full automation of drug delivery, improving user adherence while ensuring medical precision. Based on these characteristics, we utilized SOPs in sleep studies. We revealed that programmed release of exogenous melatonin from SOPs improve sleep of mice, indicating potential values for basic research and clinical treatments.</p>","<p id=\"Par2\">Microneedle patches that can actively address individual needles are challenging to realize. Here, the authors introduce a spatiotemporal on-demand patch for precise and personalized drug delivery, utilizing electrically triggered control with drug-loaded microneedles and biocompatible metallic membranes.</p>","<title>Subject terms</title>"],"string":"[\n \"<p id=\\\"Par1\\\">Transdermal drug delivery is of vital importance for medical treatments. However, user adherence to long-term repetitive drug delivery poses a grand challenge. Furthermore, the dynamic and unpredictable disease progression demands a pharmaceutical treatment that can be actively controlled in real-time to ensure medical precision and personalization. Here, we report a spatiotemporal on-demand patch (SOP) that integrates drug-loaded microneedles with biocompatible metallic membranes to enable electrically triggered active control of drug release. Precise control of drug release to targeted locations (<1 mm<sup>2</sup>), rapid drug release response to electrical triggers (<30 s), and multi-modal operation involving both drug release and electrical stimulation highlight the novelty. Solution-based fabrication ensures high customizability and scalability to tailor the SOP for various pharmaceutical needs. The wireless-powered and digital-controlled SOP demonstrates great promise in achieving full automation of drug delivery, improving user adherence while ensuring medical precision. Based on these characteristics, we utilized SOPs in sleep studies. We revealed that programmed release of exogenous melatonin from SOPs improve sleep of mice, indicating potential values for basic research and clinical treatments.</p>\",\n \"<p id=\\\"Par2\\\">Microneedle patches that can actively address individual needles are challenging to realize. Here, the authors introduce a spatiotemporal on-demand patch for precise and personalized drug delivery, utilizing electrically triggered control with drug-loaded microneedles and biocompatible metallic membranes.</p>\",\n \"<title>Subject terms</title>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<title>Supplementary information</title>","<p>\n\n\n\n\n\n</p>","<title>Source data</title>","<p>\n\n</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>\\n\\n\\n\\n\\n\\n</p>\",\n \"<title>Source data</title>\",\n \"<p>\\n\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary information</title>","<p>The online version contains supplementary material available at 10.1038/s41467-023-44532-0.</p>","<title>Acknowledgements</title>","<p>This work was supported by the start-up funds from University of North Carolina at Chapel Hill and the fund from National Science Foundation under award # ECCS-2139659 (received by W.B.). Research reported in this publication was also supported by the National Institute of Biomedical Imaging and Bioengineering at the National Institutes of Health under award # 1R01EB034332-01 (received by W.B.). This work was performed in part at the Chapel Hill Analytical and Nanofabrication Laboratory, CHANL, a member of the North Carolina Research Triangle Nanotechnology Network, RTNN, which is supported by the National Science Foundation, Grant ECCS-2025064, as part of the National Nanotechnology Coordinated Infrastructure, NNCI.</p>","<title>Author contributions</title>","<p>W.B. conceived and directed the project. Y.W. and Z.C. performed experiments and prepared supplementary information. B.D., W.L., S.X., L.Z., T.W., P.H., Z.Y., and W.X. helped fabricate the device and collect the data. Z.C., Z.H., and J.S. performed the animal experiments. Y.W., Z.C., J.S., Z.H., and W.B. wrote the paper. All authors discussed the results and commented on the manuscript.</p>","<title>Peer review</title>","<title>Peer review information</title>","<p id=\"Par66\"><italic>Nature Communications</italic> thanks Canyang Zhang, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.</p>","<title>Data availability</title>","<p>The data of this study are available within the article, the Supplementary Information. Source data are provided with this paper and available upon request. The data generated in this study are provided in the Supplementary Information and Source Data files. <xref ref-type=\"sec\" rid=\"Sec36\">Source data</xref> are provided with this paper.</p>","<title>Competing interests</title>","<p id=\"Par67\">The University of North Carolina at Chapel Hill (no. 63/343,888; filed 18 May 2023) filed a provisional patent application surrounding this work. The method to digitally automate the control of drug delivery is included in the patent. Wubin Bai, Yihang Wang, “Wearable Apparatus for Deep Tissue Sensing and Digital Automation of Drug Delivery”, provisional patent, No. 63/343,888, filed on May 19, 2022. The remaining authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41467-023-44532-0.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>This work was supported by the start-up funds from University of North Carolina at Chapel Hill and the fund from National Science Foundation under award # ECCS-2139659 (received by W.B.). Research reported in this publication was also supported by the National Institute of Biomedical Imaging and Bioengineering at the National Institutes of Health under award # 1R01EB034332-01 (received by W.B.). This work was performed in part at the Chapel Hill Analytical and Nanofabrication Laboratory, CHANL, a member of the North Carolina Research Triangle Nanotechnology Network, RTNN, which is supported by the National Science Foundation, Grant ECCS-2025064, as part of the National Nanotechnology Coordinated Infrastructure, NNCI.</p>\",\n \"<title>Author contributions</title>\",\n \"<p>W.B. conceived and directed the project. Y.W. and Z.C. performed experiments and prepared supplementary information. B.D., W.L., S.X., L.Z., T.W., P.H., Z.Y., and W.X. helped fabricate the device and collect the data. Z.C., Z.H., and J.S. performed the animal experiments. Y.W., Z.C., J.S., Z.H., and W.B. wrote the paper. All authors discussed the results and commented on the manuscript.</p>\",\n \"<title>Peer review</title>\",\n \"<title>Peer review information</title>\",\n \"<p id=\\\"Par66\\\"><italic>Nature Communications</italic> thanks Canyang Zhang, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.</p>\",\n \"<title>Data availability</title>\",\n \"<p>The data of this study are available within the article, the Supplementary Information. Source data are provided with this paper and available upon request. The data generated in this study are provided in the Supplementary Information and Source Data files. <xref ref-type=\\\"sec\\\" rid=\\\"Sec36\\\">Source data</xref> are provided with this paper.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par67\\\">The University of North Carolina at Chapel Hill (no. 63/343,888; filed 18 May 2023) filed a provisional patent application surrounding this work. The method to digitally automate the control of drug delivery is included in the patent. Wubin Bai, Yihang Wang, “Wearable Apparatus for Deep Tissue Sensing and Digital Automation of Drug Delivery”, provisional patent, No. 63/343,888, filed on May 19, 2022. The remaining authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Spatiotemporal on-demand patch for wireless, active control of drug delivery.</title><p><bold>a</bold> Schematic illustration highlighting the construction of a wirelessly controlled spatiotemporal on-demand patch (SOP) for high-precision drug delivery. The SOP features two main components: (i) an array of drug-loaded microneedles protected by active encapsulation that exploits electrochemically triggered crevice corrosion, for on-demand drug delivery; (ii) a near-field communication (NFC) module assembled on a soft printed-circuit board, for wireless control. <bold>b</bold> Exploded view of the drug-delivery interface of the SOP, including a PDMS encapsulation, an electrically triggerable gold (Au) coating, drug-loaded microneedles based on poly(D, L-lactide-co-glycolide) (PLGA), and a PLGA substrate. <bold>c</bold> Schematic illustration showing process of electrically controlled on-demand drug delivery from an individual microneedle. (i) Standby stage where an encapsulation layer protects the microneedle from releasing drug. (ii) Transitioning stage where an electrical trigger initiates crevice corrosion of the encapsulation layer to expose drug-loaded base. (iii) Releasing stage, where the exposed base starts to release drugs. <bold>d</bold> Schematic illustration demonstrating the capability of spatiotemporal control of releasing profile from the SOP. (i) Deploying an SOP at the skin interface. (ii–iv) Communicating with the NFC module of the SOP enables active control of drug release for each individual microneedle. <bold>e</bold> Optical image of a PLGA microneedle array. <bold>f</bold> Corresponding SEM image with a tilted view on the PLGA microneedle array. <bold>g</bold> Optical image of a PLGA microneedle array loaded with Rhodamine B. <bold>h</bold> Optical image of a PLGA microneedle array protected with an electrically triggerable encapsulation (Au, thickness 150 nm). The length and base diameter of the microneedles in <bold>e</bold>–<bold>h</bold> is around 1.2 mm and 270 μm, respectively; 3 experiments are repeated with similar results for each in <bold>e</bold>–<bold>h</bold>; the scale bar in <bold>e</bold>–<bold>h</bold> is 1 mm.</p></caption></fig>","<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Electrically triggerable encapsulation for active control of drug release.</title><p><bold>a</bold> Optical images and the corresponding SEM to demonstrate an SOP undergoing the process of electrically controlled crevice corrosion. Initially (0 min), the drug-loaded microneedles are fully protected by a layer of gold (150 nm). At 0.5 min, an electrical trigger (2.5 V, in 1X Dulbecco’s phosphate-buffered saline) activates the crevice corrosion of the gold encapsulation layer. Then (2 min), the exposed drug embedded inside the microneedle core diffuses to the fluidic environment. Scale bar: 1 mm in optical images, 30 μm in SEM images. <bold>b</bold>, <bold>c</bold> Element analyses by energy-dispersive X-ray spectroscopy (EDXS) of the three stages (as shown in <bold>a</bold>) of the on-demand releasing process of the drug: <bold>b</bold> tip areas of the microneedle; <bold>c</bold> waist areas of the microneedle. Data in <bold>b</bold> and <bold>c</bold> are presented as mean values +/− standard deviation of element weight% from iterations of analysis (<italic>n</italic> = 2). <bold>d</bold> Schematic illustration indicating the corresponding areas of a microneedle analyzed by EDXS element mapping. <bold>e</bold> Amperometry characterization of the crevice corrosion process of the gold layer (150 nm) coated on microneedles with 1.2 mm height. <bold>f</bold> Measured relationship between corrosion time and trigger potential applied on the gold layer (150 nm), based on the amperometry curve in <bold>e</bold>. <bold>g</bold> Analysis of thermal effect during crevice corrosion process. The comparison between two conditions (with and without current) indicates negligible amount of heat generated in the system. <bold>h</bold> The simulation of crevice corrosion depth on microneedle under 2.5-V in 1X Dulbecco’s phosphate-buffered saline. <bold>i</bold> The corresponding corrosion profile on a microneedle in 1 min (2.5-V, 1X Dulbecco’s phosphate-buffered saline) related to <bold>h</bold>. <bold>j</bold> The SEM image of the cross-section of an MN (150 nm gold coated, 1.5 mm). Gold surface, gold cross-section, and PLGA cross-section are colored in yellow, orange, and light blue, respectively. 3 measurements are repeated with similar results to <bold>j</bold>. Scale bar: 1 μm. Source data are provided as a Source Data file.</p></caption></fig>","<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Wireless drug release control of SOP via near-field communication.</title><p><bold>a</bold> Optical images of microneedles with and without gold coating, respectively. The gold layer remains stable in an artificial tissue (0.5% agar in 1X Dulbecco’s phosphate-buffered saline, PBS) for more than 10 days with no observable degradation. Scale bar: 500 μm. <bold>b</bold> The release profile of Rhodamine B loaded in microneedles with and without gold coating. Height of the microneedles, 1.2-mm; Thickness of the gold coating, 100 nm. All samples were immersed in 1X Dulbecco’s PBS at 45 °C. <bold>c</bold> Optical image of a wireless SOP. Scale bar: 5 mm. <bold>d</bold> Top-down view of the wireless module used in the SOP, featuring the receiving coil (labeled as Rec. coil), the full-bridge rectifier and the regulator (circled in blue), the power amplifier (circled in green), the System-on-Chip (SoC) module (circled in red), and the ground (GND) & output channels. Both GND and output channels are connected to gold-coated MN arrays via conductive traces, which serve as a counter electrode and a working electrode, respectively. Scale bar: 5 mm. <bold>e</bold> Measurement on power harvesting of the wireless SOP (without SoC module). Black, input AC signal (peak-to-peak 4 V, 39 MHz); Red, rectified received signal (3.4 V, stand deviation (s.d.) ~ 0.2 V); Blue, regulated output signal (2.6 V, s.d. ~ 0.1 V). <bold>f</bold> The frequency matching characterization of the wireless power transfer. Black dots: the output voltage after rectification without regulation; Red dots: the output voltage after rectification and 2.5-V regulation.10-V peak-to-peak input signal is used, and the working range from 32–40 MHz is determined at 36 MHz. The potential rectification achieves stable bias around 2.5 V as the electrical trigger. <bold>g</bold> The circuit diagram of the energy harvesting module of SOP with correspondence to <bold>e</bold> and <bold>d</bold>. Inductive coil (Ind. coil) is not present in <bold>c</bold> and <bold>d</bold>. The power amplification and SoC module are abbreviated as Amp and SoC. CE stands for counter electrodes. Source data are provided as a Source Data file.</p></caption></fig>","<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Characterization of the spatiotemporal control of drug release.</title><p><bold>a</bold> Schematic illustration of a multi-domain SOP, with a zoom-in view of a microneedle domain at the releasing stage. <bold>b</bold> The stepwise release profile of a multistage drug release simulated by Rhodamine B: the four-step release of Rhodamine B is electrically triggered at 0, 30, 60, and 90 min, corresponding to labels i, ii, iii, and iv, respectively. The SOP demonstrated here has microneedles with a height of 1.2 mm, coated with a 150-nm gold layer, and protected by a 10-μm PDMS layer. <bold>c</bold> Schematic illustration showing the sequential electrical-triggering schedule on the multi-array SOP. The electrical triggering uses a DC voltage of 2.5 V for ~ 30 s. <bold>d</bold> Optical images of the multi-domain SOP undergoing a sequential electrical trigger. The domain triggered at each stage is labeled by red hexagonal dashed frames. The images from Stage 0 to Stage iv correspond to <bold>b</bold> and <bold>c</bold>. 2 experiments are repeated with similar results to <bold>d</bold>. Scale bar: 5 mm. Source data are provided as a Source Data file.</p></caption></fig>","<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>In vivo demonstration of SOP.</title><p><bold>a</bold> Optical images of an intracranial microneedle (height, 3 mm) at various fabrication stages: (1) PLGA needle base; (2) loading melatonin (20%); (3) coating with 150-nm gold. The drug-concentrated region is circled with a red frame. (3) measurements are repeated with similar results to <bold>a</bold> Scale bar: 1 mm. <bold>b</bold> During a fracture test, the measured force-displacement curve of a microneedle array (9 needles, 1.2 mm in length). A red frame circles the first fracture point, and the displacement in contact is labeled and measured as 1.26 mm. <bold>c</bold> The schematic illustration of a mouse brain model indicating the deployment location of SOP in the in vivo study. The red and white circles label the positions of the SOP microneedle and counter electrode (Pt wire); the blue and black circles label the positions of two separate recording electrodes (Pt wire). Scale bar: 5 mm. <bold>d</bold> Optical images of the microneedle (3 mm in length) before the test, after the pulse trigger, and after the square wave trigger, respectively. 3 measurements are repeated with similar results to <bold>d</bold>. Scale bar: 1 mm. <bold>e</bold> Measured square-wave triggers generated from the SOP microneedle at various distances to the microneedle. The gold-coated microneedle delivers the trigger (3-mm, 150-nm) as a 2.5-V 5-s square wave periodically. The recording electrodes (Rec. 1 and Rec. 2) are around 5 mm from the microneedle. <bold>f</bold> Measured pulsatile triggers generated from the SOP microneedle at various distances to the microneedle. A pulse signal (50 mV, 1 Hz, 10 ms in width) is applied by a microneedle electrode and recorded by a Pt wire at ~ 0.1, 2.0, and 5.0 mm, respectively. The signal for stimulation is attached in red. Scale bar: 5 s, 50 mV, for the <italic>x</italic> and <italic>y</italic> axis, respectively. Data are presented as mean values +/− standard deviation of peak amplitude (<italic>n</italic> = 10) <bold>g</bold>. Image of the freely moving mouse after SOP deployment. <bold>h</bold> Immunohistochemical staining images of the recovery process after microneedle implantation. The combined images include Nissl bodies (neurotrace, green), astrocytes (glial fibrillary acidic protein (GFAP), red), activated microglia (lba1, white) and nuclear DNA (4′,6-diamidino-2-phenylindole (DAPI), blue). Scale bar: 100 μm. (<italic>n</italic> = 3 independent experiments) Source data are provided as a Source Data file.</p></caption></fig>","<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Assessment of in-brain drug delivery by microneedles.</title><p>The drug delivery analysis of melatonin-loaded microneedle (Mel-MN) is illustrated from <bold>a</bold>–<bold>d</bold>, and melatonin-loaded microneedle with 200-nm gold encapsulation (Au-Mel-MN) from <bold>e</bold> to <bold>h</bold>. <bold>a</bold>, <bold>e</bold> Schematic illustration of recording brain states by implanting the microneedle and electrodes after 1 week in mice. The microneedle is implanted into the medial prefrontal cortex (mPFC) of mice brain, while electrodes for signal recording are chronically implanted through the skull (for electroencephalogram, EEG) or into trapezius muscles (for electromyography, EMG). The microneedle in <bold>a</bold> and <bold>e</bold> are both loaded with 25 wt% melatonin (Mel-MN), while the microneedle in <bold>e</bold> is further coated with a 200-nm gold layer (Au-Mel-MN). <bold>b</bold>, <bold>f</bold> EEG power density analysis. The recording is a 3-h period from 19:00 to 22:00 in <bold>b</bold> and a 6-h period from 19:00-1:00 in <bold>f</bold>. <bold>c</bold>, <bold>g</bold> Time course of different brain states (active period) immediately after the light turns off. The lengths of non-rapid eye movement (NREM) sleep, wakefulness, and rapid eye movement (REM) sleep are calculated every 30 min. Two groups of 5 mice (melatonin-delivered mice and the control, respectively) are compared, where melatonin-delivered mice showed longer NREM sleep and shorter wakefulness in both <bold>c</bold> and <bold>g</bold>. The implanted Au-Mel-MN showed a more substantial effect of melatonin, suggesting the delayed release of melatonin as a result of gold protection. <bold>d</bold>, <bold>h</bold> Duration of different brain states (NREM, wake, and REM) during the recording time, corresponding to <bold>c</bold> and <bold>h</bold>, respectively. Data in <bold>c</bold>, <bold>d</bold>, <bold>g</bold>, <bold>h</bold> are presented as mean values +/− SEM of peak amplitude (<italic>n</italic> = 5 in all melatonin loaded and control groups) Paired two-sided <italic>t</italic>-tests are used for comparisons between the two groups in <bold>d</bold>, <bold>h</bold>, <bold>b</bold>, and <bold>f</bold>. Two-way ANOVAs are used to perform group comparisons with Sidak’s multiple comparisons test in <bold>c</bold> and <bold>g</bold>. All statistical tests were two-tailed. Source data are provided as a Source Data file.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Fig. 1</label><caption><title>Spatiotemporal on-demand patch for wireless, active control of drug delivery.</title><p><bold>a</bold> Schematic illustration highlighting the construction of a wirelessly controlled spatiotemporal on-demand patch (SOP) for high-precision drug delivery. The SOP features two main components: (i) an array of drug-loaded microneedles protected by active encapsulation that exploits electrochemically triggered crevice corrosion, for on-demand drug delivery; (ii) a near-field communication (NFC) module assembled on a soft printed-circuit board, for wireless control. <bold>b</bold> Exploded view of the drug-delivery interface of the SOP, including a PDMS encapsulation, an electrically triggerable gold (Au) coating, drug-loaded microneedles based on poly(D, L-lactide-co-glycolide) (PLGA), and a PLGA substrate. <bold>c</bold> Schematic illustration showing process of electrically controlled on-demand drug delivery from an individual microneedle. (i) Standby stage where an encapsulation layer protects the microneedle from releasing drug. (ii) Transitioning stage where an electrical trigger initiates crevice corrosion of the encapsulation layer to expose drug-loaded base. (iii) Releasing stage, where the exposed base starts to release drugs. <bold>d</bold> Schematic illustration demonstrating the capability of spatiotemporal control of releasing profile from the SOP. (i) Deploying an SOP at the skin interface. (ii–iv) Communicating with the NFC module of the SOP enables active control of drug release for each individual microneedle. <bold>e</bold> Optical image of a PLGA microneedle array. <bold>f</bold> Corresponding SEM image with a tilted view on the PLGA microneedle array. <bold>g</bold> Optical image of a PLGA microneedle array loaded with Rhodamine B. <bold>h</bold> Optical image of a PLGA microneedle array protected with an electrically triggerable encapsulation (Au, thickness 150 nm). The length and base diameter of the microneedles in <bold>e</bold>–<bold>h</bold> is around 1.2 mm and 270 μm, respectively; 3 experiments are repeated with similar results for each in <bold>e</bold>–<bold>h</bold>; the scale bar in <bold>e</bold>–<bold>h</bold> is 1 mm.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Fig. 2</label><caption><title>Electrically triggerable encapsulation for active control of drug release.</title><p><bold>a</bold> Optical images and the corresponding SEM to demonstrate an SOP undergoing the process of electrically controlled crevice corrosion. Initially (0 min), the drug-loaded microneedles are fully protected by a layer of gold (150 nm). At 0.5 min, an electrical trigger (2.5 V, in 1X Dulbecco’s phosphate-buffered saline) activates the crevice corrosion of the gold encapsulation layer. Then (2 min), the exposed drug embedded inside the microneedle core diffuses to the fluidic environment. Scale bar: 1 mm in optical images, 30 μm in SEM images. <bold>b</bold>, <bold>c</bold> Element analyses by energy-dispersive X-ray spectroscopy (EDXS) of the three stages (as shown in <bold>a</bold>) of the on-demand releasing process of the drug: <bold>b</bold> tip areas of the microneedle; <bold>c</bold> waist areas of the microneedle. Data in <bold>b</bold> and <bold>c</bold> are presented as mean values +/− standard deviation of element weight% from iterations of analysis (<italic>n</italic> = 2). <bold>d</bold> Schematic illustration indicating the corresponding areas of a microneedle analyzed by EDXS element mapping. <bold>e</bold> Amperometry characterization of the crevice corrosion process of the gold layer (150 nm) coated on microneedles with 1.2 mm height. <bold>f</bold> Measured relationship between corrosion time and trigger potential applied on the gold layer (150 nm), based on the amperometry curve in <bold>e</bold>. <bold>g</bold> Analysis of thermal effect during crevice corrosion process. The comparison between two conditions (with and without current) indicates negligible amount of heat generated in the system. <bold>h</bold> The simulation of crevice corrosion depth on microneedle under 2.5-V in 1X Dulbecco’s phosphate-buffered saline. <bold>i</bold> The corresponding corrosion profile on a microneedle in 1 min (2.5-V, 1X Dulbecco’s phosphate-buffered saline) related to <bold>h</bold>. <bold>j</bold> The SEM image of the cross-section of an MN (150 nm gold coated, 1.5 mm). Gold surface, gold cross-section, and PLGA cross-section are colored in yellow, orange, and light blue, respectively. 3 measurements are repeated with similar results to <bold>j</bold>. Scale bar: 1 μm. Source data are provided as a Source Data file.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Fig. 3</label><caption><title>Wireless drug release control of SOP via near-field communication.</title><p><bold>a</bold> Optical images of microneedles with and without gold coating, respectively. The gold layer remains stable in an artificial tissue (0.5% agar in 1X Dulbecco’s phosphate-buffered saline, PBS) for more than 10 days with no observable degradation. Scale bar: 500 μm. <bold>b</bold> The release profile of Rhodamine B loaded in microneedles with and without gold coating. Height of the microneedles, 1.2-mm; Thickness of the gold coating, 100 nm. All samples were immersed in 1X Dulbecco’s PBS at 45 °C. <bold>c</bold> Optical image of a wireless SOP. Scale bar: 5 mm. <bold>d</bold> Top-down view of the wireless module used in the SOP, featuring the receiving coil (labeled as Rec. coil), the full-bridge rectifier and the regulator (circled in blue), the power amplifier (circled in green), the System-on-Chip (SoC) module (circled in red), and the ground (GND) & output channels. Both GND and output channels are connected to gold-coated MN arrays via conductive traces, which serve as a counter electrode and a working electrode, respectively. Scale bar: 5 mm. <bold>e</bold> Measurement on power harvesting of the wireless SOP (without SoC module). Black, input AC signal (peak-to-peak 4 V, 39 MHz); Red, rectified received signal (3.4 V, stand deviation (s.d.) ~ 0.2 V); Blue, regulated output signal (2.6 V, s.d. ~ 0.1 V). <bold>f</bold> The frequency matching characterization of the wireless power transfer. Black dots: the output voltage after rectification without regulation; Red dots: the output voltage after rectification and 2.5-V regulation.10-V peak-to-peak input signal is used, and the working range from 32–40 MHz is determined at 36 MHz. The potential rectification achieves stable bias around 2.5 V as the electrical trigger. <bold>g</bold> The circuit diagram of the energy harvesting module of SOP with correspondence to <bold>e</bold> and <bold>d</bold>. Inductive coil (Ind. coil) is not present in <bold>c</bold> and <bold>d</bold>. The power amplification and SoC module are abbreviated as Amp and SoC. CE stands for counter electrodes. Source data are provided as a Source Data file.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Fig. 4</label><caption><title>Characterization of the spatiotemporal control of drug release.</title><p><bold>a</bold> Schematic illustration of a multi-domain SOP, with a zoom-in view of a microneedle domain at the releasing stage. <bold>b</bold> The stepwise release profile of a multistage drug release simulated by Rhodamine B: the four-step release of Rhodamine B is electrically triggered at 0, 30, 60, and 90 min, corresponding to labels i, ii, iii, and iv, respectively. The SOP demonstrated here has microneedles with a height of 1.2 mm, coated with a 150-nm gold layer, and protected by a 10-μm PDMS layer. <bold>c</bold> Schematic illustration showing the sequential electrical-triggering schedule on the multi-array SOP. The electrical triggering uses a DC voltage of 2.5 V for ~ 30 s. <bold>d</bold> Optical images of the multi-domain SOP undergoing a sequential electrical trigger. The domain triggered at each stage is labeled by red hexagonal dashed frames. The images from Stage 0 to Stage iv correspond to <bold>b</bold> and <bold>c</bold>. 2 experiments are repeated with similar results to <bold>d</bold>. Scale bar: 5 mm. Source data are provided as a Source Data file.</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Fig. 5</label><caption><title>In vivo demonstration of SOP.</title><p><bold>a</bold> Optical images of an intracranial microneedle (height, 3 mm) at various fabrication stages: (1) PLGA needle base; (2) loading melatonin (20%); (3) coating with 150-nm gold. The drug-concentrated region is circled with a red frame. (3) measurements are repeated with similar results to <bold>a</bold> Scale bar: 1 mm. <bold>b</bold> During a fracture test, the measured force-displacement curve of a microneedle array (9 needles, 1.2 mm in length). A red frame circles the first fracture point, and the displacement in contact is labeled and measured as 1.26 mm. <bold>c</bold> The schematic illustration of a mouse brain model indicating the deployment location of SOP in the in vivo study. The red and white circles label the positions of the SOP microneedle and counter electrode (Pt wire); the blue and black circles label the positions of two separate recording electrodes (Pt wire). Scale bar: 5 mm. <bold>d</bold> Optical images of the microneedle (3 mm in length) before the test, after the pulse trigger, and after the square wave trigger, respectively. 3 measurements are repeated with similar results to <bold>d</bold>. Scale bar: 1 mm. <bold>e</bold> Measured square-wave triggers generated from the SOP microneedle at various distances to the microneedle. The gold-coated microneedle delivers the trigger (3-mm, 150-nm) as a 2.5-V 5-s square wave periodically. The recording electrodes (Rec. 1 and Rec. 2) are around 5 mm from the microneedle. <bold>f</bold> Measured pulsatile triggers generated from the SOP microneedle at various distances to the microneedle. A pulse signal (50 mV, 1 Hz, 10 ms in width) is applied by a microneedle electrode and recorded by a Pt wire at ~ 0.1, 2.0, and 5.0 mm, respectively. The signal for stimulation is attached in red. Scale bar: 5 s, 50 mV, for the <italic>x</italic> and <italic>y</italic> axis, respectively. Data are presented as mean values +/− standard deviation of peak amplitude (<italic>n</italic> = 10) <bold>g</bold>. Image of the freely moving mouse after SOP deployment. <bold>h</bold> Immunohistochemical staining images of the recovery process after microneedle implantation. The combined images include Nissl bodies (neurotrace, green), astrocytes (glial fibrillary acidic protein (GFAP), red), activated microglia (lba1, white) and nuclear DNA (4′,6-diamidino-2-phenylindole (DAPI), blue). Scale bar: 100 μm. (<italic>n</italic> = 3 independent experiments) Source data are provided as a Source Data file.</p></caption></fig>\",\n \"<fig id=\\\"Fig6\\\"><label>Fig. 6</label><caption><title>Assessment of in-brain drug delivery by microneedles.</title><p>The drug delivery analysis of melatonin-loaded microneedle (Mel-MN) is illustrated from <bold>a</bold>–<bold>d</bold>, and melatonin-loaded microneedle with 200-nm gold encapsulation (Au-Mel-MN) from <bold>e</bold> to <bold>h</bold>. <bold>a</bold>, <bold>e</bold> Schematic illustration of recording brain states by implanting the microneedle and electrodes after 1 week in mice. The microneedle is implanted into the medial prefrontal cortex (mPFC) of mice brain, while electrodes for signal recording are chronically implanted through the skull (for electroencephalogram, EEG) or into trapezius muscles (for electromyography, EMG). The microneedle in <bold>a</bold> and <bold>e</bold> are both loaded with 25 wt% melatonin (Mel-MN), while the microneedle in <bold>e</bold> is further coated with a 200-nm gold layer (Au-Mel-MN). <bold>b</bold>, <bold>f</bold> EEG power density analysis. The recording is a 3-h period from 19:00 to 22:00 in <bold>b</bold> and a 6-h period from 19:00-1:00 in <bold>f</bold>. <bold>c</bold>, <bold>g</bold> Time course of different brain states (active period) immediately after the light turns off. The lengths of non-rapid eye movement (NREM) sleep, wakefulness, and rapid eye movement (REM) sleep are calculated every 30 min. Two groups of 5 mice (melatonin-delivered mice and the control, respectively) are compared, where melatonin-delivered mice showed longer NREM sleep and shorter wakefulness in both <bold>c</bold> and <bold>g</bold>. The implanted Au-Mel-MN showed a more substantial effect of melatonin, suggesting the delayed release of melatonin as a result of gold protection. <bold>d</bold>, <bold>h</bold> Duration of different brain states (NREM, wake, and REM) during the recording time, corresponding to <bold>c</bold> and <bold>h</bold>, respectively. Data in <bold>c</bold>, <bold>d</bold>, <bold>g</bold>, <bold>h</bold> are presented as mean values +/− SEM of peak amplitude (<italic>n</italic> = 5 in all melatonin loaded and control groups) Paired two-sided <italic>t</italic>-tests are used for comparisons between the two groups in <bold>d</bold>, <bold>h</bold>, <bold>b</bold>, and <bold>f</bold>. Two-way ANOVAs are used to perform group comparisons with Sidak’s multiple comparisons test in <bold>c</bold> and <bold>g</bold>. All statistical tests were two-tailed. Source data are provided as a Source Data file.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":["<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$I={i}_{{corr}}[\\exp ({{{{{\\rm{\\hbox{-}}}}}}}\\sigma {nF}\\eta /{RT}){{{{{\\rm{\\hbox{-}}}}}}}\\exp ((1{{{{{\\rm{\\hbox{-}}}}}}}\\sigma ){nF}\\eta /{RT})]$$\\end{document}</tex-math><mml:math 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Wound Care\"], \"year\": [\"2021\"], \"volume\": [\"10\"], \"fpage\": [\"204\"], \"lpage\": [\"219\"], \"pub-id\": [\"10.1089/wound.2019.1122\"]}, {\"label\": [\"16.\"], \"surname\": [\"Ning\"], \"given-names\": [\"T\"], \"article-title\": [\"Synergistically detachable microneedle dressing for programmed treatment of chronic wounds\"], \"source\": [\"Adv. Healthc Mater\"], \"year\": [\"2022\"], \"volume\": [\"11\"], \"fpage\": [\"2102180\"], \"pub-id\": [\"10.1002/adhm.202102180\"]}, {\"label\": [\"17.\"], \"surname\": [\"Hsu\"], \"given-names\": [\"WL\"], \"article-title\": [\"On-skin glucose-biosensing and on-demand insulin-zinc hexamers delivery using microneedles for syringe-free diabetes management\"], \"source\": [\"Chem. Eng. J.\"], \"year\": [\"2020\"], \"volume\": [\"398\"], \"fpage\": [\"125536\"], \"pub-id\": [\"10.1016/j.cej.2020.125536\"]}, {\"label\": [\"33.\"], \"surname\": [\"Jamaledin\"], \"given-names\": [\"R\"], \"article-title\": [\"Engineered microneedle patches for controlled release of active compounds: recent advances in release profile tuning\"], \"source\": [\"Adv. Ther.\"], \"year\": [\"2020\"], \"volume\": [\"3\"], \"fpage\": [\"2000171\"], \"pub-id\": [\"10.1002/adtp.202000171\"]}, {\"label\": [\"38.\"], \"surname\": [\"Qiu\", \"Huo\", \"Xia\"], \"given-names\": [\"J\", \"D\", \"Y\"], \"article-title\": [\"Phase-change materials for controlled release and related applications\"], \"source\": [\"Adv. 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Copper, Silver, and Gold. in \"], \"italic\": [\"Standard Potentials in Aqueous Solution\"]}]"},"glossary":{"kind":"string","value":"{\n \"acronym\": [],\n \"definition\": []\n}"},"n_references":{"kind":"number","value":97,"string":"97"},"license":{"kind":"string","value":"CC BY"},"retracted":{"kind":"string","value":"no"},"last_updated":{"kind":"string","value":"2024-01-15 23:41:58"},"citation":{"kind":"string","value":"Nat Commun. 2024 Jan 13; 15:511"},"package_file":{"kind":"string","value":"oa_package/eb/ad/PMC10787768.tar.gz"}}},{"rowIdx":99,"cells":{"accession_id":{"kind":"string","value":"PMC10787769"},"pmid":{"kind":"string","value":"38218940"},"introduction":{"kind":"list like","value":[],"string":"[]","isConversational":false},"methods":{"kind":"list like","value":["<title>Materials and methods</title>","<title>Chemicals</title>","<p id=\"Par18\">All solvents for synthesis were purchased from VWR and used as received. Water (18.2 MΩ∙cm) was collected from an Arium (Sartorius) laboratory-grade water purification system. Gadolinium(III) nitrate hexahydrate (Gd(NO<sub>3</sub>)<sub>3</sub>·6H<sub>2</sub>O), sodium tungstate (Na<sub>2</sub>WO<sub>4</sub>) and potassium chloride (KCl) were purchased from VWR. Hexadecyl-trimethylammonium bromide (CTAB, 99 + %) was purchased from ACROS ORGANICS. 2-methoxy(polyethyleneoxy)propyltrimethoxysilane (90%, 6–9 PEG units) was purchased from ABCR Chemie. Triethylamine (TEA, > 99.5%) and hydrochloric acid (HCl, 32 wt. % in H<sub>2</sub>O) were purchased from Merck. Sulfo-cyanine3 (Cy3) NHS ester (95%) was purchased from Lumiprobe. For substrate preparation and printing, Toluene, chloroform, ethanol, dimethyl sulfoxide (DMSO), and glycerol were purchased from Sigma-Aldrich (Germany). Tamra amine, 5-isomer (NH<sub>2</sub>-Tamra) was bought from Lumiprobe GmbH (Germany). Thiol-PEG-Biotin, MW 2000 (SH-Biotin) and Fluorescein-PEG-Thiol (SH-FITC) were obtained from Nanocs Company (USA). Biotin-dPEG<sub>7</sub>-NH<sub>2</sub> (NH<sub>2</sub>-biotin), streptavidin Cyanine-3 (SA-Cy3), 3-methacryloxypropyltrimethoxysilane (MPS-silane), bovine serum albumin (BSA), and phosphate-buffered saline (PBS) were obtained from Sigma-Aldrich (Germany). A 10X PBS solution contains NaCl: 1.37 M, KCl: 27 mM, Na<sub>2</sub>HPO4: 100 mM, KH<sub>2</sub>PO<sub>4</sub>: 18 mM.</p>","<title>POM synthesis</title>","<p id=\"Par19\">All reactants and solvents were of commercially available grade and used without any further purification and all reactions were carried out under aerobic conditions. Synthesis of K<sub>12.5</sub>Na<sub>1.5</sub>[NaP<sub>5</sub>W<sub>30</sub>O<sub>110</sub>]·15H<sub>2</sub>O abbreviated as {NaP<sub>5</sub>W<sub>30</sub>}-POM: This POM ligand was synthesized according to published procedure<sup>##UREF##7##12##</sup>. Initially, 33 g (100.1 mmol) of sodium tungstate was dissolved in 30 mL of water in a beaker. Subsequently, 26.5 mL of 85% phosphoric acid was added with stirring. Once a clear solution was obtained, it was transferred into autoclaves and prepared for solvothermal synthesis. The autoclaves, containing the reaction solution, were then heated in the oven at 120 °C overnight. After cooling, 15 mL of water were added, and 10 g of KCl were introduced to the solution. A white-yellow precipitate formed, which was filtered off and washed with a 2 M potassium acetate solution and methanol. The resulting white-yellow solid was subjected to two rounds of recrystallization in water to eliminate the {P<sub>2</sub>W<sub>18</sub>}-POM side-product. K<sub>12</sub>[GdP<sub>5</sub>W<sub>30</sub>O<sub>110</sub>]·15H<sub>2</sub>O (abbreviated to {GdP<sub>5</sub>W<sub>30</sub>} in the main text) was prepared following a previously described method with slight modification<sup>##UREF##7##12##</sup>. Briefly, 1 g (0,12 mmol) of the {NaP<sub>5</sub>W<sub>30</sub>}-POM ligand was dissolved in 12 mL H<sub>2</sub>O and heated to 65 °C while stirring. In the process, the POM dissolved completely. A solution of 3 mL H<sub>2</sub>O and 2 equation (0,24 mmol) Gd(NO<sub>3</sub>)<sub>3</sub>·6H<sub>2</sub>O was prepared. This solution was added in small portions to the POM solution with stirring. A suspension formed which was placed into the autoclave. This was heated in the oven at 180°C overnight. After the solution had cooled to room temperature, the product was isolated by the addition of 4 g of solid KCl. The precipitate formed was isolated from the mother liquor by filtration and dried in air. On drying, the product was characterized by IR spectroscopy (Fig. ##SUPPL##0##S7##).</p>","<title>Preparation of MSPs with amine-groups functionalized pore walls (MSPs<sup>+</sup>)</title>","<p id=\"Par20\">Pristine MSPs were prepared according to a previously described procedure<sup>##UREF##27##51##</sup>. Briefly, to a stirring dispersion of CTAB (100 mg, 0.27 mmol) in an H<sub>2</sub>O/EtOH mixture (60/30 v/v, 90 mL), containing NH<sub>3</sub> (28 wt%, 710 µL), TEOS (250 µL, 1.13 mmol) was added. The resulting reaction mixture was stirred for 16 h at room temperature. Subsequently, 1/3 of the particle dispersion was taken and used for further functionalization steps. To this dispersion of MSPs (30 mL), 2-methoxy(polyethyleneoxy)propyltrimethoxysilane (183.0 µL; 0.38 mmol) was added and the reaction mixture was stirred for 4 h at 60 °C. Subsequently, the particles were recovered by centrifugation (20 min, 3500 rpm) and washed with EtOH (20 mL, 2x) by cycle sonication and subsequent centrifugation.</p>","<p id=\"Par21\">The surfactant was removed from the pores of the MSPs by extraction in EtOH. For this purpose, the particles were dispersed in EtOH (50 mL), diluted HCl (50 µL; 250 µL HCl diluted in 2 mL EtOH) was added, and the dispersion was refluxed for 12 h. The particles were collected by centrifugation and washed by sonication and centrifugation in EtOH (50 mL, 2×). The collected precipitate was refluxed again in EtOH as described above, but without HCl and for a time of 4 h. Subsequently, the particles were washed with EtOH (50 mL, 2×).</p>","<p id=\"Par22\">To functionalize the surfactant-free pore walls of the particles, the surfactant-extracted MSP were dispersed in EtOH (10 mL) and (3-aminopropyl)trimethoxysilane (66.0 µL, 0.38 mmol) and triethylamine (66 µL, 0.47 mmol) were added. The resulting reaction mixture was stirred at room temperature for 12 h. The particles were then collected by centrifugation and washed by sonication and centrifugation with EtOH (20 mL, 2×) and finally air dried.</p>","<title>POM loading of MSPs<sup>+</sup> (MSPs-POM)</title>","<p id=\"Par23\">To determine the optimal loading process in which no POM crystals are visible on the surface of the particles or next to the nanoparticles, we developed an optimized loading and washing process. To a dispersion of MSPs<sup>+</sup> (10 mg) in water (1 mL) POM (40 mg) was added and the resulting mixture was briefly sonicated and stirred at room temperature for 12h. The particles were then collected by centrifugation and subsequently washed with EtOH (1×) and finally with H<sub>2</sub>O (5×).</p>","<title>MSPs-POM stability tests</title>","<p id=\"Par24\">In a glass vial (2 mL) with a magnetic stirrer, MSPs-POM was dispersed in 10X PBS buffer (pH = 7.5) by brief sonication (c<sub>particles</sub> = 0.01 mg·mL<sup>−1</sup>). The resulting particle dispersion was sealed and stirred at 37 °C for 5 days. Over time, aliquots were taken and used to measure the DLS and Z-potential of the sample.</p>","<title>Dynamic light scattering (DLS)</title>","<p id=\"Par25\">The measurements were performed using a Malvern ZetaSizer Nano instrument. The intensity of the scattered light was measured at a fixed angle (173°). The wavelength of the laser light used for the light scattering experiments was 633 nm. Data analysis was performed according to standard procedures using the Malvern software (v3.30, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.malvernpanalytical.com/\">https://www.malvernpanalytical.com/</ext-link>). Briefly, the decay rates were determined by the following relationship, where η is the viscosity of the medium, D<sub>h</sub> is the hydrodynamic diameter, and θ is the scattering angle.</p>","<p id=\"Par26\">The method of cumulants was used to fit the autocorrelation function, which in turn allows the determination of the diffusion coefficient (d), from which the hydrodynamic diameter (D<sub>h</sub>) of the aggregates is calculated using the Stokes–Einstein equation (see below), where k<sub>b</sub> is the Boltzmann constant, T is the temperature, and η is the viscosity of the medium.</p>","<title>Z-potential measurements</title>","<p id=\"Par27\">The measurements were performed using the Malvern ZetaSizer Nano instrument. A viscosity of μ = 0.8984 cP and a dielectric constant of ε = 79 and a refractive index of 1.33304 were used. Analysis of data was performed utilizing Zetasizer Software 6.12 (Malvern Instruments GmbH, Germany) based on the model of Smoluchowski.</p>","<title>Surface amino functionalized and fluorescent MSPs-POM (H<sub>2</sub>N-MSPs-POM)</title>","<p id=\"Par28\">First, the Sulfo-Cy3 alkoxysilane used for fluorescent labeling of the particles is prepared. For this purpose, APTMS (8 µmol, from a 100 µM stock solution in dry DMSO) is added to a solution of Sulfo-Cy3-NHS ester (0.6 mg, 0.8 µmol) in dry DMSO (50 µL) in a glass vial, mixed briefly, and allowed to react for 30 min at room temperature. A dispersion of the MSPs-POM (10 mg) in toluene (500 µL) was also prepared. To this particle dispersion, the crude SulfoCy3-silane mixture and APTMS (10 µL) were added, and the reaction mixture was stirred for 12 h at 60 °C. The particles were then collected by centrifugation and washed with EtOH until no sulfo-Cy3 was detected in the supernatant of the wash solutions. The particles were then dried under reduced pressure.</p>","<title>Fourier-transform infrared (FT-IR) spectroscopy</title>","<p id=\"Par29\">FT-IR spectroscopy was employed to analyze the acquired compounds using the ATR module of a NicoletTM iS50 spectrometer, and the resulting data was visualized using Origin 2018b.</p>","<title>Preparation of substrates</title>","<p id=\"Par30\">Glass coverslips (diameter 15 mm, VWR, Germany) were cleaned by sonication in chloroform, ethanol, and water for 5 min each and then dried with nitrogen. Straight after, 5 min of plasma treatment (10 sccm O<sub>2</sub>, 0.2 mbar, and 100 W, ATTO plasma system, Diener electronics, Germany) was done under oxygen. Subsequently, the freshly hydroxylated substrates were immersed in a freshly prepared MPS-silane solution in toluene (1%, v/v) overnight at room temperature, and then the substrates were washed with toluene, ethanol, and water, and then dried under a nitrogen stream. Finally, the MPS-modified glass samples were stored in a desiccator.</p>","<title>Ink solutions preparation</title>","<p id=\"Par31\">Ink solutions for click reaction were prepared by mixing thiol-containing or amine-containing compounds in a mixture of DMSO/TEA (10:3, v/v). To avoid fast evaporation of the ink solvent, an amount of 30% (v/v) of glycerol was added to the ink solutions. The final concentration of the ink solutions was 2 mg/mL.</p>","<title>Microarray patterning</title>","<p id=\"Par32\">For µCS, the patterns were created on an NLP 2000 instrument (Nanoink, USA), which was equipped with a microchannel cantilever (SPT-S-C30S, Bioforce, Nanosciences, USA). Before loading inks (0.2 μL), the tips were plasma cleaned by oxygen (0.2 mbar, 100 W, 20 sccm O<sub>2</sub>, 2 min) to promote ink transfer, and then the inks were pushed into the microchannel by blowing with a nitrogen stream. All patterning processes (10 × 10 spot array with a pitch of 50 µm) were done at a humidity of 40% and dwell time of 0.5 s. After printing, the samples were heated at 37°C for 30 min before being allowed to rest overnight at room temperature (RT) to complete the click reaction, and then washed with water to remove the excess ink. The biotin-bearing microarrays for the comparison of binding efficiency of the thiol-ene and amine-ene coupling routes were incubated as follows. First, the samples were blocked against unspecific protein binding by incubation with 10% BSA in PBS for 30 min. Then washed by pipetting on and off 30 μL of PBS three times and subsequently incubated with 100 μL of 1 mg mL<sup>–1</sup> SA-Cy3 in PBS (1:100) at 37 °C for 30 min in a dark environment. Finally, samples were rinsed 3 times with PBS and blown dry under a stream of nitrogen before evaluation in fluorescence microscopy. For capillary spotting, a custom-made setup was used as previously reported for liquid metal deposition<sup>##UREF##38##66##</sup>, but without connection of a microfluidic pump. In the present study, the capillary tips were simply immersed in a reservoir of the MSPs-POM solution, enabling the loading of ink via capillary forces. Microarrays with MSPs-POM (5 × 5 spot array with 300 µm pitch) were obtained with a glass capillary tip of approx. 100 µm aperture in an NLP 2000 system (Nanoink, USA) under 40% relative humidity with a dwell time for each spot of 1s. The samples were then incubated at 37°C for 3 h and left at RT overnight to complete the click reaction. Finally, excess ink was removed by washing with water.</p>","<title>Physico-chemical characterization of substrates</title>","<p id=\"Par33\">The static WCA was measured on an OCA-20 contact angle analyzer (DataPhysics Instruments GmbH, Germany) at room temperature. Concisely, a 3 μL water drop was dispensed on a sample surface, and the measurements were repeated three times for each sample to obtain the means and standard deviations. The roughness and topography of the sample surface was evaluated on an AFM (Dimension Icon, Bruker, Germany), at room temperature in the air in tapping mode (40 N m<sup>-1</sup>, 325 kHz, HQ:NSC15/Al BS, MicroMasch, Germany). Three random positions were scanned for each sample (in 5 × 5 μm), and the original roughness Ra was extracted by the onboard software of the instrument. Apart from this, the analysis of chemical compositions of the surface in each step was identified by X-ray photoelectron spectroscopy (XPS) using a Thermo Scientific K-Alpha system (XPS, Thermo Fisher Scientific, East Grinstead, UK) with a base pressure of about 2 × 10<sup>–9</sup> mbar. Excitation was done using monochromatic Al-Ka-X-rays. The energy calibration of the system was done according to ISO 15472:2001 using copper, silver and gold reference samples. The transmission function was determined using the build-in thermo standard method on a silver reference sample. Quantification of the measurement results was done using modified scofield sensitivity factors. A 400 µm X-ray spot was used for the analysis. On non-conducting samples, a flood gun was used for compensation of charging. XPS data was processed using the CasaXPS software<sup>##UREF##39##67##</sup> (suite version 2.3.25). The energy scale of the spectra was set to 285 eV based on the C–C/C–H part of the C1s signal. A Shirley background was used for the evaluation of the high-resolution spectra.</p>","<title>Optical imaging</title>","<p id=\"Par34\">The optical images were captured on a Nikon Eclipse 80i upright fluorescence microscope (Nikon, Germany) equipped with an Intensilight illumination (Nikon, Germany), a Nikon DS Qi2 camera, and FITC and Tamra filters (Nikon Y-2E/C). The microscope collected the fluorescence intensity data with the built-in NIS-element software (Nikon, Germany).</p>","<title>Electron microscopy</title>","<p id=\"Par35\">The SEM images were acquired with a Zeiss Ultra-Plus SEM at 3 kV and, 5kV. The EDX measurements were performed with a Zeiss Leo 1530 SEM operating at 20 kV. The EDX data were acquired with Oxford Instruments AZtec software using an Oxford X-Max<sup>N</sup> 50 detector.</p>","<title>Statistical analysis</title>","<p id=\"Par36\">All data shown in this work were described as means ± standard deviations. The values of fluorescence intensity were obtained by an on-board software (NIS Elements AR 5.02.01, Nikon) of the microscope. The original data of WCA and roughness were obtained by measuring 3 random points of each sample, and the means and standard deviations were computed on excel by STDEVA formula.</p>"],"string":"[\n \"<title>Materials and methods</title>\",\n \"<title>Chemicals</title>\",\n \"<p id=\\\"Par18\\\">All solvents for synthesis were purchased from VWR and used as received. Water (18.2 MΩ∙cm) was collected from an Arium (Sartorius) laboratory-grade water purification system. Gadolinium(III) nitrate hexahydrate (Gd(NO<sub>3</sub>)<sub>3</sub>·6H<sub>2</sub>O), sodium tungstate (Na<sub>2</sub>WO<sub>4</sub>) and potassium chloride (KCl) were purchased from VWR. Hexadecyl-trimethylammonium bromide (CTAB, 99 + %) was purchased from ACROS ORGANICS. 2-methoxy(polyethyleneoxy)propyltrimethoxysilane (90%, 6–9 PEG units) was purchased from ABCR Chemie. Triethylamine (TEA, > 99.5%) and hydrochloric acid (HCl, 32 wt. % in H<sub>2</sub>O) were purchased from Merck. Sulfo-cyanine3 (Cy3) NHS ester (95%) was purchased from Lumiprobe. For substrate preparation and printing, Toluene, chloroform, ethanol, dimethyl sulfoxide (DMSO), and glycerol were purchased from Sigma-Aldrich (Germany). Tamra amine, 5-isomer (NH<sub>2</sub>-Tamra) was bought from Lumiprobe GmbH (Germany). Thiol-PEG-Biotin, MW 2000 (SH-Biotin) and Fluorescein-PEG-Thiol (SH-FITC) were obtained from Nanocs Company (USA). Biotin-dPEG<sub>7</sub>-NH<sub>2</sub> (NH<sub>2</sub>-biotin), streptavidin Cyanine-3 (SA-Cy3), 3-methacryloxypropyltrimethoxysilane (MPS-silane), bovine serum albumin (BSA), and phosphate-buffered saline (PBS) were obtained from Sigma-Aldrich (Germany). A 10X PBS solution contains NaCl: 1.37 M, KCl: 27 mM, Na<sub>2</sub>HPO4: 100 mM, KH<sub>2</sub>PO<sub>4</sub>: 18 mM.</p>\",\n \"<title>POM synthesis</title>\",\n \"<p id=\\\"Par19\\\">All reactants and solvents were of commercially available grade and used without any further purification and all reactions were carried out under aerobic conditions. Synthesis of K<sub>12.5</sub>Na<sub>1.5</sub>[NaP<sub>5</sub>W<sub>30</sub>O<sub>110</sub>]·15H<sub>2</sub>O abbreviated as {NaP<sub>5</sub>W<sub>30</sub>}-POM: This POM ligand was synthesized according to published procedure<sup>##UREF##7##12##</sup>. Initially, 33 g (100.1 mmol) of sodium tungstate was dissolved in 30 mL of water in a beaker. Subsequently, 26.5 mL of 85% phosphoric acid was added with stirring. Once a clear solution was obtained, it was transferred into autoclaves and prepared for solvothermal synthesis. The autoclaves, containing the reaction solution, were then heated in the oven at 120 °C overnight. After cooling, 15 mL of water were added, and 10 g of KCl were introduced to the solution. A white-yellow precipitate formed, which was filtered off and washed with a 2 M potassium acetate solution and methanol. The resulting white-yellow solid was subjected to two rounds of recrystallization in water to eliminate the {P<sub>2</sub>W<sub>18</sub>}-POM side-product. K<sub>12</sub>[GdP<sub>5</sub>W<sub>30</sub>O<sub>110</sub>]·15H<sub>2</sub>O (abbreviated to {GdP<sub>5</sub>W<sub>30</sub>} in the main text) was prepared following a previously described method with slight modification<sup>##UREF##7##12##</sup>. Briefly, 1 g (0,12 mmol) of the {NaP<sub>5</sub>W<sub>30</sub>}-POM ligand was dissolved in 12 mL H<sub>2</sub>O and heated to 65 °C while stirring. In the process, the POM dissolved completely. A solution of 3 mL H<sub>2</sub>O and 2 equation (0,24 mmol) Gd(NO<sub>3</sub>)<sub>3</sub>·6H<sub>2</sub>O was prepared. This solution was added in small portions to the POM solution with stirring. A suspension formed which was placed into the autoclave. This was heated in the oven at 180°C overnight. After the solution had cooled to room temperature, the product was isolated by the addition of 4 g of solid KCl. The precipitate formed was isolated from the mother liquor by filtration and dried in air. On drying, the product was characterized by IR spectroscopy (Fig. ##SUPPL##0##S7##).</p>\",\n \"<title>Preparation of MSPs with amine-groups functionalized pore walls (MSPs<sup>+</sup>)</title>\",\n \"<p id=\\\"Par20\\\">Pristine MSPs were prepared according to a previously described procedure<sup>##UREF##27##51##</sup>. Briefly, to a stirring dispersion of CTAB (100 mg, 0.27 mmol) in an H<sub>2</sub>O/EtOH mixture (60/30 v/v, 90 mL), containing NH<sub>3</sub> (28 wt%, 710 µL), TEOS (250 µL, 1.13 mmol) was added. The resulting reaction mixture was stirred for 16 h at room temperature. Subsequently, 1/3 of the particle dispersion was taken and used for further functionalization steps. To this dispersion of MSPs (30 mL), 2-methoxy(polyethyleneoxy)propyltrimethoxysilane (183.0 µL; 0.38 mmol) was added and the reaction mixture was stirred for 4 h at 60 °C. Subsequently, the particles were recovered by centrifugation (20 min, 3500 rpm) and washed with EtOH (20 mL, 2x) by cycle sonication and subsequent centrifugation.</p>\",\n \"<p id=\\\"Par21\\\">The surfactant was removed from the pores of the MSPs by extraction in EtOH. For this purpose, the particles were dispersed in EtOH (50 mL), diluted HCl (50 µL; 250 µL HCl diluted in 2 mL EtOH) was added, and the dispersion was refluxed for 12 h. The particles were collected by centrifugation and washed by sonication and centrifugation in EtOH (50 mL, 2×). The collected precipitate was refluxed again in EtOH as described above, but without HCl and for a time of 4 h. Subsequently, the particles were washed with EtOH (50 mL, 2×).</p>\",\n \"<p id=\\\"Par22\\\">To functionalize the surfactant-free pore walls of the particles, the surfactant-extracted MSP were dispersed in EtOH (10 mL) and (3-aminopropyl)trimethoxysilane (66.0 µL, 0.38 mmol) and triethylamine (66 µL, 0.47 mmol) were added. The resulting reaction mixture was stirred at room temperature for 12 h. The particles were then collected by centrifugation and washed by sonication and centrifugation with EtOH (20 mL, 2×) and finally air dried.</p>\",\n \"<title>POM loading of MSPs<sup>+</sup> (MSPs-POM)</title>\",\n \"<p id=\\\"Par23\\\">To determine the optimal loading process in which no POM crystals are visible on the surface of the particles or next to the nanoparticles, we developed an optimized loading and washing process. To a dispersion of MSPs<sup>+</sup> (10 mg) in water (1 mL) POM (40 mg) was added and the resulting mixture was briefly sonicated and stirred at room temperature for 12h. The particles were then collected by centrifugation and subsequently washed with EtOH (1×) and finally with H<sub>2</sub>O (5×).</p>\",\n \"<title>MSPs-POM stability tests</title>\",\n \"<p id=\\\"Par24\\\">In a glass vial (2 mL) with a magnetic stirrer, MSPs-POM was dispersed in 10X PBS buffer (pH = 7.5) by brief sonication (c<sub>particles</sub> = 0.01 mg·mL<sup>−1</sup>). The resulting particle dispersion was sealed and stirred at 37 °C for 5 days. Over time, aliquots were taken and used to measure the DLS and Z-potential of the sample.</p>\",\n \"<title>Dynamic light scattering (DLS)</title>\",\n \"<p id=\\\"Par25\\\">The measurements were performed using a Malvern ZetaSizer Nano instrument. The intensity of the scattered light was measured at a fixed angle (173°). The wavelength of the laser light used for the light scattering experiments was 633 nm. Data analysis was performed according to standard procedures using the Malvern software (v3.30, <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"https://www.malvernpanalytical.com/\\\">https://www.malvernpanalytical.com/</ext-link>). Briefly, the decay rates were determined by the following relationship, where η is the viscosity of the medium, D<sub>h</sub> is the hydrodynamic diameter, and θ is the scattering angle.</p>\",\n \"<p id=\\\"Par26\\\">The method of cumulants was used to fit the autocorrelation function, which in turn allows the determination of the diffusion coefficient (d), from which the hydrodynamic diameter (D<sub>h</sub>) of the aggregates is calculated using the Stokes–Einstein equation (see below), where k<sub>b</sub> is the Boltzmann constant, T is the temperature, and η is the viscosity of the medium.</p>\",\n \"<title>Z-potential measurements</title>\",\n \"<p id=\\\"Par27\\\">The measurements were performed using the Malvern ZetaSizer Nano instrument. A viscosity of μ = 0.8984 cP and a dielectric constant of ε = 79 and a refractive index of 1.33304 were used. Analysis of data was performed utilizing Zetasizer Software 6.12 (Malvern Instruments GmbH, Germany) based on the model of Smoluchowski.</p>\",\n \"<title>Surface amino functionalized and fluorescent MSPs-POM (H<sub>2</sub>N-MSPs-POM)</title>\",\n \"<p id=\\\"Par28\\\">First, the Sulfo-Cy3 alkoxysilane used for fluorescent labeling of the particles is prepared. For this purpose, APTMS (8 µmol, from a 100 µM stock solution in dry DMSO) is added to a solution of Sulfo-Cy3-NHS ester (0.6 mg, 0.8 µmol) in dry DMSO (50 µL) in a glass vial, mixed briefly, and allowed to react for 30 min at room temperature. A dispersion of the MSPs-POM (10 mg) in toluene (500 µL) was also prepared. To this particle dispersion, the crude SulfoCy3-silane mixture and APTMS (10 µL) were added, and the reaction mixture was stirred for 12 h at 60 °C. The particles were then collected by centrifugation and washed with EtOH until no sulfo-Cy3 was detected in the supernatant of the wash solutions. The particles were then dried under reduced pressure.</p>\",\n \"<title>Fourier-transform infrared (FT-IR) spectroscopy</title>\",\n \"<p id=\\\"Par29\\\">FT-IR spectroscopy was employed to analyze the acquired compounds using the ATR module of a NicoletTM iS50 spectrometer, and the resulting data was visualized using Origin 2018b.</p>\",\n \"<title>Preparation of substrates</title>\",\n \"<p id=\\\"Par30\\\">Glass coverslips (diameter 15 mm, VWR, Germany) were cleaned by sonication in chloroform, ethanol, and water for 5 min each and then dried with nitrogen. Straight after, 5 min of plasma treatment (10 sccm O<sub>2</sub>, 0.2 mbar, and 100 W, ATTO plasma system, Diener electronics, Germany) was done under oxygen. Subsequently, the freshly hydroxylated substrates were immersed in a freshly prepared MPS-silane solution in toluene (1%, v/v) overnight at room temperature, and then the substrates were washed with toluene, ethanol, and water, and then dried under a nitrogen stream. Finally, the MPS-modified glass samples were stored in a desiccator.</p>\",\n \"<title>Ink solutions preparation</title>\",\n \"<p id=\\\"Par31\\\">Ink solutions for click reaction were prepared by mixing thiol-containing or amine-containing compounds in a mixture of DMSO/TEA (10:3, v/v). To avoid fast evaporation of the ink solvent, an amount of 30% (v/v) of glycerol was added to the ink solutions. The final concentration of the ink solutions was 2 mg/mL.</p>\",\n \"<title>Microarray patterning</title>\",\n \"<p id=\\\"Par32\\\">For µCS, the patterns were created on an NLP 2000 instrument (Nanoink, USA), which was equipped with a microchannel cantilever (SPT-S-C30S, Bioforce, Nanosciences, USA). Before loading inks (0.2 μL), the tips were plasma cleaned by oxygen (0.2 mbar, 100 W, 20 sccm O<sub>2</sub>, 2 min) to promote ink transfer, and then the inks were pushed into the microchannel by blowing with a nitrogen stream. All patterning processes (10 × 10 spot array with a pitch of 50 µm) were done at a humidity of 40% and dwell time of 0.5 s. After printing, the samples were heated at 37°C for 30 min before being allowed to rest overnight at room temperature (RT) to complete the click reaction, and then washed with water to remove the excess ink. The biotin-bearing microarrays for the comparison of binding efficiency of the thiol-ene and amine-ene coupling routes were incubated as follows. First, the samples were blocked against unspecific protein binding by incubation with 10% BSA in PBS for 30 min. Then washed by pipetting on and off 30 μL of PBS three times and subsequently incubated with 100 μL of 1 mg mL<sup>–1</sup> SA-Cy3 in PBS (1:100) at 37 °C for 30 min in a dark environment. Finally, samples were rinsed 3 times with PBS and blown dry under a stream of nitrogen before evaluation in fluorescence microscopy. For capillary spotting, a custom-made setup was used as previously reported for liquid metal deposition<sup>##UREF##38##66##</sup>, but without connection of a microfluidic pump. In the present study, the capillary tips were simply immersed in a reservoir of the MSPs-POM solution, enabling the loading of ink via capillary forces. Microarrays with MSPs-POM (5 × 5 spot array with 300 µm pitch) were obtained with a glass capillary tip of approx. 100 µm aperture in an NLP 2000 system (Nanoink, USA) under 40% relative humidity with a dwell time for each spot of 1s. The samples were then incubated at 37°C for 3 h and left at RT overnight to complete the click reaction. Finally, excess ink was removed by washing with water.</p>\",\n \"<title>Physico-chemical characterization of substrates</title>\",\n \"<p id=\\\"Par33\\\">The static WCA was measured on an OCA-20 contact angle analyzer (DataPhysics Instruments GmbH, Germany) at room temperature. Concisely, a 3 μL water drop was dispensed on a sample surface, and the measurements were repeated three times for each sample to obtain the means and standard deviations. The roughness and topography of the sample surface was evaluated on an AFM (Dimension Icon, Bruker, Germany), at room temperature in the air in tapping mode (40 N m<sup>-1</sup>, 325 kHz, HQ:NSC15/Al BS, MicroMasch, Germany). Three random positions were scanned for each sample (in 5 × 5 μm), and the original roughness Ra was extracted by the onboard software of the instrument. Apart from this, the analysis of chemical compositions of the surface in each step was identified by X-ray photoelectron spectroscopy (XPS) using a Thermo Scientific K-Alpha system (XPS, Thermo Fisher Scientific, East Grinstead, UK) with a base pressure of about 2 × 10<sup>–9</sup> mbar. Excitation was done using monochromatic Al-Ka-X-rays. The energy calibration of the system was done according to ISO 15472:2001 using copper, silver and gold reference samples. The transmission function was determined using the build-in thermo standard method on a silver reference sample. Quantification of the measurement results was done using modified scofield sensitivity factors. A 400 µm X-ray spot was used for the analysis. On non-conducting samples, a flood gun was used for compensation of charging. XPS data was processed using the CasaXPS software<sup>##UREF##39##67##</sup> (suite version 2.3.25). The energy scale of the spectra was set to 285 eV based on the C–C/C–H part of the C1s signal. A Shirley background was used for the evaluation of the high-resolution spectra.</p>\",\n \"<title>Optical imaging</title>\",\n \"<p id=\\\"Par34\\\">The optical images were captured on a Nikon Eclipse 80i upright fluorescence microscope (Nikon, Germany) equipped with an Intensilight illumination (Nikon, Germany), a Nikon DS Qi2 camera, and FITC and Tamra filters (Nikon Y-2E/C). The microscope collected the fluorescence intensity data with the built-in NIS-element software (Nikon, Germany).</p>\",\n \"<title>Electron microscopy</title>\",\n \"<p id=\\\"Par35\\\">The SEM images were acquired with a Zeiss Ultra-Plus SEM at 3 kV and, 5kV. The EDX measurements were performed with a Zeiss Leo 1530 SEM operating at 20 kV. The EDX data were acquired with Oxford Instruments AZtec software using an Oxford X-Max<sup>N</sup> 50 detector.</p>\",\n \"<title>Statistical analysis</title>\",\n \"<p id=\\\"Par36\\\">All data shown in this work were described as means ± standard deviations. The values of fluorescence intensity were obtained by an on-board software (NIS Elements AR 5.02.01, Nikon) of the microscope. The original data of WCA and roughness were obtained by measuring 3 random points of each sample, and the means and standard deviations were computed on excel by STDEVA formula.</p>\"\n]","isConversational":false},"results":{"kind":"list like","value":["<title>Results and discussion</title>","<title>Immobilization strategy</title>","<p id=\"Par6\">To imprint POMs on surfaces (Fig. ##FIG##1##2##), we aimed to use MSPs whose pore wall is functionalized with amino groups (MSPs<sup>+</sup>), that are protonated and thus positively charged in an aqueous environment. Due to the size of the mesopores (≈ 3 nm) and the polyanionic nature of the POM, these cargo molecules will be retained in the positively charged pores<sup>##REF##26161980##48##</sup>. Immobilization of the POM-loaded MSP (MSPs-POM) will then be enabled by functionalization of the particle surface with reactive amino groups, which can engage in amine-ene Michael additions to alkene-functionalized surfaces. The advantage of MSPs, therefore, is the possibility to circumvent the use of monofunctionalized POMs<sup>##UREF##25##49##,##UREF##26##50##</sup>, whose synthesis requires considerable effort, while providing a generalizable strategy for a wide range of POMs to be immobilized on solid substrates.</p>","<title>Synthesis of POM-loaded MSPs</title>","<p id=\"Par7\">To prepare POM-loaded MSPs, first MSPs functionalized with amino groups on the pore wall surfaces (MSPs<sup>+</sup>) were prepared. To this end, pristine spherical nanometer-sized MSPs were synthesized following a previously reported modified Stöber synthesis protocol using the surfactant cetyltrimethylammonium bromide (CTAB) as the structure-directing agent<sup>##UREF##27##51##</sup>. This synthesis method was chosen because the resulting particles are in the nanometer range, have a spherical shape, have a high specific surface area (1243 m<sup>2</sup> g<sup>-1</sup>), and are mesoporous (pore diameter ≈ 3 nm)—all properties required for subsequent effective loading of POM. Scanning electron microscopy (SEM) analysis revealed that the pristine MSPs were spherical with an average diameter of (283 ± 25) nm (Fig. ##SUPPL##0##S1##), which is in good agreement with previously published results.</p>","<p id=\"Par8\">The pristine MSPs were then passivated on the outer surfaces with a short polyethylene glycol chain (6–9 units) alkoxysilane<sup>##REF##34009950##52##</sup> and then CTAB was extracted from the mesopores of the particles. Next, the CTAB-free pore walls were covalently grafted with amino groups using (3-aminopropyl)trimethoxysilane (APTMS), resulting in MSPs whose pore walls are functionalized with amino groups (MSPs<sup>+</sup>). The surface passivation and amino functionalization (Fig. ##SUPPL##0##S2##A) were investigated by zeta potential (Z-pot) analysis (Fig. ##SUPPL##0##S2##B), which revealed a nearly neutral surface charge (0.4 ± 1.2) mV of MSPs<sup>+</sup>. This reduced negative Z-pot value (for non-functionalized and surfactant-extracted MSP, the Z-pot is around − 30 to − 40 mV) can be explained by the surface passivation that took place, which reduces the amount of deprotonated and negatively charged hydroxyl groups on the surface of the particles, as well as by charge balancing due to the positive R-NH<sub>3</sub><sup>+</sup> groups present on the pore entrances.</p>","<p id=\"Par9\">Next, we optimized the procedure for loading MSPs<sup>+</sup> with POM (MSPs-POM) to eliminate the presence of non-loaded POM crystals. In suboptimal loading procedures, where not all POM was loaded within the pores of MSPs<sup>+</sup>, POM crystals were observed near or on the surface of MSPs, as demonstrated by SEM analysis (Fig. ##SUPPL##0##S3##A). By optimizing the mass ratio between POM and MSPs<sup>+</sup> used for the loading and adding several washing steps with EtOH and water, we were able to obtain MSPs-POM (Figs. ##FIG##2##3##A and ##SUPPL##0##S3##B), which showed no crystalline POM precipitates (MSPs-POM). Z-pot measurements on MSPs-POM indicated more negative values compared to MSPs<sup>+</sup> particles, suggesting successful adsorption of POMs within the pores of the particles (Fig. ##SUPPL##0##S2##B). Attenuated total reflectance Fourier infrared spectroscopy (ATR-FTIR) recorded on MSPs-POM (Fig. ##FIG##2##3##B) shows the characteristic POM-related transmission bands supporting the presence of POM in the mesopores of the silica nanoparticles (gray background). The FTIR spectrum of MSPs-POM exhibits vibrational bands within the 1200–400 cm<sup>-1</sup> region, which closely resemble the characteristic bands found in the pristine POM. POMs feature unique metal–oxygen vibrational modes within the fingerprint region. The distinctive peaks at 1134 and 1054 cm<sup>-1</sup> are attributed to the P–O vibrations of {GdP<sub>5</sub>W<sub>30</sub>} POM. The peak at 907 cm<sup>-1</sup> could be assigned to terminal <bold>ν</bold><sub>as</sub>(W══O<sub>t</sub>) vibration. The features around 709 cm<sup>-1</sup> could be attributed to the edge-sharing <bold>ν</bold><sub>as</sub>(W–O–W). All these bands are considered as pure vibrations of the POM skeleton. The peak at 1629 cm<sup>-1</sup> can be attributed to the δ(O–H) of the lattice water molecules. The broad transmission band at 3441 cm<sup>-1</sup> can be attributed to the presence of -OH groups on the outer surface of MSPs-POM, while the broad bands in the range between 3308 and 3010 cm<sup>-1</sup> can be attributed to the presence of R-NH<sub>2</sub>/R-NH<sub>3</sub><sup>+</sup> groups<sup>##UREF##28##53##,##REF##27145200##54##</sup>. The three relatively sharp transmission bands at 2917, 2840, and 1460 cm<sup>-1</sup> are representative of the C<sub>sp3</sub>-H stretching vibration of the organic functional groups of the MSPs-POM particles, <italic>i.e.</italic>, the PEG caps and the condensed APTMS groups<sup>##UREF##29##55##</sup>. Energy dispersive X-ray (EDX) analysis (Figs. ##FIG##2##3##C and ##SUPPL##0##S4##) also confirmed that the POM is colocalized with the particles.</p>","<p id=\"Par10\">To evaluate the stability of MSPs-POM under physiological conditions, their hydrodynamic diameter (<italic>D</italic><sub>h</sub>) was monitored over time in 10X phosphate salt buffer (PBS, pH 7.4 at 37 °C). Figure ##FIG##3##4## shows the DLS size distribution of the particles at time points 0, 1 day, and 5 days. The size distribution remains little affected over 5 d, indicating that both the spherical shape and size of the particles remain intact under physiological conditions. The stability of the particles is achieved by covalent surface passivation of the terminal silanol groups of the silica with PEG silane, which effectively prevents hydrolysis of the silica<sup>##UREF##30##56##,##UREF##31##57##</sup>. We also measured the Z-pot to determine whether the POMs could leak out of the mesopores over time, which would be indicated by a clear shift to more positive Z-pot values of the particles. However, over 5 days, the zeta potential of the particles dispersed in PBS showed no significant shift towards more positive Z-pot values (Fig. S5), which proves that the POMs stay adsorbed on the silica surface.</p>","<title>Preparation and characterization of substrate</title>","<p id=\"Par11\">The alkene-bearing reactive surfaces were prepared by silanization of 3-methacryloxypropyltrimethoxysilane (MPS-silane) on hydroxylated glass substrates, which can participate thiol-ene and thiol-amine click reactions (Fig. ##FIG##4##5##A)<sup>##UREF##32##58##,##UREF##33##59##</sup>. To achieve efficient silanization, the cleaned glass coverslips were treated with oxygen plasma to remove remaining contaminants and to endow a high density of hydroxyl groups on the glass surfaces. Then, the activated glass slips were immersed in an MPS-silane solution in toluene to obtain the MPS-modified substrates. The characterization of MPS-modified substrates was carried out using atomic force microscopy (AFM), water contact angle (WCA), and x-ray photoelectron spectroscopy (XPS). The results are shown in Fig. ##FIG##4##5##B–D.</p>","<p id=\"Par12\">The glass coverslips usually show a WCA at around 43° (Fig. ##SUPPL##0##S6##A), while just after plasma treatment, the value declines to around 0° (Fig. ##SUPPL##0##S6##B) due to the densely grafted hydroxyl groups. As shown in Fig. ##FIG##4##5##B, after silanization of the plasma-treated glass surface with MPS-silane the WCA increases to a higher value at around 64.3°, which is also an indication of a successful silanization process. The mean surface roughness (Ra) of silanized surfaces obtained by AFM is around 0.251 nm, which is consistent with former reports concerning self-assembled monolayers (SAMs)<sup>##UREF##34##60##,##REF##30350707##61##</sup>. AFM images of a glass coverslip before and after silanization are given in Figs. ##SUPPL##0##S6##C and ##FIG##4##5##C, respectively. To further confirm successful silanization, high-resolution XPS (HR-XPS) was implemented on MPS-modified and bare glasses for comparison, the corresponding spectra at the C 1s region are illustrated in Fig. ##FIG##4##5##D. The deconvoluted HR-XP spectra exhibit three peaks a 284.8 eV, 286.5 eV, and 288.9 eV, which are derived from (C–C, C–H), (C–O), and (O–C=O), respectively. Comparing the XP spectra of the bare glass and MPS-modified samples, the ratio of (C–O):(C–C, C–H) increases from 0.21 to 0.35, which indicates the formation of the MPS-modified SAM.</p>","<p id=\"Par13\">To validate the reactivity of the MPS-modified glass substrates and probe the possibility of creating microarrays via microchannel cantilever spotting (µCS) on these, small fluorescent molecules with either a thiol or amine moiety were chosen as test probes, namely NH<sub>2</sub>-Tetramethylrhodamine (NH<sub>2</sub>-Tamra) and SH-Fluorescein (SH-FITC) (Fig. ##FIG##4##5##A). µCS works by loading a microchannel cantilever with a µL amount of ink containing the desired molecules, and then bringing cantilever repeatedly into contact with the substrate in a scanning probe lithography (SPL) setup allowing for high positioning control, as well as controlled humidity and dwell time. Each spot in the resulting droplet microarrays can be regarded as a microreactor permitting click reactions between the alkene terminal groups and thiol or amine moieties to take place. After the desired incubation time, excess ink can simply be washed away from the surface. As shown in Fig. ##FIG##5##6##A, the NH<sub>2</sub>-Tamra firmly binds to the MPS-modified surface as the fluorescent pattern is strongly visible in the red channel. Similarly, the SH-FITC also works well with MPS-modified surfaces as shown in Fig. ##FIG##5##6##B. Therefore, we confirm that the amine and thiol moieties all bind well to the alkene functionalized surface.</p>","<p id=\"Par14\">As a final test of reactivity and to allow a direct comparison of binding efficiency between thiol-ene and amine-ene routes, two biotinylated compounds, NH<sub>2</sub>-biotin and SH-biotin, were printed. When incubated with Cy3 conjugated streptavidin (SA-Cy3), the printed microarrays can bind the SA-Cy3 via the strong affinity between biotin and avidin<sup>##REF##24057405##62##</sup>, and the binding density of the two microarrays can be directly compared over fluorescence intensity<sup>##UREF##34##60##,##UREF##35##63##,##UREF##36##64##</sup>. After incubation of bovine serum albumin (BSA) for blocking unspecific binding followed by incubation of SA-Cy3 (both in phosphate buffered saline (PBS)) on the biotinylated arrays, the biotin spots are easily readout on a fluorescent microscope as shown in Fig. ##FIG##5##6##C and D. The corresponding fluorescence intensity comparison is shown in Fig. ##FIG##5##6##E. The yield of the thiol-ene click route (9620.86 ± 1148.15) a.u. is about 21% higher than amine-ene route (7585.39 ± 1046.44) a.u. from the fluorescence, though both routes appear similar within the fluctuations, both routes deliver a comparable and stable binding to the surface.</p>","<title>Patterned immobilization of MSPs-POM</title>","<p id=\"Par15\">After having obtained amine-functionalized MSPs-POM (H<sub>2</sub>N-MSPs-POM, see Materials and methods) and the matching alkene-functionalized glass surface, capillary spotting was adopted to generate microarrays with MSPs-POM ink. The glass capillary tip has a larger aperture compared to µCS tips and can be tailored to different opening sizes, making it better suited for nanoparticle spotting. After spotting, the obtained microarrays were incubated at 37°C to complete the click-reaction and then washed with water to remove the excess ink. The results of the capillary spotting-based microarray are shown in Fig. ##FIG##6##7##.</p>","<p id=\"Par16\">Directly after spotting, the microarray droplets are visible in bright-field and (due to labeling of the MSPs-POM with Cy3) fluorescence microscopy images (Fig. ##FIG##6##7##A, B). After the click-reaction had taken place and washing, the microarray is not visible anymore in the bright-field, but the fluorescence images revealed the immobilization of MSPs-POM (Fig. ##FIG##6##7##C). Close-up of individual immobilized MSPs-POM particles were obtained with SEM (Fig. ##FIG##6##7##G) and AFM (Fig. ##FIG##6##7##H), further confirming the successful binding to the surface and integrity of the modified MSPs-POM.</p>"],"string":"[\n \"<title>Results and discussion</title>\",\n \"<title>Immobilization strategy</title>\",\n \"<p id=\\\"Par6\\\">To imprint POMs on surfaces (Fig. ##FIG##1##2##), we aimed to use MSPs whose pore wall is functionalized with amino groups (MSPs<sup>+</sup>), that are protonated and thus positively charged in an aqueous environment. Due to the size of the mesopores (≈ 3 nm) and the polyanionic nature of the POM, these cargo molecules will be retained in the positively charged pores<sup>##REF##26161980##48##</sup>. Immobilization of the POM-loaded MSP (MSPs-POM) will then be enabled by functionalization of the particle surface with reactive amino groups, which can engage in amine-ene Michael additions to alkene-functionalized surfaces. The advantage of MSPs, therefore, is the possibility to circumvent the use of monofunctionalized POMs<sup>##UREF##25##49##,##UREF##26##50##</sup>, whose synthesis requires considerable effort, while providing a generalizable strategy for a wide range of POMs to be immobilized on solid substrates.</p>\",\n \"<title>Synthesis of POM-loaded MSPs</title>\",\n \"<p id=\\\"Par7\\\">To prepare POM-loaded MSPs, first MSPs functionalized with amino groups on the pore wall surfaces (MSPs<sup>+</sup>) were prepared. To this end, pristine spherical nanometer-sized MSPs were synthesized following a previously reported modified Stöber synthesis protocol using the surfactant cetyltrimethylammonium bromide (CTAB) as the structure-directing agent<sup>##UREF##27##51##</sup>. This synthesis method was chosen because the resulting particles are in the nanometer range, have a spherical shape, have a high specific surface area (1243 m<sup>2</sup> g<sup>-1</sup>), and are mesoporous (pore diameter ≈ 3 nm)—all properties required for subsequent effective loading of POM. Scanning electron microscopy (SEM) analysis revealed that the pristine MSPs were spherical with an average diameter of (283 ± 25) nm (Fig. ##SUPPL##0##S1##), which is in good agreement with previously published results.</p>\",\n \"<p id=\\\"Par8\\\">The pristine MSPs were then passivated on the outer surfaces with a short polyethylene glycol chain (6–9 units) alkoxysilane<sup>##REF##34009950##52##</sup> and then CTAB was extracted from the mesopores of the particles. Next, the CTAB-free pore walls were covalently grafted with amino groups using (3-aminopropyl)trimethoxysilane (APTMS), resulting in MSPs whose pore walls are functionalized with amino groups (MSPs<sup>+</sup>). The surface passivation and amino functionalization (Fig. ##SUPPL##0##S2##A) were investigated by zeta potential (Z-pot) analysis (Fig. ##SUPPL##0##S2##B), which revealed a nearly neutral surface charge (0.4 ± 1.2) mV of MSPs<sup>+</sup>. This reduced negative Z-pot value (for non-functionalized and surfactant-extracted MSP, the Z-pot is around − 30 to − 40 mV) can be explained by the surface passivation that took place, which reduces the amount of deprotonated and negatively charged hydroxyl groups on the surface of the particles, as well as by charge balancing due to the positive R-NH<sub>3</sub><sup>+</sup> groups present on the pore entrances.</p>\",\n \"<p id=\\\"Par9\\\">Next, we optimized the procedure for loading MSPs<sup>+</sup> with POM (MSPs-POM) to eliminate the presence of non-loaded POM crystals. In suboptimal loading procedures, where not all POM was loaded within the pores of MSPs<sup>+</sup>, POM crystals were observed near or on the surface of MSPs, as demonstrated by SEM analysis (Fig. ##SUPPL##0##S3##A). By optimizing the mass ratio between POM and MSPs<sup>+</sup> used for the loading and adding several washing steps with EtOH and water, we were able to obtain MSPs-POM (Figs. ##FIG##2##3##A and ##SUPPL##0##S3##B), which showed no crystalline POM precipitates (MSPs-POM). Z-pot measurements on MSPs-POM indicated more negative values compared to MSPs<sup>+</sup> particles, suggesting successful adsorption of POMs within the pores of the particles (Fig. ##SUPPL##0##S2##B). Attenuated total reflectance Fourier infrared spectroscopy (ATR-FTIR) recorded on MSPs-POM (Fig. ##FIG##2##3##B) shows the characteristic POM-related transmission bands supporting the presence of POM in the mesopores of the silica nanoparticles (gray background). The FTIR spectrum of MSPs-POM exhibits vibrational bands within the 1200–400 cm<sup>-1</sup> region, which closely resemble the characteristic bands found in the pristine POM. POMs feature unique metal–oxygen vibrational modes within the fingerprint region. The distinctive peaks at 1134 and 1054 cm<sup>-1</sup> are attributed to the P–O vibrations of {GdP<sub>5</sub>W<sub>30</sub>} POM. The peak at 907 cm<sup>-1</sup> could be assigned to terminal <bold>ν</bold><sub>as</sub>(W══O<sub>t</sub>) vibration. The features around 709 cm<sup>-1</sup> could be attributed to the edge-sharing <bold>ν</bold><sub>as</sub>(W–O–W). All these bands are considered as pure vibrations of the POM skeleton. The peak at 1629 cm<sup>-1</sup> can be attributed to the δ(O–H) of the lattice water molecules. The broad transmission band at 3441 cm<sup>-1</sup> can be attributed to the presence of -OH groups on the outer surface of MSPs-POM, while the broad bands in the range between 3308 and 3010 cm<sup>-1</sup> can be attributed to the presence of R-NH<sub>2</sub>/R-NH<sub>3</sub><sup>+</sup> groups<sup>##UREF##28##53##,##REF##27145200##54##</sup>. The three relatively sharp transmission bands at 2917, 2840, and 1460 cm<sup>-1</sup> are representative of the C<sub>sp3</sub>-H stretching vibration of the organic functional groups of the MSPs-POM particles, <italic>i.e.</italic>, the PEG caps and the condensed APTMS groups<sup>##UREF##29##55##</sup>. Energy dispersive X-ray (EDX) analysis (Figs. ##FIG##2##3##C and ##SUPPL##0##S4##) also confirmed that the POM is colocalized with the particles.</p>\",\n \"<p id=\\\"Par10\\\">To evaluate the stability of MSPs-POM under physiological conditions, their hydrodynamic diameter (<italic>D</italic><sub>h</sub>) was monitored over time in 10X phosphate salt buffer (PBS, pH 7.4 at 37 °C). Figure ##FIG##3##4## shows the DLS size distribution of the particles at time points 0, 1 day, and 5 days. The size distribution remains little affected over 5 d, indicating that both the spherical shape and size of the particles remain intact under physiological conditions. The stability of the particles is achieved by covalent surface passivation of the terminal silanol groups of the silica with PEG silane, which effectively prevents hydrolysis of the silica<sup>##UREF##30##56##,##UREF##31##57##</sup>. We also measured the Z-pot to determine whether the POMs could leak out of the mesopores over time, which would be indicated by a clear shift to more positive Z-pot values of the particles. However, over 5 days, the zeta potential of the particles dispersed in PBS showed no significant shift towards more positive Z-pot values (Fig. S5), which proves that the POMs stay adsorbed on the silica surface.</p>\",\n \"<title>Preparation and characterization of substrate</title>\",\n \"<p id=\\\"Par11\\\">The alkene-bearing reactive surfaces were prepared by silanization of 3-methacryloxypropyltrimethoxysilane (MPS-silane) on hydroxylated glass substrates, which can participate thiol-ene and thiol-amine click reactions (Fig. ##FIG##4##5##A)<sup>##UREF##32##58##,##UREF##33##59##</sup>. To achieve efficient silanization, the cleaned glass coverslips were treated with oxygen plasma to remove remaining contaminants and to endow a high density of hydroxyl groups on the glass surfaces. Then, the activated glass slips were immersed in an MPS-silane solution in toluene to obtain the MPS-modified substrates. The characterization of MPS-modified substrates was carried out using atomic force microscopy (AFM), water contact angle (WCA), and x-ray photoelectron spectroscopy (XPS). The results are shown in Fig. ##FIG##4##5##B–D.</p>\",\n \"<p id=\\\"Par12\\\">The glass coverslips usually show a WCA at around 43° (Fig. ##SUPPL##0##S6##A), while just after plasma treatment, the value declines to around 0° (Fig. ##SUPPL##0##S6##B) due to the densely grafted hydroxyl groups. As shown in Fig. ##FIG##4##5##B, after silanization of the plasma-treated glass surface with MPS-silane the WCA increases to a higher value at around 64.3°, which is also an indication of a successful silanization process. The mean surface roughness (Ra) of silanized surfaces obtained by AFM is around 0.251 nm, which is consistent with former reports concerning self-assembled monolayers (SAMs)<sup>##UREF##34##60##,##REF##30350707##61##</sup>. AFM images of a glass coverslip before and after silanization are given in Figs. ##SUPPL##0##S6##C and ##FIG##4##5##C, respectively. To further confirm successful silanization, high-resolution XPS (HR-XPS) was implemented on MPS-modified and bare glasses for comparison, the corresponding spectra at the C 1s region are illustrated in Fig. ##FIG##4##5##D. The deconvoluted HR-XP spectra exhibit three peaks a 284.8 eV, 286.5 eV, and 288.9 eV, which are derived from (C–C, C–H), (C–O), and (O–C=O), respectively. Comparing the XP spectra of the bare glass and MPS-modified samples, the ratio of (C–O):(C–C, C–H) increases from 0.21 to 0.35, which indicates the formation of the MPS-modified SAM.</p>\",\n \"<p id=\\\"Par13\\\">To validate the reactivity of the MPS-modified glass substrates and probe the possibility of creating microarrays via microchannel cantilever spotting (µCS) on these, small fluorescent molecules with either a thiol or amine moiety were chosen as test probes, namely NH<sub>2</sub>-Tetramethylrhodamine (NH<sub>2</sub>-Tamra) and SH-Fluorescein (SH-FITC) (Fig. ##FIG##4##5##A). µCS works by loading a microchannel cantilever with a µL amount of ink containing the desired molecules, and then bringing cantilever repeatedly into contact with the substrate in a scanning probe lithography (SPL) setup allowing for high positioning control, as well as controlled humidity and dwell time. Each spot in the resulting droplet microarrays can be regarded as a microreactor permitting click reactions between the alkene terminal groups and thiol or amine moieties to take place. After the desired incubation time, excess ink can simply be washed away from the surface. As shown in Fig. ##FIG##5##6##A, the NH<sub>2</sub>-Tamra firmly binds to the MPS-modified surface as the fluorescent pattern is strongly visible in the red channel. Similarly, the SH-FITC also works well with MPS-modified surfaces as shown in Fig. ##FIG##5##6##B. Therefore, we confirm that the amine and thiol moieties all bind well to the alkene functionalized surface.</p>\",\n \"<p id=\\\"Par14\\\">As a final test of reactivity and to allow a direct comparison of binding efficiency between thiol-ene and amine-ene routes, two biotinylated compounds, NH<sub>2</sub>-biotin and SH-biotin, were printed. When incubated with Cy3 conjugated streptavidin (SA-Cy3), the printed microarrays can bind the SA-Cy3 via the strong affinity between biotin and avidin<sup>##REF##24057405##62##</sup>, and the binding density of the two microarrays can be directly compared over fluorescence intensity<sup>##UREF##34##60##,##UREF##35##63##,##UREF##36##64##</sup>. After incubation of bovine serum albumin (BSA) for blocking unspecific binding followed by incubation of SA-Cy3 (both in phosphate buffered saline (PBS)) on the biotinylated arrays, the biotin spots are easily readout on a fluorescent microscope as shown in Fig. ##FIG##5##6##C and D. The corresponding fluorescence intensity comparison is shown in Fig. ##FIG##5##6##E. The yield of the thiol-ene click route (9620.86 ± 1148.15) a.u. is about 21% higher than amine-ene route (7585.39 ± 1046.44) a.u. from the fluorescence, though both routes appear similar within the fluctuations, both routes deliver a comparable and stable binding to the surface.</p>\",\n \"<title>Patterned immobilization of MSPs-POM</title>\",\n \"<p id=\\\"Par15\\\">After having obtained amine-functionalized MSPs-POM (H<sub>2</sub>N-MSPs-POM, see Materials and methods) and the matching alkene-functionalized glass surface, capillary spotting was adopted to generate microarrays with MSPs-POM ink. The glass capillary tip has a larger aperture compared to µCS tips and can be tailored to different opening sizes, making it better suited for nanoparticle spotting. After spotting, the obtained microarrays were incubated at 37°C to complete the click-reaction and then washed with water to remove the excess ink. The results of the capillary spotting-based microarray are shown in Fig. ##FIG##6##7##.</p>\",\n \"<p id=\\\"Par16\\\">Directly after spotting, the microarray droplets are visible in bright-field and (due to labeling of the MSPs-POM with Cy3) fluorescence microscopy images (Fig. ##FIG##6##7##A, B). After the click-reaction had taken place and washing, the microarray is not visible anymore in the bright-field, but the fluorescence images revealed the immobilization of MSPs-POM (Fig. ##FIG##6##7##C). Close-up of individual immobilized MSPs-POM particles were obtained with SEM (Fig. ##FIG##6##7##G) and AFM (Fig. ##FIG##6##7##H), further confirming the successful binding to the surface and integrity of the modified MSPs-POM.</p>\"\n]","isConversational":false},"discussion":{"kind":"list like","value":["<title>Results and discussion</title>","<title>Immobilization strategy</title>","<p id=\"Par6\">To imprint POMs on surfaces (Fig. ##FIG##1##2##), we aimed to use MSPs whose pore wall is functionalized with amino groups (MSPs<sup>+</sup>), that are protonated and thus positively charged in an aqueous environment. Due to the size of the mesopores (≈ 3 nm) and the polyanionic nature of the POM, these cargo molecules will be retained in the positively charged pores<sup>##REF##26161980##48##</sup>. Immobilization of the POM-loaded MSP (MSPs-POM) will then be enabled by functionalization of the particle surface with reactive amino groups, which can engage in amine-ene Michael additions to alkene-functionalized surfaces. The advantage of MSPs, therefore, is the possibility to circumvent the use of monofunctionalized POMs<sup>##UREF##25##49##,##UREF##26##50##</sup>, whose synthesis requires considerable effort, while providing a generalizable strategy for a wide range of POMs to be immobilized on solid substrates.</p>","<title>Synthesis of POM-loaded MSPs</title>","<p id=\"Par7\">To prepare POM-loaded MSPs, first MSPs functionalized with amino groups on the pore wall surfaces (MSPs<sup>+</sup>) were prepared. To this end, pristine spherical nanometer-sized MSPs were synthesized following a previously reported modified Stöber synthesis protocol using the surfactant cetyltrimethylammonium bromide (CTAB) as the structure-directing agent<sup>##UREF##27##51##</sup>. This synthesis method was chosen because the resulting particles are in the nanometer range, have a spherical shape, have a high specific surface area (1243 m<sup>2</sup> g<sup>-1</sup>), and are mesoporous (pore diameter ≈ 3 nm)—all properties required for subsequent effective loading of POM. Scanning electron microscopy (SEM) analysis revealed that the pristine MSPs were spherical with an average diameter of (283 ± 25) nm (Fig. ##SUPPL##0##S1##), which is in good agreement with previously published results.</p>","<p id=\"Par8\">The pristine MSPs were then passivated on the outer surfaces with a short polyethylene glycol chain (6–9 units) alkoxysilane<sup>##REF##34009950##52##</sup> and then CTAB was extracted from the mesopores of the particles. Next, the CTAB-free pore walls were covalently grafted with amino groups using (3-aminopropyl)trimethoxysilane (APTMS), resulting in MSPs whose pore walls are functionalized with amino groups (MSPs<sup>+</sup>). The surface passivation and amino functionalization (Fig. ##SUPPL##0##S2##A) were investigated by zeta potential (Z-pot) analysis (Fig. ##SUPPL##0##S2##B), which revealed a nearly neutral surface charge (0.4 ± 1.2) mV of MSPs<sup>+</sup>. This reduced negative Z-pot value (for non-functionalized and surfactant-extracted MSP, the Z-pot is around − 30 to − 40 mV) can be explained by the surface passivation that took place, which reduces the amount of deprotonated and negatively charged hydroxyl groups on the surface of the particles, as well as by charge balancing due to the positive R-NH<sub>3</sub><sup>+</sup> groups present on the pore entrances.</p>","<p id=\"Par9\">Next, we optimized the procedure for loading MSPs<sup>+</sup> with POM (MSPs-POM) to eliminate the presence of non-loaded POM crystals. In suboptimal loading procedures, where not all POM was loaded within the pores of MSPs<sup>+</sup>, POM crystals were observed near or on the surface of MSPs, as demonstrated by SEM analysis (Fig. ##SUPPL##0##S3##A). By optimizing the mass ratio between POM and MSPs<sup>+</sup> used for the loading and adding several washing steps with EtOH and water, we were able to obtain MSPs-POM (Figs. ##FIG##2##3##A and ##SUPPL##0##S3##B), which showed no crystalline POM precipitates (MSPs-POM). Z-pot measurements on MSPs-POM indicated more negative values compared to MSPs<sup>+</sup> particles, suggesting successful adsorption of POMs within the pores of the particles (Fig. ##SUPPL##0##S2##B). Attenuated total reflectance Fourier infrared spectroscopy (ATR-FTIR) recorded on MSPs-POM (Fig. ##FIG##2##3##B) shows the characteristic POM-related transmission bands supporting the presence of POM in the mesopores of the silica nanoparticles (gray background). The FTIR spectrum of MSPs-POM exhibits vibrational bands within the 1200–400 cm<sup>-1</sup> region, which closely resemble the characteristic bands found in the pristine POM. POMs feature unique metal–oxygen vibrational modes within the fingerprint region. The distinctive peaks at 1134 and 1054 cm<sup>-1</sup> are attributed to the P–O vibrations of {GdP<sub>5</sub>W<sub>30</sub>} POM. The peak at 907 cm<sup>-1</sup> could be assigned to terminal <bold>ν</bold><sub>as</sub>(W══O<sub>t</sub>) vibration. The features around 709 cm<sup>-1</sup> could be attributed to the edge-sharing <bold>ν</bold><sub>as</sub>(W–O–W). All these bands are considered as pure vibrations of the POM skeleton. The peak at 1629 cm<sup>-1</sup> can be attributed to the δ(O–H) of the lattice water molecules. The broad transmission band at 3441 cm<sup>-1</sup> can be attributed to the presence of -OH groups on the outer surface of MSPs-POM, while the broad bands in the range between 3308 and 3010 cm<sup>-1</sup> can be attributed to the presence of R-NH<sub>2</sub>/R-NH<sub>3</sub><sup>+</sup> groups<sup>##UREF##28##53##,##REF##27145200##54##</sup>. The three relatively sharp transmission bands at 2917, 2840, and 1460 cm<sup>-1</sup> are representative of the C<sub>sp3</sub>-H stretching vibration of the organic functional groups of the MSPs-POM particles, <italic>i.e.</italic>, the PEG caps and the condensed APTMS groups<sup>##UREF##29##55##</sup>. Energy dispersive X-ray (EDX) analysis (Figs. ##FIG##2##3##C and ##SUPPL##0##S4##) also confirmed that the POM is colocalized with the particles.</p>","<p id=\"Par10\">To evaluate the stability of MSPs-POM under physiological conditions, their hydrodynamic diameter (<italic>D</italic><sub>h</sub>) was monitored over time in 10X phosphate salt buffer (PBS, pH 7.4 at 37 °C). Figure ##FIG##3##4## shows the DLS size distribution of the particles at time points 0, 1 day, and 5 days. The size distribution remains little affected over 5 d, indicating that both the spherical shape and size of the particles remain intact under physiological conditions. The stability of the particles is achieved by covalent surface passivation of the terminal silanol groups of the silica with PEG silane, which effectively prevents hydrolysis of the silica<sup>##UREF##30##56##,##UREF##31##57##</sup>. We also measured the Z-pot to determine whether the POMs could leak out of the mesopores over time, which would be indicated by a clear shift to more positive Z-pot values of the particles. However, over 5 days, the zeta potential of the particles dispersed in PBS showed no significant shift towards more positive Z-pot values (Fig. S5), which proves that the POMs stay adsorbed on the silica surface.</p>","<title>Preparation and characterization of substrate</title>","<p id=\"Par11\">The alkene-bearing reactive surfaces were prepared by silanization of 3-methacryloxypropyltrimethoxysilane (MPS-silane) on hydroxylated glass substrates, which can participate thiol-ene and thiol-amine click reactions (Fig. ##FIG##4##5##A)<sup>##UREF##32##58##,##UREF##33##59##</sup>. To achieve efficient silanization, the cleaned glass coverslips were treated with oxygen plasma to remove remaining contaminants and to endow a high density of hydroxyl groups on the glass surfaces. Then, the activated glass slips were immersed in an MPS-silane solution in toluene to obtain the MPS-modified substrates. The characterization of MPS-modified substrates was carried out using atomic force microscopy (AFM), water contact angle (WCA), and x-ray photoelectron spectroscopy (XPS). The results are shown in Fig. ##FIG##4##5##B–D.</p>","<p id=\"Par12\">The glass coverslips usually show a WCA at around 43° (Fig. ##SUPPL##0##S6##A), while just after plasma treatment, the value declines to around 0° (Fig. ##SUPPL##0##S6##B) due to the densely grafted hydroxyl groups. As shown in Fig. ##FIG##4##5##B, after silanization of the plasma-treated glass surface with MPS-silane the WCA increases to a higher value at around 64.3°, which is also an indication of a successful silanization process. The mean surface roughness (Ra) of silanized surfaces obtained by AFM is around 0.251 nm, which is consistent with former reports concerning self-assembled monolayers (SAMs)<sup>##UREF##34##60##,##REF##30350707##61##</sup>. AFM images of a glass coverslip before and after silanization are given in Figs. ##SUPPL##0##S6##C and ##FIG##4##5##C, respectively. To further confirm successful silanization, high-resolution XPS (HR-XPS) was implemented on MPS-modified and bare glasses for comparison, the corresponding spectra at the C 1s region are illustrated in Fig. ##FIG##4##5##D. The deconvoluted HR-XP spectra exhibit three peaks a 284.8 eV, 286.5 eV, and 288.9 eV, which are derived from (C–C, C–H), (C–O), and (O–C=O), respectively. Comparing the XP spectra of the bare glass and MPS-modified samples, the ratio of (C–O):(C–C, C–H) increases from 0.21 to 0.35, which indicates the formation of the MPS-modified SAM.</p>","<p id=\"Par13\">To validate the reactivity of the MPS-modified glass substrates and probe the possibility of creating microarrays via microchannel cantilever spotting (µCS) on these, small fluorescent molecules with either a thiol or amine moiety were chosen as test probes, namely NH<sub>2</sub>-Tetramethylrhodamine (NH<sub>2</sub>-Tamra) and SH-Fluorescein (SH-FITC) (Fig. ##FIG##4##5##A). µCS works by loading a microchannel cantilever with a µL amount of ink containing the desired molecules, and then bringing cantilever repeatedly into contact with the substrate in a scanning probe lithography (SPL) setup allowing for high positioning control, as well as controlled humidity and dwell time. Each spot in the resulting droplet microarrays can be regarded as a microreactor permitting click reactions between the alkene terminal groups and thiol or amine moieties to take place. After the desired incubation time, excess ink can simply be washed away from the surface. As shown in Fig. ##FIG##5##6##A, the NH<sub>2</sub>-Tamra firmly binds to the MPS-modified surface as the fluorescent pattern is strongly visible in the red channel. Similarly, the SH-FITC also works well with MPS-modified surfaces as shown in Fig. ##FIG##5##6##B. Therefore, we confirm that the amine and thiol moieties all bind well to the alkene functionalized surface.</p>","<p id=\"Par14\">As a final test of reactivity and to allow a direct comparison of binding efficiency between thiol-ene and amine-ene routes, two biotinylated compounds, NH<sub>2</sub>-biotin and SH-biotin, were printed. When incubated with Cy3 conjugated streptavidin (SA-Cy3), the printed microarrays can bind the SA-Cy3 via the strong affinity between biotin and avidin<sup>##REF##24057405##62##</sup>, and the binding density of the two microarrays can be directly compared over fluorescence intensity<sup>##UREF##34##60##,##UREF##35##63##,##UREF##36##64##</sup>. After incubation of bovine serum albumin (BSA) for blocking unspecific binding followed by incubation of SA-Cy3 (both in phosphate buffered saline (PBS)) on the biotinylated arrays, the biotin spots are easily readout on a fluorescent microscope as shown in Fig. ##FIG##5##6##C and D. The corresponding fluorescence intensity comparison is shown in Fig. ##FIG##5##6##E. The yield of the thiol-ene click route (9620.86 ± 1148.15) a.u. is about 21% higher than amine-ene route (7585.39 ± 1046.44) a.u. from the fluorescence, though both routes appear similar within the fluctuations, both routes deliver a comparable and stable binding to the surface.</p>","<title>Patterned immobilization of MSPs-POM</title>","<p id=\"Par15\">After having obtained amine-functionalized MSPs-POM (H<sub>2</sub>N-MSPs-POM, see Materials and methods) and the matching alkene-functionalized glass surface, capillary spotting was adopted to generate microarrays with MSPs-POM ink. The glass capillary tip has a larger aperture compared to µCS tips and can be tailored to different opening sizes, making it better suited for nanoparticle spotting. After spotting, the obtained microarrays were incubated at 37°C to complete the click-reaction and then washed with water to remove the excess ink. The results of the capillary spotting-based microarray are shown in Fig. ##FIG##6##7##.</p>","<p id=\"Par16\">Directly after spotting, the microarray droplets are visible in bright-field and (due to labeling of the MSPs-POM with Cy3) fluorescence microscopy images (Fig. ##FIG##6##7##A, B). After the click-reaction had taken place and washing, the microarray is not visible anymore in the bright-field, but the fluorescence images revealed the immobilization of MSPs-POM (Fig. ##FIG##6##7##C). Close-up of individual immobilized MSPs-POM particles were obtained with SEM (Fig. ##FIG##6##7##G) and AFM (Fig. ##FIG##6##7##H), further confirming the successful binding to the surface and integrity of the modified MSPs-POM.</p>"],"string":"[\n \"<title>Results and discussion</title>\",\n \"<title>Immobilization strategy</title>\",\n \"<p id=\\\"Par6\\\">To imprint POMs on surfaces (Fig. ##FIG##1##2##), we aimed to use MSPs whose pore wall is functionalized with amino groups (MSPs<sup>+</sup>), that are protonated and thus positively charged in an aqueous environment. Due to the size of the mesopores (≈ 3 nm) and the polyanionic nature of the POM, these cargo molecules will be retained in the positively charged pores<sup>##REF##26161980##48##</sup>. Immobilization of the POM-loaded MSP (MSPs-POM) will then be enabled by functionalization of the particle surface with reactive amino groups, which can engage in amine-ene Michael additions to alkene-functionalized surfaces. The advantage of MSPs, therefore, is the possibility to circumvent the use of monofunctionalized POMs<sup>##UREF##25##49##,##UREF##26##50##</sup>, whose synthesis requires considerable effort, while providing a generalizable strategy for a wide range of POMs to be immobilized on solid substrates.</p>\",\n \"<title>Synthesis of POM-loaded MSPs</title>\",\n \"<p id=\\\"Par7\\\">To prepare POM-loaded MSPs, first MSPs functionalized with amino groups on the pore wall surfaces (MSPs<sup>+</sup>) were prepared. To this end, pristine spherical nanometer-sized MSPs were synthesized following a previously reported modified Stöber synthesis protocol using the surfactant cetyltrimethylammonium bromide (CTAB) as the structure-directing agent<sup>##UREF##27##51##</sup>. This synthesis method was chosen because the resulting particles are in the nanometer range, have a spherical shape, have a high specific surface area (1243 m<sup>2</sup> g<sup>-1</sup>), and are mesoporous (pore diameter ≈ 3 nm)—all properties required for subsequent effective loading of POM. Scanning electron microscopy (SEM) analysis revealed that the pristine MSPs were spherical with an average diameter of (283 ± 25) nm (Fig. ##SUPPL##0##S1##), which is in good agreement with previously published results.</p>\",\n \"<p id=\\\"Par8\\\">The pristine MSPs were then passivated on the outer surfaces with a short polyethylene glycol chain (6–9 units) alkoxysilane<sup>##REF##34009950##52##</sup> and then CTAB was extracted from the mesopores of the particles. Next, the CTAB-free pore walls were covalently grafted with amino groups using (3-aminopropyl)trimethoxysilane (APTMS), resulting in MSPs whose pore walls are functionalized with amino groups (MSPs<sup>+</sup>). The surface passivation and amino functionalization (Fig. ##SUPPL##0##S2##A) were investigated by zeta potential (Z-pot) analysis (Fig. ##SUPPL##0##S2##B), which revealed a nearly neutral surface charge (0.4 ± 1.2) mV of MSPs<sup>+</sup>. This reduced negative Z-pot value (for non-functionalized and surfactant-extracted MSP, the Z-pot is around − 30 to − 40 mV) can be explained by the surface passivation that took place, which reduces the amount of deprotonated and negatively charged hydroxyl groups on the surface of the particles, as well as by charge balancing due to the positive R-NH<sub>3</sub><sup>+</sup> groups present on the pore entrances.</p>\",\n \"<p id=\\\"Par9\\\">Next, we optimized the procedure for loading MSPs<sup>+</sup> with POM (MSPs-POM) to eliminate the presence of non-loaded POM crystals. In suboptimal loading procedures, where not all POM was loaded within the pores of MSPs<sup>+</sup>, POM crystals were observed near or on the surface of MSPs, as demonstrated by SEM analysis (Fig. ##SUPPL##0##S3##A). By optimizing the mass ratio between POM and MSPs<sup>+</sup> used for the loading and adding several washing steps with EtOH and water, we were able to obtain MSPs-POM (Figs. ##FIG##2##3##A and ##SUPPL##0##S3##B), which showed no crystalline POM precipitates (MSPs-POM). Z-pot measurements on MSPs-POM indicated more negative values compared to MSPs<sup>+</sup> particles, suggesting successful adsorption of POMs within the pores of the particles (Fig. ##SUPPL##0##S2##B). Attenuated total reflectance Fourier infrared spectroscopy (ATR-FTIR) recorded on MSPs-POM (Fig. ##FIG##2##3##B) shows the characteristic POM-related transmission bands supporting the presence of POM in the mesopores of the silica nanoparticles (gray background). The FTIR spectrum of MSPs-POM exhibits vibrational bands within the 1200–400 cm<sup>-1</sup> region, which closely resemble the characteristic bands found in the pristine POM. POMs feature unique metal–oxygen vibrational modes within the fingerprint region. The distinctive peaks at 1134 and 1054 cm<sup>-1</sup> are attributed to the P–O vibrations of {GdP<sub>5</sub>W<sub>30</sub>} POM. The peak at 907 cm<sup>-1</sup> could be assigned to terminal <bold>ν</bold><sub>as</sub>(W══O<sub>t</sub>) vibration. The features around 709 cm<sup>-1</sup> could be attributed to the edge-sharing <bold>ν</bold><sub>as</sub>(W–O–W). All these bands are considered as pure vibrations of the POM skeleton. The peak at 1629 cm<sup>-1</sup> can be attributed to the δ(O–H) of the lattice water molecules. The broad transmission band at 3441 cm<sup>-1</sup> can be attributed to the presence of -OH groups on the outer surface of MSPs-POM, while the broad bands in the range between 3308 and 3010 cm<sup>-1</sup> can be attributed to the presence of R-NH<sub>2</sub>/R-NH<sub>3</sub><sup>+</sup> groups<sup>##UREF##28##53##,##REF##27145200##54##</sup>. The three relatively sharp transmission bands at 2917, 2840, and 1460 cm<sup>-1</sup> are representative of the C<sub>sp3</sub>-H stretching vibration of the organic functional groups of the MSPs-POM particles, <italic>i.e.</italic>, the PEG caps and the condensed APTMS groups<sup>##UREF##29##55##</sup>. Energy dispersive X-ray (EDX) analysis (Figs. ##FIG##2##3##C and ##SUPPL##0##S4##) also confirmed that the POM is colocalized with the particles.</p>\",\n \"<p id=\\\"Par10\\\">To evaluate the stability of MSPs-POM under physiological conditions, their hydrodynamic diameter (<italic>D</italic><sub>h</sub>) was monitored over time in 10X phosphate salt buffer (PBS, pH 7.4 at 37 °C). Figure ##FIG##3##4## shows the DLS size distribution of the particles at time points 0, 1 day, and 5 days. The size distribution remains little affected over 5 d, indicating that both the spherical shape and size of the particles remain intact under physiological conditions. The stability of the particles is achieved by covalent surface passivation of the terminal silanol groups of the silica with PEG silane, which effectively prevents hydrolysis of the silica<sup>##UREF##30##56##,##UREF##31##57##</sup>. We also measured the Z-pot to determine whether the POMs could leak out of the mesopores over time, which would be indicated by a clear shift to more positive Z-pot values of the particles. However, over 5 days, the zeta potential of the particles dispersed in PBS showed no significant shift towards more positive Z-pot values (Fig. S5), which proves that the POMs stay adsorbed on the silica surface.</p>\",\n \"<title>Preparation and characterization of substrate</title>\",\n \"<p id=\\\"Par11\\\">The alkene-bearing reactive surfaces were prepared by silanization of 3-methacryloxypropyltrimethoxysilane (MPS-silane) on hydroxylated glass substrates, which can participate thiol-ene and thiol-amine click reactions (Fig. ##FIG##4##5##A)<sup>##UREF##32##58##,##UREF##33##59##</sup>. To achieve efficient silanization, the cleaned glass coverslips were treated with oxygen plasma to remove remaining contaminants and to endow a high density of hydroxyl groups on the glass surfaces. Then, the activated glass slips were immersed in an MPS-silane solution in toluene to obtain the MPS-modified substrates. The characterization of MPS-modified substrates was carried out using atomic force microscopy (AFM), water contact angle (WCA), and x-ray photoelectron spectroscopy (XPS). The results are shown in Fig. ##FIG##4##5##B–D.</p>\",\n \"<p id=\\\"Par12\\\">The glass coverslips usually show a WCA at around 43° (Fig. ##SUPPL##0##S6##A), while just after plasma treatment, the value declines to around 0° (Fig. ##SUPPL##0##S6##B) due to the densely grafted hydroxyl groups. As shown in Fig. ##FIG##4##5##B, after silanization of the plasma-treated glass surface with MPS-silane the WCA increases to a higher value at around 64.3°, which is also an indication of a successful silanization process. The mean surface roughness (Ra) of silanized surfaces obtained by AFM is around 0.251 nm, which is consistent with former reports concerning self-assembled monolayers (SAMs)<sup>##UREF##34##60##,##REF##30350707##61##</sup>. AFM images of a glass coverslip before and after silanization are given in Figs. ##SUPPL##0##S6##C and ##FIG##4##5##C, respectively. To further confirm successful silanization, high-resolution XPS (HR-XPS) was implemented on MPS-modified and bare glasses for comparison, the corresponding spectra at the C 1s region are illustrated in Fig. ##FIG##4##5##D. The deconvoluted HR-XP spectra exhibit three peaks a 284.8 eV, 286.5 eV, and 288.9 eV, which are derived from (C–C, C–H), (C–O), and (O–C=O), respectively. Comparing the XP spectra of the bare glass and MPS-modified samples, the ratio of (C–O):(C–C, C–H) increases from 0.21 to 0.35, which indicates the formation of the MPS-modified SAM.</p>\",\n \"<p id=\\\"Par13\\\">To validate the reactivity of the MPS-modified glass substrates and probe the possibility of creating microarrays via microchannel cantilever spotting (µCS) on these, small fluorescent molecules with either a thiol or amine moiety were chosen as test probes, namely NH<sub>2</sub>-Tetramethylrhodamine (NH<sub>2</sub>-Tamra) and SH-Fluorescein (SH-FITC) (Fig. ##FIG##4##5##A). µCS works by loading a microchannel cantilever with a µL amount of ink containing the desired molecules, and then bringing cantilever repeatedly into contact with the substrate in a scanning probe lithography (SPL) setup allowing for high positioning control, as well as controlled humidity and dwell time. Each spot in the resulting droplet microarrays can be regarded as a microreactor permitting click reactions between the alkene terminal groups and thiol or amine moieties to take place. After the desired incubation time, excess ink can simply be washed away from the surface. As shown in Fig. ##FIG##5##6##A, the NH<sub>2</sub>-Tamra firmly binds to the MPS-modified surface as the fluorescent pattern is strongly visible in the red channel. Similarly, the SH-FITC also works well with MPS-modified surfaces as shown in Fig. ##FIG##5##6##B. Therefore, we confirm that the amine and thiol moieties all bind well to the alkene functionalized surface.</p>\",\n \"<p id=\\\"Par14\\\">As a final test of reactivity and to allow a direct comparison of binding efficiency between thiol-ene and amine-ene routes, two biotinylated compounds, NH<sub>2</sub>-biotin and SH-biotin, were printed. When incubated with Cy3 conjugated streptavidin (SA-Cy3), the printed microarrays can bind the SA-Cy3 via the strong affinity between biotin and avidin<sup>##REF##24057405##62##</sup>, and the binding density of the two microarrays can be directly compared over fluorescence intensity<sup>##UREF##34##60##,##UREF##35##63##,##UREF##36##64##</sup>. After incubation of bovine serum albumin (BSA) for blocking unspecific binding followed by incubation of SA-Cy3 (both in phosphate buffered saline (PBS)) on the biotinylated arrays, the biotin spots are easily readout on a fluorescent microscope as shown in Fig. ##FIG##5##6##C and D. The corresponding fluorescence intensity comparison is shown in Fig. ##FIG##5##6##E. The yield of the thiol-ene click route (9620.86 ± 1148.15) a.u. is about 21% higher than amine-ene route (7585.39 ± 1046.44) a.u. from the fluorescence, though both routes appear similar within the fluctuations, both routes deliver a comparable and stable binding to the surface.</p>\",\n \"<title>Patterned immobilization of MSPs-POM</title>\",\n \"<p id=\\\"Par15\\\">After having obtained amine-functionalized MSPs-POM (H<sub>2</sub>N-MSPs-POM, see Materials and methods) and the matching alkene-functionalized glass surface, capillary spotting was adopted to generate microarrays with MSPs-POM ink. The glass capillary tip has a larger aperture compared to µCS tips and can be tailored to different opening sizes, making it better suited for nanoparticle spotting. After spotting, the obtained microarrays were incubated at 37°C to complete the click-reaction and then washed with water to remove the excess ink. The results of the capillary spotting-based microarray are shown in Fig. ##FIG##6##7##.</p>\",\n \"<p id=\\\"Par16\\\">Directly after spotting, the microarray droplets are visible in bright-field and (due to labeling of the MSPs-POM with Cy3) fluorescence microscopy images (Fig. ##FIG##6##7##A, B). After the click-reaction had taken place and washing, the microarray is not visible anymore in the bright-field, but the fluorescence images revealed the immobilization of MSPs-POM (Fig. ##FIG##6##7##C). Close-up of individual immobilized MSPs-POM particles were obtained with SEM (Fig. ##FIG##6##7##G) and AFM (Fig. ##FIG##6##7##H), further confirming the successful binding to the surface and integrity of the modified MSPs-POM.</p>\"\n]","isConversational":false},"conclusion":{"kind":"list like","value":["<title>Conclusions</title>","<p id=\"Par17\">Our proof-of-concept study shows, that the loading of MSPs with POM in conjunction with SPL techniques is a viable route to create surface-immobilized microarrays of MSPs-POM. Encapsulation of POM in MSPs increases stability and enables immobilization of a larger amount of POM, which can outperform direct immobilization methods that typically use molecularly dissolved POM to form surface monolayers. The MSPs-POM is stable under physiological conditions for several days and can therefore be used also for applications in cell cultures. The microarray spotting approach of POMs presented here paves the way for future surface-based applications of POM composites. In particular, the Gd(III)-substituted POM presented here has potential implications for surface microarray-based sensing via surface NMR, acting as a strong molecular NMR chemosensor. The approach is versatile and can, for example, be easily transferred to the loading of other POMs for specific applications. Keeping in mind the capabilities of SPL techniques, in particular the possibility of multiplexing (i.e., deposition of different materials within the same micropatterns) and highly localized deposition (e.g., to deliver functional materials exclusively to prestructures and microdevices like electrodes or sensor devices)<sup>##UREF##37##65##</sup>, complex functional constructs can be fabricated. Taken together, we think that this MSPs-POM offers an attractive for the immobilization of functional POMs into surface-bound patterns for future applications in biomedical sensing and catalysis.</p>"],"string":"[\n \"<title>Conclusions</title>\",\n \"<p id=\\\"Par17\\\">Our proof-of-concept study shows, that the loading of MSPs with POM in conjunction with SPL techniques is a viable route to create surface-immobilized microarrays of MSPs-POM. Encapsulation of POM in MSPs increases stability and enables immobilization of a larger amount of POM, which can outperform direct immobilization methods that typically use molecularly dissolved POM to form surface monolayers. The MSPs-POM is stable under physiological conditions for several days and can therefore be used also for applications in cell cultures. The microarray spotting approach of POMs presented here paves the way for future surface-based applications of POM composites. In particular, the Gd(III)-substituted POM presented here has potential implications for surface microarray-based sensing via surface NMR, acting as a strong molecular NMR chemosensor. The approach is versatile and can, for example, be easily transferred to the loading of other POMs for specific applications. Keeping in mind the capabilities of SPL techniques, in particular the possibility of multiplexing (i.e., deposition of different materials within the same micropatterns) and highly localized deposition (e.g., to deliver functional materials exclusively to prestructures and microdevices like electrodes or sensor devices)<sup>##UREF##37##65##</sup>, complex functional constructs can be fabricated. Taken together, we think that this MSPs-POM offers an attractive for the immobilization of functional POMs into surface-bound patterns for future applications in biomedical sensing and catalysis.</p>\"\n]","isConversational":false},"front":{"kind":"list like","value":["<p id=\"Par1\">Polyoxometalates (POM) are anionic oxoclusters of early transition metals that are of great interest for a variety of applications, including the development of sensors and catalysts. A crucial step in the use of POM in functional materials is the production of composites that can be further processed into complex materials, e.g. by printing on different substrates. In this work, we present an immobilization approach for POMs that involves two key processes: first, the stable encapsulation of POMs in the pores of mesoporous silica nanoparticles (MSPs) and, second, the formation of microstructured arrays with these POM-loaded nanoparticles. Specifically, we have developed a strategy that leads to water-stable, POM-loaded mesoporous silica that can be covalently linked to alkene-bearing surfaces by amine-Michael addition and patterned into microarrays by scanning probe lithography (SPL). The immobilization strategy presented facilitates the printing of hybrid POM-loaded nanomaterials onto different surfaces and provides a versatile method for the fabrication of POM-based composites. Importantly, POM-loaded MSPs are useful in applications such as microfluidic systems and sensors that require frequent washing. Overall, this method is a promising way to produce surface-printed POM arrays that can be used for a wide range of applications.</p>","<title>Subject terms</title>","<p>Open Access funding enabled and organized by Projekt DEAL.</p>"],"string":"[\n \"<p id=\\\"Par1\\\">Polyoxometalates (POM) are anionic oxoclusters of early transition metals that are of great interest for a variety of applications, including the development of sensors and catalysts. A crucial step in the use of POM in functional materials is the production of composites that can be further processed into complex materials, e.g. by printing on different substrates. In this work, we present an immobilization approach for POMs that involves two key processes: first, the stable encapsulation of POMs in the pores of mesoporous silica nanoparticles (MSPs) and, second, the formation of microstructured arrays with these POM-loaded nanoparticles. Specifically, we have developed a strategy that leads to water-stable, POM-loaded mesoporous silica that can be covalently linked to alkene-bearing surfaces by amine-Michael addition and patterned into microarrays by scanning probe lithography (SPL). The immobilization strategy presented facilitates the printing of hybrid POM-loaded nanomaterials onto different surfaces and provides a versatile method for the fabrication of POM-based composites. Importantly, POM-loaded MSPs are useful in applications such as microfluidic systems and sensors that require frequent washing. Overall, this method is a promising way to produce surface-printed POM arrays that can be used for a wide range of applications.</p>\",\n \"<title>Subject terms</title>\",\n \"<p>Open Access funding enabled and organized by Projekt DEAL.</p>\"\n]","isConversational":false},"body":{"kind":"list like","value":["<p id=\"Par2\">POMs are anionic oxo-clusters of early transition metals in their high oxidation states, which display unique chemical and physical properties on the basis of their structures, composition, sizes, rich redox chemistry and charges<sup>##UREF##0##1##,##UREF##1##2##</sup>. Prominent POM structures are the Keggin-type structure, [XM<sub>12</sub>O<sub>40</sub>]<sup>n−</sup>, and the Wells–Dawson-type structure, [X<sub>2</sub>M<sub>18</sub>O<sub>62</sub>]<sup>n−</sup><sup>##UREF##2##3##</sup>. Their properties have been widely exploited in a variety of areas, including catalysis, materials science, photochemistry, molecular magnetism and medicine<sup>##REF##31793568##4##–##UREF##4##6##</sup>. The high negative charge and multiple oxo-donor sites make them useful multidentate building blocks for constructing transition and lanthanide metal-based cluster complexes. Moreover, their capacity to engage with organic moieties enables the formation of hybrid assemblies with distinctive functionalities<sup>##REF##35356680##7##</sup>. Such assemblies have exhibited significant promise as artificial photosynthesis systems, e.g. in water oxidation<sup>##REF##30510216##8##</sup> and carbon dioxide reduction<sup>##REF##21158388##9##</sup>. Among the larger investigated POM clusters, the well-known Preyssler<italic>-</italic>type anion<sup>##UREF##5##10##,##UREF##6##11##</sup>, of general formula [M<sup><italic>n</italic>+</sup>P<sub>5</sub>W<sub>30</sub>O<sub>110</sub>]<sup>(15−<italic>n</italic>)−</sup>(abbreviated as {MP<sub>5</sub>W<sub>30</sub>}) is a doughnut-shaped molecule with an inner cavity and 30 terminal O atoms. This polyanion can capture different cations with suitable size such as alkali metals and lanthanide ions (Ln) on the inner surface of the POM. The members of the {MP<sub>5</sub>W<sub>30</sub>} POM family are among the most robust, stable, and processable POM complexes, which are also stable over a wide pH range (pH 1–12)<sup>##UREF##7##12##</sup>. In addition, their unique biological compatibility and high surface charges result in a flexible architecture for interaction with target substrates, making these POMs a promising molecular sensor material<sup>##UREF##7##12##,##UREF##8##13##</sup>.</p>","<p id=\"Par3\">In this work, we use Preyssler-type POM K<sub>12</sub>[GdP<sub>5</sub>W<sub>30</sub>O<sub>110</sub>]·15H<sub>2</sub>O (hereafter shortened as {GdP<sub>5</sub>W<sub>30</sub>}, structure shown in Fig. ##FIG##0##1##), synthesized from a Preyssler anion [NaP<sub>5</sub>W<sub>30</sub>0<sub>110</sub>]<sup>##UREF##9##14##</sup> in which the central Na<sup>+</sup> cation was replaced by the Gd<sup>III</sup> ion under hydrothermal conditions. Gd(III)-substituted POMs play a key role in the development of advanced nanomaterial-based contrast agents for nuclear magnetic resonance (NMR) imaging<sup>##REF##32812616##15##,##UREF##10##16##</sup> and thus have great potential for molecular quantum sensing applications<sup>##REF##34079892##17##,##REF##36776813##18##</sup>. These contrast agents are compounds used to enhance the visibility of various tissues, structures, substances, or pathological conditions. They achieve this by emphasizing disparities or boundaries between different substances or tissue types. In the field of imaging, Gd-containing POMs have been found to exhibit a higher <italic>r</italic><sub>1</sub> value compared to commercial contrast agents. This improvement is attributed to the significant molecular weight and the rigid framework structure of POMs, which lead to an extended rotational correlation time and an increased <italic>r</italic><sub>1</sub> value<sup>##REF##24158750##19##,##REF##31384871##20##</sup>.</p>","<p id=\"Par4\">However, pure inorganic POMs are usually soluble in many polar solvents, causing difficulties in the recovery, separation, and recycling of the materials<sup>##UREF##11##21##</sup>. This poses challenges in biomedical and catalysis applications and also for surface-based sensing, where immobilization at high-density would be desirable. To address this problem, various methods for immobilizing POMs into heterogeneous matrices have been developed. The resulting hybrid composites have found applications in various fields, including catalysis<sup>##REF##27230969##22##</sup>, energy conversion and storage<sup>##UREF##12##23##,##UREF##13##24##</sup>, molecular sensors and electronics<sup>##REF##35372262##25##</sup>. The immobilization of POMs into cationic silica nanoparticles through covalent or electrostatic binding can yield a novel material, potentially creating a biocompatible nanomaterial with luminescent properties suitable for biosensing and imaging applications<sup>##REF##32760699##26##–##UREF##15##28##</sup>. In particular, the immobilization of Gd-containing POMs into structured microarrays could enable the use of surface NMR-based methods for molecular quantum sensing, as demonstrated recently in diamond films<sup>##REF##26847544##29##,##REF##35082146##30##</sup>, with the direct use of Gd enhancing the NMR signal instead of nitrogen-vacancy (NV) centers acting as a proxy for readout.</p>","<p id=\"Par5\">In our attempt to find a straightforward approach to immobilize {GdP<sub>5</sub>W<sub>30</sub>} POM on a glass surface, we decided to use nanometer-sized mesoporous silica particles (MSP)<sup>##UREF##16##31##–##REF##17580976##33##</sup> as the POM carriers that, can be covalently tethered onto solid substrates. The choice of MSPs to serve as the POM carrier bears several advantages. First, MSPs can be prepared with tunable porosity (2–50 nm)<sup>##REF##23363241##34##</sup>, sizes and shape<sup>##UREF##17##35##,##UREF##18##36##</sup>, and possess high surface areas (up to 1000 m<sup>2</sup>∙g<sup>−1</sup>)<sup>##REF##23387478##37##</sup>. Second, MSPs can be functionalized with a variety of organic functional groups via alkoxysilane chemistry<sup>##UREF##19##38##,##UREF##20##39##</sup>. The functionalization of MSP can occur on the outer surface of the particles or the surface of their inner pore walls<sup>##UREF##21##40##,##UREF##22##41##</sup>. For example, \"clickable\" functional groups can be grafted onto silica surfaces by sol–gel chemistry with organoalkoxysilanes bearing amine<sup>##UREF##23##42##,##REF##34907672##43##</sup>, or azide functional groups<sup>##REF##32261290##44##,##UREF##24##45##</sup>, which has been used in the past for covalent immobilization of silica and silica-based (nano)particles on solid substrates<sup>##REF##20356283##46##,##REF##20644765##47##</sup>.</p>","<title>Supplementary Information</title>","<p>\n</p>"],"string":"[\n \"<p id=\\\"Par2\\\">POMs are anionic oxo-clusters of early transition metals in their high oxidation states, which display unique chemical and physical properties on the basis of their structures, composition, sizes, rich redox chemistry and charges<sup>##UREF##0##1##,##UREF##1##2##</sup>. Prominent POM structures are the Keggin-type structure, [XM<sub>12</sub>O<sub>40</sub>]<sup>n−</sup>, and the Wells–Dawson-type structure, [X<sub>2</sub>M<sub>18</sub>O<sub>62</sub>]<sup>n−</sup><sup>##UREF##2##3##</sup>. Their properties have been widely exploited in a variety of areas, including catalysis, materials science, photochemistry, molecular magnetism and medicine<sup>##REF##31793568##4##–##UREF##4##6##</sup>. The high negative charge and multiple oxo-donor sites make them useful multidentate building blocks for constructing transition and lanthanide metal-based cluster complexes. Moreover, their capacity to engage with organic moieties enables the formation of hybrid assemblies with distinctive functionalities<sup>##REF##35356680##7##</sup>. Such assemblies have exhibited significant promise as artificial photosynthesis systems, e.g. in water oxidation<sup>##REF##30510216##8##</sup> and carbon dioxide reduction<sup>##REF##21158388##9##</sup>. Among the larger investigated POM clusters, the well-known Preyssler<italic>-</italic>type anion<sup>##UREF##5##10##,##UREF##6##11##</sup>, of general formula [M<sup><italic>n</italic>+</sup>P<sub>5</sub>W<sub>30</sub>O<sub>110</sub>]<sup>(15−<italic>n</italic>)−</sup>(abbreviated as {MP<sub>5</sub>W<sub>30</sub>}) is a doughnut-shaped molecule with an inner cavity and 30 terminal O atoms. This polyanion can capture different cations with suitable size such as alkali metals and lanthanide ions (Ln) on the inner surface of the POM. The members of the {MP<sub>5</sub>W<sub>30</sub>} POM family are among the most robust, stable, and processable POM complexes, which are also stable over a wide pH range (pH 1–12)<sup>##UREF##7##12##</sup>. In addition, their unique biological compatibility and high surface charges result in a flexible architecture for interaction with target substrates, making these POMs a promising molecular sensor material<sup>##UREF##7##12##,##UREF##8##13##</sup>.</p>\",\n \"<p id=\\\"Par3\\\">In this work, we use Preyssler-type POM K<sub>12</sub>[GdP<sub>5</sub>W<sub>30</sub>O<sub>110</sub>]·15H<sub>2</sub>O (hereafter shortened as {GdP<sub>5</sub>W<sub>30</sub>}, structure shown in Fig. ##FIG##0##1##), synthesized from a Preyssler anion [NaP<sub>5</sub>W<sub>30</sub>0<sub>110</sub>]<sup>##UREF##9##14##</sup> in which the central Na<sup>+</sup> cation was replaced by the Gd<sup>III</sup> ion under hydrothermal conditions. Gd(III)-substituted POMs play a key role in the development of advanced nanomaterial-based contrast agents for nuclear magnetic resonance (NMR) imaging<sup>##REF##32812616##15##,##UREF##10##16##</sup> and thus have great potential for molecular quantum sensing applications<sup>##REF##34079892##17##,##REF##36776813##18##</sup>. These contrast agents are compounds used to enhance the visibility of various tissues, structures, substances, or pathological conditions. They achieve this by emphasizing disparities or boundaries between different substances or tissue types. In the field of imaging, Gd-containing POMs have been found to exhibit a higher <italic>r</italic><sub>1</sub> value compared to commercial contrast agents. This improvement is attributed to the significant molecular weight and the rigid framework structure of POMs, which lead to an extended rotational correlation time and an increased <italic>r</italic><sub>1</sub> value<sup>##REF##24158750##19##,##REF##31384871##20##</sup>.</p>\",\n \"<p id=\\\"Par4\\\">However, pure inorganic POMs are usually soluble in many polar solvents, causing difficulties in the recovery, separation, and recycling of the materials<sup>##UREF##11##21##</sup>. This poses challenges in biomedical and catalysis applications and also for surface-based sensing, where immobilization at high-density would be desirable. To address this problem, various methods for immobilizing POMs into heterogeneous matrices have been developed. The resulting hybrid composites have found applications in various fields, including catalysis<sup>##REF##27230969##22##</sup>, energy conversion and storage<sup>##UREF##12##23##,##UREF##13##24##</sup>, molecular sensors and electronics<sup>##REF##35372262##25##</sup>. The immobilization of POMs into cationic silica nanoparticles through covalent or electrostatic binding can yield a novel material, potentially creating a biocompatible nanomaterial with luminescent properties suitable for biosensing and imaging applications<sup>##REF##32760699##26##–##UREF##15##28##</sup>. In particular, the immobilization of Gd-containing POMs into structured microarrays could enable the use of surface NMR-based methods for molecular quantum sensing, as demonstrated recently in diamond films<sup>##REF##26847544##29##,##REF##35082146##30##</sup>, with the direct use of Gd enhancing the NMR signal instead of nitrogen-vacancy (NV) centers acting as a proxy for readout.</p>\",\n \"<p id=\\\"Par5\\\">In our attempt to find a straightforward approach to immobilize {GdP<sub>5</sub>W<sub>30</sub>} POM on a glass surface, we decided to use nanometer-sized mesoporous silica particles (MSP)<sup>##UREF##16##31##–##REF##17580976##33##</sup> as the POM carriers that, can be covalently tethered onto solid substrates. The choice of MSPs to serve as the POM carrier bears several advantages. First, MSPs can be prepared with tunable porosity (2–50 nm)<sup>##REF##23363241##34##</sup>, sizes and shape<sup>##UREF##17##35##,##UREF##18##36##</sup>, and possess high surface areas (up to 1000 m<sup>2</sup>∙g<sup>−1</sup>)<sup>##REF##23387478##37##</sup>. Second, MSPs can be functionalized with a variety of organic functional groups via alkoxysilane chemistry<sup>##UREF##19##38##,##UREF##20##39##</sup>. The functionalization of MSP can occur on the outer surface of the particles or the surface of their inner pore walls<sup>##UREF##21##40##,##UREF##22##41##</sup>. For example, \\\"clickable\\\" functional groups can be grafted onto silica surfaces by sol–gel chemistry with organoalkoxysilanes bearing amine<sup>##UREF##23##42##,##REF##34907672##43##</sup>, or azide functional groups<sup>##REF##32261290##44##,##UREF##24##45##</sup>, which has been used in the past for covalent immobilization of silica and silica-based (nano)particles on solid substrates<sup>##REF##20356283##46##,##REF##20644765##47##</sup>.</p>\",\n \"<title>Supplementary Information</title>\",\n \"<p>\\n</p>\"\n]","isConversational":false},"back":{"kind":"list like","value":["<title>Supplementary Information</title>","<p>The online version contains supplementary material available at 10.1038/s41598-023-50846-2.</p>","<title>Acknowledgements</title>","<p>This work was partly carried out with the support of the Karlsruhe Nano Micro Facility (KNMFi, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.knmf.kit.edu\">www.knmf.kit.edu</ext-link>), a Helmholtz Research Infrastructure at Karlsruhe Institute of Technology (KIT, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.kit.edu\">www.kit.edu</ext-link>). This work was supported by KNMFi project Nr. 2020-023-028480. B.Y. and W.W. acknowledge support by the China Scholarship Council fellowship (No. 201807040067, and No. 202106240010, respectively, CSC, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.csc.edu.cn\">www.csc.edu.cn</ext-link>). S.M. acknowledges support from the Helmholtz Association via the Program NACIP (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.nacip.kit.edu\">http://www.nacip.kit.edu</ext-link>).</p>","<title>Author contributions</title>","<p>Experiment Design: B.Y., Y.W., M.I., M.H., A.P.; POM synthesis: M.I., K.B.; Synthesis and characterization of MSPs-POM: P.P., K.B.; Printing experiments: B.Y. W.W., C.S., Optical microscopy: B.Y., W.W., C.S.; Electron microscopy and analysis: S.M.; XPS measurements: D.M., A.S.; Data analysis: B.Y., P.P., W.W., Y.W., D.M., A.S.; Data curation: M.I., M.H., A.P.; Supervision: M.I., M.H., A.P.; Writing initial manuscript: B.Y., P.P., M.I., M.H.; All authors were involved in revising the manuscript.</p>","<title>Funding</title>","<p>Open Access funding enabled and organized by Projekt DEAL.</p>","<title>Data availability</title>","<p>The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.</p>","<title>Competing interests</title>","<p id=\"Par37\">The authors declare no competing interests.</p>"],"string":"[\n \"<title>Supplementary Information</title>\",\n \"<p>The online version contains supplementary material available at 10.1038/s41598-023-50846-2.</p>\",\n \"<title>Acknowledgements</title>\",\n \"<p>This work was partly carried out with the support of the Karlsruhe Nano Micro Facility (KNMFi, <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"http://www.knmf.kit.edu\\\">www.knmf.kit.edu</ext-link>), a Helmholtz Research Infrastructure at Karlsruhe Institute of Technology (KIT, <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"http://www.kit.edu\\\">www.kit.edu</ext-link>). This work was supported by KNMFi project Nr. 2020-023-028480. B.Y. and W.W. acknowledge support by the China Scholarship Council fellowship (No. 201807040067, and No. 202106240010, respectively, CSC, <ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"http://www.csc.edu.cn\\\">www.csc.edu.cn</ext-link>). S.M. acknowledges support from the Helmholtz Association via the Program NACIP (<ext-link ext-link-type=\\\"uri\\\" xlink:href=\\\"http://www.nacip.kit.edu\\\">http://www.nacip.kit.edu</ext-link>).</p>\",\n \"<title>Author contributions</title>\",\n \"<p>Experiment Design: B.Y., Y.W., M.I., M.H., A.P.; POM synthesis: M.I., K.B.; Synthesis and characterization of MSPs-POM: P.P., K.B.; Printing experiments: B.Y. W.W., C.S., Optical microscopy: B.Y., W.W., C.S.; Electron microscopy and analysis: S.M.; XPS measurements: D.M., A.S.; Data analysis: B.Y., P.P., W.W., Y.W., D.M., A.S.; Data curation: M.I., M.H., A.P.; Supervision: M.I., M.H., A.P.; Writing initial manuscript: B.Y., P.P., M.I., M.H.; All authors were involved in revising the manuscript.</p>\",\n \"<title>Funding</title>\",\n \"<p>Open Access funding enabled and organized by Projekt DEAL.</p>\",\n \"<title>Data availability</title>\",\n \"<p>The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.</p>\",\n \"<title>Competing interests</title>\",\n \"<p id=\\\"Par37\\\">The authors declare no competing interests.</p>\"\n]","isConversational":false},"figure":{"kind":"list like","value":["<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Structure of Preyssler-type POM [GdP<sub>5</sub>W<sub>30</sub>O<sub>110</sub>]<sup>##UREF##7##12##</sup> (<bold>A</bold>) Top view, (<bold>B</bold>) side view. Polyhedral color scheme: WO<sub>6</sub> green, PO<sub>4</sub> pink. Color scheme for balls: Gd = violet, O = red. The structure was visualized using Diamond 4 software<sup>##UREF##9##14##</sup> with a CIF file obtained from single-crystal X-ray diffraction analysis.</p></caption></fig>","<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Scheme of the immobilization strategy. MSPs are loaded with POMs to create POM-loaded MSPs. These are then spotted via scanning probe lithography (SPL) methods into microarrays on alkene-bearing surfaces. The POM-loaded MSPs are then immobilized on the surface by amine-Michael addition. Figure created with BioRender.com.</p></caption></fig>","<fig id=\"Fig3\"><label>Figure 3</label><caption><p>(<bold>A</bold>) SEM image of MSPs-POM. (<bold>B</bold>) ATR-FTIR spectra of MSPs, MSPs-POM, and {GdP<sub>5</sub>W<sub>30</sub>} POM. (<bold>C</bold>) Left: representative SEM image of MSPs-POM and the region (white rectangle) subjected to EDX analysis. Right: atomic weight fractions and errors from EDX analysis of MSPs-POM.</p></caption></fig>","<fig id=\"Fig4\"><label>Figure 4</label><caption><p>(<bold>A</bold>) Number intensity weighted DLS size distribution of MSPs-POM in PBS buffer (10X, pH 7.5 at 37 °C) and (<bold>B</bold>) corresponding autocorrelation functions (ACF) over 5 days.</p></caption></fig>","<fig id=\"Fig5\"><label>Figure 5</label><caption><p>(<bold>A</bold>) Schematic illustration of MPS-modified surfaces functionalization by thiol-ene and amine-ene click reactions and chemical structure of probes used to test the surface reactivity. Results of the characterization of MPS-modified surfaces by (<bold>B</bold>) WCA, yielding (64.3 ± 2.3), (<bold>C</bold>) AFM (Ra = (0.251 ± 0.051) nm), and (<bold>D</bold>) XP spectra at C 1s region of bare glass and MPS-modified surfaces.</p></caption></fig>","<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Fluorescent images of microarrays for (<bold>A</bold>) NH<sub>2</sub>-Tamra (exposure time 300 ms) and (<bold>B</bold>) SH-FITC (exposure time 1 s) spots after finished click-reactions and washing of MPS-modified surfaces. Fluorescent images of (<bold>C</bold>) NH<sub>2</sub>-Biotin and (<bold>D</bold>) SH-Biotin spotted MPS-modified surfaces after incubation of SA-Cy3 (Exposure time 500 ms). (<bold>E</bold>) Fluorescence intensity of SA-Cy3 coupled patterns collected from images in C and D. Scale bars equal 100 μm in all images.</p></caption></fig>","<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Patterned immobilized MSPs-POM. (<bold>A</bold>) Bright field and (<bold>B</bold>) fluorescence microscopy image of capillary printed microarrays of MSPs-POM ink, before the heating and washing step. (<bold>C</bold>) Fluorescence microscopy image of the microarray after washing. Images of the droplet circled in A, in (<bold>D</bold>) bright field, and fluorescence microscopy images (<bold>E</bold>) before and (<bold>F</bold>) after the heating and washing step. (<bold>G</bold>) SEM image of two immobilized MSPs-POM particles. (<bold>H</bold>) AFM image of an immobilized MSPs-POM particle. Scale bars in (<bold>A–C</bold>) represent 200 µm, in (<bold>D–F</bold>) 50 µm, in (<bold>G</bold>) 200 nm, and in (<bold>H</bold>) 400 nm.</p></caption></fig>"],"string":"[\n \"<fig id=\\\"Fig1\\\"><label>Figure 1</label><caption><p>Structure of Preyssler-type POM [GdP<sub>5</sub>W<sub>30</sub>O<sub>110</sub>]<sup>##UREF##7##12##</sup> (<bold>A</bold>) Top view, (<bold>B</bold>) side view. Polyhedral color scheme: WO<sub>6</sub> green, PO<sub>4</sub> pink. Color scheme for balls: Gd = violet, O = red. The structure was visualized using Diamond 4 software<sup>##UREF##9##14##</sup> with a CIF file obtained from single-crystal X-ray diffraction analysis.</p></caption></fig>\",\n \"<fig id=\\\"Fig2\\\"><label>Figure 2</label><caption><p>Scheme of the immobilization strategy. MSPs are loaded with POMs to create POM-loaded MSPs. These are then spotted via scanning probe lithography (SPL) methods into microarrays on alkene-bearing surfaces. The POM-loaded MSPs are then immobilized on the surface by amine-Michael addition. Figure created with BioRender.com.</p></caption></fig>\",\n \"<fig id=\\\"Fig3\\\"><label>Figure 3</label><caption><p>(<bold>A</bold>) SEM image of MSPs-POM. (<bold>B</bold>) ATR-FTIR spectra of MSPs, MSPs-POM, and {GdP<sub>5</sub>W<sub>30</sub>} POM. (<bold>C</bold>) Left: representative SEM image of MSPs-POM and the region (white rectangle) subjected to EDX analysis. Right: atomic weight fractions and errors from EDX analysis of MSPs-POM.</p></caption></fig>\",\n \"<fig id=\\\"Fig4\\\"><label>Figure 4</label><caption><p>(<bold>A</bold>) Number intensity weighted DLS size distribution of MSPs-POM in PBS buffer (10X, pH 7.5 at 37 °C) and (<bold>B</bold>) corresponding autocorrelation functions (ACF) over 5 days.</p></caption></fig>\",\n \"<fig id=\\\"Fig5\\\"><label>Figure 5</label><caption><p>(<bold>A</bold>) Schematic illustration of MPS-modified surfaces functionalization by thiol-ene and amine-ene click reactions and chemical structure of probes used to test the surface reactivity. Results of the characterization of MPS-modified surfaces by (<bold>B</bold>) WCA, yielding (64.3 ± 2.3), (<bold>C</bold>) AFM (Ra = (0.251 ± 0.051) nm), and (<bold>D</bold>) XP spectra at C 1s region of bare glass and MPS-modified surfaces.</p></caption></fig>\",\n \"<fig id=\\\"Fig6\\\"><label>Figure 6</label><caption><p>Fluorescent images of microarrays for (<bold>A</bold>) NH<sub>2</sub>-Tamra (exposure time 300 ms) and (<bold>B</bold>) SH-FITC (exposure time 1 s) spots after finished click-reactions and washing of MPS-modified surfaces. Fluorescent images of (<bold>C</bold>) NH<sub>2</sub>-Biotin and (<bold>D</bold>) SH-Biotin spotted MPS-modified surfaces after incubation of SA-Cy3 (Exposure time 500 ms). (<bold>E</bold>) Fluorescence intensity of SA-Cy3 coupled patterns collected from images in C and D. Scale bars equal 100 μm in all images.</p></caption></fig>\",\n \"<fig id=\\\"Fig7\\\"><label>Figure 7</label><caption><p>Patterned immobilized MSPs-POM. (<bold>A</bold>) Bright field and (<bold>B</bold>) fluorescence microscopy image of capillary printed microarrays of MSPs-POM ink, before the heating and washing step. (<bold>C</bold>) Fluorescence microscopy image of the microarray after washing. Images of the droplet circled in A, in (<bold>D</bold>) bright field, and fluorescence microscopy images (<bold>E</bold>) before and (<bold>F</bold>) after the heating and washing step. (<bold>G</bold>) SEM image of two immobilized MSPs-POM particles. (<bold>H</bold>) AFM image of an immobilized MSPs-POM particle. Scale bars in (<bold>A–C</bold>) represent 200 µm, in (<bold>D–F</bold>) 50 µm, in (<bold>G</bold>) 200 nm, and in (<bold>H</bold>) 400 nm.</p></caption></fig>\"\n]","isConversational":false},"table":{"kind":"list like","value":[],"string":"[]","isConversational":false},"formula":{"kind":"list like","value":["<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Gamma } = {\\text{D}}_{{\\text{h}}} \\cdot \\left( {\\frac{{4{{\\uppi \\upeta }}}}{{\\uplambda }}{ } \\cdot \\sin \\left( {\\frac{{\\uptheta }}{2}} \\right)} \\right)^{2}$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">Γ</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mtext>D</mml:mtext><mml:mtext>h</mml:mtext></mml:msub><mml:mo>·</mml:mo><mml:msup><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mfrac><mml:mrow><mml:mn>4</mml:mn><mml:mrow><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mi mathvariant=\"normal\">η</mml:mi></mml:mrow></mml:mrow><mml:mi mathvariant=\"normal\">λ</mml:mi></mml:mfrac><mml:mrow/><mml:mo>·</mml:mo><mml:mo>sin</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mfrac><mml:mi mathvariant=\"normal\">θ</mml:mi><mml:mn>2</mml:mn></mml:mfrac></mml:mfenced></mml:mrow></mml:mfenced><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math></alternatives></disp-formula>","<disp-formula id=\"Equb\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{D}}_{{\\text{h}}} = \\frac{{{\\text{k}}_{{\\text{b}}} \\cdot {\\text{T}}}}{{3 \\cdot {\\uppi } \\cdot {\\upeta } \\cdot {\\text{d}}}}.$$\\end{document}</tex-math><mml:math id=\"M4\" display=\"block\"><mml:mrow><mml:msub><mml:mtext>D</mml:mtext><mml:mtext>h</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mtext>k</mml:mtext><mml:mtext>b</mml:mtext></mml:msub><mml:mo>·</mml:mo><mml:mtext>T</mml:mtext></mml:mrow><mml:mrow><mml:mn>3</mml:mn><mml:mo>·</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>·</mml:mo><mml:mi mathvariant=\"normal\">η</mml:mi><mml:mo>·</mml:mo><mml:mtext>d</mml:mtext></mml:mrow></mml:mfrac><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>"],"string":"[\n \"<disp-formula id=\\\"Equa\\\"><alternatives><tex-math 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mathvariant=\\\"normal\\\">π</mml:mi><mml:mi mathvariant=\\\"normal\\\">η</mml:mi></mml:mrow></mml:mrow><mml:mi mathvariant=\\\"normal\\\">λ</mml:mi></mml:mfrac><mml:mrow/><mml:mo>·</mml:mo><mml:mo>sin</mml:mo><mml:mfenced close=\\\")\\\" open=\\\"(\\\"><mml:mfrac><mml:mi mathvariant=\\\"normal\\\">θ</mml:mi><mml:mn>2</mml:mn></mml:mfrac></mml:mfenced></mml:mrow></mml:mfenced><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math></alternatives></disp-formula>\",\n \"<disp-formula id=\\\"Equb\\\"><alternatives><tex-math id=\\\"M3\\\">\\\\documentclass[12pt]{minimal}\\n\\t\\t\\t\\t\\\\usepackage{amsmath}\\n\\t\\t\\t\\t\\\\usepackage{wasysym} \\n\\t\\t\\t\\t\\\\usepackage{amsfonts} \\n\\t\\t\\t\\t\\\\usepackage{amssymb} \\n\\t\\t\\t\\t\\\\usepackage{amsbsy}\\n\\t\\t\\t\\t\\\\usepackage{mathrsfs}\\n\\t\\t\\t\\t\\\\usepackage{upgreek}\\n\\t\\t\\t\\t\\\\setlength{\\\\oddsidemargin}{-69pt}\\n\\t\\t\\t\\t\\\\begin{document}$${\\\\text{D}}_{{\\\\text{h}}} = \\\\frac{{{\\\\text{k}}_{{\\\\text{b}}} 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PMC9284965 | 35840902 | [
"<title>Background</title>",
"<p id=\"Par19\">Coronaviruses are large, enveloped, single-stranded RNA viruses found in humans and other animals, such as dogs, cats, bats, chickens, cattle, pigs, and birds. These viruses have the potential to cause respiratory, enteric, hepatic, and neurologic diseases. The most c... | [
"<title>Methods</title>",
"<title>Design, setting, and participants</title>",
"<p id=\"Par21\">This cross-sectional retrospective cohort study included 380 hospitalized adult patients (18 years or older) with sCoV or COVID19 across four AMITA Health hospitals located in the Chicago metropolitan area. A total of... | [
"<title>Results</title>",
"<title>Demographics and baseline characteristics</title>",
"<p id=\"Par26\">The median age of the base cohort was 72 years (IQR, 59.0–83.0 years; range 21–98 years) and 203 (53.4%) were male. Among patients with sCoV infection, the Human Coronavirus (HCoV)-OC43 was the most common cor... | [
"<title>Discussion</title>",
"<p id=\"Par32\">This retrospective cohort study examined the characteristics and clinical outcomes of hospitalized patients with sCoV infection compared to patients with COVID-19. Patients with COVID-19 presented a higher case fatality rate and an almost fourfold increased risk of de... | [
"<title>Conclusions</title>",
"<p id=\"Par38\">In conclusion, the clinical spectrum of hospitalized patients with COVID-19 is more similar to SARS and MERS in terms of illness severity and case-fatality rate than hospitalized patients with sCoV infection. However, the demographics and baseline characteristics of ... | [
"<title>Background</title>",
"<p id=\"Par1\">Unlike SARS-CoV and MERS-C0V, SARS-CoV-2 has the potential to become a recurrent seasonal infection; hence, it is essential to compare the clinical spectrum of COVID-19 to the existent endemic coronaviruses. We conducted a retrospective cohort study of hospitalized pat... | [] | [
"<title>Acknowledgements</title>",
"<p>None.</p>",
"<title>Author contributions</title>",
"<p>GRN: Conceptualization, project administration, data curation, writing—original draft, methodology, formal analysis. GE: Project administration, data curation, writing—original draft, writing—review and editing. TD, ... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Distribution of human coronavirus species among 190 patients with seasonal coronavirus infection</p></caption></fig>"
] | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Comparison between inpatients with human seasonal coronaviruses</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">CoV-229E (N = 33)</th><th align=\"left\">CoV-HKU1 (N = 39)</th><th align=\"left\">CoV-NL6... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p><sup>a</sup>Shapiro–Wilk normality test results showed deviation from a normal distribution</p><p><sup>b</sup>P-value obtained with a Bonferroni Chi-Square residual analysis: p = .023</p><p><sup>c</sup>P-value obtained with a Bonferroni Chi-Square residual analysis: p = .018</p></table-wrap-foo... | [
"<graphic xlink:href=\"12879_2022_7555_Fig1_HTML\" id=\"MO1\"/>"
] | [] | [{"label": ["2."], "surname": ["Liu", "Liang", "Fung"], "given-names": ["DX", "JQ", "TS"], "article-title": ["Human Coronavirus-229E, -OC43, -NL63, and -HKU1 (Coronaviridae)"], "source": ["Encycl Virol."], "year": ["2021"], "pub-id": ["10.1016/B978-0-12-809633-8.21501-X"]}, {"label": ["20."], "mixed-citation": ["Key Points About Myocardial Injury and Cardiac Troponin in COVID-19. "], "ext-link": ["https://www.acc.org/latest-in-cardiology/articles/2020/07/17/08/00/key-points-about-myocardial-injury-and-cardiac-troponin-in-covid-19"]}, {"label": ["21."], "mixed-citation": ["Clinical Spectrum of SARS-CoV-2 Infection. "], "ext-link": ["https://www.covid19treatmentguidelines.nih.gov/overview/clinical-spectrum/"]}, {"label": ["36."], "mixed-citation": ["Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999\u20132019 on CDC WONDER Online Database, released in 2020. Data are from the Multiple Cause of Death Files, 1999\u20132019, as compiled from data provided by the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program. "], "ext-link": ["http://wonder.cdc.gov/ucd-icd10.html"]}, {"label": ["40."], "mixed-citation": ["National Trends for Common Human Coronaviruses. "], "ext-link": ["https://www.cdc.gov/surveillance/nrevss/coronavirus/natl-trends.html"]}, {"label": ["42."], "mixed-citation": ["Genomic epidemiology of novel coronavirus - North America-focused subsampling. "], "ext-link": ["https://nextstrain.org/ncov/gisaid/north-america?animate=2019-12-09,2022-02-11,0,0,30000&f_country=USA&gmin=15&p=full"]}] | {
"acronym": [
"AKI",
"ARDS",
"CDC WONDER",
"COVID-19",
"DNR/DNI",
"HCoV",
"IQR",
"IVM",
"LTCF",
"MERS",
"NREVSS",
"qSOFA",
"sCoV",
"SARS"
],
"definition": [
"Acute kidney injury",
"Acute respiratory distress syndrome",
"CDC Wide-ranging ONline Data for Epidemiologic Research",
"Coronavirus disease 2019",
"Do-not-resuscitate/do-not-intubate",
"Human Coronavirus",
"Interquartile range",
"Invasive mechanical ventilation",
"Long-term care facility",
"Middle East respiratory syndrome coronavirus",
"National Respiratory Enteric Virus Surveillance System",
"Quick Sequential Organ Failure Assessment",
"Seasonal coronavirus",
"Severe acute respiratory syndrome"
]
} | 43 | CC BY | no | 2024-01-15 23:35:09 | BMC Infect Dis. 2022 Jul 15; 22:618 | oa_package/dd/37/PMC9284965.tar.gz |
PMC10003871 | 36902924 | [
"<title>1. Introduction</title>",
"<p>Since the 1980s, with the rapid development of many Chinese cities, many buildings with a frame bottom floor and a masonry top floor, called bottom frame structures, have been built. Bottom frame structures are used as buildings on both sides of the street, with the lower flo... | [
"<title>3. Structural Stressing State Theory and Methods</title>",
"<title>3.1. Concept and Physical Law in Structural Stressing State Theory</title>",
"<p>The structural stressing state manifests structural response at a load level. Structural response data, such as strains and displacements, can be modeled to... | [
"<title>2.4. The Experimental Results</title>",
"<p>##TAB##5##Table 6##, ##TAB##6##Table 7## and ##TAB##7##Table 8## list the inter-story displacement angles for each level of seismic acceleration used in the shaker tests to describe the evolution of the failure mode of the bottom frame model. It should be noted ... | [
"<title>5. Discussion</title>",
"<p>The above stressing state modeling study of the bottom frame structure reveals the EPB and FS points in the failure process by establishing the stressing state mode with characteristic parameters, and further verification can be observed for the experimental phenomena listed in... | [
"<title>6. Conclusions</title>",
"<p>At present, the application of structural stressing state theory to various structures has significance in science and engineering: the scientific significance is to achieve the specific scientific discovery in the working process of a structure or a type of structure based on... | [
"<p>These authors contributed equally to this work.</p>",
"<p>As a classic issue, structural seismic bearing capacity could not be accurately predicted since it was based on a structural ultimate state with inherent uncertainty. This result led to rare research efforts to discover structures’ general and definite... | [
"<title>2. The Shaking Table Test of the Bottom Frame Model</title>",
"<title>2.1. The Bottom Frame Model</title>",
"<p>This experimental model in 1/5 scale ratio was designed referring to an actual 4-story bottom frame building close to the street based on the Code for Seismic Design of Buildings of P.R. China... | [
"<title>Acknowledgments</title>",
"<p>Thanks to Rui Zhe for contributing to revising this paper’s language. And for her excellent insights into concrete and masonry structures that have improved the paper.</p>",
"<title>Author Contributions</title>",
"<p>Conceptualization, L.Z. and R.L.; methodology, R.L. and... | [
"<fig position=\"anchor\" id=\"materials-16-01809-f002\"><label>Figure 2</label><caption><p>The bottom frame model, the layout of strain gauges and the cracking.</p></caption></fig>",
"<fig position=\"float\" id=\"materials-16-01809-f001\"><label>Figure 1</label><caption><p>The floor plans of the bottom frame str... | [
"<table-wrap position=\"float\" id=\"materials-16-01809-t001\"><object-id pub-id-type=\"pii\">materials-16-01809-t001_Table 1</object-id><label>Table 1</label><caption><p>The information of the tested bottom frame structure (mm).</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"center\" ... | [
"<disp-formula id=\"FD1-materials-16-01809\">\n<label>(1)</label>\n<mml:math id=\"mm1\" display=\"block\" overflow=\"scroll\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mi>e</mml:mi><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mstyle displaystyle=\"true\"><mml:mrow><m... | [] | [] | [] | [] | [] | [
"<fn-group><fn><p><bold>Disclaimer/Publisher’s Note:</bold> The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property res... | [
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"<graphic xlink:href=\"materials-16-01809-g001\" position=\"float\"/>",
"<graphic xlink:href=\"materials-16-01809-g003\" position=\"float\"/>",
"<graphic xlink:href=\"materials-16-01809-g004\" position=\"float\"/>",
"<graphic xlink:href... | [] | [{"label": ["1."], "person-group": ["\n"], "surname": ["Chen"], "given-names": ["J."], "source": ["Analysis on Seismic Performance of Masonry Structure with Bottom-Frame and Influence of Story Stiffness Ratio"], "publisher-name": ["Chongqing University"], "publisher-loc": ["Chongqing, China"], "year": ["2010"]}, {"label": ["2."], "person-group": ["\n"], "surname": ["Shan"], "given-names": ["R."], "source": ["Study on The Seismic Collapse Pattern and The Ruin\u2019s Structural Characteristics of Typical RC Frame Supported Masonry Structures Institute of Engineering Mechanics"], "publisher-name": ["China Earthquake Administration"], "publisher-loc": ["Beijing, China"], "year": ["2015"]}, {"label": ["3."], "person-group": ["\n"], "surname": ["Li", "Wang"], "given-names": ["B.", "Z."], "article-title": ["Lessons from the Performance of Masonry Structure with Ground RC Frame during Wenchuan Earthquake"], "source": ["ACTA Sci. Nat. Univ. Sunyatseni"], "year": ["2010"], "volume": ["49"], "fpage": ["22"], "lpage": ["27"]}, {"label": ["4."], "person-group": ["\n"], "surname": ["Gao", "Wang"], "given-names": ["X.", "Y."], "article-title": ["The Seismic Testing Study of 1/2 Scale Model of 7-Story Multi-Story Brick Building with Frame Shear Wall in First Story"], "source": ["Build. Sci."], "year": ["1995"], "volume": ["4"], "fpage": ["18"], "lpage": ["23"]}, {"label": ["5."], "person-group": ["\n"], "surname": ["Gao", "Meng", "Li", "Liao", "He", "Xiao", "Wang", "Wang", "Sun", "Yang"], "given-names": ["X.", "J.", "H.", "X.", "J.", "W.", "J.", "J.", "X.", "S."], "article-title": ["The Seismic Testing Study of a 1/3 Scale Model with R/C Frame and shear Wall in the Lower-Two Story and Masonry Structure in the Upper-Six Story"], "source": ["Build. Sci."], "year": ["1994"], "volume": ["3"], "fpage": ["12"], "lpage": ["18"]}, {"label": ["6."], "person-group": ["\n"], "surname": ["Liang", "Wang", "Liang"], "given-names": ["X.", "Q.", "Y."], "article-title": ["Experimental investigation on pseudo-dynamic test of 1/2 scale model brick masonry building with frame-shear wall structure at first two stories"], "source": ["China Civ. Eng. J."], "year": ["1999"], "volume": ["32"], "fpage": ["14"], "lpage": ["21"]}, {"label": ["7."], "person-group": ["\n"], "surname": ["Zheng"], "given-names": ["S."], "article-title": ["A reasonable and Simple determining method of aseismic wall numbers in Frame-Shear wall story of masonry building with frame structure at lower stories"], "source": ["Ind. Constr."], "year": ["2001"], "volume": ["31"], "fpage": ["22"], "lpage": ["25"]}, {"label": ["8."], "person-group": ["\n"], "surname": ["Li", "Gu"], "given-names": ["Q.", "R."], "article-title": ["Reaserch on displacement-based seismic capability of brick masonry structures with frame-shear wall at the bottom"], "source": ["World Earthq. Eng."], "year": ["2006"], "volume": ["22"], "fpage": ["64"], "lpage": ["67"]}, {"label": ["9."], "person-group": ["\n"], "surname": ["Song"], "given-names": ["L."], "source": ["Analysis and Verification on Seismic Performance of Masonry Building with Concrete Frame-Shear Wall Structure on First Two Stories"], "publisher-name": ["Chongqing University"], "publisher-loc": ["Chongqing, China"], "year": ["2011"]}, {"label": ["10."], "person-group": ["\n"], "surname": ["Zhou"], "given-names": ["G."], "source": ["Structural State-of-Stress Theory and Its Applications"], "publisher-name": ["Harbin Institute of Technology Press"], "publisher-loc": ["Harbin, China"], "year": ["2021"]}, {"label": ["11."], "person-group": ["\n"], "surname": ["Zhou", "Shi", "Li", "Li"], "given-names": ["G.", "J.", "P.", "H."], "article-title": ["Characteristics of structural state of stress for steel frame in progressive collapse"], "source": ["J. Constr. Steel Res."], "year": ["2019"], "volume": ["160"], "fpage": ["444"], "lpage": ["456"], "pub-id": ["10.1016/j.jcsr.2019.05.026"]}, {"label": ["13."], "person-group": ["\n"], "surname": ["Shi", "Xiao", "Zheng", "Shen", "Zhou"], "given-names": ["J.", "H.", "K.", "J.", "G."], "article-title": ["Essential stressing state features of a large-curvature continuous steel box-girder bridge model revealed by modeling experimental data"], "source": ["Thin-Walled Struct."], "year": ["2019"], "volume": ["143"], "fpage": ["106247"], "pub-id": ["10.1016/j.tws.2019.106247"]}, {"label": ["14."], "person-group": ["\n"], "surname": ["Shen", "Huang", "Yang", "Shi", "Zheng"], "given-names": ["J.", "W.", "X.", "J.", "K."], "article-title": ["Stressing State Analysis of CFST Arch Supports in Deep Roadway Based on NSF Method"], "source": ["Appl. Sci."], "year": ["2019"], "volume": ["9"], "elocation-id": ["4238"], "pub-id": ["10.3390/app9204238"]}, {"label": ["15."], "person-group": ["\n"], "surname": ["Shi", "Yang", "Zheng", "Shen", "Zhou", "Huang"], "given-names": ["J.", "K.", "K.", "J.", "G.", "Y."], "article-title": ["An Investigation into Working Behavior Characteristics of Parabolic Cfst Arches Applying Structural Stressing State Theory"], "source": ["J. Civ. Eng. Manag."], "year": ["2019"], "volume": ["25"], "fpage": ["215"], "lpage": ["227"], "pub-id": ["10.3846/jcem.2019.8102"]}, {"label": ["16."], "person-group": ["\n"], "surname": ["Chen", "Zhao", "Shao", "Zhou"], "given-names": ["Z.", "Y.", "Y.", "G."], "article-title": ["Essential state-of-stress features of HBBC connections revealed by modeling simulative strain energy"], "source": ["Eng. Struct."], "year": ["2021"], "volume": ["230"], "fpage": ["111463"], "pub-id": ["10.1016/j.engstruct.2020.111463"]}, {"label": ["17."], "person-group": ["\n"], "surname": ["Shen", "Liu", "Cheng", "Zhou"], "given-names": ["Z.", "B.", "X.", "G."], "article-title": ["Stressing state features of H-steel columns under cyclic biaxial bending action revealed from experimental residual strains"], "source": ["Case Stud. Constr. Mater."], "year": ["2023"], "volume": ["18"], "fpage": ["e01518"], "pub-id": ["10.1016/j.cscm.2022.e01518"]}, {"label": ["19."], "person-group": ["\n"], "surname": ["Shen", "Liu", "Zhou"], "given-names": ["Z.", "B.", "G."], "article-title": ["Stressing State Analysis of SRC Column with Modeling Test and Finite Element Model Data"], "source": ["Appl. Sci."], "year": ["2022"], "volume": ["12"], "elocation-id": ["8866"], "pub-id": ["10.3390/app12178866"]}, {"label": ["20."], "person-group": ["\n"], "surname": ["Liu", "Zhang", "Li", "Zhou", "Zhao"], "given-names": ["B.", "Y.", "R.", "G.", "Y."], "article-title": ["Essential stressing state features of spirally reinforced concrete short columns revealed by modeling experimental strain data"], "source": ["Structures"], "year": ["2020"], "volume": ["25"], "fpage": ["1"], "lpage": ["7"], "pub-id": ["10.1016/j.istruc.2020.02.006"]}, {"label": ["21."], "person-group": ["\n"], "surname": ["Liu", "Zhao", "Liu", "Zhou"], "given-names": ["B.", "Y.", "H.", "G."], "article-title": ["The Hysteretic Failure Features of Reinforced Masonry Shear Walls Revealed by Modeling Experimental Residual Strain Data"], "source": ["J. Earthq. Eng."], "year": ["2022"], "volume": ["26"], "fpage": ["7353"], "lpage": ["7365"], "pub-id": ["10.1080/13632469.2021.1961943"]}, {"label": ["22."], "person-group": ["\n"], "surname": ["Shen", "Liu", "Zhou"], "given-names": ["Z.", "B.", "G."], "article-title": ["Stressing state analysis of concrete airport pavement by modeling experimental strain data"], "source": ["Case Stud. Constr. Mater."], "year": ["2022"], "volume": ["17"], "fpage": ["e01635"], "pub-id": ["10.1016/j.cscm.2022.e01635"]}, {"label": ["23."], "std": ["GB 50011-2010"], "source": ["Code for Seismic Design of Buildings"], "publisher-name": ["China Architecture and Building Press"], "publisher-loc": ["Beijing, China"], "year": ["2010"]}, {"label": ["24."], "person-group": ["\n"], "surname": ["Mann"], "given-names": ["H.B."], "article-title": ["Non-parametric tests against trend"], "source": ["Econometrical"], "year": ["1945"], "volume": ["13"], "fpage": ["163"], "lpage": ["171"], "pub-id": ["10.2307/1907187"]}, {"label": ["25."], "person-group": ["\n"], "surname": ["Kendall"], "given-names": ["M.G."], "source": ["Rank Correlation Methods"], "publisher-name": ["Oxford University Press"], "publisher-loc": ["New York, NY, USA"], "year": ["1990"]}] | {
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} | 25 | CC BY | no | 2024-01-15 23:35:11 | Materials (Basel). 2023 Feb 22; 16(5):1809 | oa_package/3e/bd/PMC10003871.tar.gz |
PMC10034578 | 36934759 | [
"<title>Introduction</title>",
"<p>The success of evolutionary game theory (EGT) since Maynard Smith and Price published their classic 1973 <italic toggle=\"yes\">Nature</italic> paper ‘The logic of animal conflict’ [##UREF##0##1##] is well known. In the span of just a few years, it became one of the main modelli... | [
"<title>A method for behavioural ecology: a case study</title>",
"<p>Both Brockmann's and Riechert's pathways testify to the importance of collaborative ventures in this first flurry of EGT application by fieldworkers. To analyse one's data in EGT terms, collaborating with colleagues better versed in mathematical... | [] | [] | [] | [
"<p>One contribution of 18 to a theme issue ‘<ext-link xlink:href=\"http://dx.doi.org/10.1098/rstb/378/1876\" ext-link-type=\"uri\">Half a century of evolutionary games: a synthesis of theory, application and future directions</ext-link>’.</p>",
"<p>Though the first attempts to introduce game theory into evolutio... | [
"<title>Evolutionary game theory: first steps</title>",
"<p>The reception given to the theory of games developed by von Neumann and Morgenstern [##UREF##5##8##] in the mid-1940s provides a useful starting point. Albeit surprisingly, but abundantly documented by historians, game theory met with only lukewarm inter... | [
"<title>Acknowledgements</title>",
"<p>We express our very sincere gratitude to all the scientists contacted for this paper, who so generously shared their recollections; to the staff of the British Library, London, for kindly allowing us to consult their archives; and to John Welch, two anonymous reviewers and e... | [] | [] | [] | [] | [] | [
"<disp-quote><p>Someday, particularly for social traits, we will have to work out some more formal principles for applying natural selection than are commonly employed: you routinely think in terms of selection pressures operating simultaneously on several individuals at the same time, but this is not common, and i... | [] | [] | [] | [] | [] | [{"label": ["1"], "x": ["."], "surname": ["Maynard Smith", "Price"], "given-names": ["J", "GR"], "year": ["1973"], "article-title": ["The logic of animal conflict"], "source": ["Nature"], "bold": ["246"], "fpage": ["15"], "lpage": ["18"], "pub-id": ["10.1038/246015a0"]}, {"label": ["3"], "x": ["."], "surname": ["Sigmund"], "given-names": ["K"], "year": ["2005"], "comment": ["John Maynard Smith. ", ")"], "italic": ["Biogr. Mems Fell. R. 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M Gerstenhaber"], "fpage": ["62"], "lpage": ["87"], "publisher-loc": ["Providence, RI"], "publisher-name": ["American Mathematical Society"]}, {"label": ["101"], "x": ["."], "surname": ["Lewontin"], "given-names": ["RC"], "year": ["1974"], "source": ["The genetic basis of evolutionary change"], "publisher-loc": ["New York, NY"], "publisher-name": ["Columbia University Press"]}, {"label": ["102"], "x": ["."], "surname": ["Lewontin"], "given-names": ["RC"], "year": ["1982"], "article-title": ["Keeping it clean"], "source": ["Nature"], "bold": ["300"], "fpage": ["113"], "lpage": ["114"]}, {"label": ["103"], "x": ["."], "surname": ["Lewontin"], "given-names": ["R"], "year": ["1989"], "article-title": ["A natural selection"], "source": ["Nature"], "bold": ["339"], "fpage": ["107"], "pub-id": ["10.1038/339107a0"]}, {"label": ["105"], "x": ["."], "surname": ["Maynard Smith"], "given-names": ["J"], "year": ["1981"], "article-title": ["Will a sexual population evolve to an ESS?"], "source": ["Am. Nat."], "bold": ["117"], "fpage": ["1015"], "lpage": ["1018"], "pub-id": ["10.1086/283788"]}, {"label": ["106"], "x": ["."], "surname": ["Eshel"], "given-names": ["I"], "year": ["1982"], "article-title": ["Evolutionarily stable strategies and viability selection in Mendelian populations"], "source": ["Theor. Popul. Biol."], "bold": ["22"], "fpage": ["204"], "lpage": ["217"], "pub-id": ["10.1016/0040-5809(82)90042-9"]}, {"label": ["107"], "x": ["."], "surname": ["Darwin"], "given-names": ["CR"], "year": ["1871"], "source": ["The descent of man, and selection in relation to sex"], "publisher-loc": ["London, UK"], "publisher-name": ["John Murray"]}, {"label": ["109"], "x": ["."], "surname": ["Fisher"], "given-names": ["RA"], "year": ["1930"], "source": ["The genetical theory of natural selection"], "publisher-loc": ["Oxford, UK"], "publisher-name": ["Oxford University Press"]}, {"label": ["110"], "x": ["."], "surname": ["Gardner"], "given-names": ["A"], "year": ["2023"], "article-title": ["The rarer-sex effect"], "source": ["Phil. Trans. R. Soc. B"], "bold": ["378"], "comment": ["20210500"], "pub-id": ["10.1098/rstb.2021.0500"]}, {"label": ["111"], "x": ["."], "surname": ["Shaw"], "given-names": ["RR"], "year": ["2014"], "source": ["Itinerant professor: a memoir"], "publisher-name": ["Banfill Books"]}, {"label": ["112"], "x": ["."], "surname": ["Charnov"], "given-names": ["EL"], "year": ["1982"], "source": ["The theory of sex allocation"], "publisher-loc": ["Princeton, NJ"], "publisher-name": ["Princeton University Press"]}, {"label": ["113"], "x": ["."], "surname": ["Bull", "Charnov"], "given-names": ["J", "E"], "year": ["1988"], "article-title": ["How fundamental are Fisherian sex ratios?"], "source": ["Oxford Surv. Evol. Biol."], "bold": ["5"], "fpage": ["96"], "lpage": ["135"]}, {"label": ["114"], "x": ["."], "surname": ["West"], "given-names": ["SA"], "year": ["2009"], "source": ["Sex allocation"], "publisher-loc": ["Princeton, NJ"], "publisher-name": ["Princeton University Press"]}, {"label": ["115"], "x": ["."], "surname": ["Wynne-Edwards"], "given-names": ["VC"], "year": ["1962"], "source": ["Animal dispersion in relation to social behaviour"], "publisher-loc": ["Edinburgh, UK"], "publisher-name": ["Oliver & Boyd"]}, {"label": ["116"], "x": ["."], "surname": ["Schoener", "Kamil", "Krebs", "Pulliam"], "given-names": ["TW", "AC", "JR", "HR"], "year": ["1987"], "article-title": ["A brief history of optimal foraging ecology"], "source": ["Foraging behavior"], "fpage": ["5"], "lpage": ["67"], "publisher-loc": ["New York, NY"], "publisher-name": ["Plenum"]}, {"label": ["118"], "x": ["."], "surname": ["Charnov", "Maynard Smith", "Bull"], "given-names": ["EL", "J", "JJ"], "year": ["1976"], "article-title": ["Why be an hermaphrodite?"], "source": ["Nature"], "bold": ["263"], "fpage": ["125"], "lpage": ["126"], "pub-id": ["10.1038/263125a0"]}, {"label": ["119"], "x": ["."], "surname": ["Charnov"], "given-names": ["EL"], "year": ["1979"], "article-title": ["Natural selection and sex change in pandalid shrimp: test of a life-history theory"], "source": ["Am. Nat."], "bold": ["113"], "fpage": ["715"], "lpage": ["734"], "pub-id": ["10.1086/283428"]}, {"label": ["120"], "x": ["."], "surname": ["Charnov"], "given-names": ["EL"], "year": ["1979"], "article-title": ["The genetical evolution of patterns of sexuality: Darwinian fitness"], "source": ["Am. Nat."], "bold": ["113"], "fpage": ["465"], "lpage": ["480"], "pub-id": ["10.1086/283407"]}, {"label": ["122"], "x": ["."], "surname": ["Hines", "Bishop"], "given-names": ["W", "D"], "year": ["1983"], "article-title": ["Evolutionarily stable strategies in diploid populations with general inheritance patterns"], "source": ["J. Appl. Prob."], "bold": ["20"], "fpage": ["395"], "lpage": ["399"], "pub-id": ["10.2307/3213812"]}, {"label": ["123"], "x": ["."], "surname": ["Guicciardini"], "given-names": ["N"], "year": ["1999"], "source": ["Reading the "], "italic": ["Principia"], "publisher-loc": ["Cambridge, UK"], "publisher-name": ["Cambridge University Press"]}, {"label": ["126"], "x": ["."], "surname": ["Eshel"], "given-names": ["I"], "year": ["1978"], "article-title": ["On a prey-predator nonzero-sum game and the evolution of gregarious behavior of evasive prey"], "source": ["Am. Nat."], "bold": ["112"], "fpage": ["787"], "lpage": ["795"], "pub-id": ["10.1086/283323"]}, {"label": ["127"], "x": ["."], "surname": ["Eshel"], "given-names": ["I"], "year": ["1991"], "article-title": ["Game theory and population dynamics in complex genetical systems: the role of sex in short term and in long term evolution"], "source": ["Game equilibrium models I"], "fpage": ["6"], "lpage": ["28"], "publisher-loc": ["Berlin, Germany"], "publisher-name": ["Springer"]}, {"label": ["128"], "x": ["."], "surname": ["Eshel", "Feldman", "Bergman"], "given-names": ["I", "MW", "A"], "year": ["1998"], "article-title": ["Long-term evolution, short-term evolution, and population genetic theory"], "source": ["J. Theor. Biol."], "bold": ["191"], "fpage": ["391"], "lpage": ["396"], "pub-id": ["10.1006/jtbi.1997.0597"]}, {"label": ["129"], "x": ["."], "surname": ["Eshel", "Feldman", "Orzack", "Sober"], "given-names": ["I", "MW", "SH", "E"], "year": ["2001"], "article-title": ["Optimality and evolutionary stability under short-term and long-term selection"], "source": ["Adaptationism and optimality"], "fpage": ["161"], "lpage": ["190"], "publisher-loc": ["Cambridge, UK"], "publisher-name": ["Cambridge University Press"]}, {"label": ["134"], "x": ["."], "surname": ["Grafen"], "given-names": ["A"], "year": ["2014"], "article-title": ["The formal Darwinism project in outline"], "source": ["Biol. Phil."], "bold": ["29"], "fpage": ["155"], "lpage": ["174"], "pub-id": ["10.1007/s10539-013-9414-y"]}, {"label": ["135"], "x": ["."], "surname": ["Grafen", "Hendricks", "Hansen"], "given-names": ["A", "VF", "PG"], "year": ["2007"], "article-title": ["Alan Grafen"], "source": ["Game theory: five questions"], "fpage": ["61"], "lpage": ["74"], "publisher-loc": ["New York, NY"], "publisher-name": ["Automatic Press"]}] | {
"acronym": [],
"definition": []
} | 135 | CC BY | no | 2024-01-15 23:43:51 | Philos Trans R Soc Lond B Biol Sci.; 378(1876):20210493 | oa_package/72/16/PMC10034578.tar.gz |
PMC10184354 | 37189133 | [
"<title>Introduction</title>",
"<p id=\"Par33\">The endospore-forming <italic>Bacillus cereus</italic> is a Gram-positive, food poisoning-associated pathogen, with the ability to cause severe gastrointestinal tract infections [##UREF##0##1##, ##REF##33525722##2##]. Besides, there is a growing body of evidence sup... | [
"<title>Methods</title>",
"<title><italic>B. cereus</italic> strains and growth conditions</title>",
"<p id=\"Par64\">The enteropathogenic <italic>B. cereus</italic> strain NVH0075-95, isolated from vegetables after a large food poisoning outbreak in Norway [##REF##8768516##7##], and its isogenic mutant strains... | [
"<title>Results</title>",
"<title><italic>B. cereus</italic> secretes EVs into the culture supernatant, containing the multicomponent enterotoxin Nhe and the membrane active enzyme SMase</title>",
"<p id=\"Par38\">In recent years, extracellular prokaryotic membrane vesicles have been reported to play an importa... | [
"<title>Discussion</title>",
"<p id=\"Par51\">EVs secreted by clinically relevant bacteria are considered important mediators of host–pathogen interactions. In recent years, there is growing evidence that pathogenic Gram-positive bacteria deliver multiple virulence factors via EVs in a protected manner to target ... | [
"<title>Conclusion</title>",
"<p id=\"Par62\">Though considerable progress has been made in the characterization of EVs from Gram-positive bacteria [##REF##26324094##30##, ##REF##32989035##31##], their role in <italic>B. cereus</italic> pathogenicity remained elusive. The discovery of <italic>B. cereus</italic> v... | [
"<title>Background</title>",
"<p id=\"Par1\">Extracellular vesicles (EVs) from Gram-positive bacteria have gained considerable importance as a novel transport system of virulence factors in host–pathogen interactions. <italic>Bacillus cereus</italic> is a Gram-positive human pathogen, causing gastrointestinal tox... | [
"<title>Limitation of this study</title>",
"<p id=\"Par60\">Currently, there are no minimal guidelines for the isolation of bacterial extracellular vesicles, in contrast to the minimal information for studies of eukaryotic extracellular vesicles defined in 2018 by a position paper of the International Society for... | [
"<title>Acknowledgements</title>",
"<p>We thank Richard Dietrich (Department of Veterinary Sciences, Faculty of Veterinary Medicine, Ludwig-Maximilians-University, Munich, Germany) for the generous gift of the antibodies. The excellent technical support by Tatjana Svoboda (Institute of Microbiology, University of... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p><italic>B. cereus</italic> releases EVs in vitro. EVs of <italic>B. cereus</italic> NVH0095-75 were characterized using nanoparticle tracking analysis (NTA), transmission electron microscopy and Fourier transform infrared (FTIR) spectroscopy. <bold>A</bold> Purifie... | [] | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>",
"<supplementary-material ... | [
"<fn-group><fn><p>The original version of this article was revised: The family name of author Markus Kranzler has been corrected.</p></fn><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p><... | [
"<graphic xlink:href=\"12964_2023_1132_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"12964_2023_1132_Fig2_HTML\" id=\"MO2\"/>",
"<graphic xlink:href=\"12964_2023_1132_Fig3_HTML\" id=\"MO3\"/>",
"<graphic xlink:href=\"12964_2023_1132_Fig4_HTML\" id=\"MO4\"/>",
"<graphic xlink:href=\"12964_2023_1132_Fig5_... | [
"<media xlink:href=\"12964_2023_1132_MOESM6_ESM.mp4\" id=\"MOESM6\"><caption><p>Video Abstract</p></caption></media>",
"<media xlink:href=\"12964_2023_1132_MOESM1_ESM.pdf\"><caption><p><bold>Additional file 1: Figure S1.</bold></p></caption></media>",
"<media xlink:href=\"12964_2023_1132_MOESM2_ESM.pdf\"><capti... | [{"label": ["1."], "mixed-citation": ["Ehling-Schulz M, Lereclus D, Koehler TM. The "], "italic": ["Bacillus cereus", "Bacillus"]}, {"label": ["11."], "surname": ["Ehling-Schulz", "Svensson", "Guinebretiere", "Lindb\u00e4ck", "Andersson", "Schulz"], "given-names": ["M", "B", "MH", "T", "M", "A"], "article-title": ["Emetic toxin formation of Bacillus cereus is restricted to a single evolutionary lineage of closely related strains"], "source": ["Microbiology (N Y)"], "year": ["2005"], "volume": ["151"], "issue": ["1"], "fpage": ["183"], "lpage": ["197"]}, {"label": ["15."], "surname": ["Lindback"], "given-names": ["T"], "article-title": ["Characterization of the Bacillus cereus Nhe enterotoxin"], "source": ["Microbiology (N Y)"], "year": ["2004"], "volume": ["150"], "issue": ["12"], "fpage": ["3959"], "lpage": ["3967"]}, {"label": ["22."], "surname": ["Beecher", "Wong"], "given-names": ["DJ", "ACL"], "article-title": ["Cooperative, synergistic and antagonistic haemolytic interactions between haemolysin BL, phosphatidylcholine phospholipase C and sphingomyelinase from Bacillus cereus"], "source": ["Microbiology (N Y)"], "year": ["2000"], "volume": ["146"], "issue": ["12"], "fpage": ["3033"], "lpage": ["3039"]}, {"label": ["32."], "surname": ["Nagakubo", "Nomura", "Toyofuku"], "given-names": ["T", "N", "M"], "article-title": ["Cracking open bacterial membrane vesicles"], "source": ["Front Microbiol"], "year": ["2020"], "volume": ["17"], "fpage": ["10"]}, {"label": ["47."], "surname": ["Tartaglia", "Breyne", "Meyer", "Cauty", "Jardin", "Chr\u00e9tien"], "given-names": ["NR", "K", "E", "C", "J", "D"], "article-title": ["Staphylococcus aureus extracellular vesicles elicit an immunostimulatory response in vivo on the murine mammary gland"], "source": ["Front Cell Infect Microbiol"], "year": ["2018"], "volume": ["22"], "fpage": ["8"]}, {"label": ["49."], "mixed-citation": ["Brown L, Kessler A, Cabezas-Sanchez P, Luque-Garcia JL, Casadevall A. Extracellular vesicles produced by the Gram-positive bacterium "], "italic": ["Bacillus subtilis"]}, {"label": ["50."], "surname": ["Wagner", "Joshi", "Janice", "Askarian", "\u0160kalko-Basnet", "Hagestad"], "given-names": ["T", "B", "J", "F", "N", "OC"], "article-title": ["Enterococcus faecium produces membrane vesicles containing virulence factors and antimicrobial resistance related proteins"], "source": ["J Proteomics"], "year": ["2018"], "volume": ["15"], "issue": ["187"], "fpage": ["28"], "lpage": ["38"], "pub-id": ["10.1016/j.jprot.2018.05.017"]}, {"label": ["54."], "surname": ["Typas", "Banzhaf", "Gross", "Vollmer"], "given-names": ["A", "M", "CA", "W"], "article-title": ["From the regulation of peptidoglycan synthesis to bacterial growth and morphology"], "source": ["Nat Rev Microbiol"], "year": ["2012"], "volume": ["10"], "issue": ["2"], "fpage": ["123"], "lpage": ["136"], "pub-id": ["10.1038/nrmicro2677"]}, {"label": ["66."], "surname": ["Jessberger", "Krey", "Rademacher", "B\u00f6hm", "Mohr", "Ehling-Schulz"], "given-names": ["N", "VM", "C", "ME", "AK", "M"], "article-title": ["From genome to toxicity: a combinatory approach highlights the complexity of enterotoxin production in Bacillus cereus"], "source": ["Front Microbiol"], "year": ["2015"], "volume": ["10"], "fpage": ["6"]}, {"label": ["68."], "surname": ["Fagerlund", "Lindb\u00e4ck", "Storset", "Granum", "Hardy"], "given-names": ["A", "T", "AK", "PE", "SP"], "article-title": ["Bacillus cereus Nhe is a pore-forming toxin with structural and functional properties similar to the ClyA (HlyE, SheA) family of haemolysins, able to induce osmotic lysis in epithelia"], "source": ["Microbiology (N Y)"], "year": ["2008"], "volume": ["154"], "issue": ["3"], "fpage": ["693"], "lpage": ["704"]}, {"label": ["69."], "surname": ["Mehanny", "Koch", "Lehr", "Fuhrmann"], "given-names": ["M", "M", "CM", "G"], "article-title": ["Streptococcal extracellular membrane vesicles are rapidly internalized by immune cells and alter their cytokine release"], "source": ["Front Immunol"], "year": ["2020"], "volume": ["14"], "fpage": ["11"]}, {"label": ["73."], "mixed-citation": ["Mehdiani A, Maier A, Pinto A, Barth M, Akhyari P, Lichtenberg A. An innovative method for exosome quantification and size measurement. J Vis Exp. 2015;(95)50974."]}] | {
"acronym": [
"ABC",
"Caco2",
"CME",
"DTT",
"EVs",
"FASP",
"FCS",
"FDR",
"FTIR",
"Hbl",
"IAA",
"LB",
"LC–MS/MS",
"LRS",
"MISEV",
"Nhe",
"NTA",
"OMVs",
"PBMCs",
"PBP",
"PBS",
"PFA",
"PLC",
"R18",
"SMase",
"TCA",
"TEM",
"TFA",
"TNF-α"
],
"definition": [
"ATP-binding cassette",
"Human colon carcinoma cell line 2",
"Clathrin-mediated endocytosis",
"Dithiothreitol",
"Extracellular vesicles",
"Filter aided sample preparation",
"Fetal calf serum",
"False discovery rate",
"Fourier tansform infrared spectroscopy",
"Hemolysin BL",
"Iodacetamide",
"Lysogeny broth",
"Liquid Chromatography-Tandem Mass Spectrometry",
"Leukocyte reduction system",
"Minimal information for studies of erxtracellular vesicles",
"Non-hemolytic enterotoxin",
"Nanoparticle tracking analysis",
"Outer membrane vesicles",
"Peripheral blood mononuclear cells",
"Penicillin binding protein",
"Phosphate-buffered saline",
"Paraformaldehyde",
"Phospholipase C",
"Octadecyl rhodamine B chloride",
"Sphingomyelinase",
"Thrichloroacetic acid",
"Transmission electron microscopy",
"Trifluoroacetic",
"Tumor necrosis factor-alpha"
]
} | 85 | CC BY | no | 2024-01-15 23:35:09 | Cell Commun Signal. 2023 May 15; 21:112 | oa_package/1f/17/PMC10184354.tar.gz |
PMC10229423 | 37198474 | [] | [
"<title>Methods</title>",
"<title>Cell lines</title>",
"<p id=\"Par24\">K562 (CCL-243), 293T (CRL-3216), HeLa (CCL-2), A375 (CRL-1619), A2058 (CRL-11147), SH4 (CRL-7724), MDA-MB-435S (HTB-129), SK-MEL-5 (HTB-70), SK-MEL-30 (HTB-63) and THP1 (TIB-202) cell lines were obtained from the American Type Culture Colle... | [] | [] | [] | [
"<p id=\"Par1\">Glucose is vital for life, serving as both a source of energy and carbon building block for growth. When glucose is limiting, alternative nutrients must be harnessed. To identify mechanisms by which cells can tolerate complete loss of glucose, we performed nutrient-sensitized genome-wide genetic scr... | [
"<title>Main</title>",
"<p id=\"Par3\">We sought to identify new genes and pathways that might serve as alternative sources of energy when glucose is limiting. We transduced K562 cells with a library comprising 17,255 barcoded open reading frames (ORFs)<sup>##REF##21706014##2##</sup> and compared proliferation in... | [
"<title>Extended data</title>",
"<p id=\"Par60\">\n\n</p>",
"<p id=\"Par61\">\n\n</p>",
"<p id=\"Par62\">\n\n</p>",
"<p id=\"Par63\">\n\n</p>",
"<p id=\"Par64\">\n\n</p>",
"<p id=\"Par65\">\n\n</p>",
"<p id=\"Par66\">\n\n</p>",
"<p id=\"Par67\">\n\n</p>",
"<p id=\"Par68\">\n\n</p>",
"<title>Exte... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Uridine phosphorylase activity supports growth on uridine or RNA.</title><p><bold>a</bold>, Schematic overview of the ORF proliferation screen. <bold>b</bold>, Volcano plot representation of the screen hits after 21 d of growth in medium containing 25 mM glucos... | [] | [
"<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepack... | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>",
"<supplementary-material ... | [
"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Owen S. Skinner, Joan Blanco-Fernández.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"42255_2023_774_Fig1_HTML\" id=\"d32e510\"/>",
"<graphic xlink:href=\"42255_2023_774_Fig2_HTML\" id=\"d32e757\"/>",
"<graphic xlink:href=\"42255_2023_774_Fig3_HTML\" id=\"d32e1339\"/>",
"<graphic xlink:href=\"42255_2023_774_Fig4_HTML\" id=\"d32e1559\"/>",
"<graphic xlink:href=\"42255_2... | [
"<media xlink:href=\"42255_2023_774_MOESM1_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>",
"<media xlink:href=\"42255_2023_774_MOESM2_ESM.xlsx\"><label>Supplementary Table 1</label><caption><p>Raw results and analysis of the ORFeome, CRISPR–Cas9 and PRISM screens.</p></caption></media>",
"<media... | [{"label": ["23."], "surname": ["Goncalves", "Hopkins", "Cantley"], "given-names": ["MD", "BD", "LC"], "article-title": ["Dietary fat and sugar in promoting cancer development and progression"], "source": ["Annu. Rev. Cancer Biol."], "year": ["2019"], "volume": ["3"], "fpage": ["255"], "lpage": ["273"], "pub-id": ["10.1146/annurev-cancerbio-030518-055855"]}, {"label": ["26."], "mixed-citation": ["Chemical composition of alcoholic beverages, additives and contaminants. "], "italic": ["IARC Monogr. Eval. Carcinog. Risks Hum."], "bold": ["44"]}, {"label": ["33."], "mixed-citation": ["Nwosu, Z. C. et al. Uridine-derived ribose fuels glucose-restricted pancreatic cancer. "], "italic": ["Nature"]}] | {
"acronym": [],
"definition": []
} | 46 | CC BY | no | 2024-01-15 23:35:06 | Nat Metab. 2023 May 17; 5(5):765-776 | oa_package/05/40/PMC10229423.tar.gz |
PMC10233181 | 37264348 | [
"<title>Background</title>",
"<p id=\"Par5\">Performing physical activities is vital in maintaining good health as it plays a large role in preventing cardiovascular diseases and reducing mortality rates [##UREF##0##1##–##UREF##3##4##]. For this reason, it is imperative that we maintain our physical health by par... | [
"<title>Methods</title>",
"<title>Study design and setting</title>",
"<p id=\"Par10\">We used a cross-sectional design for this study on adolescents and young adults. Participants were recruited from the UAE’s public and private schools and universities between January and August 2022.</p>",
"<title>Participa... | [
"<title>Results</title>",
"<p id=\"Par19\">Among 131 participants (age range: 14–25 years), 81% of the participants were non-Emirati Arabs (n = 106), 13% were Asians (n = 17), and 6% were Emiratis (n = 8). Other participants characteristics are summarized in Table ##TAB##1##2##.</p>",
"<p id=\"Par20\">\n\n</p>"... | [
"<title>Discussion</title>",
"<p id=\"Par30\">This study investigated the concurrent validity of self-reported ATLS-2 and Fibion accelerometer-measured time during various activities performed during the day such as sitting, walking, cycling, high intensity and moderate intensity activities. Overall, there were n... | [
"<title>Conclusion</title>",
"<p id=\"Par39\">This study showed an overall poor correlation and low agreement between self-reported ATLS-2 and Fibion accelerometer-measured sedentary and physical activity time. Negligible to weak correlations were noted for sitting, walking, cycling, high and moderate intensity a... | [
"<title>Background</title>",
"<p id=\"Par1\">Most young adults and adolescents in the United Arab Emirates (UAE) do not meet the established internationally recommended physical activity levels per day. The Arab Teen Lifestyle Study (ATLS) physical activity questionnaire has been recommended for measuring self-re... | [] | [
"<title>Acknowledgements</title>",
"<p>The authors would like to thank Professor Hazzaa M. Al-Hazzaa for providing us the ATLS-2 questionnaire and granting permission to use it for our studies.</p>",
"<title>Author Contribution</title>",
"<p>AA – conceptualization, design, supervision, data curation, data ana... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p> A Bland-Altman plot showing agreement between the Fibion accelerometer and ATLS-2 data for sitting time. The solid horizontal line represents bias (the mean of the differences between the two methods), while the horizontal dashed lines show upper and lower limits ... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Criteria for interpreting the Spearman rho correlation coefficient</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Correlation coefficient value range</th><th align=\"left\">Strength of correlation</th></tr></thead><tbody... | [
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"<table-wrap-foot><p>Note: These criteria were adapted from Schober et al.2018 [##UREF##41##43##].</p></table-wrap-foot>",
"<table-wrap-foot><p>BMI, body mass index; SD, standard deviation</p><p>*Number</p></table-wrap-foot>",
"<table-wrap-foot><p>*Time spent: light intensity (< 3 METs [metabolic equivalents... | [
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"acronym": [
"ATLS",
"ATLS-2",
"IPAQ-SF",
"METs",
"UAE"
],
"definition": [
"Arab Teen Lifestyle Study",
"Revised version of the Arab Teen Lifestyle Study questionnaire",
"International Physical Activity Questionnaire Short Form",
"Metabolic equivalents",
"United Arab Emirates"
]
} | 47 | CC BY | no | 2024-01-15 23:35:09 | BMC Public Health. 2023 Jun 1; 23:1045 | oa_package/05/33/PMC10233181.tar.gz |
PMC10258666 | 37305907 | [] | [] | [] | [] | [] | [
"<p>\n<email>louise.rollins-smith@vumc.org</email>\n</p>",
"<p>One contribution of 14 to a theme issue ‘<ext-link xlink:href=\"http://dx.doi.org/10.1098/rstb/378/1882\" ext-link-type=\"uri\">Amphibian immunity: stress, disease and ecoimmunology</ext-link>’.</p>",
"<p>As a class of vertebrates, amphibians, are a... | [
"<title>Amphibians responding to changing environments</title>",
"<p>Amphibians are ancient creatures valued by all human societies. They play critical roles in aquatic and semiaquatic environments as important consumers or competitors of insects and as prey for other animals. They share a complex neuroendocrine ... | [
"<title>Data accessibility</title>",
"<p>This article has no additional data.</p>",
"<title>Authors' contributions</title>",
"<p>L.A.R.-S.: conceptualization, funding acquisition, project administration, writing—original draft, writing—review and editing; E.H.LS.: writing—review and editing.</p>",
"<p>Both ... | [] | [] | [] | [] | [] | [] | [] | [] | [] | [] | [] | [{"label": ["2"], "x": [". "], "surname": ["P\u00f6rtner", "P\u00f6rtner"], "given-names": ["H-O", "H-O"], "etal": ["et al.", "et al"], "year": ["2022"], "article-title": ["Technical summary"], "source": ["Climate change 2022: impacts, adaptation and vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change"], "fpage": ["37"], "lpage": ["118"], "publisher-loc": ["Cambridge, UK"], "publisher-name": ["Cambridge University Press"]}, {"label": ["3"], "x": [". "], "surname": ["Larsen"], "given-names": ["EH"], "year": ["2021"], "article-title": ["Dual skin functions in amphibian osmoregulation"], "source": ["Comp. Biochem. Physiol. A"], "bold": ["253"], "fpage": ["110869"], "pub-id": ["10.1016/j.cbpa.2020.110869"]}, {"label": ["6"], "x": [". 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"], "surname": ["Paull", "Johnson"], "given-names": ["SH", "PTJ"], "year": ["2011"], "article-title": ["High temperature enhances host pathology in a snail\u2013trematode system: possible consequences of climate change for the emergence of disease"], "source": ["Freshw. Biol."], "bold": ["56"], "fpage": ["767"], "lpage": ["776"], "pub-id": ["10.1111/j.1365-2427.2010.02547.x"]}, {"label": ["94"], "x": [". "], "surname": ["Echaubard", "Leduc", "Pauli", "Chinchar", "Robert", "Lesbarreres"], "given-names": ["P", "J", "B", "VG", "J", "D"], "year": ["2014"], "article-title": ["Environmental dependency of amphibian\u2013ranavirus genotypic interactions: evolutionary perspectives on infectious diseases"], "source": ["Evol. App."], "bold": ["7"], "fpage": ["723"], "lpage": ["733"], "pub-id": ["10.1111/eva.12169"]}, {"label": ["96"], "x": [". "], "surname": ["Collins"], "given-names": ["JP"], "year": ["2010"], "article-title": ["Amphibian decline and extinction: what we know and what we need to learn"], "source": ["Dis. Aquat. Org."], "bold": ["92"], "fpage": ["93"], "lpage": ["99"], "pub-id": ["10.3354/dao02307"]}] | {
"acronym": [],
"definition": []
} | 98 | CC BY | no | 2024-01-15 23:43:51 | Philos Trans R Soc Lond B Biol Sci.; 378(1882):20220132 | oa_package/e5/f7/PMC10258666.tar.gz |
PMC10263403 | 37341226 | [] | [] | [] | [] | [] | [
"<p>Potencial conflito de interesse</p>",
"<p>Não há conflito com o presente artigo.</p>",
"<title>Resumo</title>",
"<p>O infarto do miocárdio com artérias coronárias não obstrutivas (MINOCA) é um fenômeno clínico intrigante e de prognóstico incerto, caracterizado pela evidência de infarto do miocárdio (IM) c... | [
"<title>Introdução</title>",
"<p>O infarto do miocárdio com artérias coronárias não obstrutivas (MINOCA) é um fenômeno clínico intrigante com um prognóstico incerto caracterizado por evidência de infarto do miocárdio (IM) com artérias coronárias normais ou quase normais na angiografia.<sup>##REF##32750696##1##</s... | [] | [
"<fig position=\"float\" id=\"f01\"><label>Figura 1</label><caption><title>– A ventriculografia esquerda revelou dilatação balonística apical com acinesia e hipertrofia apical ETT.</title></caption></fig>",
"<fig position=\"float\" id=\"f02\"><label>Figura 2</label><caption><title>– A) ECG inicial. B) Coronariogr... | [] | [] | [] | [] | [] | [] | [] | [
"<fn-group><fn fn-type=\"other\"><p>Vinculação acadêmica</p><p>Não há vinculação deste estudo a programas de pós-graduação.</p></fn><fn fn-type=\"other\"><p>Aprovação ética e consentimento informado</p><p>Este artigo não contém estudos com humanos ou animais realizados por nenhum dos autores.</p></fn></fn-group>",
... | [
"<graphic xlink:href=\"0066-782X-abc-120-06-e20220705-gf01\" position=\"float\"/>",
"<graphic xlink:href=\"0066-782X-abc-120-06-e20220705-gf02\" position=\"float\"/>",
"<graphic xlink:href=\"0066-782X-abc-120-06-e20220705-gf03\" position=\"float\"/>",
"<graphic xlink:href=\"0066-782X-abc-120-06-e20220705-gf01... | [] | [{"label": ["2"], "person-group": ["\n"], "surname": ["Lypovetska", "Shved", "Gurskyi"], "given-names": ["S", "M", "V"], "article-title": ["Takotsubo syndrome masking apical hypertrophic cardiomyopathy"], "source": ["Eur J Heart Fail"], "year": ["2019"], "volume": ["21"], "issue": ["Suppl. S1"], "fpage": ["552"], "lpage": ["592"], "pub-id": ["10.1002/ejhf.1488"]}, {"label": ["13"], "person-group": ["\n"], "surname": ["Iannopollo", "Ferlini", "Kozinski", "Crimi", "Lanfranchi", "Camporotondo"], "given-names": ["G", "M", "M.", "G", "L", "R"], "etal": ["et al"], "article-title": ["Patient outcomes with STEMI caused by aneurysmal coronary artery disease and treated with primary PCI"], "source": ["J Am Coll Cardiol"], "year": ["2017"], "volume": ["69"], "issue": ["24"], "size": ["14"], "pub-id": ["10.1016/j.jacc.2017.04.030"]}] | {
"acronym": [],
"definition": []
} | 14 | CC BY | no | 2024-01-15 23:35:10 | Arq Bras Cardiol. 2023 May 26; 120(6):e20220705 | oa_package/6e/8b/PMC10263403.tar.gz |
PMC10291421 | 37381861 | [
"<title>Introduction</title>",
"<p>There is broad agreement that 15 000 years before the present (BP), almost all humans lived in small mobile foraging bands. By 5000 BP, the first states had arisen in Mesopotamia and Egypt. The intervening 10 000 years saw a transition from egalitarian societies to stratificatio... | [] | [] | [] | [
"<title>Conclusion</title>",
"<p>The IOEC theory we have developed in this paper and in Dow & Reed [##UREF##3##4##, ch. 6]) has the following causal structure.\n<list list-type=\"simple\"><list-item><label>(a)<x xml:space=\"preserve\"> </x></label><p>Improvements in climate and/or technology raise regional pr... | [
"<p>One contribution of 20 to a theme issue ‘<ext-link xlink:href=\"http://dx.doi.org/10.1098/rstb/378/1883\" ext-link-type=\"uri\">Evolutionary ecology of inequality</ext-link>’.</p>",
"<p>We examine three recent frameworks that attempt to explain early inequality. One explanation involves the emergence of dense... | [
"<title>The Holocene environment and household inheritance</title>",
"<p>The HEHI theory of early inequality originated with Borgerhoff Mulder <italic toggle=\"yes\">et al</italic>. [##REF##19900925##11##], who distinguished three kinds of wealth (embodied, material and relational) that can be passed from parents... | [
"<title>Acknowledgements</title>",
"<p>We thank Robert Kelly, Stephen Shennan, Eric Alden Smith, and three anonymous referees for helpful comments on earlier drafts. They are not responsible for our errors or opinions.</p>",
"<title>Data accessibility</title>",
"<p>The verbal discussion in our paper relies up... | [] | [] | [] | [] | [] | [] | [] | [] | [] | [] | [] | [{"label": ["1"], "x": [". "], "surname": ["Kelly"], "given-names": ["RL"], "year": ["2013"], "source": ["The lifeways of hunter-gatherers: the foraging spectrum"], "publisher-loc": ["New York, NY"], "publisher-name": ["Cambridge University Press"]}, {"label": ["2"], "x": [". "], "surname": ["Boix"], "given-names": ["C"], "year": ["2015"], "source": ["Political order and inequality: their foundations and their consequences for human welfare"], "publisher-loc": ["New York, NY"], "publisher-name": ["Cambridge University Press"]}, {"label": ["3"], "x": [". "], "surname": ["Dow", "Reed"], "given-names": ["GK", "CG"], "year": ["2015"], "article-title": ["The origins of sedentism: climate, population, and technology"], "source": ["J. Econ. Behav. Org."], "bold": ["119"], "fpage": ["56"], "lpage": ["71"], "pub-id": ["10.1016/j.jebo.2015.07.007"]}, {"label": ["4"], "x": [". "], "surname": ["Dow", "Reed"], "given-names": ["GK", "CG"], "year": ["2022"], "data-title": ["Economic prehistory: six transitions that shaped the world"], "publisher-loc": ["New York, NY"], "publisher-name": ["Cambridge University Press"]}, {"label": ["5"], "x": [". "], "surname": ["Keeley"], "given-names": ["LH"], "year": ["1988"], "article-title": ["Hunter-gatherer economic complexity and \u2018population pressure\u2019: a cross-cultural analysis"], "source": ["J. Anthropol. Archaeol."], "bold": ["7"], "fpage": ["373"], "lpage": ["411"], "pub-id": ["10.1016/0278-4165(88)90003-7"]}, {"label": ["6"], "x": [". "], "surname": ["Keeley", "Barton", "Roberts", "Roe"], "given-names": ["LH", "N", "AJ", "DA"], "year": ["1991"], "article-title": ["Ethnographic models for late glacial hunter-gatherers"], "source": ["The late glacial in northwest Europe"], "fpage": ["179"], "lpage": ["190"], "publisher-loc": ["London, UK"], "publisher-name": ["Council for British Archaeology"]}, {"label": ["7"], "x": [". "], "surname": ["Rowley-Conwy", "Panter-Brick", "Layton", "Rowley-Conwy"], "given-names": ["P", "C", "RH", "P"], "year": ["2001"], "article-title": ["Time, change and the archaeology of hunter-gatherers: how original is the 'Original Affluent Society'?"], "source": ["Hunter-gatherers: an interdisciplinary perspective"], "fpage": ["39"], "lpage": ["72"], "publisher-loc": ["Cambridge, UK"], "publisher-name": ["Cambridge University Press"]}, {"label": ["8"], "x": [". 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"], "surname": ["Codding", "Parker", "Jones"], "given-names": ["BF", "AK", "TL"], "year": ["2019"], "article-title": ["Territorial behavior among Western North American foragers: Allee effects, within group cooperation, and between group conflict"], "source": ["Quat. Int."], "bold": ["518"], "fpage": ["31"], "lpage": ["40"], "pub-id": ["10.1016/j.quaint.2017.10.045"]}, {"label": ["33"], "x": [". "], "surname": ["Jazwa", "Kennett", "Winterhalder", "Joslin"], "given-names": ["CS", "DJ", "B", "TL"], "year": ["2019"], "article-title": ["Territoriality and the rise of despotic social organization on western Santa Rosa Island, California"], "source": ["Quat. Int."], "bold": ["518"], "fpage": ["41"], "lpage": ["56"], "pub-id": ["10.1016/j.quaint.2017.11.009"]}, {"label": ["34"], "x": [". "], "surname": ["Lambert", "Walker"], "given-names": ["PM", "PL"], "year": ["1991"], "article-title": ["Physical anthropological evidence for the evolution of social complexity in coastal Southern California"], "source": ["Antiquity"], "bold": ["65"], "fpage": ["963"], "lpage": ["973"], "pub-id": ["10.1017/S0003598X00080765"]}, {"label": ["35"], "x": [". "], "surname": ["Shennan"], "given-names": ["S"], "year": ["2018"], "source": ["The first farmers of Europe: an evolutionary perspective"], "publisher-loc": ["New York, NY"], "publisher-name": ["Cambridge University Press"]}, {"label": ["37"], "x": [". "], "surname": ["Prufer"], "given-names": ["KM"], "etal": ["et al."], "year": ["2017"], "article-title": ["The Classic Period Maya transition from an ideal free to an ideal despotic settlement system at the polity of Uxbenka"], "source": ["J. Anthropol. Archaeol."], "bold": ["45"], "fpage": ["53"], "lpage": ["68"], "pub-id": ["10.1016/j.jaa.2016.11.003"]}, {"label": ["38"], "x": [". "], "surname": ["Hayden", "D Price", "Feinman"], "given-names": ["B", "T", "GM"], "year": ["1995"], "article-title": ["Pathways to power: principles for creating socioeconomic inequalities"], "source": ["Foundations of social inequality"], "fpage": ["15"], "lpage": ["86"], "publisher-loc": ["New York, NY"], "publisher-name": ["Plenum Press"]}, {"label": ["39"], "x": [". "], "surname": ["Arnold"], "given-names": ["JE"], "year": ["1993"], "article-title": ["Labor and the rise of complex hunter-gatherers"], "source": ["J. Anthropol. Archaeol."], "bold": ["12"], "fpage": ["75"], "lpage": ["119"], "pub-id": ["10.1006/jaar.1993.1003"]}, {"label": ["40"], "x": [". "], "surname": ["Arnold", "D Price", "Feinman"], "given-names": ["JE", "T", "GM"], "year": ["1995"], "article-title": ["Social inequality, marginalization, and economic process"], "source": ["Foundations of social inequality"], "fpage": ["87"], "lpage": ["103"], "publisher-loc": ["New York, NY"], "publisher-name": ["Plenum Press"]}, {"label": ["41"], "x": [". "], "surname": ["Flannery", "Marcus"], "given-names": ["K", "J"], "year": ["2012"], "source": ["The creation of inequality: how our prehistoric ancestors set the stage for monarchy, slavery, and empire"], "publisher-loc": ["Cambridge, MA"], "publisher-name": ["Harvard University Press"]}] | {
"acronym": [],
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} | 41 | CC BY | no | 2024-01-15 23:43:51 | Philos Trans R Soc Lond B Biol Sci.; 378(1883):20220293 | oa_package/1a/f7/PMC10291421.tar.gz |
PMC10368488 | 37490942 | [
"<title>Introduction</title>",
"<p>Animals must gather, process and act on information about their surroundings to efficiently forage. Rather than relying solely on private information through direct sampling of the environment, animals can use information provided by other individuals (often inadvertently) to re... | [
"<title>Material and methods</title>",
"<p>Adult fish were wild caught in 2011 and 2012 from Enos Lake (Vancouver Island, BC) and Paxton and Priest Lakes (Texada Island, BC). Canada considers these fish to be endangered and so carefully controls collection, limiting sample sizes. Only fish that resembled limnetic... | [
"<title>Results</title>",
"<title>Are there species differences in use of social information?</title>",
"<title>First choice</title>",
"<p>We first asked whether species in both lakes with intact species-pairs (Paxton and Priest) differed in their propensity to first choose the local enhancement side. Benthic... | [
"<title>Discussion</title>",
"<p>We tested individual threespine stickleback fish on the ability to use the feeding behaviour of others to locate a food patch. In two lakes with reproductively isolated species adapted to different ecological niches, we found strong evidence for parallel evolution of species diffe... | [] | [
"<p>Electronic supplementary material is available online at <uri xlink:href=\"https://doi.org/10.6084/m9.figshare.c.6743102\">https://doi.org/10.6084/m9.figshare.c.6743102</uri>.</p>",
"<p>Individuals can reduce sampling costs and increase foraging efficiency by using information provided by others. One simple f... | [] | [
"<title>Acknowledgements</title>",
"<p>W. Fetzner and J. Martinez assisted with data collection. Members of the Boughman laboratory gave valuable support. Members of Alison M. Bell's laboratory gave valuable feedback on an earlier manuscript version. We also thank two anonymous reviewers for their constructive co... | [
"<fig position=\"float\" id=\"RSBL20230208F1\"><label>Figure 1<x xml:space=\"preserve\">. </x></label><caption><p>Social information use in benthic and limnetic sticklebacks. (<italic toggle=\"yes\">a</italic>) Diagram of experimental set-up during a feeding. (<italic toggle=\"yes\">b</italic>) Benthic fish in both... | [
"<table-wrap position=\"float\" id=\"RSBL20230208TB1\"><label>Table 1<x xml:space=\"preserve\">. </x></label><caption><p>Preference score, intact species-pair lakes (Paxton and Priest Lakes). (Italics indicate statistically significant effect, <italic toggle=\"yes\">p</italic> < 0.05.)</p></caption><table frame=... | [] | [] | [] | [] | [] | [] | [] | [
"<graphic xlink:href=\"rsbl20230208f01\" position=\"float\"/>",
"<graphic xlink:href=\"rsbl20230208f02\" position=\"float\"/>"
] | [] | [{"label": ["2"], "x": [". "], "surname": ["Brown", "Laland"], "given-names": ["C", "KN"], "year": ["2003"], "article-title": ["Social learning in fishes: a review"], "source": ["Fish Fish."], "bold": ["4"], "fpage": ["280"], "lpage": ["288"], "pub-id": ["10.1046/j.1467-2979.2003.00122.x"]}, {"label": ["3"], "x": [". "], "surname": ["Galef", "Laland"], "given-names": ["BG", "KN"], "year": ["2005"], "article-title": ["Social learning in animals: empirical studies and theoretical models"], "source": ["BioScience"], "bold": ["55"], "fpage": ["489"], "lpage": ["499"], "pub-id": ["10.1641/0006-3568(2005)055[0489:SLIAES]2.0.CO;2"]}, {"label": ["4"], "x": [". "], "surname": ["Hoppitt", "Laland"], "given-names": ["W", "KN"], "year": ["2008"], "article-title": ["Social processes influencing learning in animals: a review of the evidence"], "source": ["Adv. Study Behav."], "bold": ["38"], "fpage": ["105"], "lpage": ["165"], "pub-id": ["10.1016/S0065-3454(08)00003-X"]}, {"label": ["5"], "x": [". "], "surname": ["Coolen", "Bergen", "Day", "Laland"], "given-names": ["I", "YV", "RL", "KN"], "year": ["2003"], "article-title": ["Species difference in adaptive use of public information in sticklebacks"], "source": ["Proc. R. Soc. Lond. B"], "bold": ["270"], "fpage": ["2413"], "lpage": ["2419"], "pub-id": ["10.1098/rspb.2003.2525"]}, {"label": ["7"], "x": [". "], "surname": ["Harcourt", "Biau", "Johnstone", "Manica"], "given-names": ["JL", "S", "R", "A"], "year": ["2010"], "article-title": ["Boldness and information use in three-spined sticklebacks"], "source": ["Ethology"], "bold": ["116"], "fpage": ["440"], "lpage": ["447"], "pub-id": ["10.1111/j.1439-0310.2010.01757.x"]}, {"label": ["8"], "x": [". "], "surname": ["Dunbar"], "given-names": ["RI"], "year": ["1998"], "article-title": ["The social brain hypothesis"], "source": ["Evol. Anthropol."], "bold": ["6"], "fpage": ["178"], "lpage": ["190"]}, {"label": ["9"], "x": [". "], "surname": ["Humphrey", "Bateson", "Hinde"], "given-names": ["NK", "PPG", "RA"], "year": ["1976"], "article-title": ["The social function of intellect"], "source": ["Growing points in ethology"], "fpage": ["303"], "lpage": ["317"], "publisher-loc": ["Cambridge, UK"], "publisher-name": ["Cambridge University Press"]}, {"label": ["10"], "x": [". "], "surname": ["Krause", "Ruxton"], "given-names": ["J", "G"], "year": ["2002"], "source": ["Living in groups"], "publisher-loc": ["Oxford, UK"], "publisher-name": ["Oxford University Press"]}, {"label": ["11"], "x": [". "], "surname": ["McKinnon", "Rundle"], "given-names": ["JS", "HD"], "year": ["2002"], "article-title": ["Speciation in nature: the threespine stickleback model systems"], "source": ["Trends Ecol. Evol."], "bold": ["17"], "fpage": ["480"], "lpage": ["488"], "pub-id": ["10.1016/S0169-5347(02)02579-X"]}, {"label": ["12"], "x": [". "], "surname": ["McPhail", "Bell", "Foster"], "given-names": ["J", "M", "S"], "year": ["1994"], "article-title": ["Speciation and the evolution of reproductive isolation in the sticklebacks ("], "italic": ["Gasterosteus"], "source": ["The evolutionary biology of the threespine stickleback"], "fpage": ["399"], "lpage": ["437"], "publisher-loc": ["Oxford, UK"], "publisher-name": ["Oxford Science Publications"]}, {"label": ["13"], "x": [". "], "surname": ["Larson"], "given-names": ["GL"], "year": ["1976"], "article-title": ["Social behavior and feeding ability of two phenotypes of "], "italic": ["Gasterosteus aculeatus"], "source": ["Can. J. Zool."], "bold": ["54"], "fpage": ["107"], "lpage": ["121"], "pub-id": ["10.1139/z76-012"]}, {"label": ["14"], "x": [". "], "surname": ["Vamosi"], "given-names": ["SM"], "year": ["2002"], "article-title": ["Predation sharpens the adaptive peaks: survival trade-offs in sympatric sticklebacks"], "source": ["Annales Zool. Fennici"], "bold": ["39"], "fpage": ["237"], "lpage": ["248"]}, {"label": ["16"], "x": [". "], "surname": ["Vamosi", "Schluter"], "given-names": ["SM", "D"], "year": ["2002"], "article-title": ["Impacts of trout predation on fitness of sympatric sticklebacks and their hybrids"], "source": ["Proc. R. Soc. Lond. B"], "bold": ["269"], "fpage": ["923"], "lpage": ["930"], "pub-id": ["10.1098/rspb.2001.1946"]}, {"label": ["17"], "x": [". "], "surname": ["McPhail"], "given-names": ["JD"], "year": ["1993"], "article-title": ["Ecology and evolution of sympatric sticklebacks ("], "italic": ["Gasterosteus"], "source": ["Can. J. Zool."], "bold": ["71"], "fpage": ["515"], "lpage": ["523"], "pub-id": ["10.1139/z93-072"]}, {"label": ["19"], "x": [". "], "surname": ["Taylor", "Boughman", "Groenenboom", "Sniatynski", "Schluter", "Gow"], "given-names": ["EB", "JW", "M", "M", "D", "JL"], "year": ["2005"], "article-title": ["Speciation in reverse: morphological and genetic evidence of the collapse of a three-spined stickleback ("], "italic": ["Gasterosteus aculeatus"], "source": ["Mol. Ecol."], "bold": ["15"], "fpage": ["343"], "lpage": ["355"], "pub-id": ["10.1111/j.1365-294X.2005.02794.x"]}, {"label": ["20"], "x": [". "], "surname": ["Taylor", "Piercey"], "given-names": ["EB", "RS"], "year": ["2018"], "article-title": ["Going, going, gone: evidence for loss of an endemic species pair of threespine sticklebacks ("], "italic": ["Gasterosteus aculeatus"], "source": ["Conserv. Genet."], "bold": ["19"], "fpage": ["297"], "lpage": ["308"], "pub-id": ["10.1007/s10592-017-1000-4"]}, {"label": ["21"], "x": [". "], "surname": ["McPhail"], "given-names": ["JD"], "year": ["1984"], "article-title": ["Ecology and evolution of sympatric sticklebacks ("], "italic": ["Gasterosteus"], "source": ["Can. J. Zool."], "bold": ["62"], "fpage": ["1402"], "lpage": ["1408"], "pub-id": ["10.1139/z84-201"]}, {"label": ["22"], "x": [". "], "surname": ["Lackey", "Boughman"], "given-names": ["ACR", "JW"], "year": ["2014"], "article-title": ["Female discrimination against heterospecific mates does not depend on mating habitat"], "source": ["Behav. Ecol."], "bold": ["25"], "fpage": ["1256"], "lpage": ["1267"], "pub-id": ["10.1093/beheco/aru111"]}, {"label": ["23"], "x": [". "], "surname": ["Webster", "Laland"], "given-names": ["MM", "KN"], "year": ["2011"], "article-title": ["Reproductive state affects reliance on public information in sticklebacks"], "source": ["Proc. R. Soc. B"], "bold": ["278"], "fpage": ["619"], "lpage": ["627"], "pub-id": ["10.1098/rspb.2010.1562"]}, {"label": ["24"], "x": [". "], "collab": ["R Core Team"], "year": ["2020"], "source": ["R: a language and environment for statistical computing"], "publisher-loc": ["Vienna, Austria"], "publisher-name": ["R Foundation for Statistical Computing"], "uri": ["https://www.R-project.org/"]}, {"label": ["25"], "x": [". "], "surname": ["Kleiber", "Zeileis"], "given-names": ["C", "A"], "year": ["2008"], "source": ["Applied econometrics with R"], "publisher-loc": ["New York, NY"], "publisher-name": ["Springer-Verlag"], "uri": ["https://CRAN.R-project.org/package=AER"]}, {"label": ["26"], "x": [". "], "surname": ["Fox", "Weisberg"], "given-names": ["J", "S"], "year": ["2019"], "source": ["An R companion to applied regression"], "edition": ["3rd edn"], "publisher-loc": ["Thousand Oaks, CA"], "publisher-name": ["Sage"], "uri": ["https://socialsciences.mcmaster.ca/jfox/Books/Companion"]}, {"label": ["27"], "x": [". "], "surname": ["Wickham"], "given-names": ["H"], "year": ["2016"], "source": ["Ggplot2: elegant graphics for data analysis"], "publisher-loc": ["Berlin, Germany"], "publisher-name": ["Springer-Verlag"]}, {"label": ["28"], "x": [". "], "surname": ["Odling-Smee", "Boughman", "Braithwaite"], "given-names": ["LC", "JW", "VA"], "year": ["2008"], "article-title": ["Sympatric species of threespine stickleback differ in their performance in a spatial learning task"], "source": ["Behav. Ecol. Sociobiol."], "bold": ["62"], "fpage": ["1935"], "lpage": ["1945"], "pub-id": ["10.1007/s00265-008-0625-1"]}, {"label": ["31"], "x": [". "], "surname": ["Martinez", "Keagy", "Wurst", "Fetzner", "Boughman"], "given-names": ["J", "J", "B", "W", "JW"], "year": ["2016"], "article-title": ["The relative roles of genes and rearing environment on the spatial cognitive ability of two sympatric species of threespine stickleback"], "source": ["Evol. Ecol. Res."], "bold": ["17"], "fpage": ["565"], "lpage": ["581"]}, {"label": ["32"], "x": [". "], "surname": ["Keagy", "Braithwaite", "Boughman"], "given-names": ["J", "VA", "JW"], "year": ["2018"], "article-title": ["Brain differences in ecologically differentiated sticklebacks"], "source": ["Cur. Zool."], "bold": ["64"], "fpage": ["243"], "lpage": ["250"], "pub-id": ["10.1093/cz/zox074"]}, {"label": ["33"], "x": [". "], "surname": ["Healy", "Rowe"], "given-names": ["SD", "C"], "year": ["2007"], "article-title": ["A critique of comparative studies of brain size"], "source": ["Proc. R. Soc. B"], "bold": ["274"], "fpage": ["453"], "lpage": ["464"], "pub-id": ["10.1098/rspb.2006.3748"]}, {"label": ["37"], "x": [". "], "surname": ["Bond", "Kamil", "Balda"], "given-names": ["AB", "AC", "RP"], "year": ["2003"], "article-title": ["Social complexity and transitive inference in corvids"], "source": ["Anim. Behav."], "bold": ["65"], "fpage": ["479"], "lpage": ["487"], "pub-id": ["10.1006/anbe.2003.2101"]}, {"label": ["39"], "x": [". "], "surname": ["Keagy", "Lehto", "Minter", "Machniak", "Baird", "Boughman"], "given-names": ["J", "W", "R", "S", "O", "JW"], "year": ["2023"], "data-title": ["Data from: Repeated parallel differentiation of social learning differences in benthic and limnetic threespine stickleback fish"], "italic": ["Dryad Digital Repository"], "pub-id": ["10.5061/dryad.8931zcrwb"]}, {"label": ["40"], "x": [". "], "surname": ["Keagy", "Lehto", "Minter", "Machniak", "Baird", "Boughman"], "given-names": ["J", "W", "R", "S", "O", "JW"], "year": ["2023"], "comment": ["Repeated parallel differentiation of social learning differences in benthic and limnetic threespine stickleback fish. Figshare. (", ")"], "pub-id": ["10.6084/m9.figshare.c.6743102"]}] | {
"acronym": [],
"definition": []
} | 40 | CC BY | no | 2024-01-15 23:43:51 | Biol Lett.; 19(7):20230208 | oa_package/ab/ce/PMC10368488.tar.gz |
PMC10394125 | 36892404 | [
"<title>Introduction</title>",
"<p>With increasing concerns about global sustainability, the transition from today’s fossil-based economy toward a more sustainable biobased economy has received significant interest worldwide (##UREF##11##Menon and Rao, 2012##). Lignocellulosic biomass represents the Earth’s large... | [] | [] | [] | [] | [
"<title>Abstract</title>",
"<p>Lignocellulosic biomass is a renewable source of energy, chemicals and materials. Many applications of this resource require the depolymerization of one or more of its polymeric constituents. Efficient enzymatic depolymerization of cellulose to glucose by cellulases and accessory en... | [
"<title>Protein engineering of cellulolytic enzymes</title>",
"<p>Depending on their application, cellulases are either used individually (e.g. paper manufacturing, cellulose fiber upgrading or fruit juice extraction) or in a cocktail (e.g. biofuel industry, animal feed production and detergent industry). Accordi... | [
"<title>Acknowledgments</title>",
"<p>We are grateful for helpful discussions with numerous colleagues and apologize to all who have done cellulase engineering work that could not be included in this short review.</p>",
"<title>Funding</title>",
"<p>This work was supported by the Research Council of Norway th... | [
"<fig position=\"float\" id=\"ga1\"></fig>",
"<fig position=\"float\" id=\"f1\"><label>Fig. 1</label><caption><p>Degradation of cellulose by the synergistic action of cellulases (cellobiohydrolases, endoglucanases), β-glucosidases and LPMOs. The concomitant action of cellobiohydrolases (CBHs), endoglucanases (EGs... | [] | [] | [] | [] | [] | [] | [
"<supplementary-material id=\"sup1\" position=\"float\" content-type=\"local-data\"><label>PEDS22_0053_R1_Supplementary_material_Clean_gzad002</label></supplementary-material>"
] | [] | [
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"<media xlink:href=\"peds22_0053_r1_supplementary_material_clean_gzad002.docx\"><caption><p>Click here for additional data file.</p></caption></media>"
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Ed."], "volume": ["58"], "fpage": ["36"], "lpage": ["40"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Chen"], "given-names": ["H."], "year": ["2014"], "source": ["Biotechnology of lignocellulose"], "publisher-name": ["Dordrecht, Heidelberg, New York, London: Springer"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Contreras", "Thiele", "Pramanik", "Rozhkova", "Dotsenko", "Zorov", "Sinitsyn", "Davari", "Schwaneberg"], "given-names": ["F.", "M.J.", "S.", "A.M.", "A.S.", "I.N.", "A.P.", "M.D.", "U."], "year": ["2020b"], "source": ["ACS "], "italic": ["Sustain. Chem. 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Sci."], "volume": ["7"], "fpage": ["2308"], "lpage": ["2315"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Kari", "Olsen", "Jensen", "Badino", "Krogh", "Borch", "Westh"], "given-names": ["J.", "J.P.", "K.", "S.F.", "K.B.", "K.", "P."], "year": ["2018"], "source": ["ACS Catal."], "volume": ["8"], "fpage": ["11966"], "lpage": ["11972"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Mabee", "McFarlane", "Saddler"], "given-names": ["W.", "P.", "J."], "year": ["2011"], "source": ["Biomass Bioenergy"], "volume": ["35"], "fpage": ["4519"], "lpage": ["4529"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Menon", "Rao"], "given-names": ["V.", "M."], "year": ["2012"], "source": ["Prog. Energy. Combust. Sci."], "volume": ["38"], "fpage": ["522"], "lpage": ["550"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Mondal", "Halder", "Mondal"], "given-names": ["S.", "S.K.", "K.C."], "year": ["2019"], "source": ["Recent advancement in white biotechnology through fungi"], "publisher-name": ["Cham, Switzerland: Springer"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Paul", "Dutta"], "given-names": ["S.", "A."], "year": ["2018"], "source": ["Resour. Conserv. Recycl."], "volume": ["130"], "fpage": ["164"], "lpage": ["174"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Rahikainen", "Ceccherini", "Molinier"], "given-names": ["J.", "S.", "M."], "etal": ["et al."], "year": ["2019"], "source": ["Cellul."], "volume": ["26"], "fpage": ["5085"], "lpage": ["5103"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Sadat"], "given-names": ["F."], "year": ["2017"], "source": ["Artif. Cells Nanomed. Biotechnol."], "volume": ["7"], "fpage": ["1"], "lpage": ["7"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Schaller", "Molina", "Kari", "Schiano-di-Cola", "S\u00f8rensen", "Borch", "Peters", "Westh"], "given-names": ["K.S.", "G.A.", "J.", "C.", "T.H.", "K.", "G.n.H.", "P."], "year": ["2022"], "source": ["ACS Catal."], "volume": ["12"], "fpage": ["7427"], "lpage": ["7435"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Sidar", "Albuquerque", "Voshol", "Ram", "Vijgenboom", "Punt"], "given-names": ["A.", "E.D.", "G.P.", "A.F.", "E.", "P.J."], "year": ["2020"], "source": ["Front. Bioeng. 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Acta Proteins "], "italic": ["Proteom."], "volume": ["1834"], "fpage": ["1539"], "lpage": ["1544"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Wyman", "Goodman"], "given-names": ["C.E.", "B.J."], "year": ["1993"], "source": ["Appl. Biochem. Biotechnol."], "volume": ["39-40"], "fpage": ["41"], "lpage": ["59"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Xia", "Xu", "Qian", "Shi", "Bai", "Luo", "Ma", "Yao"], "given-names": ["W.", "X.", "L.", "P.", "Y.", "H.", "R.", "B."], "year": ["2016"], "source": ["Biotechnol Biofuels Bioprod"], "volume": ["9"], "fpage": ["1"], "lpage": ["12"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Yamaguchi", "Sunagawa", "Tachioka", "Igarashi", "Samejima"], "given-names": ["S.", "N.", "M.", "K.", "M."], "year": ["2020"], "source": ["J. Appl. Glycosci."], "volume": ["67"], "fpage": ["79"], "lpage": ["86"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Zakariassen", "Eijsink", "S\u00f8rlie"], "given-names": ["H.", "V.G.", "M."], "year": ["2010"], "source": ["Carbohydr. Polym."], "volume": ["81"], "fpage": ["14"], "lpage": ["20"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Zhang", "Cao", "Bi", "Li"], "given-names": ["R.", "C.", "J.", "Y."], "year": ["2021"], "source": ["Appl. Microbiol. Biotechnol."], "fpage": ["1"], "lpage": ["24"]}] | {
"acronym": [],
"definition": []
} | 141 | CC BY | no | 2024-01-15 23:35:08 | Protein Eng Des Sel. 2023 Mar 24; 36:gzad002 | oa_package/70/c3/PMC10394125.tar.gz |
PMC10404922 | 37545307 | [
"<title>Introduction</title>",
"<p>Metacognition, the capacity to monitor one's own uncertainty, is important for adaptive behaviour [##REF##26906503##1##–##REF##25032495##3##]. In a noisy restaurant, if we feel unsure whether the waiter said ‘beans’ or ‘greens’, we may ask him to repeat it [##REF##22492746##4##–... | [
"<title>Methods</title>",
"<title>Participants</title>",
"<p>Fifteen participants were initially recruited, but one did not finish the study. Therefore, 14 participants were included in the analysis (two males, two left handers, mean age: 19.5, range: 18–22). The number of participants was a convenience sample.... | [
"<title>Results</title>",
"<p>In the following, we present the key results organized in line with the results figures and electronic supplementary material, tables (main statistical analyses). The complete statistical results are presented only in the tables (see the electronic supplementary material).</p>",
"<... | [
"<title>Discussion</title>",
"<p>This study investigated how human observers form confidence judgements when presented with spoken syllables, articulatory lip movements or their congruent and McGurk combinations. In such multisensory information integration tasks, observers need to monitor two intimately related ... | [] | [
"<p>One contribution of 16 to a theme issue ‘<ext-link xlink:href=\"http://dx.doi.org/10.1098/rstb/378/1886\" ext-link-type=\"uri\">Decision and control processes in multisensory perception</ext-link>’.</p>",
"<p>Electronic supplementary material is available online at <uri xlink:href=\"https://doi.org/10.6084/m9... | [] | [
"<title>Acknowledgements</title>",
"<p>We thank Sonal Patel for help with data acquisition.</p>",
"<title>Ethics</title>",
"<p>The study was approved by the research ethics committee of the University of Birmingham (ERN_11-0470AP4). All participants provided written informed consent to participate in the stud... | [
"<fig position=\"float\" id=\"RSTB20220348F1\"><label>Figure 1<x xml:space=\"preserve\">. </x></label><caption><p>Bayesian causal inference model. (<italic toggle=\"yes\">a</italic>) Generative model: the generative model of Bayesian causal inference explicitly models the potential causal structures (C = 1 or C = 2... | [] | [
"<inline-formula><mml:math id=\"IM1\"><mml:msub><mml:mi>S</mml:mi><mml:mrow><mml:mi>A</mml:mi><mml:mi>V</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>",
"<inline-formula><mml:math id=\"IM2\"><mml:msub><mml:mi>S</mml:mi><mml:mi>A</mml:mi></mml:msub></mml:math></inline-formula>",
"<inline-formula><mml... | [] | [] | [] | [] | [] | [] | [
"<graphic xlink:href=\"rstb20220348f01\" position=\"float\"/>",
"<graphic xlink:href=\"rstb20220348f02\" position=\"float\"/>",
"<graphic xlink:href=\"rstb20220348f03\" position=\"float\"/>",
"<graphic xlink:href=\"rstb20220348f04\" position=\"float\"/>",
"<graphic xlink:href=\"rstb20220348f05\" position=\"... | [] | [{"label": ["5"], "x": [". "], "surname": ["Flavell"], "given-names": ["JH"], "year": ["1979"], "article-title": ["Metacognition and cognitive monitoring: a new area of cognitive\u2013developmental inquiry"], "source": ["Am. Psychol."], "bold": ["34"], "fpage": ["906"], "lpage": ["911"], "pub-id": ["10.1037/0003-066x.34.10.906"]}, {"label": ["10"], "x": [". "], "surname": ["Aitchison", "Bang", "Bahrami", "Latham"], "given-names": ["L", "D", "B", "PE"], "year": ["2015"], "article-title": ["Doubly Bayesian analysis of confidence in perceptual decision-making"], "source": ["PLoS Comput. Biol."], "bold": ["11"], "pub-id": ["10.1371/journal.pcbi.1004519"]}, {"label": ["21"], "x": [". "], "surname": ["Rohe", "Noppeney"], "given-names": ["T", "U"], "year": ["2015"], "article-title": ["Sensory reliability shapes perceptual inference via two mechanisms"], "source": ["J. Vis."], "bold": ["15"], "fpage": ["22"], "pub-id": ["10.1167/15.5.22"]}, {"label": ["31"], "x": [". "], "surname": ["McGurk", "Macdonald"], "given-names": ["H", "J"], "year": ["1976"], "article-title": ["Hearing lips and seeing voices"], "source": ["Nature"], "bold": ["264"], "fpage": ["691"], "lpage": ["811"], "pub-id": ["10.1038/264746a0"]}, {"label": ["32"], "x": [". "], "surname": ["Hillis", "Ernst", "Banks", "Landy"], "given-names": ["JM", "MO", "MS", "MS"], "year": ["2002"], "article-title": ["Combining sensory information: mandatory fusion within, but not between, senses"], "source": ["Science (N.Y.)"], "bold": ["298"], "fpage": ["1627"], "lpage": ["1630"], "pub-id": ["10.1126/science.1075396"]}, {"label": ["34"], "x": [". "], "surname": ["Tiippana", "Hayes", "M\u00f6tt\u00f6nen", "Kraus", "Sams"], "given-names": ["K", "E", "R", "N", "M"], "year": ["2010"], "article-title": ["The McGurk effect at various auditory signal-to-noise ratios in American and Finnish listeners"], "conf-name": ["Proc. of the Int. Conf. on Auditory-Visual Speech Processing"], "italic": ["September 30\u2013October 3, 2010"], "fpage": ["166"], "lpage": ["169"], "publisher-loc": ["Hakone, Kanagawa, Japan"], "publisher-name": ["International Speech Communication Association (ISCA)"], "uri": ["https://isca-speech.org/archive"]}, {"label": ["35"], "x": [". "], "surname": ["Tiippana", "Vainio", "Tiainen"], "given-names": ["K", "M", "M"], "year": ["2011"], "article-title": ["Audiovisual speech perception: acoustic and visual phonetic features contributing to the McGurk effect"], "source": ["I-Perception"], "bold": ["2"], "fpage": ["768"], "pub-id": ["10.1068/ic768"]}, {"label": ["40"], "x": [". "], "collab": ["R Core Team"], "year": ["2023"], "source": ["R: a language and environment for statistical computing"], "publisher-loc": ["Vienna, Austria"], "publisher-name": ["R Foundation for Statistical Computing"], "comment": ["See\u00a0"], "uri": ["https://www.Rproject.org/"]}, {"label": ["41"], "x": [". "], "surname": ["Brooks", "Kristensen", "van Benthem", "Magnusson", "Berg", "Nielsen", "Skaug", "Maechler", "Bolker"], "given-names": ["ME", "K", "KJ", "A", "CW", "A", "HJ", "M", "BM"], "year": ["2017"], "article-title": ["glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling"], "source": ["R J."], "bold": ["9"], "fpage": ["378"], "lpage": ["400"], "pub-id": ["10.32614/RJ-2017-066"]}, {"label": ["42"], "x": [". "], "surname": ["Elff"], "given-names": ["M"], "year": ["2022"], "comment": ["mclogit: multinomial logit models, with or without random effects or overdispersion. R package version 0.9.6. See "], "uri": ["https://CRAN.R-project.org/package=mclogit"]}, {"label": ["43"], "x": [". "], "surname": ["Lenth"], "given-names": ["R"], "year": ["2023"], "comment": ["emmeans: estimated marginal means, aka least-squares means. R package version 1.8.6. See "], "uri": ["https://CRAN.R-project.org/package=emmeans"]}, {"label": ["44"], "x": [". "], "surname": ["L\u00fcdecke"], "given-names": ["D"], "year": ["2018"], "article-title": ["ggeffects: tidy data frames of marginal effects from regression models"], "source": ["J. Open Source Softw."], "bold": ["3"], "fpage": ["772"], "pub-id": ["10.21105/joss.00772"]}, {"label": ["45"], "x": [". "], "surname": ["Christensen"], "given-names": ["RHB"], "year": ["2022"], "comment": ["Ordinal \u2013 regression models for ordinal data. R package version 2022.11\u201316. See "], "uri": ["https://CRAN.R-project.org/package=ordinal"]}, {"label": ["46"], "x": [". "], "surname": ["Kubinec"], "given-names": ["R"], "year": ["2022"], "article-title": ["Ordered beta regression: a parsimonious, well-fitting model for continuous data with lower and upper bounds"], "source": ["Polit. Analysis"], "fpage": ["1"], "lpage": ["18"], "pub-id": ["10.1017/pan.2022.20"]}, {"label": ["52"], "x": [". "], "surname": ["Meijer", "Noppeney", "Sathian", "Ramachandran"], "given-names": ["D", "U", "K", "VS"], "year": ["2020"], "article-title": ["Computational models of multisensory integration"], "source": ["Multisensory perception: from laboratory to clinic"], "fpage": ["113"], "lpage": ["133"], "publisher-loc": ["New York, NY"], "publisher-name": ["Academic Press"], "pub-id": ["10.1016/B978-0-12-812492-5.00005-X"]}, {"label": ["55"], "x": [". "], "surname": ["Peters"], "given-names": ["MAK"], "year": ["2022"], "comment": ["Confidence in decision-making. "], "italic": ["Oxford research encyclopedia of neuroscience"], "pub-id": ["10.1093/acrefore/9780190264086.013.371"]}, {"label": ["58"], "x": [". "], "surname": ["Kimmet", "Pedersen", "Cardenas", "Rubiera", "Johnson", "Sans", "Baldwin", "Odegaard"], "given-names": ["F", "S", "V", "C", "G", "A", "M", "B"], "year": ["2023"], "article-title": ["Metacognition and causal inference in audiovisual speech"], "source": ["Multisensory Res."], "bold": ["36"], "fpage": ["289"], "lpage": ["311"], "pub-id": ["10.1163/22134808-bja10094"]}, {"label": ["59"], "x": [". "], "surname": ["Meijer", "Noppeney"], "given-names": ["D", "U"], "year": ["2023"], "data-title": ["Metacognition in the audiovisual McGurk illusion: perceptual and causal confidence"], "pub-id": ["10.6084/m9.figshare.c.6751342"]}] | {
"acronym": [],
"definition": []
} | 59 | CC BY | no | 2024-01-15 23:43:51 | Philos Trans R Soc Lond B Biol Sci.; 378(1886):20220348 | oa_package/d4/d7/PMC10404922.tar.gz |
PMC10404924 | 37545312 | [
"<title>Introduction</title>",
"<p>Metacognition has previously been defined as the capacity for ‘thinking about thinking’ [##UREF##0##1##] and perceptual metacognition can be defined as the capacity to monitor the quality and fidelity of one's own perceptions. Studies now provide various behavioural and computat... | [
"<title>Experiment—method</title>",
"<title>Participants</title>",
"<p>Forty-six undergraduate students at the University of Florida (33 women, 13 men, mean age = 19.02 years, s.d. = 3.05) volunteered to participate to earn course credit. Participants began the experimental session by completing an informed con... | [
"<title>Results</title>",
"<p>As shown in ##FIG##0##figure 1##<italic toggle=\"yes\">a</italic>, we were able to successfully create stimulus conditions that frequently resulted in both ‘fission’ and ‘fusion’ illusions. For example, participants in the 1F2B condition (the ‘fission’ illusion condition) frequently ... | [
"<title>Discussion</title>",
"<p>In this investigation, we aimed to study if metacognitive confidence judgements differed between congruent and integrated (illusory) multisensory stimuli, and if confidence differed between these two conditions when the reported percept was the same. Using the sound-induced flash ... | [] | [
"<p>One contribution of 16 to a theme issue ‘<ext-link xlink:href=\"http://dx.doi.org/10.1098/rstb/378/1886\" ext-link-type=\"uri\">Decision and control processes in multisensory perception</ext-link>’.</p>",
"<p>Hundreds (if not thousands) of multisensory studies provide evidence that the human brain can integra... | [] | [
"<title>Ethics</title>",
"<p>All research was conducted in accordance with IRB201902462 at the University of Florida.</p>",
"<title>Data accessibility</title>",
"<p>Our data are available from the Open Science Framework website: <uri xlink:href=\"https://osf.io/p6f3k/\">https://osf.io/p6f3k/</uri>. If you hav... | [
"<fig position=\"float\" id=\"RSTB20220347F1\"><label>Figure 1<x xml:space=\"preserve\">. </x></label><caption><p>Average behavioural reports across subjects and individual averages in the beep–flash illusion conditions. All error bars represent standard error of the mean. (<italic toggle=\"yes\">a</italic>) The av... | [] | [] | [] | [] | [] | [] | [] | [] | [
"<graphic xlink:href=\"rstb20220347f01\" position=\"float\"/>"
] | [] | [{"label": ["1"], "x": [". "], "surname": ["Flavell"], "given-names": ["JH"], "year": ["1979"], "article-title": ["Metacognition and cognitive monitoring: a new area of cognitive\u2013developmental inquiry"], "source": ["Am. Psychol."], "bold": ["34"], "fpage": ["906"], "lpage": ["911"], "pub-id": ["10.1037/0003-066X.34.10.906"]}, {"label": ["2"], "x": [". "], "surname": ["Maniscalco", "Lau"], "given-names": ["B", "H"], "year": ["2014"], "article-title": ["Signal detection theory analysis of type 1 and type 2 data: Meta-D\u2032, response-specific Meta-D\u2032, and the unequal variance SDT model"], "source": ["The cognitive neuroscience of metacognition"], "fpage": ["25"], "lpage": ["66"], "publisher-loc": ["Berlin, Germany"], "publisher-name": ["Springer"]}, {"label": ["20"], "x": [". "], "surname": ["Lau"], "given-names": ["H"], "year": ["2019"], "comment": ["Consciousness, metacognition, & perceptual reality monitoring"], "italic": ["PsyArXiv"], "pub-id": ["10.31234/osf.io/ckbyf"]}, {"label": ["21"], "x": [". "], "surname": ["Gershman"], "given-names": ["SJ"], "year": ["2019"], "article-title": ["The generative adversarial brain"], "source": ["Front. Artif. Intell. Appl."], "bold": ["2"], "fpage": ["18"], "pub-id": ["10.3389/frai.2019.00018"]}, {"label": ["25"], "x": [". "], "surname": ["Pick", "Warren", "Hay"], "given-names": ["HL", "DH", "JC"], "year": ["1969"], "article-title": ["Sensory conflict in judgements of spatial direction"], "source": ["Percept. Psychophys."], "bold": ["6"], "fpage": ["203"], "lpage": ["205"], "pub-id": ["10.3758/BF03207017"]}, {"label": ["32"], "x": [". "], "surname": ["Battaglia", "Jacobs", "Aslin"], "given-names": ["PW", "RA", "RN"], "year": ["2003"], "article-title": ["Bayesian integration of visual and auditory signals for spatial localization"], "source": ["J. Opt. Soc. Am., Opt. Image Sci. Vis."], "bold": ["20"], "fpage": ["1391"], "lpage": ["1397"], "pub-id": ["10.1364/JOSAA.20.001391"]}, {"label": ["44"], "x": [". "], "surname": ["Anderson", "Tiippana", "Sams"], "given-names": ["TS", "K", "M"], "year": ["2004"], "article-title": ["Factors influencing audiovisual fission and fusion illusions"], "source": ["Cogn. Brain Res."], "bold": ["21"], "fpage": ["301"], "lpage": ["308"], "pub-id": ["10.1016/j.cogbrainres.2004.06.004"]}, {"label": ["48"], "x": [". "], "surname": ["Buda", "Fornito", "Bergstr\u00f6m", "Simons"], "given-names": ["M", "A", "ZM", "JS"], "year": ["2011"], "article-title": ["A specific brain structural basis for individual differences in reality monitoring"], "source": ["J. Neurosci."], "bold": ["31"], "fpage": ["14 308"], "lpage": ["14 313"], "pub-id": ["10.1523/JNEUROSCI.3595-11.2011"]}, {"label": ["53"], "x": [". "], "surname": ["Gau", "Noppeney"], "given-names": ["R", "U"], "year": ["2016"], "article-title": ["How prior expectations shape multisensory perception"], "source": ["Neuroimage A"], "bold": ["124"], "fpage": ["876"], "lpage": ["886"], "pub-id": ["10.1016/j.neuroimage.2015.09.045"]}, {"label": ["63"], "x": [". "], "surname": ["Knotts", "Shams"], "given-names": ["JD", "L"], "year": ["2016"], "article-title": ["Clarifying signal detection theoretic interpretations of the M\u00fcller\u2013Lyer and sound-induced flash illusions"], "source": ["J. Vis."], "bold": ["16"], "fpage": ["18"], "pub-id": ["10.1167/16.11.18"]}, {"label": ["64"], "x": [". "], "surname": ["Witt", "Taylor", "Sugovic", "Wixted"], "given-names": ["JK", "JET", "M", "JT"], "year": ["2016"], "article-title": ["Further clarifying signal detection theoretic interpretations of the M\u00fcller\u2013Lyer and sound-induced flash illusions"], "source": ["J. Vis."], "bold": ["16"], "fpage": ["19"], "pub-id": ["10.1167/16.11.19"]}, {"label": ["69"], "x": [". "], "surname": ["De Haas", "Kanai", "Jalkanen", "Rees"], "given-names": ["B", "R", "L", "G"], "year": ["2012"], "article-title": ["Grey matter volume in early human visual cortex predicts proneness to the sound-induced flash illusion"], "source": ["Proc. R. Soc. B"], "bold": ["279"], "fpage": ["4955"], "lpage": ["4961"], "pub-id": ["10.1098/rspb.2012.2132"]}, {"label": ["75"], "x": [". "], "surname": ["Beierholm", "Quartz", "Shams"], "given-names": ["UR", "SR", "L"], "year": ["2009"], "article-title": ["Bayesian priors are encoded independently from likelihoods in human multisensory perception"], "source": ["J. Vis."], "bold": ["9"], "fpage": ["23.1"], "lpage": ["23.9"], "pub-id": ["10.1167/9.5.23"]}, {"label": ["77"], "x": [". "], "surname": ["Macmillan", "Creelman"], "given-names": ["NA", "CD"], "year": ["2005"], "source": ["Detection theory: a user's guide"], "edition": ["2nd edn"], "publisher-name": ["Mahwah, NJ: Lawrence Erlbaum Associates Publishers"]}, {"label": ["78"], "x": [". "], "surname": ["Morales", "Lau"], "given-names": ["J", "H"], "year": ["2022"], "comment": ["Confidence tracks consciousness. In "], "italic": ["Qualitative consciousness: themes from the philosophy of David Rosenthal"]}, {"label": ["80"], "x": [". "], "surname": ["Roberts", "McMillan", "Musolino", "Cole"], "given-names": ["WA", "N", "E", "M"], "year": ["2012"], "article-title": ["Information seeking in animals: metacognition?"], "source": ["Comp. Cogn. Behav. Rev."], "bold": ["7"], "fpage": ["85"], "lpage": ["109"], "pub-id": ["10.3819/ccbr.2012.70005"]}] | {
"acronym": [],
"definition": []
} | 81 | CC BY | no | 2024-01-15 23:43:51 | Philos Trans R Soc Lond B Biol Sci.; 378(1886):20220347 | oa_package/83/ac/PMC10404924.tar.gz |
PMC10408178 | 37553651 | [
"<title>Introduction</title>",
"<p id=\"Par5\">Human ABO antigens are located on the red cell surface, they play an active role in the cells’ physiology and pathology. They are oligosaccharide structures found on leukocytes, platelets, and tissues. Also, they are present in a soluble form in sweat, saliva, breast... | [
"<title>Methods</title>",
"<p id=\"Par9\">The study was conducted from August to December 2019 at the Department of Endoscopy and/or the Clinical Gastroenterology outpatient service of the Educational Republican Hospital in Sana’a City. One hundred and three adult patients were included in the study if they had s... | [
"<title>Results</title>",
"<title>Socio-Demographic and risk factors of H. pylori infection</title>",
"<p id=\"Par23\">A total of 103 patients were included in the study, with 40 (38.8%) being male and 63 (61.2%) female. The median age of H. pylori-infected patients was 25 years, with ages ranging from 19 to 57... | [
"<title>Discussion</title>",
"<p id=\"Par41\">The prevalence of H. pylori infection in Yemen was found to be high at 80.6%, which is similar to other countries in the Middle East and North Africa region [##UREF##12##30##]. However, a recent study from Yemen found a lower rate of infection at 19.3%, which could be... | [
"<title>Conclusion</title>",
"<p id=\"Par51\">Based on our findings, it is important to consider the blood group of individuals when assessing their risk for H. pylori infection. Those with O blood may be more susceptible to infection due to their increased likelihood of being secretors. Additionally, the presenc... | [
"<title>Background</title>",
"<p id=\"Par1\">The ABO and Lewis blood group antigens are potential factors in susceptibility to H. pylori infection. This research aimed to examine the prevalence of Helicobater pylori (H.pylori) infection and its association with ABO, Lewis blood group systems, and secretory status... | [] | [
"<title>Acknowledgements</title>",
"<p>We want to thank our patients, without whom this study would not be possible.</p>",
"<title>Authors’ contributions</title>",
"<p>Mohammed AW. Almorish made the conception and design the research. Mohammed AW. Almorish, Boshra Al-absi did the experiments and collected dat... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Prevalence of H. pylori infection according to stool antigen assay and serum antibody assay</p></caption></fig>"
] | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Socio-Demographic and Risk Factors of H. pylori Infection</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Characteristics</th><th align=\"left\" colspan=\"2\">No. of Patients (103)</th></tr><tr><th align=\"l... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>Stool antigen test considered as gold standard</p></table-wrap-foot>",
"<fn-group><fn><p>This article has been updated to correct an author name.</p></fn><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and insti... | [
"<graphic xlink:href=\"12879_2023_8496_Fig1_HTML\" id=\"d32e800\"/>"
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"acronym": [],
"definition": []
} | 64 | CC BY | no | 2024-01-15 23:35:09 | BMC Infect Dis. 2023 Aug 8; 23:520 | oa_package/da/58/PMC10408178.tar.gz |
PMC10410216 | 37553994 | [
"<title>Introduction</title>",
"<p>In limbless animals, lateral undulatory locomotion is the most common paradigm, in which the body bends laterally in a sinusoidal shape. This type of locomotion has long attracted the interest of scientists from the perspectives of evolutionary biology [##UREF##0##1##,##UREF##1#... | [
"<title>Material and methods</title>",
"<title>Mathematical modelling</title>",
"<p>In our model, we assumed an animal body as one-dimensional and discretized it into <italic toggle=\"yes\">N</italic> links. The links are joined by viscoelastic springs to represent the endogenous parameters of the body. The ben... | [
"<title>Results</title>",
"<title>Equivalent bending stiffness of joints</title>",
"<p>Equivalent bending stiffness, <italic toggle=\"yes\">k</italic>, is calculated using equation (3.1). The proof is provided in electronic supplementary material, S1:</p>",
"<p>Here <italic toggle=\"yes\">F</italic> is the fo... | [
"<title>Discussion</title>",
"<p>We investigated the effects of passive stiffness and environment on lateral undulatory locomotion by comparing mathematical and physical models to determine the performance optimization criteria. Our findings suggest a strong correlation between the resultant locomotion and the su... | [
"<title>Conclusion</title>",
"<p>We analysed and found a correlation between body stiffness and the environment for limbless undulatory locomotion in a dry friction environment. Our mathematical results are in agreement with physical experiments. The results suggest that the interdependence between passive body s... | [
"<p>Electronic supplementary material is available online at <uri xlink:href=\"https://doi.org/10.6084/m9.figshare.c.6751782\">https://doi.org/10.6084/m9.figshare.c.6751782</uri>.</p>",
"<p>The current study investigates the body–environment interaction and exploits the passive viscoelastic properties of the body... | [] | [
"<title>Ethics</title>",
"<p>This work did not require ethical approval from a human subject or animal welfare committee.</p>",
"<title>Data accessibility</title>",
"<p>The data are provided in the electronic supplementary material [##UREF##29##49##].</p>",
"<title>Authors' contributions</title>",
"<p>B.Y... | [
"<fig position=\"float\" id=\"RSIF20230330F1\"><label>Figure 1<x xml:space=\"preserve\">. </x></label><caption><p>Schematic of the body discretized into five links. <italic toggle=\"yes\">k</italic><sub>1</sub>, <italic toggle=\"yes\">k</italic><sub>2</sub> and <italic toggle=\"yes\">k</italic><sub>3</sub> represen... | [
"<table-wrap position=\"float\" id=\"RSIF20230330TB1\"><label>Table 1<x xml:space=\"preserve\">. </x></label><caption><p>Comparison of optimum stiffness distribution found in simulations and experimentally using the qualitative results suggested by simulations.</p></caption><table frame=\"hsides\" rules=\"groups\">... | [
"<disp-formula id=\"RSIF20230330M2.1\"><label>2.1</label><mml:math id=\"DM1\" display=\"block\"><mml:msub><mml:mrow><mml:mover><mml:mi>T</mml:mi><mml:mo stretchy=\"false\">→</mml:mo></mml:mover></mml:mrow><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:mi>g</mml:mi><mml:msub><mml:mrow><mml:mi>... | [] | [] | [] | [] | [] | [
"<fn-group><title>Endnote</title><fn id=\"FN1\"><label>1</label><p>See <uri xlink:href=\"https://www.kinovea.org/\">https://www.kinovea.org/</uri>.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"rsif20230330f01\" position=\"float\"/>",
"<graphic xlink:href=\"rsif20230330f02\" position=\"float\"/>",
"<graphic xlink:href=\"rsif20230330f03\" position=\"float\"/>",
"<graphic xlink:href=\"rsif20230330f04\" position=\"float\"/>",
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Biol."], "bold": ["8"], "fpage": ["354"], "lpage": ["398"], "pub-id": ["10.1371/journal.pcbi.1002810"]}, {"label": ["26"], "x": ["."], "surname": ["Rodella", "Mazzolai", "Pugno"], "given-names": ["A", "B", "N"], "year": ["2022"], "article-title": ["Undulatory locomotion of an elastic slender body in granular media: a comparison between finite-discrete element simulations and resistive force theory"], "source": ["SSRN Electron. J."], "bold": ["2"], "fpage": ["54"], "lpage": ["59"], "pub-id": ["10.2139/ssrn.4299835"]}, {"label": ["29"], "x": ["."], "surname": ["Persson"], "given-names": ["BNJ"], "year": ["2001"], "article-title": ["Theory of rubber friction and contact mechanics"], "source": ["J. Chem. Phys."], "bold": ["115"], "fpage": ["3840"], "lpage": ["3861"], "pub-id": ["10.1063/1.1388626"]}, {"label": ["32"], "x": ["."], "surname": ["Sarrafan", "Malayjerdi", "Behboudi", "Akbarzadeh"], "given-names": ["S", "M", "M", "A"], "year": ["2013"], "comment": ["Determination of optimum number of ground contact points in modeling a snake-like robot with maneuverability in three dimensions"], "conf-name": ["2013 1st RSI/ISM Int. Conf. on Robotics and Mechatronics (ICRoM), Tehran, Iran, 13\u201315 February 2013"], "fpage": ["158"], "lpage": ["164"], "pub-id": ["10.1109/ICRoM.2013.6510098"]}, {"label": ["33"], "x": ["."], "surname": ["Purcell"], "given-names": ["EM"], "year": ["1977"], "article-title": ["Life at low Reynolds number"], "source": ["Am. J. 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Neurosci."], "bold": ["4"], "fpage": ["354"], "lpage": ["398"], "pub-id": ["10.3389/fnbeh.2010.00049"]}, {"label": ["47"], "x": ["."], "surname": ["Gillis"], "given-names": ["GB"], "year": ["1998"], "article-title": ["Environmental effects on undulatory locomotion in the American eel "], "italic": ["Anguilla rostrata"], "source": ["J. Exp. Biol."], "bold": ["201"], "fpage": ["949"], "lpage": ["961"], "pub-id": ["10.1242/jeb.201.7.949"]}, {"label": ["49"], "x": ["."], "surname": ["Yaqoob", "Dottore", "Mondini", "Rodella", "Mazzolai", "Pugno"], "given-names": ["B", "ED", "A", "A", "B", "NM"], "year": ["2023"], "comment": ["Towards the optimization of passive undulatory locomotion on land: mathematical and physical models. Figshare. (", ")"], "pub-id": ["10.6084/m9.figshare.c.6751782"]}] | {
"acronym": [],
"definition": []
} | 49 | CC BY | no | 2024-01-15 23:43:51 | J R Soc Interface.; 20(205):20230330 | oa_package/37/4b/PMC10410216.tar.gz |
PMC10426048 | 37580698 | [
"<title>Introduction</title>",
"<p id=\"Par6\">Psittacosis is caused by <italic>Chlamydia psittaci</italic>, an intracellular gram-negative bacterium. It is a zoonosis that commonly infects birds. Although not always present, exposure to birds is a major risk factor for infection [##REF##28891690##1##]. Psittacos... | [
"<title>Materials and methods</title>",
"<title>Study design and patients</title>",
"<p id=\"Par8\">This retrospective study included patients with severe CAP and AHRF caused by psittacosis who were admitted to the 19 tertiary hospitals of China from April 2018 to May 2021. Patients were included if they fulfil... | [
"<title>Results</title>",
"<p id=\"Par14\">From April 2018 to May 2021, 45 patients with severe CAP and AHRF were diagnosed with psittacosis. <italic>C. psittaci</italic> was detected using mNGS on BALF samples. Droplet digital PCR validation was carried out while preparing this study, all the samples showed posi... | [
"<title>Discussion</title>",
"<p id=\"Par27\">To the best of our knowledge, this was the largest cohort study of psittacosis accompanied by AHRF from mainland China. In this study, we found psittacosis may present with varying AHRF severity, and the common seasons of psittacosis were autumn and winter. Fluoroquin... | [
"<title>Conclusion</title>",
"<p id=\"Par34\">Psittacosis caused severe CAP was not rare, especially in the patients with the history of exposure to poultry or birds. It may present with varying AHRF severity. Novel microbiological technologies may improve the diagnostic potential. Empirical treatment that covers... | [
"<title>Introduction</title>",
"<p id=\"Par1\">Psittacosis can cause severe community-acquired pneumonia (CAP). The clinical manifestations of psittacosis range from subclinical to fulminant psittacosis with multi-organ failure. It is essential to summarize the clinical characteristic of patients with severe psit... | [] | [
"<title>Acknowledgements</title>",
"<p>Not applicable.</p>",
"<title>Authors’ contributions</title>",
"<p>Z.H.T., Q.L., and B.S. conceived the idea, designed, and supervised the study. Z.H.Y., Q.L., and B.S. had full access to all of the data and took responsibility for the integrity of the data. X.T. and N.W... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Cumulative number of cases by PaO<sub>2</sub>/FiO<sub>2</sub> in 12 months</p><p>FiO<sub>2</sub>, fraction of inspired oxygen; PaO<sub>2</sub>, arterial partial pressure of oxygen</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Lung compu... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of patients with psittacosis-induced severe community-acquired pneumonia and acute hypoxic respiratory failure</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Patients (n = 45)</th></tr... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>APACHE, Acute Physiology and Chronic Health Evaluation; BMI, body mass index; ICU, intensive care unit; SOFA, sequential organ failure assessment</p></table-wrap-foot>",
"<table-wrap-foot><p>FiO<sub>2</sub>, fraction of inspired oxygen; PaCO<sub>2</sub>, arterial carbon dioxide partial pressu... | [
"<graphic xlink:href=\"12879_2023_8283_Fig2_HTML\" id=\"d32e558\"/>",
"<graphic xlink:href=\"12879_2023_8283_Fig1_HTML\" id=\"d32e1184\"/>"
] | [] | [{"label": ["8."], "surname": ["Dunnahoo", "Hampton"], "given-names": ["GL", "BC"], "article-title": ["Psittacosis: occurrence in the United States and Report of 97% mortality in a shipment of Psittacine Birds while under Quarantine"], "source": ["Public Health Reports"], "year": ["1945"], "volume": ["60"], "issue": ["13"], "fpage": ["354"], "pub-id": ["10.2307/4585218"]}, {"label": ["27."], "mixed-citation": ["Li T, Mbala-Kingebeni P, Naccache SN, Theze J, Bouquet J, Federman S, et al. Metagenomic next-generation sequencing of the 2014 Ebola Virus Disease Outbreak in the Democratic Republic of the Congo. J Clin Microbiol. 2019;57(9)."]}, {"label": ["31."], "mixed-citation": ["Cunha BA. The chlamydial pneumonias. Drugs Today (Barc). 1998;34(12):1005\u201312."]}] | {
"acronym": [
"AHRF",
"APACHE",
"CAP",
"CT",
"DDPCR",
"ICU",
"IMV",
"IQR",
"mNGS",
"NIPPV",
"PaO2",
"PCR",
"PCT",
"SD",
"SOFA"
],
"definition": [
"Acute hypoxic respiratory failure",
"Acute Physiology and Chronic Health Evaluation",
"Community-acquired pneumonia",
"Computed tomography",
"Droplet digital PCR validation",
"Intensive care units",
"Invasive mechanical ventilation",
"Interquartile range",
"Metagenomic next-generation sequencing",
"Non-invasive positive pressure ventilation",
"Partial oxygen pressure",
"Polymerase chain reaction",
"Procalcitonin",
"Standard deviation",
"Sequential organ failure assessment"
]
} | 31 | CC BY | no | 2024-01-15 23:35:08 | BMC Infect Dis. 2023 Aug 14; 23:532 | oa_package/dd/a8/PMC10426048.tar.gz |
PMC10542848 | 36917408 | [] | [
"<title>Methods</title>",
"<title>Participants</title>",
"<p id=\"Par9\">The sample consisted of 140 six- to ten-year-old children (M<sub>age</sub> = 7.9 years; SD <sub>age</sub> = 1.1 years) and caregivers participating in a laboratory visit as part of a larger study on youth with and without ADHD and for whom... | [
"<title>Results</title>",
"<title>Preliminary Analyses</title>",
"<p id=\"Par20\">We observed one outlier (> 3 SD from the mean) on child disruptive behavior problems. Because results did not change with or without its inclusion, the outlier was not excluded from the dataset, and results are based on all ava... | [
"<title>Discussion</title>",
"<p id=\"Par32\">The present study aimed to improve understanding of real-time antecedents and consequences of child noncompliance, a behavior problem theorized to arise from bidirectional relational processes (Granic & Patterson, ##REF##16478303##2006##; Kalb & Loeber, ##REF#... | [
"<title>Conclusion</title>",
"<p id=\"Par41\">To prevent recurring child externalizing behavior problems, we must elucidate both antecedents and consequences of child noncompliance as it occurs during parent–child interactions. The current study suggests that parents’ behavior precipitates the onset of child nonc... | [
"<p id=\"Par1\">Given that noncompliance is the most common externalizing problem during middle childhood and reliably predicts significant conduct problems, innovations in elucidating its etiology are sorely needed. Evaluation of in-the-moment antecedents and consequences of child noncompliance improves traction o... | [
"<p id=\"Par2\">Children’s noncompliance with parental requests represents the most common externalizing problem for which parents seek child mental health services (Kalb & Loeber, ##REF##12612249##2003##; Owen et al., ##REF##22918669##2012##). Temporally, parents’ <italic>immediate</italic> response to their c... | [
"<title>Author Contributions</title>",
"<p>Conceptualization: Somers & Lee; Data Curation: Stiles & Shen; Formal Analysis: Somers; Funding Acquisition & Investigation: Lee; Writing – original draft: Somers, Stiles, MacNaughton, & Schiff; Writing – review & editing: Somers, Stiles, MacNaughton,... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Conceptual Model. Neg Talk = Parent negative talk. Noncomp = Child noncompliance. Child BP = Child disruptive behavior problems or ADHD symptoms</p></caption></fig>"
] | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Sample Characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">ADHD<break/>(N = 65)</th><th align=\"left\">Non-ADHD<break/>(N = 75)</th></tr></thead><tbody><tr><td align=\"left\">Child Age—M<it... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>Results of the tetrachoric correlations are summarized with the posterior distribution medians. If the 95% credible interval contained 0, the effect is considered null (i.e., non-significant); null effects are shown in plain text</p></table-wrap-foot>",
"<table-wrap-foot><p>Unstandardized est... | [
"<graphic xlink:href=\"10802_2023_1045_Fig1_HTML\" id=\"MO1\"/>"
] | [
"<media xlink:href=\"10802_2023_1045_MOESM1_ESM.docx\"><caption><p>Supplementary file1 (DOCX 32 KB)</p></caption></media>"
] | [{"surname": ["Achenbach", "Rescorla"], "given-names": ["TM", "LA"], "source": ["Manual for the ASEBA school-age forms & profiles"], "year": ["2001"], "publisher-name": ["University of Vermont, Research Center for Children, Youth, and Families"]}, {"surname": ["Achenbach", "Dumenci", "Rescorla"], "given-names": ["TM", "L", "LA"], "article-title": ["DSM-Oriented and empirically based approaches to constructing scales from the same item pools"], "source": ["Journal of Clinical Child and Adolescent Psychology"], "year": ["2003"], "volume": ["32"], "issue": ["3"], "fpage": ["332"], "lpage": ["340"], "pub-id": ["10.1207/S15374424JCCP3203_02"]}, {"surname": ["Asparouhov", "Muth\u00e9n"], "given-names": ["T", "B"], "article-title": ["Latent variable centering of predictors and mediators in multilevel and time-series models"], "source": ["Structural Equation Modeling"], "year": ["2019"], "volume": ["26"], "issue": ["1"], "fpage": ["119"], "lpage": ["142"], "pub-id": ["10.1080/10705511.2018.1511375"]}, {"surname": ["Asparouhov", "Hamaker", "Muthen"], "given-names": ["T", "EL", "B"], "article-title": ["Dynamic structural equation models"], "source": ["Structural Equation Modeling: A Multidisciplinary Journal"], "year": ["2018"], "volume": ["25"], "issue": ["3"], "fpage": ["359"], "lpage": ["388"], "pub-id": ["10.1080/10705511.2017.1406803"]}, {"surname": ["Brown"], "given-names": ["TA"], "source": ["Confirmatory factor analysis for applied research"], "year": ["2006"], "publisher-name": ["The Guilford Press"]}, {"mixed-citation": ["Eyberg, S.M., Nelson, M.M., Duke, M., & Boggs, S.R. (2005). "], "italic": ["Manual for the dyadic parent-child interaction coding system"]}, {"surname": ["Garcia", "Medina", "Sibley"], "given-names": ["AM", "D", "MH"], "article-title": ["Conflict between parents and adolescents with ADHD: Situational triggers and the role of comorbidity"], "source": ["Journal of Child and Family Studies"], "year": ["2019"], "volume": ["28"], "fpage": ["3338"], "lpage": ["3345"], "pub-id": ["10.1007/s10826-019-01512-7"]}, {"surname": ["Gelman", "Rubin"], "given-names": ["A", "DB"], "article-title": ["Inference from Iterative Simulation Using Multiple Sequences"], "source": ["Statistical Science"], "year": ["1992"], "volume": ["7"], "issue": ["4"], "fpage": ["457"], "lpage": ["472"], "pub-id": ["10.1214/ss/1177011136"]}, {"surname": ["Granic", "Lougheed", "Dishion", "Snyder"], "given-names": ["I", "JP", "TJ", "JJ"], "article-title": ["The role of anxiety in coercive family processes with aggressive children"], "source": ["The Oxford handbook of coercive relationship dynamics"], "year": ["2016"], "publisher-name": ["Oxford University Press"], "fpage": ["231"], "lpage": ["248"]}, {"mixed-citation": ["Muth\u00e9n, L.K. & Muth\u00e9n, B.O. (1998\u20132017). "], "italic": ["Mplus User\u2019s Guide."]}, {"surname": ["Patterson", "Reid", "Patterson", "Snyder"], "given-names": ["GR", "JB", "GR", "J"], "article-title": ["The early development of coercive family processes"], "source": ["Antisocial behavior in children and adolescents: A developmental analysis and model for intervention"], "year": ["2002"], "publisher-name": ["American Psychological Association"], "fpage": ["25"], "lpage": ["44"]}, {"mixed-citation": ["Preacher, K.J., Curran, P.J., & Bauer, D.J. (2006). Computational tools for\u00a0probing interaction effects in multiple linear regression, multilevel modeling and latent curve analysis. "], "italic": ["Journal of Educational and Behavioral\u00a0Statistics, 31"]}, {"surname": ["Seymour", "Miller"], "given-names": ["KE", "L"], "article-title": ["ADHD and depression: The role of poor frustration tolerance"], "source": ["Current Developmental Disorders Report"], "year": ["2017"], "volume": ["4"], "issue": ["1"], "fpage": ["14"], "lpage": ["18"], "pub-id": ["10.1007/s40474-017-0105-2"]}, {"surname": ["Schultzberg", "Muth\u00e9n"], "given-names": ["M", "B"], "article-title": ["Number of subjects and time points needed for multilevel time-series analysis: A simulation study of dynamic structural equation modeling"], "source": ["Structural Equation Modeling"], "year": ["2018"], "volume": ["25"], "issue": ["4"], "fpage": ["495"], "lpage": ["515"], "pub-id": ["10.1080/10705511.2017.1392862"]}, {"surname": ["Tung", "Li", "Lee"], "given-names": ["I", "JJ", "SS"], "article-title": ["Child sex moderates the association between negative parenting and childhood conduct problems"], "source": ["Aggressive Behavior"], "year": ["2015"], "volume": ["38"], "issue": ["3"], "fpage": ["239"], "lpage": ["251"], "pub-id": ["10.1002/ab.21423"]}, {"surname": ["Zimmerman", "Schunk", "Zimmerman", "Schunk"], "given-names": ["BJ", "DH", "BJ", "DH"], "article-title": ["Self-regulated learning and performance: An introduction and an overview"], "source": ["Handbook of self-regulation of learning and performance"], "year": ["2011"], "publisher-name": ["Routledge"], "fpage": ["15"], "lpage": ["26"]}] | {
"acronym": [],
"definition": []
} | 48 | CC BY | no | 2024-01-15 23:42:01 | Res Child Adolesc Psychopathol. 2024 Mar 14; 52(1):7-19 | oa_package/33/74/PMC10542848.tar.gz |
PMC10561706 | 37818117 | [
"<title>INTRODUCTION</title>",
"<p>The massive consumption of fossil fuels all over the world has led to excessive CO<sub>2</sub> emissions into the atmosphere, which has caused serious environmental issues and energy crises [##REF##31117420##1–3##]. Electrochemical CO<sub>2</sub> reduction (ECR) by renewable ele... | [
"<title>METHODS</title>",
"<title>Synthesis of PI-linked metallophthalocyanine-metalloporphyrin COFs</title>",
"<p>Synthesis of NiPc-2HPor COF: A Pyrex tube measuring 10 × 200 mm (o.d × length) was charged with NiPc (23.1 mg), 2HPor (16.8 mg), H<sub>2</sub>O (2.5 mL). After sonication for about 60 minutes, the ... | [
"<title>RESULTS AND DISCUSSION</title>",
"<title>Synthesis and structure of NiPc-MPor COF</title>",
"<p>As shown in Scheme ##FIG##0##1## and Fig. ##FIG##1##1##, a [4 + 4] condensation reaction is applied to synthesize NiPc-2HPor COF. Specifically, NiPc-2HPor COF was synthesized by condensation between 2,3,9,10,... | [
"<title>RESULTS AND DISCUSSION</title>",
"<title>Synthesis and structure of NiPc-MPor COF</title>",
"<p>As shown in Scheme ##FIG##0##1## and Fig. ##FIG##1##1##, a [4 + 4] condensation reaction is applied to synthesize NiPc-2HPor COF. Specifically, NiPc-2HPor COF was synthesized by condensation between 2,3,9,10,... | [
"<title>CONCLUSION</title>",
"<p>In conclusion, we rationally designed and synthesized two stable PI phthalocyanine-porphyrin bifunctional COFs in pure water by a hydrothermal method for electrocatalytic cathodic CO<sub>2</sub> reduction coupled with anodic CH<sub>3</sub>OH oxidation. The dual Ni sites in NiPc-Ni... | [
"<p>Equally contributed to this work.</p>",
"<title>ABSTRACT</title>",
"<p>Electrocatalytic CO<sub>2</sub> reduction (ECR) coupled with organic oxidation is a promising strategy to produce high value-added chemicals and improve energy efficiency. However, achieving the efficient redox coupling reaction is still... | [
"<title>Supplementary Material</title>"
] | [
"<title>FUNDING</title>",
"<p>This work was supported by the National Natural Science Foundation of China (22225109, 22071109, 22105080 and 22201083), the Project funded by the China Postdoctoral Science Foundation (2020M682748 and 2021M701270), the Guangdong Basic and Applied Basic Research Foundation (2023A1515... | [
"<fig position=\"float\" id=\"sch1\"><label>Scheme 1.</label><caption><p>Schematic representation of design and synthesis Pc-based COFs. This work and previous works. (Mesitylene (Mes), 1,2-dichloroethane (DCE), Boron trifluoride etherate (BTE), N, N-Dimethylacetamide (DMA), Triethylamine (TEA), N-methylpyrrolidone... | [] | [] | [] | [] | [] | [] | [
"<supplementary-material id=\"sup1\" position=\"float\" content-type=\"local-data\"><label>nwad226_Supplemental_File</label></supplementary-material>"
] | [] | [
"<graphic xlink:href=\"nwad226sc1\" position=\"float\"/>",
"<graphic xlink:href=\"nwad226fig1\" position=\"float\"/>",
"<graphic xlink:href=\"nwad226fig2\" position=\"float\"/>",
"<graphic xlink:href=\"nwad226fig3\" position=\"float\"/>",
"<graphic xlink:href=\"nwad226fig4\" position=\"float\"/>",
"<graph... | [
"<media xlink:href=\"nwad226_supplemental_file.pdf\"><caption><p>Click here for additional data file.</p></caption></media>"
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"given-names": ["BP", "S", "M"], "etal": ["et al."], "article-title": ["Nonlinear optical switching in regioregular porphyrin covalent organic frameworks"], "source": ["Angew Chem Int Ed"], "year": ["2019"], "volume": ["58"], "fpage": ["6896"], "lpage": ["900"], "pub-id": ["10.1002/anie.201814412"]}, {"label": ["45."], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Xie", "Li", "Cui"], "given-names": ["W", "H", "G"], "etal": ["et al."], "article-title": ["NiSn atomic pair on an integrated electrode for synergistic electrocatalytic CO"], "sub": ["2"], "source": ["Angew Chem Int Ed"], "year": ["2021"], "volume": ["60"], "fpage": ["7382"], "lpage": ["8"], "pub-id": ["10.1002/anie.202014655"]}, {"label": ["47."], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Sun", "Dong", "Li"], "given-names": ["S-N", "L-Z", "J-R"], "etal": ["et al."], "article-title": ["Redox-active crystalline coordination catalyst for hybrid 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"acronym": [],
"definition": []
} | 47 | CC BY | no | 2024-01-15 23:35:07 | Natl Sci Rev. 2023 Sep 2; 10(11):nwad226 | oa_package/4f/f0/PMC10561706.tar.gz |
PMC10693699 | 38042773 | [
"<title>Background</title>",
"<p id=\"Par6\">In older clients’ home care, medication management has a key role in the daily tasks of home care professionals [##UREF##0##1##, ##UREF##1##2##]. In addition, the professionals take care of older home care clients with personal assistance for eating and nursing treatme... | [
"<title>Methods</title>",
"<title>Study design</title>",
"<p id=\"Par12\">A pragmatic non-randomized controlled clinical trial with three data collection points (at baseline, 1 month and 2 months) design [##REF##34800676##22##] was carried out in Finland in 2021 (Fig. ##FIG##0##1##). It was designed to evaluate... | [
"<title>Results</title>",
"<title>Demographic characteristics</title>",
"<p id=\"Par30\">The clients were mostly female in both groups (IG: n = 46, 71.9%; CG: n = 35, 76.1%, p = 0.621). Their mean age was 79.3 years, SD 6.4 (IG: 79.1 y, SD 6.6; CG: 79.6 y, SD 6.0, p = 0.718). There were no statistically signifi... | [
"<title>Discussion</title>",
"<p id=\"Par35\">This study aimed to examine the effect of using a robot for medication management on home care professionals’ use of working time. The study produced new knowledge about the effect of a digital solution on home care professionals’ use of working time for medication ma... | [
"<title>Conclusions</title>",
"<p id=\"Par43\">Using a robot for medication management had a decreasing effect on home care professionals’ use of working time. Consequently, it can lead to better health outcomes, improved satisfaction among older clients, and a reduction in readmissions to healthcare settings. Ad... | [
"<title>Background</title>",
"<p id=\"Par1\">Medication management has a key role in the daily tasks of home care professionals delivered to older clients in home care. The aim of this study was to examine the effect of using a robot for medication management on home care professionals´ use of working time.</p>",... | [] | [
"<title>Acknowledgements</title>",
"<p>The authors would like to sincerely thank all home care professionals who participated in this study.</p>",
"<title>Author Contributions</title>",
"<p>S.K.-U., M.V., M.K. and R.T. designed the study and collected the data. S.K.-U., M.V., J.K., M.K. and R.T. analyzed the ... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The CONSORT diagram of the study</p></caption></fig>"
] | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Home care professionals’ background factors (n = 315)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Background factor</th><th align=\"left\">n (%)</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"2\">\n<bold>Occ... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p><sup>a</sup>Five-day mean in minutes per client</p><p>IG: Intervention Group; CG: Control Group; SD: Standard Deviation</p></table-wrap-foot>",
"<table-wrap-foot><p><sup>a</sup>Five-day Estimated Marginal Mean in minutes per group</p><p>Abbreviations: IG, Intervention Group; CG, Control Group... | [
"<graphic xlink:href=\"12913_2023_10367_Fig1_HTML\" id=\"d32e431\"/>"
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"acronym": [
"CG",
"CI",
"CONSORT",
"ECTS",
"EQF",
"F",
"IG",
"SD",
"SPSS"
],
"definition": [
"Control group",
"Confidence Interval",
"Consolidated Standards of Reporting Trials",
"The European Credit Transfer and Accumulation System",
"the European Qualifications Framework",
"Frequency",
"Intervention group",
"Standard deviation",
"Statistical Package for the Social Sciences"
]
} | 44 | CC BY | no | 2024-01-15 23:35:10 | BMC Health Serv Res. 2023 Dec 2; 23:1344 | oa_package/c6/36/PMC10693699.tar.gz |
PMC10695030 | 38055528 | [
"<title>INTRODUCTION</title>",
"<p>In the human species, the genetic determination of gender is chromosomal, homogametic for the female and heterogametic for the male, which conditions gonadal development, with some possible and infrequent variations. In a complex process mediated by the consecutive secretion of ... | [
"<title>METHODS</title>",
"<p>Reflective study in which the most common dilemmas encountered when carrying out a narrative bibliographic review on nursing care for transgender people are analyzed. The analysis will be carried out around the four bioethical principles that constitute the so-called “Hierarchical Pr... | [
"<title>RESULTS</title>",
"<p>As indicated in article 1.2 of the Code of Ethics for Nurses of the International Council of Nursing (ICN) (2021): “Nurses promote an environment in which everyone recognizes and respects the human rights, values, customs, religious and spiritual beliefs of the person, families and c... | [] | [] | [
"<p>EDITOR IN CHIEF: Antonio José de Almeida Filho</p>",
"<p>ASSOCIATE EDITOR: Hugo Fernandes</p>",
"<title>ABSTRACT</title>",
"<title>Objectives:</title>",
"<p>to discuss ethical aspects in nursing care for transgender people.</p>",
"<title>Methods:</title>",
"<p>reflective study based on the dilemmas ... | [
"<title>OBJECTIVES</title>",
"<p>To discuss ethical aspects in nursing care for transgender people.</p>",
"<title>FINAL CONSIDERATIONS</title>",
"<p>Currently, the increase in the prevalence of trans people is worrying, contemplating several hypotheses and requiring more consistent studies. Therefore, especia... | [] | [] | [] | [] | [] | [] | [] | [] | [] | [
"<fn-group><fn fn-type=\"other\"><p>Erratum</p><p>In the article “The ethics of nursing care for transgender people”, with DOI number: <ext-link xlink:href=\"https://doi.org/10.1590/0034-7167-2022-0797\" ext-link-type=\"uri\">https://doi.org/10.1590/0034-7167-2022-0797</ext-link>, published in Revista Brasileira de... | [] | [] | [{"label": ["2"], "person-group": ["\n"], "surname": ["S\u00e1ez-Sesma"], "given-names": ["S."], "source": ["Sexo b\u00e1sico"], "publisher-loc": ["Madrid"], "publisher-name": ["Fundamentos;"], "year": ["2017"]}, {"label": ["4"], "person-group": ["\n"], "surname": ["Errasti", "P\u00e9rez"], "given-names": ["J", "M."], "source": ["Nadie nace en un cuerpo equivocado"], "publisher-loc": ["Barcelona"], "publisher-name": ["Deusto;"], "year": ["2022"]}, {"label": ["7"], "person-group": ["\n"], "surname": ["Miss\u00e9"], "given-names": ["M."], "source": ["A la conquista Del cuerpo equivocado"], "publisher-loc": ["Barcelona/Madrid"], "publisher-name": ["Egales;"], "year": ["2018"]}, {"label": ["8"], "person-group": ["\n"], "surname": ["Beauchamp", "Childress"], "given-names": ["TL", "JF."], "source": ["Principles of Biomedical Ethics"], "edition": ["6\u00aa"], "publisher-loc": ["New York"], "publisher-name": ["Oxford University Press;"], "year": ["2009"]}, {"label": ["9"], "person-group": ["\n"], "surname": ["Gracia"], "given-names": ["D."], "source": ["Fundamentos de bio\u00e9tica"], "edition": ["2\u00aa"], "publisher-loc": ["Madrid"], "publisher-name": ["Triacastela;"], "year": ["2007"]}, {"label": ["10"], "person-group": ["\n"], "collab": ["International Counsil of Nurses (ICN)"], "source": ["The ICN code of ethics for nurses: revised 2021[Internet]"], "publisher-loc": ["Ginebra"], "publisher-name": ["ICN;"], "year": ["2021"], "date-in-citation": ["cited 2022 Dec 12"], "comment": ["Available from: "], "ext-link": ["https://www.icn.ch/system/files/2021-10/ICN_Code-of-Ethics_EN_Web_0.pdf"]}] | {
"acronym": [],
"definition": []
} | 10 | CC BY | no | 2024-01-15 23:35:08 | Rev Bras Enferm.; 76(Suppl 3):e20220797 | oa_package/ed/ed/PMC10695030.tar.gz |
PMC10726224 | 38116399 | [] | [] | [] | [] | [] | [
"<p>Guest Editor of the Roadmap.</p>",
"<title>Abstract</title>",
"<p>Optical sensors and sensing technologies are playing a more and more important role in our modern world. From micro-probes to large devices used in such diverse areas like medical diagnosis, defence, monitoring of industrial and environmental... | [
"<title>Optical fibre sensors</title>",
"<title>Gilberto Brambilla<sup>1</sup> and Luc Thévenaz<sup>2</sup>\n</title>",
"<p>\n<sup>1</sup>University of Southampton, United Kingdom</p>",
"<p>\n<sup>2</sup>EPFL, Switzerland</p>",
"<title>Status</title>",
"<p>Optical fibre sensors (OFSs) are devices which ex... | [
"<title>Data availability statement</title>",
"<p>No new data were created or analysed in this study.</p>"
] | [
"<fig position=\"float\" id=\"joptad0e85f1\"><label>Figure 1.</label><caption id=\"joptad0e85fc1\"><p>Optical fibre sensor opportunities for development.</p></caption><alternatives></alternatives></fig>",
"<fig position=\"float\" id=\"joptad0e85f2\"><label>Figure 2.</label><caption id=\"joptad0e85fc2\"><p>Selecte... | [] | [] | [] | [] | [] | [] | [] | [] | [
"<graphic xlink:href=\"joptad0e85f1\" specific-use=\"print-only\" content-type=\"print\" id=\"joptad0e85f1_eps\" position=\"float\"/>",
"<graphic xlink:href=\"joptad0e85f1_lr\" content-type=\"low\" id=\"joptad0e85f1_lr\" position=\"float\"/>",
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} | 151 | CC BY | no | 2024-01-15 23:35:10 | J Opt. 2024 Jan 1; 26(1):013001 | oa_package/16/5d/PMC10726224.tar.gz |
PMC10730679 | 38130663 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Accurate exposure assessment is central to risk management and relies upon a spatial representation of the hazard, within which exposed people and/or assets can be quantified. Regional multi-volcano analyses that consider population exposure typically consider hazard... | [
"<title>Methods</title>",
"<title>Hazard modelling</title>",
"<p id=\"Par4\">This study relies on the probabilistic simulations of Jenkins et al. (##UREF##26##2022a##), which assessed the hazard associated with tephra fall loads (using Tephra2; Bonadonna et al. ##UREF##8##2005##), impact from large clasts (Ross... | [] | [] | [
"<title>Conclusions and future directions</title>",
"<p id=\"Par17\">Estimating volcanic hazards as concentric radii facilitates comparison across multiple volcanoes and is therefore a popular approach in regional or global assessments. This study provides the first benchmark between this radii approach and the u... | [
"<p>Editorial responsibility: C. Gregg</p>",
"<p id=\"Par1\">Effective risk management requires accurate assessment of population exposure to volcanic hazards. Assessment of this exposure at the large-scale has often relied on circular footprints of various sizes around a volcano to simplify challenges associated... | [
"<title>Hazard extent</title>",
"<p id=\"Par6\">Figure ##FIG##1##2## shows the variability in maximum distance reached by the hazard footprints simulated in our study. Tephra fall is the farthest-reaching hazard and the most variable in maximum extent reached. In the present modelling framework (i.e., fixed total... | [
"<title>Acknowledgements</title>",
"<p>We would like to thank Eduardo Rossi for his support in implementing the large clast model, Álvaro Aravena Ponce for his support with ECMapProb, Rudiger Escobar-Wolf for providing the MATLAB implementation of LAHARZ, Chris Gregg for his seamless editorial work and two anonym... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Location of the selected volcanoes in Indonesia (pink) and in the Philippines (orange). White volcanoes are volcanoes in the GVP database that are not considered in the analysis. Gridded population dataset is from LandScan 2018. White labels are regions defined in ... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Maximum distance reached by all hazards for specific probabilities of occurrence estimated from model simulations. All distance data are provided in Online Resource ##SUPPL##0##1##</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>"
] | [] | [
"<graphic xlink:href=\"445_2023_1686_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"445_2023_1686_Fig2_HTML\" id=\"MO2\"/>",
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"<graphic xlink:href=\"445_2023_1686_Fig4_HTML\" id=\"MO4\"/>",
"<graphic xlink:href=\"445_2023_1686_Fig5_HTML\" id=... | [
"<media xlink:href=\"445_2023_1686_MOESM1_ESM.docx\"><caption><p>Supplementary file1 (DOCX 15 KB) Online resource 1: Maximum hazard footprint distance data from simulations available at https://doi.org/10.21979/N9/GWNX5K.</p></caption></media>",
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"acronym": [],
"definition": []
} | 48 | CC BY | no | 2024-01-15 23:35:07 | Bull Volcanol. 2024 Dec 19; 86(1):3 | oa_package/00/b8/PMC10730679.tar.gz |
PMC10740281 | 38129898 | [
"<title>Background</title>",
"<p id=\"Par5\">\nJuvenile idiopathic arthritis (JIA) is the most common persistent rheumatic condition in children [##UREF##0##1##]. By nature, it is chronic; in a Nordic cohort, at a time point 18 years after diagnosis, the disease was still active in 46% of patients, with 15% being... | [
"<title>Methods</title>",
"<p id=\"Par10\">This was a retrospective, real-life study based on our clinical practices in the transition of patients with JIA. PETRA questionnaire was developed and inspired by a Canadian Good 2 Go questionnaire (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.sickkids.ca/en/... | [
"<title>Results</title>",
"<p id=\"Par16\">The clinical characteristics of the patients are shown in Table ##TAB##0##1##.</p>",
"<p id=\"Par17\">\n\n</p>",
"<p id=\"Par18\">Sixteen of the 83 patients who filled out the questionnaire did not need the transition to the adult clinic when they reached the transit... | [
"<title>Discussion</title>",
"<p id=\"Par27\">In our study, the transition was classified as successful in 71% of the JIA. The main issues behind unsatisfactory results for the transition were poor adherence to medication, inability to comply with scheduled appointments, and the adolescent’s lack of independence ... | [
"<title>Conclusion</title>",
"<p id=\"Par33\">In this study, we developed a usable instrument for evaluating transition readiness in JIA. Based on our findings, the timing of the transition from paediatric care to the adult site should be flexible, allowing the young person to achieve better readiness, capability... | [
"<title>Background</title>",
"<p id=\"Par1\">With chronic diseases, the responsibility for care transfers to adult clinics at some point. Juvenile idiopathic arthritis (JIA) is the most common persistent rheumatic condition in children. A successful transition requires sufficient self-management skills to manage ... | [
"<title>Analyses</title>",
"<p id=\"Par15\">Data were presented as means with standard deviation (SD) and as counts with percentages. The Kaplan–Meier method was used to estimate the crude cumulative transition rate. Receiver operating characteristic (ROC) curves were used to determine an optimal cut-off value of... | [
"<title>Acknowledgements</title>",
"<p>We thank all the patients involved in the study.</p>",
"<title>Author contributions</title>",
"<p>KM, KR, HK, and KA designed the study. KM, KR, KA collected data. KM, KR, HK and KA performed data analysis and data interpretation and drafted the manuscript. All authors r... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Cumulative transition rate from paediatric to adult care in patients with JIA. Kaplan–Meier estimates of the cumulative transition. The grey area represents a 95% confidence interval</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>ROC cur... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>The clinical characteristics of the patients at the time when they filled in the questionnaire at the paediatric site and, later, at their first adult clinic visit</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th alig... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>*According to the ILAR classification criteria [##REF##14760812##31##]. **According to Wallace’s preliminary criteria [##REF##15517647##15##]</p><p>RF = rheumatoid factor, VAS = visual analogy scale, ESR = erythrocyte sedimentation rate, DMARD = disease-modifying antirheumatic drug</p></table-w... | [
"<graphic xlink:href=\"12969_2023_938_Fig1_HTML\" id=\"d32e566\"/>",
"<graphic xlink:href=\"12969_2023_938_Fig2_HTML\" id=\"d32e772\"/>"
] | [] | [{"label": ["1."], "surname": ["Ravelli", "Martini"], "given-names": ["A", "A"], "article-title": ["Juvenile idiopathic arthritis"], "source": ["Lancet Lond Engl"], "year": ["2007"], "volume": ["369"], "issue": ["9563"], "fpage": ["767"], "lpage": ["78"], "pub-id": ["10.1016/S0140-6736(07)60363-8"]}, {"label": ["2."], "surname": ["Glerup", "Rypdal", "Arnstad", "Ekelund", "Peltoniemi", "Aalto"], "given-names": ["M", "V", "ED", "M", "S", "K"], "article-title": ["Ym. Long-term outcomes in juvenile idiopathic arthritis: eighteen years of Follow-Up in the Population-based nordic Juvenile Idiopathic Arthritis Cohort"], "source": ["Arthritis Care Res"], "year": ["2020"], "volume": ["72"], "issue": ["4"], "fpage": ["507"], "lpage": ["16"], "pub-id": ["10.1002/acr.23853"]}, {"label": ["9."], "mixed-citation": ["Rypdal V, Glerup M, Songstad NT, Bertelsen G, Christoffersen T, Arnstad ED. Ym. Uveitis in Juvenile Idiopathic Arthritis. Ophthalmol Rochester Minn [Internet]. 2020;(Journal Article). 10.1016/j.ophtha.2020.08.024."]}, {"label": ["10."], "surname": ["Adams", "Chien", "Wisk"], "given-names": ["JS", "AT", "LE"], "article-title": ["Mental Illness among Youth with Chronic Physical conditions"], "source": ["Pediatr Evanst"], "year": ["2019"], "volume": ["144"], "issue": ["1"], "fpage": ["e20181819"], "pub-id": ["10.1542/peds.2018-1819"]}, {"label": ["12."], "surname": ["Kyll\u00f6nen", "Ebeling", "Kautiainen", "Puolakka", "V\u00e4h\u00e4salo"], "given-names": ["MS", "H", "H", "K", "P"], "article-title": ["Psychiatric disorders in incident patients with juvenile idiopathic arthritis - a case-control cohort study"], "source": ["Pediatr Rheumatol"], "year": ["2021"], "volume": ["19"], "issue": ["1"], "fpage": ["105"], "pub-id": ["10.1186/s12969-021-00599-x"]}, {"label": ["13."], "surname": ["Lozano", "Houtrow"], "given-names": ["P", "A"], "article-title": ["Supporting self-management in children and adolescents with Complex Chronic conditions"], "source": ["Pediatrics"], "year": ["2018"], "volume": ["141"], "issue": ["Suppl 3"], "fpage": ["233"], "lpage": ["41"], "pub-id": ["10.1542/peds.2017-1284H"]}, {"label": ["21."], "mixed-citation": ["Hosmer DW, Lemeshow S. Applied logistic regression, chap. 5. Teoksessa: Applied logistic regression. 2 nd edition. New York: John Wiley and sons; 160\u20134."]}, {"label": ["28."], "surname": ["Mikola", "Rebane", "Arnstad", "Berntson", "Fasth", "Glerup"], "given-names": ["K", "K", "ED", "L", "A", "M"], "article-title": ["Ym. Transitioning patients with juvenile idiopathic arthritis to adult care: the nordic experience"], "source": ["Pediatr Rheumatol"], "year": ["2022"], "volume": ["20"], "issue": ["1"], "fpage": ["84"], "pub-id": ["10.1186/s12969-022-00742-2"]}] | {
"acronym": [
"JIA",
"DAS28",
"sDMARD",
"bDMARD",
"PETRA",
"HUS",
"HAQ",
"VAS",
"SD",
"ROC",
"AUC",
"OR",
"IQR",
"EULAR",
"PReS",
"ILAR"
],
"definition": [
"juvenile idiopathic arthritis",
"Disease Activity Score",
"synthetic disease-modifying antirheumatic drug",
"biologic disease-modifying antirheumatic drug",
"pediatric transition readiness to adult care",
"hospital district of Helsinki and Uusimaa",
"the Health Assessment Questionnaire",
"visual analogy scale",
"standard deviation",
"receiver operating characteristic",
"area under curve",
"odd ratio",
"interquartile range",
"European League Against Rheumatism",
"Paediatric Rheumatology European Society",
"the International League of Associations for Rheumatology"
]
} | 31 | CC BY | no | 2024-01-15 23:35:09 | Pediatr Rheumatol Online J. 2023 Dec 21; 21:149 | oa_package/fe/aa/PMC10740281.tar.gz |
PMC10750394 | 38148946 | [
"<title>Introduction</title>",
"<p>Major depressive disorder (MDD) is a prevalent and debilitating psychiatric disorder that affects 4.7% of the global population and is the second leading cause of disability worldwide (##REF##23922765##Ferrari et al., 2013##). Neuroimaging studies have made significant efforts t... | [
"<title>Methods</title>",
"<title>Participants</title>",
"<p>We utilized rs-fMRI data from the REST-meta-MDD consortium (##UREF##1##Chen et al., 2023##), comprising 1,300 MDD patients and 1,128 NCs across 23 sites. Each participant underwent a T1-weighted structural scan and an rs-fMRI scan. The patient inclusi... | [
"<title>Results</title>",
"<title>Characteristics of participants</title>",
"<p>As shown in ##TAB##0##Table 1##, two MDD subgroups had no significant differences than NCs in age and gender (<italic>p</italic> > 0.05), but showed lower education than NCs (<italic>p</italic> < 0.001). Recurrent MDD showed a... | [
"<title>Discussion</title>",
"<p>This study used GCA to explore alterations in EC within and between resting-state networks in FEDN and recurrent MDD patients in a large-sample Chinese population. We found that: (1) recurrent and total MDD showed altered EC in the FPN, SMN, and CN compared to NCs, while FEDN and ... | [
"<title>Conclusion</title>",
"<p>The present study used the GCA method to investigate differences in EC of large-scale brain networks in FEDN and recurrent MDD patients. We found that recurrent MDD showed altered EC in the FPN, SMN, and CN, while FEDN showed altered inter-network EC from SMN to FPN compared with ... | [
"<p>Edited by: Zhiyong Zhao, Zhejiang University, China</p>",
"<p>Reviewed by: Chao Li, The First Affiliated Hospital of China Medical University, China; Lanxin Ji, New York University, United States; Zhengyuan Yang, University of Macau, China</p>",
"<p><sup>†</sup>These authors have contributed equally to this... | [
"<title>Data availability statement</title>",
"<p>Publicly available datasets were analyzed in this study. This data can be found here: <ext-link xlink:href=\"http://rfmri.org/REST-meta-MDD\" ext-link-type=\"uri\">http://rfmri.org/REST-meta-MDD</ext-link>.</p>",
"<title>Author contributions</title>",
"<p>YZ: ... | [
"<title>Conflict of interest</title>",
"<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>",
"<title>Publisher’s note</title>",
"<p>All claims expressed in this article are solely t... | [
"<fig position=\"float\" id=\"fig1\"><label>Figure 1</label><caption><p>Brain networks from DOS-160 atlas used in the present study. DMN, default mode network; FPN, fronto-parietal network; CON, cingulo-opercular network; SMN, sensorimotor network; VN, visual network; CN, cerebellum network.</p></caption></fig>",
... | [
"<table-wrap position=\"float\" id=\"tab1\"><label>Table 1</label><caption><p>Demographic characteristics of participants.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\" colspan=\"1\"/><th align=\"center\" valign=\"middle\" colspan=\"3\" rowspan=\"1\">MDD vs NC</th><th align=\"ce... | [] | [] | [] | [] | [] | [] | [] | [
"<graphic xlink:href=\"fnins-17-1308551-g001\" position=\"float\"/>",
"<graphic xlink:href=\"fnins-17-1308551-g002\" position=\"float\"/>",
"<graphic xlink:href=\"fnins-17-1308551-g003\" position=\"float\"/>",
"<graphic xlink:href=\"fnins-17-1308551-g004\" position=\"float\"/>"
] | [] | [{"collab": ["American Psychiatric Association"], "year": ["2013"], "source": ["Diagnostic and statistical manual of mental disorders: DSM-5"], "publisher-name": ["American psychiatric association Washington"], "publisher-loc": ["DC"]}, {"surname": ["Chen", "Bin", "Wang", "Li", "Wang", "Li"], "given-names": ["X.", "L.", "Y.-W.", "X.-Y.", "Z.-H.", "H.-X."], "year": ["2023"], "article-title": ["The complexity of functional connectivity profiles of the Subgenual anterior cingulate cortex and dorsal lateral prefrontal cortex in major depressive disorder: a DIRECT consortium study"], "source": ["bioRxiv"], "pub-id": ["10.1101/2023.03.09.531726"]}, {"surname": ["Guha"], "given-names": ["Martin"], "year": ["2014"], "article-title": ["Diagnostic and statistical manual of mental disorders: DSM-5"], "source": ["Ref. Rev."], "publisher-name": ["American Psychiatric Publishing, Inc."], "publisher-loc": ["Arlington, VA"]}, {"surname": ["Jiao", "Zou", "Cao", "Qian", "Ma"], "given-names": ["Z.-Q.", "L.", "Y.", "N.", "Z.-H."], "year": ["2014"], "article-title": ["Effective connectivity analysis of fMRI data based on network motifs"], "source": ["J. Supercomput."], "volume": ["67"], "fpage": ["806"], "lpage": ["819"], "pub-id": ["10.1007/s11227-013-1010-z"]}, {"surname": ["Sun", "Zhongming", "Ma", "Chen", "Wang", "Guo"], "given-names": ["J.", "D.", "Y.", "L.", "Z.", "C."], "year": ["2022b"], "article-title": ["Altered functional connectivity in first-episode and recurrent depression: a resting-state functional magnetic resonance imaging study"], "source": ["Front. Neurol."], "volume": ["13"], "fpage": ["1822"], "pub-id": ["10.3389/fneur.2022.922207"]}, {"surname": ["Wang", "Chen", "Liu", "Zhang", "Zhou", "Feng"], "given-names": ["Y.", "X.", "R.", "Z.", "J.", "Y."], "year": ["2022"], "article-title": ["Disrupted effective connectivity of the default, salience and dorsal attention networks in major depressive disorder: a study using spectral dynamic causal modelling of resting-state fMRI"], "source": ["J. Psychiatry Neurosci."], "volume": ["47"], "fpage": ["E421"], "lpage": ["E434"], "pub-id": ["10.1503/jpn.220038"]}, {"surname": ["West", "Welch", "Galecki"], "given-names": ["Brady T", "Kathleen B", "Andrzej T"], "year": ["2022"], "source": ["Linear mixed models: a practical guide using statistical software"], "publisher-name": ["Crc Press"], "publisher-loc": ["Boca Raton, FL"]}] | {
"acronym": [],
"definition": []
} | 62 | CC BY | no | 2024-01-15 23:35:07 | Front Neurosci. 2023 Dec 11; 17:1308551 | oa_package/5b/7d/PMC10750394.tar.gz |
PMC10755656 | 38096002 | [
"<title>Introduction</title>",
"<title>Adherence to Oral Anticancer Treatments</title>",
"<p>Metastatic breast cancer (MBC) is an incurable disease, wherein the available medications are primarily focused on deferring disease progression and symptom mitigation, thereby prolonging survival rate as well as preser... | [
"<title>Methods</title>",
"<title>Primary End Point Analysis</title>",
"<p>The primary end point was to assess the effectiveness of the DSS web-based solution and the machine learning web application in promoting adherence to OATs in a sample of 100 patients with MBC at 3 months. The adherence is evaluated usin... | [
"<title>Results</title>",
"<p>The recruitment process started in May 2023 and is expected to be concluded on December 2023. Data analysis will be performed between 2023 and 2024. This project has been funded by a Pfizer grant: Enhancing therapy adherence among patients with metastatic breast cancer (65080791).</p... | [
"<title>Discussion</title>",
"<title>Expected Findings</title>",
"<p>Adherence to anticancer treatment is fundamental in the clinical management of patients with MBC, as adherence can lead to a series of crucial clinical outcomes (eg, prolonged survival time, better monitoring and management of side effects, im... | [
"<title>Conclusions</title>",
"<p>Considering the poor evidence on OATs and the need to develop validated instruments to evaluate medication adherence to improve patient clinical outcomes [##REF##19294501##14##,##UREF##3##16##], the development of integrated and shared DSSs able to foster adherence behaviors and ... | [
"<title>Background</title>",
"<p>Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is partic... | [] | [
"<p>The MMAS-8 Scale, content, name, and trademarks are protected by US copyright and trademark laws. Permission for use of the scale and its coding is required. A license agreement is available from MMAR, LLC., www.moriskyscale.com.</p>",
"<p>This project has been funded by a Pfizer grant: Enhancing therapy adhe... | [
"<fig position=\"float\" id=\"figure1\"><label>Figure 1</label><caption><p>The 4 main sections of the decision support system in Italian. Section A: metastatic breast cancer; Section B: adherence to cancer therapies; Section C: promoting adherence; Section D: my adherence diary. The screenshot with the decision sup... | [] | [] | [] | [] | [] | [] | [] | [
"<fn-group><fn fn-type=\"con\"><p>Authors' Contributions: GP, MM, RP, and EM conceived and designed this study. GP coordinated this study. RP, LT, MV, and AM performed statistical analyses. MM, GES, VS, RP, LT, MV, AM, EM, EF, MP, and GP handled the drafting and writing of the manuscript. All authors have read and ... | [
"<graphic xlink:href=\"resprot_v12i1e48852_fig1\" position=\"float\"/>",
"<graphic xlink:href=\"resprot_v12i1e48852_fig2\" position=\"float\"/>"
] | [] | [{"label": ["5"], "surname": ["Montagna", "Zagami", "Masiero", "Mazzocco", "Pravettoni", "Munzone"], "given-names": ["E", "P", "M", "K", "G", "E"], "article-title": ["Assessing predictors of tamoxifen nonadherence in patients with early breast cancer"], "source": ["PPA"], "year": ["2021"], "month": ["09"], "volume": ["Volume 15"], "fpage": ["2051"], "lpage": ["2061"], "pub-id": ["10.2147/ppa.s285768"]}, {"label": ["8"], "surname": ["Sabat\u00e9"], "given-names": ["E"], "source": ["Adherence to Long-Term Therapies: Evidence for Action"], "year": ["2003"], "publisher-loc": ["Geneva"], "publisher-name": ["World Health Organization"]}, {"label": ["15"], "surname": ["Shah", "Touchette", "Marrs"], "given-names": ["KK", "DR", "JC"], "article-title": ["Research and scholarly methods: Measuring medication adherence"], "source": ["J Am Coll Clin Pharm"], "year": ["2023"], "month": ["03"], "day": ["06"], "volume": ["6"], "issue": ["4"], "fpage": ["416"], "lpage": ["426"], "pub-id": ["10.1002/jac5.1771"]}, {"label": ["16"], "surname": ["DiBonaventura", "Copher", "Basurto"], "given-names": ["DD", "R", "E"], "article-title": ["Patient preferences and treatment adherence among women diagnosed with metastatic breast cancer"], "source": ["Am Heal Drug Benefits"], "year": ["2014"], "volume": ["7"], "fpage": ["386"], "lpage": ["396"], "pub-id": ["25525495"]}, {"label": ["17"], "surname": ["Horne", "Cooper", "Wileman", "Chan"], "given-names": ["R", "V", "V", "A"], "article-title": ["Supporting adherence to medicines for long-term conditions"], "source": ["European Psychologist"], "year": ["2019"], "month": ["01"], "volume": ["24"], "issue": ["1"], "fpage": ["82"], "lpage": ["96"], "pub-id": ["10.1027/1016-9040/a000353"]}, {"label": ["32"], "surname": ["Lewis", "Abell"], "given-names": ["SJ", "N"], "article-title": ["Development and evaluation of the Adherence Attitude Inventory"], "source": ["Research on Social Work Practice"], "year": ["2016"], "month": ["08"], "day": ["18"], "volume": ["12"], "issue": ["1"], "fpage": ["107"], "lpage": ["123"], "pub-id": ["10.1177/104973150201200108"]}, {"label": ["34"], "surname": ["Scott", "Fayers", "Aaronson", "Bottomley", "de Graeff", "Groenvold", "Gundy", "Koller", "Petersen", "Sprangers"], "given-names": ["NW", "P", "N", "A", "A", "M", "C", "M", "MA", "MAG"], "collab": ["et al"], "source": ["EORTC QLQ-C30 Reference Values"], "year": ["2008"], "date-in-citation": ["2023-11-13"], "publisher-loc": ["Brussels, Belgium"], "publisher-name": ["EORTC Quality of Life Group"], "comment": ["\n"], "ext-link": ["https://www.eortc.org/app/uploads/sites/2/2018/02/reference_values_manual2008.pdf"]}, {"label": ["35"], "surname": ["Sprangers", "Groenvold", "Arraras", "Franklin", "te Velde", "Muller", "Franzini", "Williams", "de Haes", "Hopwood", "Cull", "Aaronson"], "given-names": ["MA", "M", "JI", "J", "A", "M", "L", "A", "HC", "P", "A", "NK"], "article-title": ["The European organization for research and treatment of breast cancer-specific quality-of-life questionnaire module: first results from a three-country field study"], "source": ["JCO"], "year": ["1996"], "month": ["10"], "volume": ["14"], "issue": ["10"], "fpage": ["2756"], "lpage": ["2768"], "pub-id": ["10.1200/jco.1996.14.10.2756"]}, {"label": ["36"], "surname": ["Cleeland", "Ryan"], "given-names": ["CS", "KM"], "article-title": ["Pain assessment: global use of the Brief Pain Inventory"], "source": ["Ann Acad Med Singapore"], "year": ["1994"], "volume": ["13"], "issue": ["1"], "fpage": ["129"], "lpage": ["138"], "pub-id": ["10.1097/01893697-199513010-00022"]}, {"label": ["37"], "surname": ["Spielberger", "Gonzalez-Reigosa", "Martinez-Urrutia", "Natalicio", "Natalicio"], "given-names": ["CD", "F", "A", "LF", "DS"], "article-title": ["The State-Trait Anxiety inventory"], "source": ["Rev Interam Psicol J Psychol"], "year": ["1971"], "volume": ["5"], "fpage": ["4"], "pub-id": ["10.1002/9780470479216.corpsy0943"]}, {"label": ["38"], "surname": ["Pedrabissi", "Santinello"], "given-names": ["L", "M"], "article-title": ["Verifica della validit\u00e0 dello STAI forma Y di Spielberger"], "source": ["Giunti Organ Spec"], "year": ["1989"], "volume": ["191-192"], "fpage": ["11"], "lpage": ["14"], "comment": ["\n"]}, {"label": ["39"], "surname": ["Beck", "Steer", "Brown"], "given-names": ["AT", "RA", "G"], "article-title": ["Beck Depression Inventory\u2013II (BDI-II)"], "source": ["APA PsycTests"], "year": ["1996"], "fpage": ["1996"], "pub-id": ["10.1037/t00742-000"]}, {"label": ["40"], "surname": ["Sica", "Ghisi"], "given-names": ["C", "M"], "article-title": ["The Italian versions of the Beck Anxiety Inventory and the Beck Depression Inventory-II: Psychometric properties and discriminant power"], "source": ["Flore"], "year": ["2007"], "date-in-citation": ["2023-11-13"], "comment": ["\n"], "ext-link": ["https://flore.unifi.it/handle/2158/258900"]}, {"label": ["41"], "surname": ["Ubbiali", "Chiorri", "Hampton", "Donati"], "given-names": ["A", "C", "P", "D"], "article-title": ["Italian Big Five Inventory. Psychometric properties of the Italian adaptation of the Big Five Inventory (BFI)"], "source": ["Bollettino di Psicologia applicata"], "year": ["2013"], "volume": ["59"], "issue": ["266"], "fpage": ["37"], "lpage": ["48"], "comment": ["\n"]}, {"label": ["45"], "surname": ["Jansen", "Appelbaum", "Klein", "Weinstein", "Cook", "Fogel", "Sulmasy"], "given-names": ["LA", "P", "W", "ND", "W", "JS", "DP"], "article-title": ["Unrealistic optimism in early-phase oncology trials"], "source": ["IRB Ethics Hum Res"], "year": ["2011"], "volume": ["33"], "issue": ["1"], "fpage": ["1"], "lpage": ["8"], "pub-id": ["21314034"]}, {"label": ["46"], "surname": ["Weinstein"], "given-names": ["ND"], "article-title": ["Unrealistic optimism about future life events"], "source": ["Journal of Personality and Social Psychology"], "year": ["1980"], "month": ["11"], "volume": ["39"], "issue": ["5"], "fpage": ["806"], "lpage": ["820"], "pub-id": ["10.1037/0022-3514.39.5.806"]}, {"label": ["48"], "article-title": ["R: A language and environment for statistical computing"], "source": ["R Foundation for Statistical Computing"], "date-in-citation": ["2023-11-13"], "comment": ["\n"], "ext-link": ["https://www.R-project.org/"]}, {"label": ["52"], "surname": ["Wang", "Kong", "Ko"], "given-names": ["Y", "M", "Y"], "article-title": ["Psychometric properties of the 8-item Morisky Medication Adherence Scale in patients taking warfarin"], "source": ["Thromb Haemost"], "year": ["2017"], "month": ["11"], "day": ["29"], "volume": ["108"], "issue": ["10"], "fpage": ["789"], "lpage": ["795"], "pub-id": ["10.1160/th12-05-0368"]}, {"label": ["53"], "surname": ["Prantner"], "given-names": ["B"], "source": ["Visualization of Imputed Values Using the R-package VIM"], "year": ["2011"], "date-in-citation": ["2023-01-01"], "comment": ["\n"], "ext-link": ["https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=50d745a08f8bb3ee5b2045fca40d17da4f693961"]}, {"label": ["54"], "surname": ["Kuhn", "Johnson"], "given-names": ["M", "K"], "source": ["Applied Predictive Modeling"], "year": ["2013"], "publisher-loc": ["New York"], "publisher-name": ["Springer"]}, {"label": ["56"], "surname": ["Greifer"], "given-names": ["N"], "article-title": ["Covariate balance tables and plots: a guide to the cobalt package"], "source": ["R-Project"], "date-in-citation": ["2023-01-01"], "comment": ["\n"], "ext-link": ["https://cloud.r-project.org/web/packages/cobalt/vignettes/cobalt.html"]}, {"label": ["58"], "source": ["Council for International Organizations of Medical Sciences"], "year": ["2016"], "date-in-citation": ["2023-11-13"], "comment": ["\n"], "ext-link": ["https://cioms.ch/"]}, {"label": ["59"], "collab": ["Department of Health\u201a Education\u201aWelfare", "National Commission for the Protection of Human Subjects of BiomedicalBehavioral Research"], "article-title": ["The Belmont Report. Ethical principles and guidelines for the protection of human subjects of research"], "source": ["J Am Coll Dent"], "year": ["2014"], "volume": ["81"], "issue": ["3"], "fpage": ["4"], "lpage": ["13"], "pub-id": ["25951677"]}, {"label": ["60"], "article-title": ["Regulation (EU) 2016/679 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data (General Data Protection Regulation \u2013 GDPR)"], "source": ["Official Journal of the European Union"], "year": ["2018"], "date-in-citation": ["2023-11-13"], "comment": ["\n"], "ext-link": ["https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R0679"]}] | {
"acronym": [
"AAI",
"BDI-II",
"BFI",
"BPI",
"CBI-B/I",
"DSS",
"EORTC-QLQ-BR23",
"EORTC-QLQ-C30",
"IEO",
"MBC",
"MMAS-8",
"OAT",
"QoL",
"REDCap",
"SOC-13",
"STAI-Y",
"TREAT"
],
"definition": [
"Adherence Attitude Inventory",
"Beck Depression Inventory-II",
"Big Five Inventory",
"Brief Pain Inventory",
"Brief Italian version of Cancer Behavior Inventory",
"decision support system",
"European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire",
"European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30",
"European Institute of Oncology",
"metastatic breast cancer",
"8-item Morisky Medication Adherence Scale",
"oral anticancer treatment",
"quality of life",
"research electronic data capture",
"13-item Sense of Coherence scale",
"State-Trait Anxiety Inventory",
"treatment adherence support"
]
} | 61 | CC BY | no | 2024-01-15 23:35:11 | JMIR Res Protoc. 2023 Dec 14; 12:e48852 | oa_package/ed/d3/PMC10755656.tar.gz |
PMC10759156 | 38055568 | [
"<title>Introduction</title>",
"<p>To successfully perform a polychromatic flow cytometry experiment, nothing is more important than correct compensation. Previously it was demonstrated that when there were no spectral differences between experimental samples and compensation controls, beads could be used to comp... | [
"<title>Materials and Methods</title>",
"<title>Cells</title>",
"<p>PBMCs from two healthy male adults were isolated from a leukocyte reduction system chamber purchased from the Oklahoma Blood Institute with Lymphoprep (STEMCELL Technologies). Isolated PBMCs were resuspended in serum-free cell freezing media (B... | [
"<title>Results</title>",
"<title>Identification of baseline variation of median mismatch</title>",
"<p>Single-cell samples were compensated/unmixed using the same cell samples (##TAB##0##Tables I##, ##TAB##1##II##). The spillover matrix is assumed to be perfect, as the same data were used for unmixing calculat... | [
"<title>Discussion</title>",
"<p>To investigate how well compensation beads appropriate experimental cells, datasets with cells and eight commercially available Ab-capture compensation beads (beads from BioLegend and Cytek were not released at the time of this study) were generated using 13 commonly used fluoroch... | [] | [
"<p>D.B. and M.L.R. conceptualized and designed the study, and D.B. and S.K.L. prepared the samples. All authors contributed to data analyses. All authors contributed equally to writing the manuscript.</p>",
"<title>Abstract</title>",
"<p>Compensation or unmixing is essential in analyzing multiparameter flow cy... | [
"<title>Supplementary Material</title>"
] | [
"<title>Acknowledgments</title>",
"<p>We thank Dr. Sumant Basu, Dr. Timothy Bushnell, Geza Paukovics, and Connie Porretta for providing insights. We also thank Anita Fauth and Frank van Diepen for invaluable suggestions.</p>",
"<title>Disclosures</title>",
"<p>The authors have no financial conflicts of intere... | [
"<fig position=\"float\" id=\"fig01\"><label>FIGURE 1.</label><caption><p>Schematic of MMI calculations.</p><p>Details of the concept of MMI are shown.</p></caption></fig>",
"<fig position=\"float\" id=\"fig02\"><label>FIGURE 2.</label><caption><p>Fluorochrome spillover affects data visualization.</p><p>PBMCs wer... | [
"<table-wrap position=\"float\" id=\"tI\"><label>Table I.</label><caption><title>Details of the Ab capture bead</title></caption><table frame=\"hsides\" rules=\"groups\"><thead valign=\"bottom\"><tr><th rowspan=\"2\" colspan=\"1\"/><th align=\"center\" colspan=\"3\" rowspan=\"1\">Thermo Fisher Scientific</th><th al... | [] | [] | [] | [] | [] | [
"<supplementary-material id=\"SMF1\" position=\"float\" content-type=\"local-data\"><label>Supplemental Figures 1 (PDF)</label></supplementary-material>"
] | [
"<table-wrap-foot><fn><p>Details of all eight Ab capture beads are presented.</p></fn></table-wrap-foot>",
"<table-wrap-foot><fn><p>The number of Ab binding sites on eight different beads is presented in terms of the number of Ab binding sites on CD4<sup>+</sup> cells. Green and yellow boxes indicate the values a... | [
"<graphic xlink:href=\"ih2300066t2\" position=\"float\"/>",
"<graphic xlink:href=\"ih2300066f1\" position=\"float\"/>",
"<graphic xlink:href=\"ih2300066f2\" position=\"float\"/>",
"<graphic xlink:href=\"ih2300066f3\" position=\"float\"/>",
"<graphic xlink:href=\"ih2300066f4\" position=\"float\"/>",
"<grap... | [
"<media xlink:href=\"IH_2300066_Supplemental_1.pdf\"><caption><p>Click here for additional data file.</p></caption></media>"
] | [{"label": ["3"], "surname": ["Roederer"], "given-names": ["M."], "year": ["2002"], "article-title": ["Compensation in flow cytometry"], "source": ["Curr. Protoc. Cytom."], "comment": ["Chapter 1:Unit 1.14 "], "ext-link": ["https://currentprotocols.onlinelibrary.wiley.com/doi/10.1002/0471142956.cy0114s22"]}, {"label": ["4"], "surname": ["Maciorowski", "Chattopadhyay", "Jain"], "given-names": ["Z.", "P. K.", "P."], "year": ["2017"], "article-title": ["Basic multicolor flow cytometry"], "source": ["Curr. Protoc. Immunol."], "volume": ["117"], "fpage": ["5.4.1"], "lpage": ["5.4.38"]}, {"label": ["7"], "surname": ["Sahir", "Mateo", "Steinhoff", "Siveen"], "given-names": ["F.", "J. M.", "M.", "K. S."], "year": ["2020"], "article-title": ["Development of a 43 color panel for the characterization of conventional and unconventional T-cell subsets, B cells, NK cells, monocytes, dendritic cells, and innate lymphoid cells using spectral flow cytometry"], "source": ["Cytometry A"], "comment": ["DOI: 10.1002/cyto.a.24288"]}, {"label": ["13"], "surname": ["Daniels", "Leite", "Morgado", "Gardner"], "given-names": ["K.", "J.", "J. M.", "R."], "year": ["2019"], "article-title": ["Beads vs cells: compensating with (un)mixed feelings"], "comment": ["Available at: "], "ext-link": ["https://api.fccf.mskcc.org/uploads/Beads_vs_Cells_CYTO_2019_3e6743ea6a.pdf?updated_at=2023-07-05T20"]}, {"label": ["14"], "surname": ["Friend", "Monard", "Paukovics", "Cordioli", "Dankar"], "given-names": ["K.", "S.", "G.", "V.", "A."], "year": ["2023"], "article-title": ["The search for the perfect particle"], "comment": ["Available at: "], "ext-link": ["https://cdn.shopify.com/s/files/1/0602/8581/2930/files/ACS_Poster_Kathryn_Simon_Geza.pdf?v=1694634750"]}, {"label": ["19"], "surname": ["Verwer"], "given-names": ["B."], "year": ["2002"], "article-title": ["DB FACSDiVa option"], "comment": ["Available at: "], "ext-link": ["https://www.yumpu.com/en/document/read/4122485/bd-facsdiva-option-bd-biosciences"]}, {"label": ["20"], "surname": ["Daniels", "Gardner"], "given-names": ["K.", "R."], "year": ["2020"], "article-title": ["Secondary index stain"], "comment": ["Available at: "], "ext-link": ["https://wi.mit.edu/sites/default/files/2021-05/20200504_Post-it_Secondary_Stain_Index_Final.pdf"]}, {"label": ["21"], "collab": ["FlowJo, LLC"], "year": ["2011"], "article-title": ["Three rules for compensation controls"], "comment": ["Available at: "], "ext-link": ["https://flowjo.typepad.com/the_daily_dongle/2011/09/three-rules-for-compensation-controls.html"]}, {"label": ["26"], "collab": ["Thermo Fisher Scientific"], "article-title": ["UltraComp eBeads compensation beads and UltraComp eBeads plus compensation beads"], "comment": ["Available at: "], "ext-link": ["https://www.thermofisher.com/search/results?query=ultracomp&persona=DocSupport&navId=4294959582&refinementAction=true"]}] | {
"acronym": [
"AF",
"MMI",
"RSD",
"tSNE"
],
"definition": [
"autofluorescence",
"median mismatch index",
"robust SD",
"t-distributed stochastic neighbor embedding"
]
} | 27 | CC BY | no | 2024-01-15 23:35:08 | Immunohorizons. 2023 Dec 6; 7(12):819-833 | oa_package/50/ad/PMC10759156.tar.gz |
PMC10762989 | 38166881 | [
"<title>Background</title>",
"<p id=\"Par6\">Sex workers, those who exchange sex for money or nonmonetary items [##UREF##0##1##], are disproportionally impacted by HIV [##REF##26914165##2##]. Those engaging in sex work practice in a variety of settings, both indoors and outdoors. In the United States, sex workers... | [
"<title>Methods</title>",
"<p id=\"Par12\">We used the ADAPT-ITT framework to guide the adaptation process. Specific steps incorporated within each phase are described within the paper and summarized in Table ##TAB##0##1##. Formative research took place in six stages between January 2019 and March 2022. Our team ... | [] | [
"<title>Discussion</title>",
"<p id=\"Par38\">This study filled a critical gap in Centering adaptation and in community-empowered research with sex workers considering Prep. While Centering has been successfully adapted, the model has never been iteratively adapted with community members and stakeholders, nor has... | [
"<title>Conclusion</title>",
"<p id=\"Par41\">The primary goal of this study was to utilize the ADAPT-ITT model phases 1–6 to adapt Centering for sex workers desiring Pre-Exposure Prophylaxis (PrEP) for HIV prevention. Centering PrEP (C-PrEP +), a group model of PrEP care, was iteratively developed to provide an ... | [
"<title>Background</title>",
"<p id=\"Par1\">Sex workers, those who trade sex for monetary or nonmonetary items, experience high rates of HIV transmission but have not been adequately included in HIV prevention and Pre-Exposure Prophylaxis (PrEP) adherence program development research. Community-empowered (C.E.) ... | [
"<title>Supplementary Information</title>",
"<p>\n</p>"
] | [
"<title>Acknowledgements</title>",
"<p>Authors would like to acknowledge our community partners, study participants, Southside Health Advocacy Resource Partnership, the community advisory board, and Howard Brown Health. For Baby T.</p>",
"<title>Author’s contributions</title>",
"<p>Authors RBS, JB, AKJ, JZ, N... | [] | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Applying the ADAPT-ITT model to guide the adaptation of centering healthcare</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Phase</th><th align=\"left\">Methodology</th><th align=\"left\">Results or Observations</th></tr... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>"
] | [
"<fn-group><fn><p>This article has been updated to correct a typo in the abstract.</p></fn><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
] | [] | [
"<media xlink:href=\"12889_2023_17508_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1: Appendix 1. </bold>Session Outline.</p></caption></media>",
"<media xlink:href=\"12889_2023_17508_MOESM2_ESM.docx\"><caption><p><bold>Additional file 2: Appendix 2. </bold>How C-PrEP+ Maintains fidelity to the Centering H... | [{"label": ["1."], "mixed-citation": ["CDC. HIV Risk Among Persons Who Exchange Sex for Money or Nonmonetary Items. 2016. Available from: "], "ext-link": ["https://www.cdc.gov/hiv/group/sexworkers.html"]}, {"label": ["6."], "surname": ["Sawicki", "Meffert", "Read", "Heinz"], "given-names": ["DA", "BN", "K", "AJ"], "article-title": ["Culturally competent health care for sex workers: an examination of myths that stigmatize sex work and hinder access to care"], "source": ["Sex Relatsh Ther"], "year": ["2019"], "volume": ["34"], "fpage": ["355"], "lpage": ["371"], "pub-id": ["10.1080/14681994.2019.1574970"]}, {"label": ["7."], "mixed-citation": ["Chicago's \"Decriminalization\" of Sex Work | INSIDE COMPLIANCE. 2021. Available from: "], "ext-link": ["https://blogs.luc.edu/compliance/?p=4195"]}, {"label": ["8."], "surname": ["Freeman"], "given-names": ["J"], "article-title": ["Legalization of Sex Work in the United States: An HIV Reduction Strategy"], "source": ["George J Leg Ethics"], "year": ["2019"], "volume": ["32"], "fpage": ["597"]}, {"label": ["10."], "mixed-citation": ["Pre-Exposure Prophylaxis (PrEP) | HIV Risk and Prevention | HIV/AIDS | CDC. Available from: "], "ext-link": ["https://www.cdc.gov/hiv/risk/prep/index.html"]}, {"label": ["11."], "surname": ["Decker", "Crago", "Chu", "Sherman", "Seshu", "Buthelezi"], "given-names": ["MR", "A-L", "SKH", "SG", "MS", "K"], "article-title": ["Human rights violations against sex workers: burden and effect on HIV"], "source": ["The Lancet"], "year": ["2015"], "volume": ["385"], "fpage": ["186"], "lpage": ["199"], "pub-id": ["10.1016/S0140-6736(14)60800-X"]}, {"label": ["12."], "mixed-citation": ["Platt L, Grenfell P, Meiksin R, Elmes J, Sherman SG, Sanders T, et al. Associations between sex work laws and sex workers' health: A systematic review and meta-analysis of quantitative and qualitative studies. Tsai AC, editor. PLOS Med. 2018;15:e1002680."]}, {"label": ["15."], "surname": ["Rivers", "Edwards", "Bhandar", "Bradford", "Packer", "Johnson"], "given-names": ["A", "R", "D", "S", "B", "R"], "article-title": ["Our Hands, Our Health: Developing a Promising Group Healthcare Model for Pediatric to Adult Transition in a Sickle Cell Disease Clinic"], "source": ["Blood"], "year": ["2018"], "volume": ["132"], "fpage": ["4713"], "lpage": ["4713"], "pub-id": ["10.1182/blood-2018-99-111848"]}, {"label": ["16."], "mixed-citation": ["Wirtz AL, Pretorius C, Beyrer C, Baral S, Decker MR, Sherman SG, et al. Epidemic Impacts of a Community Empowerment Intervention for HIV Prevention among Female Sex Workers in Generalized and Concentrated Epidemics. Niccolai LM, editor. PLoS ONE. 2014;9:e88047."]}, {"label": ["17."], "surname": ["Jason"], "given-names": ["L"], "source": ["Participatory community research: theories and methods in action"], "year": ["2004"], "edition": ["1"], "publisher-loc": ["Washington, DC"], "publisher-name": ["American Psychological Association"]}, {"label": ["19."], "surname": ["McCreary", "Kaponda", "Davis", "Kalengamaliro", "Norr"], "given-names": ["LL", "CPN", "K", "M", "KF"], "article-title": ["Empowering peer group leaders for HIV prevention in Malawi"], "source": ["J Nurs Scholarsh Off Publ Sigma Theta Tau Int Honor Soc Nurs"], "year": ["2013"], "volume": ["45"], "fpage": ["288"], "lpage": ["297"]}, {"label": ["24."], "mixed-citation": ["Overs C. An analysis of HIV prevention programming to prevent HIV transmission during commercial sex in developing countries: SEX WORKERS\u202f: PART OF THE SOLUTION. 2002."]}, {"label": ["25."], "mixed-citation": ["Benoit, C., Belle-Isle, L., Smith, M., Phillips, R., Shumka, L., Atchison, C., Jansson, M., & Loppie, C., Benoit, C., Belle-Isle, L., Smith, M., Phillips, R., Shumka,. Community empowerment & transformative learning among sex workers. Victoria, BC: University of Victoria; 2017. Available from: "], "ext-link": ["http://www.understandingsexwork.ca/sites/default/files/uploads/2017%2011%2021%20bulletin-17-sex-worker-empowerment.pdf"]}, {"label": ["26."], "surname": ["Fonner", "Dalglish", "Kennedy", "Baggaley", "O\u2019Reilly", "Koechlin"], "given-names": ["VA", "SL", "CE", "R", "KR", "FM"], "article-title": ["Effectiveness and safety of oral HIV pre-exposure prophylaxis for all populations"], "source": ["AIDS Lond Engl"], "year": ["2016"], "volume": ["30"], "fpage": ["1973"], "lpage": ["1983"], "pub-id": ["10.1097/QAD.0000000000001145"]}, {"label": ["27."], "mixed-citation": ["Pre-Exposure Prophylaxis (PrEP) | HIV Risk and Prevention | HIV/AIDS | CDC. 2019. Available from: "], "ext-link": ["https://www.cdc.gov/hiv/risk/prep/index.html"]}, {"label": ["29."], "mixed-citation": ["Ciribassi RM, Patil CL. \u201cWe don\u2019t wear it on our sleeve\u201d: Sickle cell disease and the (in)visible body in parts. Soc Sci Med. 1982;2016(148):131\u20138."]}, {"label": ["32."], "surname": ["Cavanaugh", "Wismar"], "given-names": ["CE", "A"], "article-title": ["A systematic review of community stakeholder involvement during application of the ADAPT-ITT model to adapt trauma interventions for new populations"], "source": ["Psychol Trauma Theory Res Pract Policy"], "year": ["2022"], "volume": ["14"], "fpage": ["932"], "lpage": ["939"], "pub-id": ["10.1037/tra0001174"]}, {"label": ["33."], "mixed-citation": ["Polit DF, Beck CT. Nursing Research: Generating and Assessing Evidence for Nursing Practice. Lippincott Williams & Wilkins; 2008."]}, {"label": ["34."], "surname": ["Bano", "Zowghi", "Ferrari", "Spoletini", "Donati"], "given-names": ["M", "D", "A", "P", "B"], "article-title": ["Teaching requirements elicitation interviews: an empirical study of learning from mistakes"], "source": ["Requir Eng"], "year": ["2019"], "volume": ["24"], "fpage": ["259"], "lpage": ["289"], "pub-id": ["10.1007/s00766-019-00313-0"]}] | {
"acronym": [],
"definition": []
} | 37 | CC BY | no | 2024-01-15 23:35:10 | BMC Public Health. 2024 Jan 2; 24:56 | oa_package/d0/ed/PMC10762989.tar.gz |
PMC10765273 | 38179382 | [
"<title>Introduction and background</title>",
"<p>Imbalance of supply and demand</p>",
"<p>Many surgical procedures have been associated with increasing blood product transfusions in recent years. This problem in part emerged because of the rise in incidence of numerous comorbidities such as cirrhosis, drug the... | [] | [] | [] | [
"<title>Conclusions</title>",
"<p>In summary, for the operating suite, an anesthesiologist should emphasize the importance of a multidisciplinary approach. Continuing education, regular meetings with stakeholders to review protocols, and posters can all assist in reinforcing algorithms.</p>"
] | [
"<p>Platelet dysfunction and thrombocytopenia are associated with postoperative morbidity not only from modifiable preoperative factors but also from a lack of local patient blood management algorithms. In this regard, platelet transfusions have risen after the COVID-19 pandemic. Simultaneously, there has been a sh... | [
"<title>Review</title>",
"<p>Common medications to treat coagulation-related bleeding</p>",
"<p>Epoetin Alpha </p>",
"<p>Epopoetin is often injected subcutaneously three times a week by chronic renal failure patients to obviate frequent PRBC transfusions at a similar cost of about $200 per PRBC. Prospective s... | [
"<p>The authors wish to acknowledge the Paolo Procacci Foundation for its generous support in the publication process</p>"
] | [] | [
"<table-wrap position=\"float\" id=\"TAB1\"><label>Table 1</label><caption><title>When critical organs are at risk </title><p>We list various guidelines for discontinuation of drugs prior to surgeries with a high risk of bleeding in critical regions, such as the brain, spine, and vascular, along with the cost of an... | [] | [] | [] | [] | [] | [] | [
"<fn-group content-type=\"other\"><title>Author Contributions</title><fn fn-type=\"other\"><p><bold>Acquisition, analysis, or interpretation of data:</bold> Sahar Shekoohi, Kimberly L. Skidmore, Naina Singh, Anusha Kallurkar, Hayden Cagle, Van S. Smith III, Giustino Varrassi, Alan D. Kaye</p><p><bold>Critical revi... | [] | [] | [{"label": ["2"], "article-title": ["Implications of drug use disorders on spine surgery"], "source": ["World Neurosurg"], "person-group": ["\n"], "surname": ["Ferari", "Katsevman", "Dekeseredy", "Sedney"], "given-names": ["CS", "GA", "P", "CL"], "fpage": ["0"], "lpage": ["41"], "volume": ["136"], "year": ["2020"]}, {"label": ["11"], "article-title": ["Hemostasis Management | Haemonetics\u00ae Hospital Solutions"], "date-in-citation": ["\n"], "month": ["10"], "year": ["2023"], "uri": ["https://hospital.haemonetics.com/hemostasis-management"]}, {"label": ["16"], "article-title": ["Evaluating the importance of patient blood management during COVID-19 pandemic"], "source": ["Anesth Pain Med"], "person-group": ["\n"], "surname": ["Chegini"], "given-names": ["A"], "fpage": ["0"], "volume": ["11"], "year": ["2021"]}, {"label": ["17"], "article-title": ["Handovers during cardiac surgery can increase mortality"], "date-in-citation": ["\n"], "month": ["10"], "year": ["2023"], "uri": ["https://www.AnesthesiologyNews.com/Clinical-Anesthesiology/Article/08-21/Handovers-Cardiac-Surgery-Mortality/64212"]}, {"label": ["25"], "article-title": ["Blood loss in major spine surgery: are there effective measures to decrease massive hemorrhage in major spine fusion surgery?"], "source": ["Spine (Phila Pa 1976)"], "person-group": ["\n"], "surname": ["Elgafy", "Bransford", "McGuire", "Dettori", "Fischer"], "given-names": ["H", "RJ", "RA", "JR", "D"], "fpage": ["0"], "lpage": ["56"], "volume": ["35"], "year": ["2010"]}, {"label": ["30"], "article-title": ["Procedure-related bleeding risk in patients with cirrhosis and severe thrombocytopenia"], "source": ["Eur J Clin Invest"], "person-group": ["\n"], "surname": ["Alvaro", "Caporaso", "Giannini", "Iacobellis", "Morelli", "Toniutto", "Violi"], "given-names": ["D", "N", "EG", "A", "M", "P", "F"], "fpage": ["0"], "volume": ["51"], "year": ["2021"]}, {"label": ["31"], "article-title": ["Impact of preoperative platelet count on bleeding risk and allogeneic transfusion in multilevel spine surgery"], "source": ["Spine (Phila Pa 1976)"], "person-group": ["\n"], "surname": ["Chow", "Chancer", "Mazzeffi"], "given-names": ["JH", "Z", "MA"], "fpage": ["0"], "lpage": ["72"], "volume": ["46"], "year": ["2021"]}, {"label": ["32"], "article-title": ["Perioperative oral eltrombopag versus intravenous immunoglobulin in patients with immune thrombocytopenia: a non-inferiority, multicentre, randomised trial"], "source": ["Lancet Haematol"], "person-group": ["\n"], "surname": ["Arnold", "Heddle", "Cook"], "given-names": ["DM", "NM", "RJ"], "fpage": ["640"], "lpage": ["648"], "volume": ["7"], "year": ["2020"]}, {"label": ["35"], "article-title": ["Research C for BE and. RIASTAP. FDA"], "date-in-citation": ["\n"], "month": ["9"], "year": ["2023", "2022"], "fpage": ["2023"], "volume": ["27"], "uri": ["https://www.fda.gov/vaccines-blood-biologics/approved-blood-products/riastap"]}, {"label": ["37"], "article-title": ["Results and lessons from a hospital-wide initiative incentivised by delivery system reform to improve infection prevention and sepsis care"], "source": ["BMJ Open Qual"], "person-group": ["\n"], "surname": ["Sreeramoju", "Voy-Hatter", "White"], "given-names": ["P", "K", "C"], "fpage": ["0"], "volume": ["10"], "year": ["2021"]}] | {
"acronym": [],
"definition": []
} | 39 | CC BY | no | 2024-01-15 23:35:11 | Cureus.; 15(12):e49986 | oa_package/b9/c6/PMC10765273.tar.gz |
PMC10765565 | 38179342 | [
"<title>Introduction and background</title>",
"<p>Leprosy is a contagious infection that is caused by <italic>Mycobacterium leprae</italic>. The disease causes damage to the affected area by targeting the peripheral nerves, which results in swelling of the affected area. The infection commonly targets the nerves,... | [] | [] | [] | [
"<title>Conclusions</title>",
"<p>Leprosy is a contagious infection caused by <italic>Mycobacterium leprae</italic> and <italic>Mycobacterium lepromatosis</italic>. Leprosy is a nonfatal infectious disease that is the most common cause of non-traumatic peripheral neuropathy worldwide. It is estimated that 250,000... | [
"<p>Hansen disease, known as Leprosy, is an infectious disease caused by <italic>Mycobacterium leprae</italic>. The disease was once thought to be highly contiguous, and patients with leprosy were treated poorly and had to face discrimination due to the gruesome disease’s complications. <italic>Mycobacterium leprae... | [
"<title>Review</title>",
"<p>Methods</p>",
"<p>This is a narrative review. The sources for this review are as follows: searching on PubMed, Google Scholar, Medline, and ScienceDirect using keywords: Leprosy, Blindness, Mycobacterium leprae, Armadillo, and Immunologic reactions. Sources were accessed between Aug... | [
"<p>The authors wish to acknowledge the Paolo Procacci Foundation for its generous support in the publication process.</p>"
] | [] | [
"<table-wrap position=\"float\" id=\"TAB1\"><label>Table 1</label><caption><title>The first and second-line medications for leprosy</title><p>Information adapted from Leprosy: Treatment and prevention – UpToDate [##UREF##0##1##,##UREF##9##32##]</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=... | [] | [] | [] | [] | [] | [] | [
"<fn-group content-type=\"other\"><title>Author Contributions</title><fn fn-type=\"other\"><p><bold>Concept and design:</bold> Sahar Shekoohi, Phat H. Le, Sandy Philippeaux, Tiombee Mccollins, Cassande Besong, Alexander Kellar, Vincent G. Klapper, Adalyn S. De Witt, Joseph Drinkard, Shahab Ahmadzadeh, Alan D. Kaye... | [] | [] | [{"label": ["1"], "article-title": ["Leprosy: Epidemiology, microbiology, clinical manifestations, and diagnosis"], "date-in-citation": ["\n"], "month": ["5"], "year": ["2023"], "uri": ["https://www.uptodate.com/contents/leprosy-epidemiology-microbiology-clinical-manifestations-and-diagnosis"]}, {"label": ["2"], "article-title": ["Leprosy"], "source": ["Magill\u2019s Medical Guide"], "person-group": ["\n"], "surname": ["Isola"], "given-names": ["VJ"], "publisher-name": ["Salem Press"]}, {"label": ["5"], "article-title": ["Leprosy - an overview of clinical features, diagnosis, and treatment"], "source": ["J Dtsch Dermatol Ges"], "person-group": ["\n"], "surname": ["Fischer"], "given-names": ["M"], "fpage": ["801"], "lpage": ["827"], "volume": ["15"], "year": ["2017"]}, {"label": ["6"], "article-title": ["Chapter 18: Mycobacteria and Actinomycetes"], "source": ["Lippincott\u00ae Illustrated Reviews: Microbiology (4th Edition)"], "date-in-citation": ["\n"], "month": ["5"], "year": ["2023", "2020"], "person-group": ["\n"], "surname": ["Cornelissen", "Hobbs"], "given-names": ["CN", "MM"], "fpage": ["188"], "lpage": ["200"], "publisher-name": ["Wolters Kluwer Health"], "uri": ["https://meded.lwwhealthlibrary.com/book.aspx?bookid=2605"]}, {"label": ["8"], "article-title": ["MIMS\u2019 Medical Microbiology and Immunology - Mycobacterial Diseases of the Skin (6th ed)"], "source": ["Mims\u2019 Medical Microbiology and Immunology - Mycobacterial Diseases of the Skin. 6th ed"], "date-in-citation": ["\n"], "month": ["5"], "year": ["2023", "2018"], "person-group": ["\n"], "surname": ["Goering", "Dockrell", "Zuckerman", "Chiodini"], "given-names": ["R", "HM", "M", "PL"], "publisher-name": ["Elsevier Health Sciences"], "uri": ["http://com.aucmed.idm.oclc.org/Resource/detail/0702071544/ch0027s1162"]}, {"label": ["10"], "article-title": ["https://emedicine.medscape.com/article/220455-overview?form=fpf"], "source": ["Published online May 1"], "date-in-citation": ["\n"], "month": ["11"], "year": ["2023", "2023"], "person-group": ["\n"], "surname": ["Darvin Scott", "Jade Chee"], "given-names": ["Smith", "Riopelle"], "uri": ["https://emedicine.medscape.com/article/220455-overview?form=fpf"]}, {"label": ["15"], "article-title": ["Reservoirs and transmission routes of leprosy; a systematic review"], "source": ["PLoS Negl Trop Dis"], "person-group": ["\n"], "surname": ["Ploemacher", "Faber", "Menke", "Rutten", "Pieters"], "given-names": ["T", "WR", "H", "V", "T"], "fpage": ["0"], "volume": ["14"], "year": ["2020"]}, {"label": ["22"], "article-title": ["What is Hansen\u2019s disease?"], "source": ["CDC. Published November 15"], "date-in-citation": ["\n"], "month": ["5"], "year": ["2023", "2022"], "fpage": ["2023"], "volume": ["15"], "uri": ["https://www.cdc.gov/leprosy/about/about.html"]}, {"label": ["24"], "article-title": ["Presenting symptoms of leprosy at diagnosis: clinical evidence from a cross-sectional, population-based study"], "source": ["PLoS Negl Trop Dis"], "person-group": ["\n"], "surname": ["Chen", "Zha", "Shui"], "given-names": ["X", "S", "TJ"], "fpage": ["0"], "volume": ["15"], "year": ["2021"]}, {"label": ["32"], "article-title": ["Leprosy: Treatment and prevention"], "date-in-citation": ["\n"], "month": ["5"], "year": ["2023"], "uri": ["https://www.uptodate.com/contents/leprosy-treatment-and-prevention"]}, {"label": ["33"], "article-title": ["Chapter 11: Drugs used in treatment of infectious diseases"], "source": ["BRS Pharmacology (7th Edition)"], "date-in-citation": ["\n"], "month": ["5"], "year": ["2023"], "person-group": ["\n"], "surname": ["Lerchenfeldt", "Rosenfeld", "Loose"], "given-names": ["S", "GC", "DS"], "fpage": ["241"], "lpage": ["273"], "publisher-name": [" Wolters Kluwer Health"], "uri": ["https://meded.lwwhealthlibrary.com/book.aspx?bookid=2691"]}, {"label": ["34"], "article-title": ["Antimycobacterial Drugs"], "source": ["Basic & Clinical Pharmacology (15the Edition) "], "person-group": ["\n"], "surname": ["Beauduy", "Winston"], "given-names": ["CE", "LG"], "publisher-name": ["McGraw-Hill"]}, {"label": ["35"], "article-title": ["Clinical management of leprosy reactions"], "source": ["Infect Dis Clin Pract"], "person-group": ["\n"], "surname": ["Graham", "Furlong", "Margoles", "Owusu", "Franco-Paredes"], "given-names": ["A", "S", "LM", "K", "C"], "fpage": ["235"], "lpage": ["238"], "volume": ["18"], "year": ["2010"]}, {"label": ["42"], "article-title": ["Global practices in regard to implementation of preventive measures for leprosy"], "source": ["PLoS Negl Trop Dis"], "person-group": ["\n"], "surname": ["Gillini", "Cooreman", "Wood", "Pemmaraju", "Saunderson"], "given-names": ["L", "E", "T", "VR", "P"], "fpage": ["0"], "volume": ["11"], "year": ["2017"]}] | {
"acronym": [],
"definition": []
} | 43 | CC BY | no | 2024-01-15 23:35:11 | Cureus.; 15(12):e49954 | oa_package/32/94/PMC10765565.tar.gz |
PMC10787665 | 38222235 | [
"<title>Introduction</title>",
"<p>Ewing sarcoma is a rare solid tumor, with the majority of cases occurring in bone (15%-20% of cases develop in soft tissues) [##UREF##0##1##]. It corresponds to the second most common bone neoplasm that mainly occurs in children and young adults, especially in the second decade ... | [] | [] | [
"<title>Discussion</title>",
"<p>The cell of origin of Ewing sarcoma is not established, although several hypotheses are currently considered (possible origin on mesenchymal cell, hematopoietic cell, fibroblast, or neural crest) [##UREF##1##10##]. Histologically, these neoplasms are characterized by the presence ... | [
"<title>Conclusions</title>",
"<p>Despite the low specificity of CD117, its expression and KIT gene mutations are markers of mastocytosis. Expression of CD117 is variable in Ewing sarcoma, but KIT gene mutations with gain-of-function were identified only in 2.6% of these patients. In this case, the detection of a... | [
"<p>KIT gene mutations in Ewing sarcomas are rare; however, they are much more frequent in other neoplasms, namely mastocytosis. We describe a case of an adult male with a one-year duration of recurrent episodes of pain, swelling, and redness on the proximal phalanx of the third finger of his right hand. A core bio... | [
"<title>Case presentation</title>",
"<p>A male in his early 30s presented in 2016 with pain on the proximal phalanx of the middle finger of the right hand associated with redness and swelling, with a one-year duration. Blood analysis was normal for a complete blood count, renal and liver function, electrolyte bal... | [] | [
"<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG1\"><label>Figure 1</label><caption><title>(A) Coronal T1-weighted magnetic resonance imaging of the right hand (2016). (B) Bone scintigraphy (2016).</title></caption></fig>",
"<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG2\"><label>Figure 2</label><ca... | [] | [] | [] | [] | [] | [] | [] | [
"<fn-group content-type=\"other\"><title>Author Contributions</title><fn fn-type=\"other\"><p><bold>Concept and design:</bold> Ridhi Ranchor, Manuel Magalhães, Eugénia Rosendo, André Coelho, Pedro Cardoso</p><p><bold>Acquisition, analysis, or interpretation of data:</bold> Ridhi Ranchor, Manuel Magalhães, Eugénia... | [
"<graphic xlink:href=\"cureus-0015-00000050537-i01\" position=\"float\"/>",
"<graphic xlink:href=\"cureus-0015-00000050537-i02\" position=\"float\"/>",
"<graphic xlink:href=\"cureus-0015-00000050537-i03\" position=\"float\"/>",
"<graphic xlink:href=\"cureus-0015-00000050537-i04\" position=\"float\"/>"
] | [] | [{"label": ["1"], "article-title": ["Extraskeletal Ewing\u2019s sarcoma of the little finger, a rare case"], "source": ["Glob Surg"], "person-group": ["\n"], "surname": ["Akther", "Kumar", "Shaikh"], "given-names": ["MJ", "LU", "SU"], "fpage": ["129"], "lpage": ["131"], "volume": ["2"], "year": ["2016"]}, {"label": ["10"], "article-title": ["Pathogenesis of Ewing sarcoma: a review"], "source": ["J Adv Clin Res Insights"], "person-group": ["\n"], "surname": ["Rani", "Shyamala", "Girish", "Murgod"], "given-names": ["PSV", "K", "HC", "S"], "fpage": ["164"], "lpage": ["168"], "volume": ["2"], "year": ["2015"]}, {"label": ["11"], "article-title": ["The histogenesis of Ewing sarcoma"], "source": ["Cancer Rep Rev"], "person-group": ["\n"], "surname": ["Tu", "Huo", "Gingold", "Zhao", "Shen", "Lee"], "given-names": ["J", "Z", "J", "R", "J", "DF"], "fpage": ["1"], "lpage": ["4"], "volume": ["1"], "year": ["2017"]}, {"label": ["12"], "article-title": ["Biomarkers in the Ewing sarcoma family of tumors"], "source": ["Curr Biomark Find"], "person-group": ["\n"], "surname": ["Machado", "L\u00f3pez-Guerrero", "Llombart-Bosch"], "given-names": ["I", "JA", "A"], "fpage": ["81"], "lpage": ["92"], "volume": ["4"], "year": ["2014"]}, {"label": ["18"], "article-title": ["Metachronous renal Ewing sarcoma/primitive neuroectodermal tumour in a survivor of Burkitt lymphoma"], "source": ["BMJ Case Rep"], "person-group": ["\n"], "surname": ["Khandwala", "Hilal", "Fadoo", "Minhas"], "given-names": ["K", "K", "Z", "K"], "fpage": ["0"], "volume": ["2018"], "year": ["2018"]}] | {
"acronym": [],
"definition": []
} | 20 | CC BY | no | 2024-01-15 23:41:51 | Cureus.; 15(12):e50537 | oa_package/26/07/PMC10787665.tar.gz |
PMC10787667 | 38222133 | [
"<title>Introduction</title>",
"<p>Renal transplantation is a preferred treatment for end-stage renal disease and greatly enhances quality of life [##REF##21883901##1##]. However, antibody-mediated rejection poses a significant challenge to long-term graft survival and can lead to graft dysfunction or loss [##REF... | [] | [] | [
"<title>Discussion</title>",
"<p>The occurrence of hyperacute rejection is an unusual event in this modern era due to worldwide pretransplant cross-matching to detect pre-existing donor-specific antibodies. Non-HLA antibodies have been reported as responsible in the absence of HLA antibodies [##REF##30062683##12#... | [
"<title>Conclusions</title>",
"<p>In this specific case, an innovative strategy is introduced to address hyperacute rejection, a condition that is not commonly encountered and currently lacks any established methods of treatment. This novel approach holds significant promise in effectively addressing the unique c... | [
"<p>Hyperacute rejection is a rare complication of renal transplantation. It is mainly caused by preformed human leukocyte antigen antibodies and can lead to the loss of the transplanted kidney. Renal transplantation is a highly beneficial treatment for people with end-stage renal disease, greatly improving their q... | [
"<title>Case presentation</title>",
"<p>An 18-year-old male, with a significant past medical history of focal segmental glomerulosclerosis, underwent bilateral native nephrectomy due to severe proteinuria. Subsequently, he received a living donor kidney transplant from his mother. The patient had a heterozygous <... | [
"<p>The authors greatly acknowledge the contributions of the admirable individuals who donated kidneys.</p>"
] | [
"<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG1\"><label>Figure 1</label><caption><title>Formalin-fixed permanent section of a biopsy with confirmation of neutrophil infiltration and glomerular capillary microthrombus (hematoxylin and eosin stain at 20× original magnification).</title></caption></fig>",
"... | [] | [] | [] | [] | [] | [] | [] | [
"<fn-group content-type=\"other\"><title>Author Contributions</title><fn fn-type=\"other\"><p><bold>Concept and design:</bold> Rauf Shahbazov, Reut Hod Dvorai, Irshad Hussain</p><p><bold>Drafting of the manuscript:</bold> Rauf Shahbazov, Salma Mahmoud, Irshad Hussain</p><p><bold>Supervision:</bold> Rauf Shahbazo... | [
"<graphic xlink:href=\"cureus-0015-00000050538-i01\" position=\"float\"/>",
"<graphic xlink:href=\"cureus-0015-00000050538-i02\" position=\"float\"/>",
"<graphic xlink:href=\"cureus-0015-00000050538-i03\" position=\"float\"/>"
] | [] | [{"label": ["10"], "article-title": ["Antibody-mediated rejection: new approaches in prevention and management"], "source": ["Am J Transplant"], "person-group": ["\n"], "surname": ["Montgomery", "Loupy", "Segev"], "given-names": ["RA", "A", "DL"], "fpage": ["3"], "lpage": ["17"], "volume": ["18 Suppl 3"], "year": ["2018"], "uri": ["https://www.sciencedirect.com/science/article/pii/S1600613522098410"]}] | {
"acronym": [],
"definition": []
} | 14 | CC BY | no | 2024-01-15 23:41:52 | Cureus.; 15(12):e50538 | oa_package/35/50/PMC10787667.tar.gz |
PMC10787668 | 38217785 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Among health professionals, nurses constitute the largest workforce. Thus, it is vital to increase the quality of service that nurses provide for positive patient outcomes [##REF##36918253##1##]. However, nursing is generally accepted as a high-risk profession in ter... | [
"<title>Materials and methods</title>",
"<p id=\"Par6\">This study was approved by the Institutional Ethics Committee (Approval #: E1-20-356) and performed in accordance with the ethical standards stated in the 1964 Declaration of Helsinki. Informed consent was obtained from all participants. This cross-sectional... | [
"<title>Results</title>",
"<p id=\"Par14\">The mean age of the participants was 34.8 (23–51) years. 80.2% of the participants were female and 19.8% were male. Moreover, most participants (88.1%) had a bachelor’s degree. The majority of the participants (62.4%) had more than 10 years of work experience. There were... | [
"<title>Discussion</title>",
"<p id=\"Par17\">Nurses who are overworked likely experience negative emotional states and adverse health effects due to burnout that in turn may reduce their performance and quality of care [##REF##36918253##1##, ##REF##33794917##12##, ##UREF##3##13##]. In addition, adoption of new t... | [
"<title>Conclusion</title>",
"<p id=\"Par26\">Our results show that job satisfaction, psychological well-being and QoL ratings were similar between perioperative nurses who work in robotic-assisted and pure laparoscopic surgery. In our sample, 18.9% of the nurses reported having moderate to extreme depressive sym... | [
"<p id=\"Par1\">The rapid introduction of technological developments into healthcare systems adds another layer of complexity to the already demanding jobs of nurses, particularly for those working in perioperative care. In the present study, our primary aim is job satisfaction, whereas the secondary outcomes are p... | [
"<title>Statıstıcal analysıs</title>",
"<p id=\"Par13\">Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS, Chicago, IL) version 28.0.1. Descriptive statistical data for continuous variables were expressed as mean and standard deviation. <italic>T</italic> tests were co... | [
"<title>Author contributions</title>",
"<p>All authors contributed to the study conception and design. DNT, ET, MB, AT: contributed to the conception and design of the study. DNT, AP, MT: collected data. MB, TK, OG, YA, AT: provided close supervision during the study. ET: worked on data analysis and interpretatio... | [] | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>The demographic data of the participants</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Parameters</th><th align=\"left\">Robotic-assisted laparoscopic surgery group (<italic>n</italic> = 51)</th><th align=\"left\">Pure ... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p><italic>SD</italic> standard deviation</p><p><sup>*</sup>Student’s <italic>T</italic> test</p></table-wrap-foot>",
"<table-wrap-foot><p>The scores are presented as mean ± standard deviation</p><p><italic>MSQ</italic> Minnesota Satisfaction Questionnaire, <italic>BDI</italic> Beck Depression I... | [] | [] | [{"label": ["2."], "surname": ["Misiak", "Sier\u017cantowicz", "Krajewska-Ku\u0142ak", "Lewko", "Chili\u0144ska", "Lewko"], "given-names": ["B", "R", "E", "K", "J", "J"], "article-title": ["Psychosocial work-related hazards and their relationship to the quality of life of nurses-a cross-sectional study"], "source": ["Int J Environ Res Public Health"], "year": ["2020"], "volume": ["18"], "fpage": ["755"], "pub-id": ["10.3390/ijerph17030755"]}, {"label": ["8."], "surname": ["Fisher"], "given-names": ["CD"], "article-title": ["Why do lay people believe that satisfaction and performance are correlated? Possible sources of a commonsense theory"], "source": ["J Org Behav"], "year": ["2003"], "volume": ["24"], "fpage": ["753"], "lpage": ["777"], "pub-id": ["10.1002/job.219"]}, {"label": ["9."], "surname": ["Weiss", "Dawis", "England", "Lofquist"], "given-names": ["DJ", "RV", "GW", "LH"], "source": ["Manual for the Minnesota satisfaction questionnaire"], "year": ["1967"], "publisher-loc": ["Minneapolis"], "publisher-name": ["University of Minnesota Press"]}, {"label": ["13."], "surname": ["Al Hakami", "Baker"], "given-names": ["IY", "OG"], "article-title": ["Exploring the factors influencing the nurse\u2019s work motivation"], "source": ["Iris J Nur Car"], "year": ["2018"], "volume": ["1"], "fpage": ["1"], "lpage": ["12"], "pub-id": ["10.33552/IJNC.2018.01.000503"]}] | {
"acronym": [],
"definition": []
} | 25 | CC BY | no | 2024-01-15 23:41:52 | J Robot Surg. 2024 Jan 13; 18(1):19 | oa_package/49/67/PMC10787668.tar.gz |
PMC10787671 | 37782382 | [
"<title>Background</title>",
"<p id=\"Par5\">Colorectal cancer (CRC) is the third most common cancer globally, with incidence rates that positively correlate with Human Development Index levels (HDI), a measure of societal development based on the average health, education, and income of a population [##REF##3353... | [
"<title>Methods</title>",
"<title>Study design</title>",
"<p id=\"Par8\">The Colorectal cancer low-risk study cohort consists of more than 3300 participants diagnosed with all-stage CRC in 14 hospitals in Middle Sweden from 2003 to 2009, as described elsewhere [##UREF##2##24##]. Participants were consecutively ... | [
"<title>Results</title>",
"<p id=\"Par23\">Participants missing an exact date of recurrence (<italic>n</italic> = 29), with recurrences occurring ≤ 6 months of diagnosis (<italic>n</italic> = 18) or follow-up time ≤ 6 months (<italic>n</italic> = 11) were excluded from the RFS analysis (<italic>n</italic> =58 ). ... | [
"<title>Discussion</title>",
"<p id=\"Par33\">In this cohort study, patients with CRC stage I-III and a healthy lifestyle (HL 4) had a 49% lower HR of cancer recurrence and a 48% lower HR of all-cause death compared with the least healthy (HL 1). As we found the proportional hazards assumption to be violated in o... | [
"<title>Conclusions</title>",
"<p id=\"Par43\">Our study indicates that adherence to a healthy lifestyle may increase the RFS and OS of patients with stage I-III CRC. Avoidance of smoking and being physically active were independent risk-reducing factors for these outcomes.</p>"
] | [
"<title>Purpose</title>",
"<p id=\"Par1\">Colorectal cancer (CRC) risk is associated with modifiable lifestyle factors including smoking, physical inactivity, Western diet, and excess body weight. The impact of lifestyle factors on survival is less known. A cohort study was conducted to investigate the combined e... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<p>The authors would like to thank all study participants, clinicians, and staff of the Swedish low-risk colorectal cancer study group. The late Berith Wejderot stands out for her diligent work with study inclusion and the handling of questionnaires. Professor Alicja Wolk at the Institute of Environmental Medicine... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Flow diagram of participants illustrating the study design according to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations. <italic>N</italic> number, <italic>CRC</italic> colorectal cancer, tumor stage = stage according... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of 1098 patients with stage I–III colorectal cancer in the Colorectal cancer low-risk cohort, for the population as a whole and by Healthy lifestyle and Body Mass Index score group</p></caption><table frame=\"hsides\" rules=\"groups\"><thead... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>*Healthy lifestyle and Body Mass Index score groups, 0–1 points, 2 points, 3 points, 4 points</p><p>**<italic>IQR</italic> Interquartile range</p><p>***<italic>ASA</italic> American Society of Anesthesiologists Physical Status Classification system</p><p><sup>a</sup><italic>AJCC</italic> Ame... | [
"<graphic xlink:href=\"10552_2023_1802_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"10552_2023_1802_Fig2_HTML\" id=\"MO2\"/>"
] | [
"<media xlink:href=\"10552_2023_1802_MOESM1_ESM.docx\"><caption><p>Supplementary file1 (DOCX 4549 KB)</p></caption></media>"
] | [{"label": ["2."], "surname": ["Klugman", "Rodriguez", "Choi"], "given-names": ["J", "F", "H-J"], "article-title": ["The HDI 2010: new controversies, old critiques UNDP (United Nations Development Programme)"], "source": ["J Econ Inequal"], "year": ["2011"], "volume": ["9"], "fpage": ["249"], "lpage": ["288"], "pub-id": ["10.1007/s10888-011-9178-z"]}, {"label": ["3."], "mixed-citation": ["WCRF/AICR (2018) Diet, nutrition, physical activity and cancer: a global perspective. Continuous Update Project Expert Report"]}, {"label": ["24."], "mixed-citation": ["Forsberg A, Keranen A, Vonh S, Picelli S, Papadogiannakis N, Ghazi S, et al (2017) Defining new colorectal cancer syndromes in a population-based cohort of the disease. Anticancer Res 37(4):1831\u20131835. 10.21873/anticanres.11518"]}, {"label": ["25."], "mixed-citation": ["AJCC (1997) AJCC Cancer Staging Manual - Fifth Edition Philadelphia: Lippincott - Raven Publishers"]}, {"label": ["41."], "mixed-citation": ["WHO (1998) Obesity: preventing and managing the global epidemic. Report of a WHO consultation on obesity. Geneva, June 3\u20135, 1997. Geneva: WHO"]}] | {
"acronym": [],
"definition": []
} | 52 | CC BY | no | 2024-01-15 23:41:52 | Cancer Causes Control. 2024 Oct 2; 35(2):367-376 | oa_package/4f/aa/PMC10787671.tar.gz |
PMC10787673 | 37812268 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Melittin, the major bioactive component (40–50%) [##REF##33799931##1##] of honeybee (<italic>A. melifera</italic>) venom, is a hemolytic, small, linear peptide composed of 26 amino acid residues with the following sequence: GIGAVLKVLTTGLPALISWIKRKRQQ-NH2 [##REF##3454... | [
"<title>Materials and Methods</title>",
"<title>Animals and Melittin Administration</title>",
"<p id=\"Par7\">Male, specific pathogen-free, C57/BL6 mice (20–25 g) were supplied by Vital River (Beijing Vital River Laboratory Animal Technology, Beijing, China). In all experiments, 8- to 12-week-old mice were used... | [
"<title>Results</title>",
"<title>Melittin Ameliorates Neurological Deficits and Decreased CBF in dMCAO Mice</title>",
"<p id=\"Par22\">To assess the effects of melittin on neurological deficits following cerebral ischemia, dMCAO mice were pretreated with varying concentrations of melittin (0.1, 0.2, and 0.4 μg... | [
"<title>Discussion</title>",
"<p id=\"Par32\">In traditional Chinese medicine, bee venom has long been used against chronic pain, skin diseases, arthritis, inflammation, and cancer [##REF##34543656##2##]. As the bee venom peptide, melittin is hydrophobic and amphipathic, showing archetypal membrane activity [##RE... | [
"<title>Conclusion</title>",
"<p id=\"Par40\">Our findings furnish compelling evidence that underscores the advantageous role of melittin in the therapeutic intervention of ischemic stroke, with potential applications to other neuroinflammatory diseases as well. As for the underlying mechanism, our study illumina... | [
"<p id=\"Par1\">Melittin, a principal constituent of honeybee venom, exhibits diverse biological effects, encompassing anti-inflammatory capabilities and neuroprotective actions against an array of neurological diseases. In this study, we probed the prospective protective influence of melittin on cerebral ischemia,... | [] | [
"<title>Author Contributions</title>",
"<p>XX, XZ designed the experiments and analyzed the data. XX and JF performed the behavioral experiments and assisted with getting mice tissues. CZ contributed to part of the acquisition of animal and cell data. XX, ZL, RD, and HH performed the ELISA, the western blot, and ... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Melittin ameliorates neurological deficit and cerebral blood flow, reduces infarct volume and brain edema in dMCAO mice: <bold>a</bold> mNSS scores of dMCAO mice were significantly increased from 24 to 48 h and were reduced in medium- and high-dose of melittin-trea... | [] | [] | [] | [] | [] | [] | [] | [
"<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"11064_2023_4030_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"11064_2023_4030_Fig2_HTML\" id=\"MO2\"/>",
"<graphic xlink:href=\"11064_2023_4030_Fig3_HTML\" id=\"MO3\"/>",
"<graphic xlink:href=\"11064_2023_4030_Fig4_HTML\" id=\"MO4\"/>",
"<graphic xlink:href=\"11064_2023_4030_Fig5_... | [] | [{"label": ["20"], "surname": ["Jin", "Liang", "Wang"], "given-names": ["Z", "J", "J"], "article-title": ["Delayed brain ischemia tolerance induced by electroacupuncture pretreatment is mediated via MCP-induced protein 1"], "source": ["J Neuroinflamm"], "year": ["2013"], "volume": ["10"], "fpage": ["63"], "pub-id": ["10.1186/1742-2094-10-63"]}, {"label": ["28."], "surname": ["Ahmedy", "Ibrahim", "Salem"], "given-names": ["OA", "SM", "HH"], "article-title": ["Antiulcerogenic effect of melittin via mitigating TLR4/TRAF6 mediated NF-\u03baB and p38MAPK pathways in acetic acid-induced ulcerative colitis in mice"], "source": ["Chemico-Biol Interact"], "year": ["2020"], "volume": ["331"], "fpage": ["109276"], "pub-id": ["10.1016/j.cbi.2020.109276"]}, {"label": ["37."], "surname": ["Zhu"], "given-names": ["J"], "article-title": ["Bee therapy and the prevention and treatment of neurological disorders"], "source": ["Journal of Bee"], "year": ["2010"], "volume": ["2010"], "issue": ["9"], "fpage": ["5"]}, {"label": ["43"], "surname": ["Wang", "Tang", "Yenari"], "given-names": ["Q", "XN", "MA"], "article-title": ["The inflammatory response in stroke"], "source": ["J Neuroim"], "year": ["2007"], "volume": ["184"], "issue": ["1\u20132"], "fpage": ["53"], "lpage": ["68"], "pub-id": ["10.1016/j.jneuroim.2006.11.014"]}, {"label": ["55"], "surname": ["Zhan", "Qin", "Zhang"], "given-names": ["J", "WY", "Y"], "article-title": ["Upregulation of neuronal zinc finger protein A20 expression is required for electroacupuncture to attenuate the cerebral inflammatory injury mediated by the nuclear factor-kB signaling pathway in cerebral ischemia/reperfusion rats"], "source": ["J Neuroinflamm."], "year": ["2016"], "volume": ["13"], "issue": ["1"], "fpage": ["258"], "pub-id": ["10.1186/s12974-016-0731-3"]}] | {
"acronym": [],
"definition": []
} | 58 | CC BY | no | 2024-01-15 23:41:52 | Neurochem Res. 2024 Oct 9; 49(2):348-362 | oa_package/fb/45/PMC10787673.tar.gz |
PMC10787674 | 38218743 | [
"<title>Introduction to Lymphatic Vasculature Structure and Development</title>",
"<p id=\"Par4\">The lymphatic vascular system functions to maintain homeostatic fluid and immune cell maintenance and surveillance in the body. Lymph fluid contains tissue-derived cell debris, immune cells, and/or other cells or cel... | [] | [] | [] | [
"<title>Conclusions</title>",
"<p id=\"Par22\">In this review, we described what is known about lymphatic vasculature development throughout mammary gland development and its changing morphology. The establishment of lymphatics during these functional events may also contribute to the potential of tumor cell meta... | [
"<p id=\"Par1\">Lymphatic vasculature has been shown to promote metastatic spread of breast cancer. Lymphatic vasculature, which is made up of larger collecting vessels and smaller capillaries, has specialized cell junctions that facilitate cell intravasation. Normally, these junctions are designed to collect immun... | [
"<title>Overview of Lymphatics and Mammary Gland Development</title>",
"<p id=\"Par7\">The mammary gland is first established during embryogenesis with formation of mammary rudiments from the mesenchyme in the mouse by embryonic day 10–12 [##REF##22844349##50##]. Left-right asymmetry of the mammary line formation... | [
"<title>Author Contributions</title>",
"<p>P.A.D. and T.R.L. wrote the main manuscript. P.A.D. prepared all figures using BioRender.com. All authors reviewed the manuscript.</p>",
"<title>Declarations</title>",
"<title>Competing Interests</title>",
"<p id=\"Par26\">The authors declare no competing interests... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Model demonstrating overlapping signaling mechanisms to promote lymphangiogenesis during mammary gland pubertal development and postpartum involution. Mammary gland pubertal development (left, representative terminal end bud) and postpartum involution (right, repre... | [] | [] | [] | [] | [] | [] | [] | [
"<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"10911_2023_9554_Fig1_HTML\" id=\"d32e552\"/>",
"<graphic xlink:href=\"10911_2023_9554_Fig2_HTML\" id=\"d32e836\"/>"
] | [] | [{"label": ["39."], "mixed-citation": ["Vadhan A et al. "], "italic": ["CD44 promotes Breast Cancer Metastasis through AKT-Mediated downregulation of Nuclear FOXA2"]}, {"label": ["52."], "mixed-citation": ["Biswas SK et al. "], "italic": ["The mammary gland: Basic structure and Molecular Signaling during Development"]}, {"label": ["56."], "mixed-citation": ["Yoshida N et al. "], "italic": ["Dysregulation of Amphiregulin stimulates the pathogenesis of cystic lymphangioma"]}, {"label": ["61."], "mixed-citation": ["Khan YS, Sajjad H. "], "italic": ["Anatomy, Thorax, Mammary Gland", "StatPearls"]}, {"label": ["71."], "mixed-citation": ["Skytthe MK, Graversen JH, Moestrup SK. "], "italic": ["Targeting of CD163(+) macrophages in Inflammatory and Malignant Diseases"]}, {"label": ["82."], "mixed-citation": ["Lim CL et al. "], "italic": ["Estrogen exacerbates mammary involution through neutrophil-dependent and -independent mechanism"]}, {"label": ["86."], "mixed-citation": ["Cahill TJ et al. "], "italic": ["Tissue-resident macrophages regulate lymphatic vessel growth and patterning in the developing heart"]}, {"label": ["91."], "surname": ["Zhang"], "given-names": ["B"], "article-title": ["M2-polarized tumor-associated macrophages are associated with poor prognoses resulting from accelerated lymphangiogenesis in lung adenocarcinoma"], "source": ["Clin (Sao Paulo)"], "year": ["2011"], "volume": ["66"], "issue": ["11"], "fpage": ["1879"], "lpage": ["86"], "pub-id": ["10.1590/S1807-59322011001100006"]}, {"label": ["95."], "mixed-citation": ["Hughes K, Watson CJ. "], "italic": ["The multifaceted role of STAT3 in mammary gland involution and Breast Cancer"]}, {"label": ["96."], "surname": ["Chen"], "given-names": ["Y"], "article-title": ["Quantification of STAT3 and VEGF expression for molecular diagnosis of lymph node Metastasis in Breast cancer"], "source": ["Med (Baltim)"], "year": ["2017"], "volume": ["96"], "issue": ["45"], "fpage": ["e8488"], "pub-id": ["10.1097/MD.0000000000008488"]}, {"label": ["111."], "mixed-citation": ["Cronin KA et al. "], "italic": ["Annual report to the nation on the status of cancer, part 1: National cancer statistics"]}, {"label": ["119."], "mixed-citation": ["Duffy AM, Harmey JH. "], "italic": ["Vascular Endothelial Growth Factor (VEGF) and Its Role in Non-Endothelial Cells: Autocrine Signalling by VEGF"]}] | {
"acronym": [],
"definition": []
} | 125 | CC BY | no | 2024-01-15 23:41:52 | J Mammary Gland Biol Neoplasia. 2024 Jan 13; 29(1):1 | oa_package/04/05/PMC10787674.tar.gz |
PMC10787675 | 37878148 | [
"<title>Introduction</title>",
"<p id=\"Par18\">The prevalence of obesity has been rising for decades [##REF##27115820##1##]. It is estimated that over one fifth of the female world population will be obese by the year 2025 [##REF##27115820##1##]. Obesity is a well-known risk factor for the development of breast ... | [
"<title>Methods</title>",
"<title>Study design and population</title>",
"<p id=\"Par22\">Patients were identified from the Southeast Netherlands Advanced Breast Cancer (SONABRE) registry (NCT03577197), an ongoing prospectively maintained retrospective cohort study [##UREF##0##26##]. The SONABRE registry include... | [
"<title>Results</title>",
"<title>Patient characteristics</title>",
"<p id=\"Par34\">Of 4365 patients included in the SONABRE registry between 2007 and 2020, 2709 patients were diagnosed with HR+/HER2− ABC (Fig. ##FIG##0##1##). After exclusion of patients without a BMI measurement at diagnosis (n = 764) or pati... | [
"<title>Discussion</title>",
"<p id=\"Par39\">In this study on a real-world cohort of 1456 patients diagnosed with HR+/HER2− ABC who received endocrine therapy with or without a CDK 4/6 inhibitor as first-given systemic therapy in the Netherlands between 2007 and 2020, we evaluated whether BMI is an independent p... | [] | [
"<title>Purpose</title>",
"<p id=\"Par1\">This study determines the prognostic impact of body mass index (BMI) in patients with hormone receptor-positive/human epidermal growth factor receptor-2-negative (HR+/HER2−) advanced (i.e., metastatic) breast cancer (ABC).</p>",
"<title>Methods</title>",
"<p id=\"Par2... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Acknowledgements</title>",
"<p>The SONABRE registry was funded by the Netherlands Organisation for Health Research and Development (ZonMw: 80-82500-98-8003), Roche, Pfizer, Novartis, Eli Lilly, Daiichi Sankyo, AstraZeneca, and Gilead. We would like to thank all SONABRE data managers of the department of M... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Flowchart of included patients</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Overall survival (<bold>a</bold>) and first-line progression-free survival (<bold>b</bold>) according to BMI class at diagnosis of HR+/HER2− ABC</p></caption><... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline characteristics of the study population, overall and according to BMI class at diagnosis of HR+/HER2− ABC (N [%])</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Total<break/>n = 1456 (100%)</... | [
"<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n... | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>Data in each cell represent numbers with associated percentages, unless otherwise specified</p><p>Percentages may exceed 100% due to rounding</p><p><italic>ABC </italic>advanced breast cancer, <italic>CNS </italic>central nervous system, <italic>HR+/HER2−</italic> hormone receptor-positive/huma... | [
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"<graphic xlink:href=\"10549_2023_7108_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"10549_2023_7108_Fig2_HTML\" id=\"MO2\"/>"
] | [
"<media xlink:href=\"10549_2023_7108_MOESM1_ESM.pdf\"><caption><p>Supplementary file1 (PDF 783 KB)</p></caption></media>"
] | [{"label": ["26."], "mixed-citation": ["Tjan-Heijnen VCG (2018) Southeast Netherlands Advanced Metastatic Breast Cancer Registry (SONABRE), clinicaltrials.gov. National Library of Medicine, Bethesda, MD"]}] | {
"acronym": [
"ABC",
"BMI",
"CI",
"CDK 4/6",
"DFS",
"EBC",
"HER2",
"HR+",
"HR",
"IQR",
"MFI",
"OS",
"PFS"
],
"definition": [
"Advanced breast cancer",
"Body mass index",
"Confidence interval",
"Cyclin-dependent kinase 4/6",
"Disease-free survival",
"Early breast cancer",
"Human epidermal growth factor receptor 2",
"Hormone receptor-positive",
"Hazard ratio",
"Interquartile range",
"Metastatic-free interval",
"Overall survival",
"Progression-free survival"
]
} | 39 | CC BY | no | 2024-01-15 23:41:52 | Breast Cancer Res Treat. 2024 Oct 25; 203(2):339-349 | oa_package/1e/42/PMC10787675.tar.gz |
PMC10787676 | 0 | [
"<title>Introduction</title>",
"<p id=\"Par4\">In most bird species, breeding success and nesting behaviors change with age and experience, with experienced individuals usually better concealing their nests to improve nest survival (Marzluff ##UREF##43##1988##; Kleindorfer ##UREF##25##2007a##; Öst and Steele ##RE... | [
"<title>Methods</title>",
"<title>Study site and study species</title>",
"<p id=\"Par10\">This study was conducted on Floreana Island (− 1.299829, − 90.455674) during the onset of nesting and the Darwin’s finch breeding season that peaks during February and March and coincides with the onset of heavier rains us... | [
"<title>Results</title>",
"<title>Male age and heterospecific singing activity</title>",
"<p id=\"Par26\">Older males had significantly more heterospecific singing activity near the nest (<italic>n</italic> = 55, Mean estimate [95% CrI] = 2.088 [0.447, 3.714], Table S1a) compared to younger males (Fig. ##FIG##2... | [
"<title>Discussion</title>",
"<p id=\"Par30\">The main aim of this study was to test if nest site characteristics, such as vegetation cover and the acoustic neighborhood, differed across male age in two Darwin’s finches: the small tree finch and the small ground finch. As predicted, older males built nests in are... | [] | [
"<p>Communicated by S. Bouwhuis.</p>",
"<p id=\"Par1\">Nesting success tends to increase with age in birds, in part because older birds select more concealed nest sites based on experience and/or an assessment of prevailing predation risk. In general, greater plant diversity is associated with more biodiversity a... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Acknowledgements</title>",
"<p>We thank the Galapagos National Park for permission to conduct research (permit no. PC-02–20 and PC-73-21) and the Charles Darwin Foundation for logistical support. We thank all team members for assistance with nest monitoring and data collection, especially David Arango Rol... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Changes in plumage coloration in male small tree finches with each annual molt. Males require, on average, 5 years to attain a fully black chin and crown. Brown to black 5 correspond to the color and age categories B0–B5 used, with B0 being yearling males and B5 in... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Sample size per nesting phase: the number of recordings and number of nests for each year</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\">Nesting phase</th><th align=\"left\" colspan=\"4\">Small ground finch... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>The IUCN classification is shown per species as endemic (E), native (N) or introduced (I)</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></f... | [
"<graphic xlink:href=\"10336_2023_2093_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"10336_2023_2093_Fig2_HTML\" id=\"MO2\"/>",
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"<graphic xlink:href=\"10336_2023_2093_Fig5_... | [
"<media xlink:href=\"10336_2023_2093_MOESM1_ESM.docx\"><caption><p>Supplementary file1 (DOCX 56 kb)</p></caption></media>"
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"acronym": [],
"definition": []
} | 86 | CC BY | no | 2024-01-15 23:41:52 | J Ornithol. 2024 Jul 13; 165(1):179-191 | oa_package/aa/64/PMC10787676.tar.gz |
PMC10787677 | 0 | [
"<title>Introduction</title>",
"<p id=\"Par5\">Proteins are hydrolyzed by proteases (EC 3.4.11-24) to yield amino acids and bioactive peptides. Aspartic proteases (EC 3.4.23), commonly called acid proteases, have been widely used in different foods, such as cheese, bread, beverages, meat, and bioactive peptides (... | [
"<title>Materials and methods</title>",
"<title>Strains, plasmids, and reagents</title>",
"<p id=\"Par10\">\n<italic>T. asperellum</italic> CAU126 was screened, identified, and preserved in China General Microbiological Culture Collection Center (CGMCC No. 3.5921). <italic>Escherichia coli</italic> strain DH5α ... | [
"<title>Results</title>",
"<title>Bioinformation analysis of <italic>Ta</italic>proA1</title>",
"<p id=\"Par25\">A novel aspartic protease gene (<italic>Tapro</italic>A1) from <italic>T. asperellum</italic> was cloned and identified (GenBank no. GFP56020.1). The full-length gene was 1643 bp with an intron of 77... | [
"<title>Discussion</title>",
"<p id=\"Par31\">In this study, a novel aspartic protease gene (<italic>Tapro</italic>A1) from <italic>T. asperellum</italic> was successfully mined and expressed in <italic>K. phaffii.</italic> The pro-peptide sequence plays a vital role in the folding and secretion of active proteas... | [] | [
"<title>Abstract</title>",
"<p id=\"Par1\">A novel aspartic protease gene (<italic>Tapro</italic>A1) from <italic>Trichoderma asperellum</italic> was successfully expressed in <italic>Komagataella phaffii</italic> (<italic>Pichia pastoris</italic>). <italic>Ta</italic>proA1 showed 52.8% amino acid sequence identi... | [
"<title>Supplementary information</title>",
"<p>\n</p>"
] | [
"<title>Author contribution</title>",
"<p>YBX and ZQJ conceived and designed the research. YBX and XL conducted the experiments. QJY analyzed the data. YBX and ZQJ wrote and revised the manuscript. All authors read and approved the manuscript.</p>",
"<title>Funding</title>",
"<p>This work was financially supp... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Multiple sequence alignments of <italic>Ta</italic>proA1 from <italic>T. asperellum</italic> with other A1 family aspartic proteases. NCBI GenBank accession numbers were as follows: Q9HEZ3.1 from <italic>Penicillium janthinellum</italic>, P0CU33.1 from <italic>Aspe... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Effects of protease inhibitors on <italic>Ta</italic>proA1 activity</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Inhibitors</th><th>Concentration (mM)</th><th>Specific activity (U/mg) <sup>a</sup></th><th>Relative activity <sup>b</su... | [
"<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t... | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p><sup>a</sup>The protease activity was measured under optimal conditions. All data were mean values ± standard deviations of triplicate tests</p><p><sup>b</sup>Without protease inhibitors, the specific activity was defined as 100%</p></table-wrap-foot>",
"<table-wrap-foot><p><sup>a</sup>The pr... | [
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"<media xlink:href=\"253_2023_12848_MOESM1_ESM.pdf\"><label>ESM 1</label><caption><p>(PDF 1124 kb)</p></caption></media>"
] | [{"surname": ["Azadi", "Hemmati", "Homaei", "Khalaji-Pirbalouty"], "given-names": ["MA", "R", "A", "V"], "article-title": ["A psychrophilic caseinolytic aspartic protease from the freshwater amphipod "], "italic": ["Gammarus bakhteyaricus"], "source": ["LWT-Food Sci Technol"], "year": ["2022"], "volume": ["153"], "fpage": ["112536"], "pub-id": ["10.1016/j.lwt.2021.112536"]}, {"surname": ["Deng", "Zheng", "Zhang", "Wang", "Kan"], "given-names": ["HL", "J", "FS", "Y", "JQ"], "article-title": ["Isolation of angiotensin I-converting enzyme inhibitor from pepsin hydrolysate of porcine hemoglobin"], "source": ["Eur Food Res Technol"], "year": ["2014"], "volume": ["239"], "fpage": ["933"], "lpage": ["940"], "pub-id": ["10.1007/s00217-014-2290-0"]}, {"surname": ["Deng", "Huang", "Li", "Lu", "Zhang", "Luo"], "given-names": ["JJ", "WQ", "ZW", "DL", "Y", "XC"], "article-title": ["Biocontrol activity of recombinant aspartic protease from "], "italic": ["Trichoderma harzianum"], "source": ["Enzym Microb Technol"], "year": ["2018"], "volume": ["112"], "fpage": ["35"], "lpage": ["42"], "pub-id": ["10.1016/j.enzmictec.2018.02.002"]}, {"surname": ["Gao", "He", "Wei", "Zhang"], "given-names": ["B", "L", "D", "LJ"], "article-title": ["Identification and magnetic immobilization of a pyrophilous aspartic protease from Antarctic psychrophilic fungus"], "source": ["J Food Biochem"], "year": ["2018"], "volume": ["42"], "fpage": ["e12691"], "pub-id": ["10.1111/jfbc.12691"]}, {"surname": ["Horimoto", "Dee", "Yada"], "given-names": ["Y", "DR", "RY"], "article-title": ["Multifunctional aspartic peptidase prosegments"], "source": ["New Biotechnol"], "year": ["2009"], "volume": ["25"], "fpage": ["318"], "lpage": ["324"], "pub-id": ["10.1016/j.nbt.2009.03.010"]}, {"surname": ["Hyun", "Shin"], "given-names": ["CK", "HK"], "article-title": ["Utilization of bovine blood plasma proteins for the production of angiotensin I converting enzyme inhibitory peptides"], "source": ["Process Biochem"], "year": ["2000"], "volume": ["36"], "fpage": ["65"], "lpage": ["71"], "pub-id": ["10.1016/S0032-9592(00)00176-X"]}, {"surname": ["Ichishima"], "given-names": ["E"], "article-title": ["Purification and mode of assay for acid proteinase of "], "italic": ["Aspergillus saitoi"], "source": ["Methods Enzymol"], "year": ["1970"], "volume": ["19"], "fpage": ["397"], "lpage": ["406"], "pub-id": ["10.1016/0076-6879(70)19029-X"]}, {"surname": ["Mamo", "Assefa"], "given-names": ["J", "F"], "article-title": ["The role of microbial aspartic protease enzyme in food and beverage industries"], "source": ["J Food Qual"], "year": ["2018"], "volume": ["2018"], "fpage": ["1"], "lpage": ["15"], "pub-id": ["10.1155/2018/7957269"]}, {"surname": ["Mechri", "Jaouadi", "Bouacem", "Allala", "Bouraoui", "Ferard", "Pekik", "Noiriel", "Abousalham", "Darenfed", "Hac\u00e8ne", "Lederer", "Baciou", "Jaouadi"], "given-names": ["S", "NZ", "K", "F", "A", "C", "H", "A", "A", "AB", "H", "F", "L", "B"], "article-title": ["Cloning and heterologous expression of subtilisin SAPN, a serine alkaline protease from "], "italic": ["Melghiribacillus thermohalophilus", "Escherichia coli", "Pichia pastoris"], "source": ["Process Biochem"], "year": ["2021"], "volume": ["105"], "fpage": ["27"], "lpage": ["41"], "pub-id": ["10.1016/j.procbio.2021.03.020"]}, {"surname": ["Nielsen", "Petersen", "Dambmann"], "given-names": ["PM", "D", "CJJOFS"], "article-title": ["Improved method for determining food protein degree of hydrolysis"], "source": ["J Food Sci"], "year": ["2001"], "volume": ["66"], "fpage": ["642"], "lpage": ["646"], "pub-id": ["10.1111/j.1365-2621.2001.tb04614.x"]}, {"surname": ["Shu", "Shen", "Yang", "Wang", "Wang", "Wang", "Ma"], "given-names": ["M", "W", "SH", "XJ", "F", "Y", "L"], "article-title": ["High-level expression and characterization of a novel serine protease in "], "italic": ["Pichia pastoris"], "source": ["Enzyme Microb Tech"], "year": ["2016"], "volume": ["92"], "fpage": ["56"], "lpage": ["66"], "pub-id": ["10.1016/j.enzmictec.2016.06.007"]}, {"surname": ["Siala", "Kamoun", "Hajji", "Abid", "Gharsallah", "Nasri"], "given-names": ["R", "A", "M", "L", "N", "M"], "article-title": ["Extracellular acid protease from "], "italic": ["Aspergillus niger"], "source": ["Afr J Biotechnol"], "year": ["2009"], "volume": ["8"], "fpage": ["4582"], "lpage": ["4589"]}, {"surname": ["Singh", "Aluko", "Hati", "Solanki"], "given-names": ["BP", "RE", "S", "D"], "article-title": ["Bioactive peptides in the management of lifestyle-related diseases: current trends and future perspectives"], "source": ["Crit Rev Food Sci"], "year": ["2022"], "volume": ["62"], "fpage": ["4593"], "lpage": ["4606"], "pub-id": ["10.1080/10408398.2021.1877109"]}, {"surname": ["Wei", "Chen", "Zheng", "Tao", "Yu", "Wu"], "given-names": ["M", "P", "P", "X", "X", "D"], "article-title": ["Purification and characterization of aspartic protease from "], "italic": ["Aspergillus niger"], "source": ["Microb Cell Factories"], "year": ["2023"], "volume": ["22"], "fpage": ["42"], "pub-id": ["10.1186/s12934-023-02047-9"]}, {"surname": ["Xie", "Liu", "Wang", "Li"], "given-names": ["NN", "SS", "C", "B"], "article-title": ["Stability of casein antioxidant peptide fractions during in vitro digestion/Caco-2 cell model: characteristics of the resistant peptides"], "source": ["Eur Food Res Technol"], "year": ["2014"], "volume": ["239"], "fpage": ["577"], "lpage": ["586"], "pub-id": ["10.1007/s00217-014-2253-5"]}, {"surname": ["Yue", "Chen", "Zhu", "Zeng", "Liu", "Liu", "Wang", "Sun"], "given-names": ["XP", "P", "YM", "Y", "HM", "HY", "M", "YX"], "article-title": ["Heterologous expression and characterization of "], "italic": ["Aspergillus oryzae", "Pichia pastoris"], "source": ["Chin J Biotechnol"], "year": ["2019"], "volume": ["35"], "fpage": ["415"], "lpage": ["424"]}] | {
"acronym": [],
"definition": []
} | 42 | CC BY | no | 2024-01-15 23:41:52 | Appl Microbiol Biotechnol. 2024 Jan 13; 108(1):1-12 | oa_package/8a/e9/PMC10787677.tar.gz |
PMC10787678 | 38217742 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Recent studies have highlighted that severe respiratory viral infections such as influenza or coronavirus disease 2019 (COVID-19) pose a risk for secondary fungal infections in critically ill patients [##REF##36029799##1##]. In consequence, influenza-associated pulmo... | [
"<title>Methods</title>",
"<title>Study Cohort</title>",
"<p id=\"Par5\">This is a prospective monocentric observational study performed at the University Hospital of Graz, Austria. The primary objective of this study was to describe our local experience with CAPA, and to compare the incidence of CAPA between p... | [
"<title>Results</title>",
"<title>Study Cohort</title>",
"<p id=\"Par11\">Baseline characteristics of the study cohort are displayed in Table ##TAB##0##1##. Seventy-seven patients were admitted to the ICU during the observational period, fulfilled inclusion criteria and were enrolled into the study. Twenty-seve... | [
"<title>Discussion</title>",
"<p id=\"Par19\">In this prospective monocentric cohort study in critically ill COVID-19 patients, we observed a CAPA incidence of 2.2 events per 100 ICU days in those not receiving antifungal prophylaxis, while not a single case of CAPA was observed in those with administration of mo... | [] | [
"<p>Handling Editor: Jannik Stemler.</p>",
"<p id=\"Par1\">Early after the beginning of the coronavirus disease 2019 (COVID-19)-pandemic, it was observed that critically ill patients in the intensive care unit (ICU) were susceptible to developing secondary fungal infections, particularly COVID-19 associated pulmo... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Author Contributions</title>",
"<p>JP and MH conceptualized the study. JF, ACR, MH, and JP contributed to data collection and analysis. MG contributed to methodology. JF and JP drafted the manuscript. MG, MH, ACR and PE critically revised the manuscript. All authors reviewed and approved the final version... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Kaplan–Meier survival curves between patients who received antifungal prophylaxis and those you did not for an 84-day follow-up period. No differences in probability of survival could be observed between the groups (Log-rank test: <italic>p</italic> = 0.115)</p></c... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline characteristics of study cohort</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Total (<italic>n</italic> = 77)</th><th align=\"left\">Prophylaxis group (<italic>n</italic> = 53)</th><th align... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p><italic>BMI</italic> = Body Mass Index, <italic>CAPA</italic> = COVID associated pulmonary aspergillosis, <italic>ECMO</italic> = Extracorporeal membrane oxygenation; <italic>EORTC</italic> = European Organization for Research and Treatment of Cancer, <italic>ICU</italic> = Intensive Care Unit;... | [
"<graphic xlink:href=\"11046_2023_809_Fig1_HTML\" id=\"MO1\"/>"
] | [
"<media xlink:href=\"11046_2023_809_MOESM1_ESM.docx\"><caption><p>Supplementary file1 (DOCX 13 KB)</p></caption></media>"
] | [{"label": ["8."], "surname": ["Sarden", "Sinha", "Potts", "Pernet", "Hiroki", "Hassanabad"], "given-names": ["N", "S", "KG", "E", "CH", "MF"], "article-title": ["A B1a\u2013natural IgG\u2013neutrophil axis is impaired in viral- and steroid-associated aspergillosis"], "source": ["Sci Transl Med."], "year": ["2022"], "volume": ["14"], "issue": ["674"], "fpage": ["6682"], "pub-id": ["10.1126/scitranslmed.abq6682"]}, {"label": ["11."], "surname": ["Egger", "Bussini", "Hoenigl", "Bartoletti"], "given-names": ["M", "L", "M", "M"], "article-title": ["Prevalence of COVID-19-associated pulmonary aspergillosis: critical review and conclusions"], "source": ["J Fungi."], "year": ["2022"], "volume": ["8"], "issue": ["4"], "fpage": ["390"], "pub-id": ["10.3390/jof8040390"]}, {"label": ["19."], "surname": ["Feys", "Lagrou", "Lauwers", "Haenen", "Jacobs", "Brusselmans"], "given-names": ["S", "K", "HM", "K", "C", "M"], "article-title": ["High burden of COVID-19-associated pulmonary aspergillosis (CAPA) in severely immunocompromised patients requiring mechanical ventilation"], "source": ["Clin Infect Dis."], "year": ["2023"], "volume": ["11"], "fpage": ["ciad546"], "pub-id": ["10.1093/cid/ciad546"]}] | {
"acronym": [],
"definition": []
} | 37 | CC BY | no | 2024-01-15 23:41:52 | Mycopathologia. 2024 Jan 13; 189(1):3 | oa_package/9c/44/PMC10787678.tar.gz |
PMC10787679 | 37847456 | [
"<title>Introduction</title>",
"<p id=\"Par5\">While considered an established marker of cell proliferation for decades, the potential role for Ki67 immunohistochemistry (IHC) in breast cancer management has remained unclear due largely to its high inter-observer variability and the lack of established cutoff poi... | [
"<title>Study design and methods</title>",
"<title>Setting and source population</title>",
"<p id=\"Par9\">The study was conducted within Kaiser Permanente Northern California (KPNC), an integrated health care system providing comprehensive primary and specialty care to approximately 4.4 million members at 21 h... | [
"<title>Results</title>",
"<p id=\"Par13\">Selected characteristics of the study population are provided in Table ##TAB##0##1##. In this low-risk population of breast cancer patients, there were 40 patients (or 13%) with a high (≥ 26) RS. The percent of women with high RS was similar across racial/ethnic groups, ... | [
"<title>Discussion</title>",
"<p id=\"Par18\">In our study of early-stage breast cancer patients with favorable prognosis—women aged 50 years or older, with node-negative disease and ER positive, PR positive, HER2− tumors—we found that visual assessment of Ki67 IHC after undergoing IKWG training was moderately to... | [] | [
"<title>Purpose</title>",
"<p id=\"Par1\">The International Ki67 Working Group (IKWG) has developed training for immunohistochemistry (IHC) scoring reproducibility and recommends cut points of ≤ 5% and ≥ 30% for prognosis in ER+, HER2−, stage I/II breast cancer. We examined scoring reproducibility following IKWG ... | [] | [
"<title>Author contributions</title>",
"<p>Conceptualization: VCS and LAH. Data collection and processing: VCS, RJB, WJ, RP, SSA, TKL, WG, NA, MV, LAH. Study design and statistical analysis: VCS, NA, CL, LAH. Supervision: VCS and LAH. Writing—original draft: VCS and LAH. All authors contributed to the <italic>int... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Scatter plot comparing pathologist 1 weighted Ki67 scores to pathologist 2 weighted Ki67 scores</p></caption></fig>"
] | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Selected characteristics of breast cancer cases, by 21 gene recurrence score</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristic</th><th align=\"left\">Overall<break/><italic>N</italic> = 307 (Column %)</th><th... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>*Cut points for percent staining recommended by IKWG [##REF##33369635##5##]</p><p>**Cut points for percent staining used in POETIC</p><p>***Cut points for percent staining used in MonarchE</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains ne... | [
"<graphic xlink:href=\"10549_2023_7118_Fig1_HTML\" id=\"MO1\"/>"
] | [] | [{"label": ["4."], "mixed-citation": ["U.S. Food and Drug Administration (2023) FDA expands early breast cancer indication for abemaciclib with endocrine therapy. In: U.S. Food & Drug Administration, "], "ext-link": ["https://www.fda.gov/drugs/resources-information-approved-drugs/fda-expands-early-breast-cancer-indication-abemaciclib-endocrine-therapy"]}, {"label": ["13."], "mixed-citation": ["Gradishar WJ, Moran MS, Abraham J, Aft R, Agnese D, Allison KH, Anderson B, Burstein HJ, Chew H, Dang C, Elias A, Giordano SH, Goetz MP, Goldstein LJ, Hurvitz SA, Isakoff SJ, Jankowitz RC, Javid SH, Krishnamurthy J, Leithch M, Lyons J, Mortimer J, Patel SA, Pierce LJ, Rosenberger LH, Rugo HS, Sitapati A, Smith KL, Smith ML, Soliman H, Stringer-Reasor EM, Telli ML, Ward JH, Wisinski KB, Young JS, Kumar R, Berardi R National Comprehensive Cancer Network (NCCN) version 4.2022 breast cancer. Accessed 21 June 2022."]}, {"label": ["14."], "mixed-citation": ["National Institute for Health and Care Excellence (NICE) (2018) Tumour profiling tests to guide adjuvant chemotherapy decisions in early breast cancer. Diagnostics Guidance:34. "], "ext-link": ["www.nice.org.uk/guidance/dg34"]}] | {
"acronym": [],
"definition": []
} | 26 | CC BY | no | 2024-01-15 23:41:52 | Breast Cancer Res Treat. 2024 Oct 17; 203(2):281-289 | oa_package/a7/ab/PMC10787679.tar.gz |
PMC10787680 | 37747615 | [
"<title>Introduction</title>",
"<p id=\"Par5\">Colorectal cancer is the third most diagnosed cancer and is responsible for 11% of cancer deaths in Australia [##UREF##0##1##], and other developed countries [##REF##31616522##2##, ##UREF##1##3##]. Colorectal cancer (CRC) has a five-year survival of around 70% which ... | [
"<title>Methods</title>",
"<title>Study population</title>",
"<p id=\"Par8\">This was a prospective study of an Australian population who had recently undergone a colonoscopy in a public hospital setting (Flinders Medical Centre or Noarlunga Hospital, South Australia). We reviewed clinical records to identify p... | [
"<title>Results</title>",
"<title>Study sample</title>",
"<p id=\"Par21\">The survey was sent to 644 individuals who had undergone colonoscopies between March 2017 and July 2019. Demographic details of participants are provided in Table ##TAB##0##1##. The flow chart in Figure S1 shows the categories of particip... | [
"<title>Discussion</title>",
"<p id=\"Par35\">This research aimed to assess HRQoL for individuals undergoing diagnostic or surveillance colonoscopy for CRC, using both the generic EQ-5D-5L and cancer-specific QLU-C10D and evaluate whether HRQoL differed based on the scale used. This study showed no differences in... | [
"<title>Conclusion</title>",
"<p id=\"Par42\">HRQoL does not change one year following a diagnosis of the bowel at colonoscopy and it does not differentiate between different colonoscopy diagnoses including cancer. However, patients undergoing colonoscopy because of symptoms have poorer HRQoL compared to those un... | [
"<title>Purpose</title>",
"<p id=\"Par1\">To compare the sensitivity and discriminant validity of generic and cancer-specific measures for assessing health-related quality of life (HRQoL) for individuals undergoing diagnostic or surveillance colonoscopy for colorectal cancer.</p>",
"<title>Methods</title>",
"... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Author contributions</title>",
"<p>All authors contributed to the study’s conception and design. Material preparation, data collection and analysis were performed by ES, NB, GC and EM. The first draft of the manuscript was written by NB, and all authors commented on previous versions of the manuscript. Al... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Distribution (%) of QLU-C10D dimension responses for participants with full health on EQ-5D-5L but not on QLU-C10D at baseline (<italic>n</italic> = 51)</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Distribution (%) of QLU-C10D dimensio... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Demographic characteristics for participants of each survey</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\" colspan=\"2\">Baseline (<italic>N</italic> = 246)</th><th align=\"left\" colspan=... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p><sup>a</sup>Education level above Grade 12 – technical certificate, diploma and degrees</p><p><sup>b</sup>Two sample test of proportions- difference between baseline and follow-u</p><p><sup>c</sup>Wilcoxon signed ranksum test</p><p><sup>*</sup><italic>SEIFA</italic> Socio-economic indexes for a... | [
"<graphic xlink:href=\"10552_2023_1789_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"10552_2023_1789_Fig2_HTML\" id=\"MO2\"/>"
] | [
"<media xlink:href=\"10552_2023_1789_MOESM1_ESM.docx\"><caption><p>Supplementary file1 (DOCX 763 KB)</p></caption></media>"
] | [{"label": ["1."], "mixed-citation": ["Bowel Cancer ["], "ext-link": ["https://www.cancer.org.au/cancer-information/types-of-cancer/bowel-cancer"]}, {"label": ["3."], "surname": ["Safiri", "Sepanlou", "Ikuta", "Bisignano", "Salimzadeh", "Delavari", "Ansari", "Roshandel", "Merat", "Fitzmaurice"], "given-names": ["S", "SG", "KS", "C", "H", "A", "R", "G", "S", "C"], "article-title": ["The global, regional, and national burden of colorectal cancer and its attributable risk factors in 195 countries and territories, 1990\u20132017: a systematic analysis for the Global Burden of disease study 2017"], "source": ["The Lancet Gastroenterol Hepatology"], "year": ["2019"], "volume": ["4"], "issue": ["12"], "fpage": ["913"], "lpage": ["933"], "pub-id": ["10.1016/S2468-1253(19)30345-0"]}, {"label": ["8."], "mixed-citation": ["Hewitson P, Glasziou P, Watson E, Towler B, Irwig L (2008) Cochrane Systematic Review of Colorectal Cancer Screening Using the Fecal Occult Blood Test (Hemoccult): An Update. Official journal of the American College of Gastroenterology | ACG 103 (6) 1541"]}, {"label": ["9."], "mixed-citation": ["Winawer SJ, Zauber AG, Ho MN, O'Brien MJ, Gottlieb LS, Sternberg SS, Waye JD, Schapiro M, Bond JH, Panish JF et al (1993) Prevention of colorectal cancer by colonoscopic polypectomy. The National Polyp Study Workgroup. N Engl J Med 329 (27) 1977\u20131981."]}, {"label": ["11."], "mixed-citation": ["Cancer Council Australia Surveillance Colonoscopy Guidelines Working Party: Clinical Practice Guidelines for Surveillance Colonoscopy. In"], "italic": ["."]}, {"label": ["34."], "mixed-citation": ["Taylor TH: Ceiling Effect In: "], "italic": ["Encyclopedia of Research Design."]}, {"label": ["35."], "mixed-citation": ["De Vet HCW, Terwee CB, Mokkink LB, Knol DL: Validity. In: "], "italic": ["Measurement in Medicine: A Practical Guide."]}, {"label": ["41."], "surname": ["Kim", "Yoon"], "given-names": ["K", "H"], "article-title": ["Health-related quality of life among cancer survivors depending on the occupational status"], "source": ["Int J Environ Res Public Health"], "year": ["2021"], "volume": ["18"], "issue": ["7"], "fpage": ["11"], "pub-id": ["10.3390/ijerph18073803"]}, {"label": ["53."], "mixed-citation": ["National Institute of Health and Care Excellence: Position statement on use of the EQ-5D-5L value set for England (updated October 2019). In"], "italic": ["."]}, {"label": ["54."], "mixed-citation": ["Medical Services Advisory Committee: Technical Guidelines for preparing assessment reports for the Medical Services Advisory Committee \u2013 Service Type: Investigative (Version 3.0). In"], "italic": ["."]}, {"label": ["55."], "mixed-citation": ["Pharmaceutical Benefits Advisory Committee: Guidelines for preparing a submission to the Pharmaceutical Benefits Advisory Committee. In"], "italic": ["."]}] | {
"acronym": [],
"definition": []
} | 58 | CC BY | no | 2024-01-15 23:41:52 | Cancer Causes Control. 2024 Sep 25; 35(2):347-357 | oa_package/c8/ac/PMC10787680.tar.gz |
PMC10787681 | 0 | [
"<title>Introduction</title>",
"<p id=\"Par5\">Peptidyl-lys metalloendopeptidases, more commonly known as LysN (EC 3.4.24.20), are enzymes that preferentially hydrolyze substrates at the N-terminus of the lysine residues (Barrett et al. ##UREF##0##2004##). The first LysN peptidases were isolated from various bact... | [
"<title>Materials and methods</title>",
"<title>Chemicals and equipment</title>",
"<p id=\"Par9\">Analytical grade reagents and chemicals were procured from either Merck (Darmstadt, Germany) or Carl Roth (Karlsruhe, Germany) unless stated otherwise. Azocasein was obtained from Megazyme (Limerick, Ireland). Spec... | [
"<title>Results</title>",
"<title>Production of <italic>Tc</italic>-LysN in <italic>K. phaffii</italic>, its purification and activation</title>",
"<p id=\"Par26\">\n<italic>Tc</italic>-LysN was expressed as pre-pro-protein using the native pro-protein sequence and <italic>S. cerevisiae</italic>’s α-mating fact... | [
"<title>Discussion</title>",
"<p id=\"Par31\">Purification of LysN peptidases from the fruiting bodies of organisms such as <italic>Grifola frondosa</italic> is a labor- and resource-intensive procedure as demonstrated by Nonaka et al. (##REF##8749321##1995##), Stressler et al. (##UREF##9##2014##), and more recen... | [] | [
"<title>Abstract</title>",
"<p id=\"Par1\">A novel peptidyl-lys metalloendopeptidase (<italic>Tc</italic>-LysN) from <italic>Tramates coccinea</italic> was recombinantly expressed in <italic>Komagataella phaffii</italic> using the native pro-protein sequence. The peptidase was secreted into the culture broth as z... | [
"<title>Supplementary information</title>",
"<p>\n</p>"
] | [
"<title>Author contribution</title>",
"<p>TE and KO conceived and supervised the research. TS and DH designed the synthetic gene constructs. JP and BW performed mass spectrometry experiments and drafted sections of the manuscript specific to those experiments. UA performed all molecular biology and biochemistry e... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Anion exchange (HiTrap QFF 5 mL) chromatogram (<bold>A</bold>) of concentrated BMMY supernatant after ~96 h of induction with methanol. Pooled fractions with <italic>Tc</italic>-LysN activity are enclosed within two straight lines. Sodium dodecyl sulfate–polyacryla... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Effect of organic solvents, cations, denaturing agents, metal chelators, and peptidase inhibitors on the activity of the purified <italic>Tc</italic>-LysN (at 60 °C in 50 mM sodium acetate, pH 5.0). The enzyme was assayed using azocasein as substrate</p></c... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p><sup>1</sup>The activity determined in the absence of test substances was defined as 100% activity. <sup>2</sup>Dissolved in DMSO. All the substances were dissolved in H<sub>2</sub>O<sub>dd</sub> unless stated otherwise. <sup>*</sup><italic>Tc</italic>-LysN had to be incubated overnight at 4 °C... | [
"<graphic xlink:href=\"253_2023_12986_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"253_2023_12986_Fig2_HTML\" id=\"MO2\"/>",
"<graphic xlink:href=\"253_2023_12986_Fig3_HTML\" id=\"MO3\"/>"
] | [
"<media xlink:href=\"253_2023_12986_MOESM1_ESM.pdf\"><label>ESM 1</label><caption><p>(PDF 276 kb)</p></caption></media>"
] | [{"mixed-citation": ["Barrett AJ, Rawlings ND, Woessner JF (2004) Handbook of proteolytic enzymes 2"], "sup": ["nd"]}, {"surname": ["Degraeve", "Martial-Gros"], "given-names": ["P", "A"], "article-title": ["Purification and partial characterisation of X-prolyl dipeptidyl aminopeptidase of "], "italic": ["Lactobacillus helveticus"], "source": ["Int Dairy J"], "year": ["2003"], "volume": ["13"], "issue": ["7"], "fpage": ["497"], "lpage": ["507"], "pub-id": ["10.1016/S0958-6946(03)00057-8"]}, {"surname": ["Dohmae", "Hayashi", "Miki", "Tsumuraya", "Hashimoto"], "given-names": ["N", "K", "K", "Y", "Y"], "article-title": ["Purification and characterization of intracellular proteinases in "], "italic": ["Pleurotus ostreatus"], "source": ["Biotechnol Biochem"], "year": ["1995"], "volume": ["59"], "issue": ["11"], "fpage": ["2074"], "lpage": ["2080"], "pub-id": ["10.1271/bbb.59.2074"]}, {"mixed-citation": ["Engler C, Kandzia R, Marillonnet S (2008) A one pot, one step, precision cloning method with high throughput capability. PLoS One 3(11). 10.1371/JOURNAL.PONE.0003647"]}, {"mixed-citation": ["Fuller RS, Sterne RE, Thorner J. Enzymes required for yeast prohormone processing Vol 50.; 1988. 10.1146/annurev.ph.50.030188.002021"]}, {"surname": ["Iversen", "J\u00f8rgensen"], "given-names": ["SL", "MH"], "article-title": ["Azocasein assay for alkaline protease in complex fermentation broth"], "source": ["Biotechnol Tech"], "year": ["1995"], "volume": ["9"], "fpage": ["573"], "lpage": ["576"], "pub-id": ["10.1007/BF00152446"]}, {"mixed-citation": ["Kaushik N, Rohila D, Arora U, Raut R, Lamminm\u00e4ki U, Khanna N, Batra G (2016) Casamino acids facilitate the secretion of recombinant dengue virus serotype-3 envelope domain III in "], "italic": ["Pichia pastoris"]}, {"surname": ["Lewis", "Basford", "Walton"], "given-names": ["WG", "JM", "PL"], "article-title": ["Specificity and inhibition studies of "], "italic": ["Armillaria mellea"], "source": ["BBA - Enzymol"], "year": ["1978"], "volume": ["522"], "issue": ["2"], "fpage": ["551"], "lpage": ["560"], "pub-id": ["10.1016/0005-2744(78)90087-6"]}, {"surname": ["Raijmakers", "Neerincx", "Mohammed", "Heck"], "given-names": ["R", "P", "S", "AJR"], "article-title": ["Cleavage specificities of the brother and sister proteases Lys-C and Lys-Nw"], "source": ["Chem Commun"], "year": ["2010"], "volume": ["46"], "fpage": ["8827"], "pub-id": ["10.1039/c0cc02523b"]}, {"surname": ["Stressler", "Eisele", "Kleinthom\u00e4", "Meyer", "Fischer"], "given-names": ["T", "T", "AK", "S", "L"], "article-title": ["Partial purification and characterization of Lys-N from "], "italic": ["Grifola frondosa"], "source": ["Biocatal Agric Biotechnol"], "year": ["2014"], "volume": ["3"], "issue": ["4"], "fpage": ["275"], "lpage": ["281"], "pub-id": ["10.1016/J.BCAB.2014.08.002"]}, {"mixed-citation": ["Taouatas N, Drugan MM, Heck AJR, Mohammed S. Straightforward ladder sequencing of peptides using a Lys-N metalloendopeptidase. Published online 2008. 10.1038/NMETH.1204"]}, {"mixed-citation": ["Tsai PL, Chen S-F, Huang SY (2013) Mass spectrometry-based strategies for protein disulfide bond identification. Rev Anal Chem 32(4). 10.1515/revac-2013-0011"]}, {"mixed-citation": ["Wis JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Published online 2009. 10.1038/NMETH.1322"]}, {"mixed-citation": ["Zhao M, Hao B, Li H, Cai M, Xie J, Liu H, Tan M, Zhai L, Yu Q (2020) Peptidyl-Lys metalloendopeptidase (Lys-N) purified from dry fruit of "], "italic": ["Grifola frondosa"]}] | {
"acronym": [],
"definition": []
} | 37 | CC BY | no | 2024-01-15 23:41:52 | Appl Microbiol Biotechnol. 2024 Jan 13; 108(1):1-12 | oa_package/30/09/PMC10787681.tar.gz |
PMC10787682 | 38217685 | [
"<title>Introduction</title>",
"<p id=\"Par6\">Bibliometrics is one of the few subfields involved in the measurement of science outputs [##UREF##0##1##]. Bibliometric indicators are useful tools for evaluating research performance, provided they are precise, advanced, up-to-date, combined with expert knowledge, a... | [
"<title>Materials and methods</title>",
"<p id=\"Par14\">To identify the most cited articles on dentin adhesives, our study was conducted in two stages, in which bibliometric and altmetric analysis data were collected. Institutional ethics committee approval was not necessary because the data used in this study w... | [
"<title>Results</title>",
"<p id=\"Par22\">The top 100 most-cited articles are listed in Table ##TAB##0##1## according to the number of citations. The most cited article, published in 2003 by Van Meerbeek et al. in Operative Dentistry, had 1288 citations and was a lecture on adhesion to enamel and dentin (Table #... | [
"<title>Discussion</title>",
"<p id=\"Par29\">In our study, the citation count of the top 100 articles was between 1288 and 198 on WoS, between 1464 and 208 on Scopus, between 3118 and 276 on Google Scholar, and between 1100 and 184 on Dimensions. The total number of citations was highest on Google Scholar, follo... | [
"<title>Conclusion</title>",
"<p id=\"Par35\">Most top-cited articles (89.1%) were published before 2010. In our study, the most frequently cited articles were concentrated in a few journals. As first author, Van Meerbeek B. has the highest number of articles with nine articles and a total number of 4650 citation... | [
"<title>Objective</title>",
"<p id=\"Par1\">This study aimed to identify the 100 top-cited articles on dentin adhesives utilizing comprehensive bibliometric and altmetric analyses.</p>",
"<title>Materials and methods</title>",
"<p id=\"Par2\">The Institute of Scientific Information Web of Knowledge database w... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Acknowledgements</title>",
"<p>We to thank Altmetric LLP (London, UK) for their support and permission to access the Altmetric data used in this study.</p>",
"<title>Author contributions</title>",
"<p>Conceptualization: [Mustafa Demirci]; Methodology: [Ferda Karabay]; Formal analysis and investigation... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Journal citation map of the 101 most cited articles; bubbles indicate the number of publications and colour indicates the average normalised citation. (Using VOSviewer interface, in the 'Overlay Visualisation' section, \"LinLog/modularity\" was selected as the anal... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>The 100 most cited articles on dentin adhesives</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\">Rank</th><th align=\"left\" rowspan=\"3\">Article</th><th align=\"left\" colspan=\"6\">No. of citatitons</th></... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>"
] | [
"<table-wrap-foot><p><sup><italic>*</italic></sup><italic>for WOS</italic></p></table-wrap-foot>",
"<table-wrap-foot><p><sup><italic>*</italic></sup><italic>for WoS</italic></p></table-wrap-foot>",
"<table-wrap-foot><p><sup><italic>*</italic></sup><italic>for WoS</italic></p></table-wrap-foot>",
"<fn-group><f... | [
"<graphic xlink:href=\"784_2024_5498_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"784_2024_5498_Fig2_HTML\" id=\"MO2\"/>",
"<graphic xlink:href=\"784_2024_5498_Fig3_HTML\" id=\"MO3\"/>"
] | [
"<media xlink:href=\"784_2024_5498_MOESM1_ESM.docx\"><caption><p>Supplementary file1 (DOCX 17 KB)</p></caption></media>",
"<media xlink:href=\"784_2024_5498_MOESM2_ESM.docx\"><caption><p>Supplementary file2 (DOCX 18 KB)</p></caption></media>"
] | [{"label": ["1."], "surname": ["Godin"], "given-names": ["B"], "article-title": ["On the origins of bibliometrics"], "source": ["Scientometrics"], "year": ["2006"], "volume": ["68"], "fpage": ["109"], "lpage": ["133"], "pub-id": ["10.1007/s11192-006-0086-0"]}, {"label": ["7."], "mixed-citation": ["Resources D (2019) A guide to the dimensions data approach 10.6084/m9.figshare.5783094.v7"]}, {"label": ["10."], "surname": ["Nijakowski", "Jankowski", "Lehmann"], "given-names": ["K", "J", "A"], "article-title": ["Bibliometric analysis of literature on Bulk-Fill Composite Resins in Dentistry"], "source": ["J Endod Restor Dent"], "year": ["2023"], "volume": ["1"], "fpage": ["2"], "lpage": ["7"], "pub-id": ["10.5281/zenodo.8306021"]}, {"label": ["23."], "surname": ["Ahmad", "Slots"], "given-names": ["P", "J"], "article-title": ["A bibliometric analysis of periodontology"], "source": ["Periodontol"], "year": ["2020"], "volume": ["2000"], "issue": ["85"], "fpage": ["237"], "lpage": ["240"], "pub-id": ["10.1111/prd.12376"]}, {"label": ["49."], "mixed-citation": ["Garfield E (1977 to 1993.) What is a Citation Classic? Book title., "], "ext-link": ["https://garfield.library.upenn.edu/classics.html"]}, {"label": ["51."], "surname": ["Bradford"], "given-names": ["SC"], "article-title": ["Sources of information on specific subjects"], "source": ["Eng"], "year": ["1934"], "volume": ["137"], "issue": ["4"], "fpage": ["85"], "lpage": ["86"]}, {"label": ["52."], "surname": ["Nakabayashi", "Nakamura", "Yasuda"], "given-names": ["N", "M", "N"], "article-title": ["Hybrid Layer as a Dentin-Bonding Mechanism"], "source": ["J Esthetic Restor Dentistry"], "year": ["1991"], "volume": ["3"], "fpage": ["133"], "lpage": ["138"], "pub-id": ["10.1111/j.1708-8240.1991.tb00985.x"]}] | {
"acronym": [],
"definition": []
} | 53 | CC BY | no | 2024-01-15 23:41:52 | Clin Oral Investig. 2024 Jan 13; 28(1):92 | oa_package/16/95/PMC10787682.tar.gz |
PMC10787683 | 38217625 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Studies evaluating the pain prevalence among laparoscopic surgeons are rife in the literature; there are also multiple reviews on the matter [##REF##32710214##1##–##UREF##0##3##]. However, more nuance is required to draw meaningful conclusions from this data. A previ... | [
"<title>Methods</title>",
"<p id=\"Par6\">The survey contained questions on the presence and impact of intraoperative pain or stress, as well as the intuitiveness and comfort of surgical tools for TLS and RALS. Rather than the binary answers elicited in previous studies, responses here were based on how much the ... | [
"<title>Results</title>",
"<p id=\"Par10\">There were 323 valid responses from TLS surgeons collected over the six-week period; 102 respondents also had RALS experience. The most frequently used console for RALS was the da Vinci Xi (77.2%). Most commonly, participants were 47 ± 10.6-year-old males (83%) from Euro... | [
"<title>Discussion</title>",
"<p id=\"Par16\">Compared to a previously conducted meta-analysis [##REF##32710214##1##], the prevalence rate of neck and shoulder symptoms of any severity was approximately 20% higher for both TLS and RALS. Several previous survey studies provide data on the perceived severity of int... | [] | [
"<p id=\"Par1\">It is known that over half of previously surveyed surgeons performing Robot-Assisted Laparoscopic Surgery (RALS) and three-quarters of those performing Traditional Laparoscopic Surgery (TLS) experience intraoperative pain. This survey study aimed to expand upon the ongoing impact of that pain as wel... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Acknowledgements</title>",
"<p>The authors would like to acknowledge the generous support of Prof George Hanna, President of the European Association of Endoscopic Surgeons (EAES), for coordinating survey access to the members of the EAES.</p>",
"<title>Author contributions</title>",
"<p>All authors c... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The severity (determined by impact) of intraoperative pain experienced by surgeons during Traditional Laparoscopic Surgery (TLS) and Robot-Assisted Laparoscopic Surgery (RALS). The asterisk *represents a significant difference (<italic>p</italic> < 0.05) between... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Survey questions</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Question summary</th><th align=\"left\">Answer format</th><th align=\"left\">Total responses [TLS/RALS]</th></tr></thead><tbody><tr><td align=\"left\">Infor... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>Similar questions were asked regarding Traditional Laparoscopic Surgery (TLS) and Robot-Assisted Laparoscopic Surgery (RALS). The questions that were asked separately of TLS and RALS surgeons (rather than those posed to all surgeons) are denoted by [TLS/RALS]</p></table-wrap-foot>",
"<fn-grou... | [
"<graphic xlink:href=\"11701_2023_1785_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"11701_2023_1785_Fig2_HTML\" id=\"MO2\"/>"
] | [
"<media xlink:href=\"11701_2023_1785_MOESM1_ESM.xlsx\"><caption><p>Supplementary file1 (XLSX 375 KB)</p></caption></media>"
] | [{"label": ["3."], "surname": ["Wu", "Liu", "Lommen", "Pudwell", "Pelland", "Bougie"], "given-names": ["L", "S", "J", "J", "L", "O"], "article-title": ["Prevalence of musculoskeletal pain among gynecologic surgeons performing laparoscopic procedures: a systematic review and meta-analysis"], "source": ["Int J Gynecol Obstetrics"], "year": ["2022"], "pub-id": ["10.1002/ijgo.14518"]}, {"label": ["14."], "surname": ["Sutton", "Irvin", "Zeigler", "Lee", "Park"], "given-names": ["E", "M", "C", "G", "A"], "article-title": ["The ergonomics of women in surgery"], "source": ["Surg Endosc Interv Tech"], "year": ["2014"], "volume": ["28"], "fpage": ["1051"], "lpage": ["1055"], "pub-id": ["10.1007/s00464-013-3281-0"]}, {"label": ["19"], "surname": ["Kasai", "Mizushima", "Muto", "Matsuo", "Tomizawa", "Takeda"], "given-names": ["R", "I", "T", "T", "Y", "S"], "article-title": ["Ergonomic assessment of a laparoscopic stapler"], "source": ["Adv Biomed Eng"], "year": ["2013"], "volume": ["2"], "fpage": ["11"], "lpage": ["16"], "pub-id": ["10.14326/abe.2.11"]}, {"label": ["22."], "surname": ["Reid", "Nygren"], "given-names": ["GB", "TE"], "article-title": ["The subjective workload assessment technique: a scaling procedure for measuring mental workload"], "source": ["Advances in psychology"], "year": ["1988"], "publisher-name": ["Elsevier"], "fpage": ["185"], "lpage": ["218"]}, {"label": ["23."], "surname": ["Fisher"], "given-names": ["RA"], "source": ["Statistical methods for research workers"], "year": ["1925"], "edition": ["11"], "publisher-loc": ["Edinburgh"], "publisher-name": ["Oliver and Boyd"]}, {"label": ["24"], "surname": ["Millan", "Nagpal", "Ding", "Lee", "Kapoor"], "given-names": ["B", "S", "M", "JY", "A"], "article-title": ["A scoping review of emerging and established surgical robotic platforms with applications in urologic surgery"], "source": ["Soc Int Urol J"], "year": ["2021"], "volume": ["2"], "fpage": ["300"], "lpage": ["310"], "pub-id": ["10.48083/EWWQ2677"]}, {"label": ["27."], "surname": ["Liu", "Cernat"], "given-names": ["M", "A"], "article-title": ["Item-by-item versus matrix questions: a web survey experiment"], "source": ["Soc Sci Comput Rev"], "year": ["2018"], "volume": ["36"], "fpage": ["690"], "lpage": ["706"], "pub-id": ["10.1177/0894439316674459"]}] | {
"acronym": [],
"definition": []
} | 28 | CC BY | no | 2024-01-15 23:41:52 | J Robot Surg. 2024 Jan 13; 18(1):15 | oa_package/68/8e/PMC10787683.tar.gz |
PMC10787684 | 0 | [
"<title>Introduction</title>",
"<p id=\"Par3\">Konrad Lorenz was fascinated by the behavioral repertoire and cognitive capacities of corvids. In his first scientific papers (Lorenz ##UREF##41##1927##, ##UREF##42##1931##), he describes the richness and flexible use of social behaviors in tame, free-flying Jackdaws... | [] | [] | [] | [] | [
"<p>Communicated by F. Bairlein.</p>",
"<p id=\"Par1\">Ravens and other corvids are renowned for their ‘intelligence’. For long, this reputation has been based primarily on anecdotes but in the last decades experimental evidence for impressive cognitive skills has accumulated within and across species. While we b... | [
"<title>Summary and outlook</title>",
"<p id=\"Par22\">Taken together, our studies reveal that (1) ravens meet repeatedly at foraging sites, either at the same location or at different locations; (2) foraging groups are composed of individuals with different site preferences and thus degrees of fission–fusion dyn... | [
"<title>Acknowledgements</title>",
"<p>I am grateful to all members of the Bugnyar lab in the last 20 years, the keepers and staff at the research stations, and my colleagues at the Department BeCogBio. I acknowledge financial support by the FWF (J2064, J2225, R31, I105, Y366, P29705, P3390, W1234, W1262), WWTF (... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Presence patterns of Common Ravens. <bold>a</bold> Mean number of individuals (blue line) and mean number of marked individuals (red line) counted during feedings per day at our study site; data are plotted across 6 months (June–November 2018). <bold>b</bold> Prese... | [] | [] | [] | [] | [] | [] | [] | [
"<fn-group><fn><p>This paper belongs to a Topical Collection.Please insert:\nThis article is a contribution to the Topical Collection ‘50 years anniversary of the Nobel Prize in Physiology or Medicine to Karl von Frisch, Konrad Lorenz and Niko Tinbergen in 1973’.</p></fn><fn><p><bold>Publisher's Note</bold></p><p>S... | [
"<graphic xlink:href=\"10336_2023_2111_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"10336_2023_2111_Fig2_HTML\" id=\"MO2\"/>"
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} | 119 | CC BY | no | 2024-01-15 23:41:52 | J Ornithol. 2024 Oct 4; 165(1):15-26 | oa_package/d9/57/PMC10787684.tar.gz |
PMC10787685 | 38217719 | [
"<title>Introduction</title>",
"<p id=\"Par5\">Renal stones are one of the most common urologic diseases globally, with lower pole calculi accounting for almost 25% to 35% [##REF##36721691##1##]. The best choice for lower pole stones (LPS) remains controversial, with several scholars recommending active surveilla... | [
"<title>Methods</title>",
"<p id=\"Par8\">A prospective randomized trial of 90 patients who underwent FURS with holmium laser lithotripsy for 10–20 mm LPS in our center was conducted from November 2020 to November 2022. The study protocol was approved by the local Institutional Review Board (Approval Number (KY20... | [
"<title>Results</title>",
"<p id=\"Par12\">A total of 90 patients with 10–20 mm LPS were enrolled in this study and randomized to two groups. The patients’, demographics and stone characteristics are outlined in Table ##TAB##0##1##, showing no significant difference between the two groups in terms of age, BMI, ge... | [
"<title>Discussion</title>",
"<p id=\"Par16\">The best treatment for 10–20 mm LPS remains controversial. The EAU guideline on urolithiasis recommends ESWL or endoscopy as the first-line treatment [##UREF##0##5##]. However, the SFR of ESWL is affected by many factors, such as the patient’s BMI and stone density. T... | [
"<title>Conclusion</title>",
"<p id=\"Par24\">The final SFR was significantly higher in the relocation group with an acceptable low complication rate compared to the in situ group, indicating that relocating the LPS to a favorable calyx during FURS is effective and safe for the treatment of 10–20 mm LPS.</p>"
] | [
"<title>Objective</title>",
"<p id=\"Par1\">To compare the efficacy and safety of relocating the lower pole stones to a favorable pole during flexible ureteroscopy with in situ lithotripsy for the treatment of 10–20 mm lower pole stone (LPS).</p>",
"<title>Methods</title>",
"<p id=\"Par2\">This study was a pr... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Acknowledgements</title>",
"<p>The English language of the present study was revised by Dr. Powell, and the authors thank again for his help and support provided.</p>",
"<title>Author contributions</title>",
"<p>Ru Huang: project development, data collection, and manuscript editing. Jian-Chen Chen: da... | [] | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Comparison of patients, demographic and stone characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Parameters</th><th align=\"left\">Relocation group (<italic>n</italic> = 45)</th><th align=\"left\">In situ gro... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>"
] | [
"<table-wrap-foot><p><sup>*</sup>Data represent means ± SD unless otherwise indicated</p><p><italic>BMI</italic> Body Mass Index, <italic>HU</italic> Hounsfield Unit, <italic>mm</italic> millimeter, <italic>SD</italic> Standard Deviation</p></table-wrap-foot>",
"<table-wrap-foot><p><italic>ASA</italic> American S... | [] | [
"<media xlink:href=\"345_2023_4703_MOESM1_ESM.pdf\"><caption><p>Supplementary file1 (PDF 2563 KB)</p></caption></media>",
"<media xlink:href=\"345_2023_4703_MOESM2_ESM.xls\"><caption><p>Supplementary file2 (XLS 34 KB)</p></caption></media>",
"<media xlink:href=\"345_2023_4703_MOESM3_ESM.xls\"><caption><p>Supple... | [{"label": ["5."], "mixed-citation": ["Skolarikos A, Neisius A, Pet\u0159\u00edk A, Somani B, Thomas K, Gambaro G, Davis NF, Geraghty R, Lombardo R, Tzelves L, Shepherd R. Eau guidelines on Urolithiasis. 2022;337\u2013342"]}, {"label": ["16."], "surname": ["Rapoport", "Gazimiev", "Korolev", "Tsarichenko", "Svetikova", "Enikeev", "Akopyan", "Chinenov", "Taratkin", "Enikeev"], "given-names": ["LM", "MA", "DO", "DG", "YUA", "ME", "GN", "DV", "MS", "DV"], "article-title": ["Flexible ureteroscopy for lower pole renal stones: novel superpulse thulium (TM) fiber laser lithotripsy"], "source": ["Urologiia."], "year": ["2020"], "volume": ["6"], "fpage": ["89"], "lpage": ["92"], "pub-id": ["10.18565/urology.2020.6.89-92"]}] | {
"acronym": [],
"definition": []
} | 25 | CC BY | no | 2024-01-15 23:41:53 | World J Urol. 2024 Jan 13; 42(1):30 | oa_package/d9/c3/PMC10787685.tar.gz |
PMC10787686 | 37723300 | [
"<title>Introduction</title>",
"<p id=\"Par4\">Currently, healthcare system is facing multiple burdens, related to demographic factors, social structure, and a lack of physicians [<xref ref-type=\"bibr\" rid=\"CR1\">1</xref>, <xref ref-type=\"bibr\" rid=\"CR2\">2</xref>]. These problems have resulted in considera... | [
"<title>Material and Methods</title>",
"<title>Search Strategy</title>",
"<p id=\"Par6\">In April 2023, the publication search was done in several databases, including the ACM Digital Library, CINAHL, IEEE Xplore, PubMed, Google Scholar, and <ext-link ext-link-type=\"uri\" xlink:href=\"https://clinicaltrials.go... | [
"<title>Results</title>",
"<p id=\"Par11\">Among 567 papers investigated (Fig. ##FIG##0##1##), 15 of these discussed different scenarios to use AI-based chatbots in urology. These included symptom checkers and health screening, patient education and counseling, lifestyle change and conservative management, and cl... | [
"<title>Discussion</title>",
"<p id=\"Par18\">Chatbots have been used in healthcare for decades, with the earliest known healthcare chatbot being ELIZA, created in 1966 [<xref ref-type=\"bibr\" rid=\"CR26\">26</xref>]. Since then, chatbots have been utilized in various healthcare settings, from helping patients m... | [
"<title>Conclusion</title>",
"<p id=\"Par27\">The use of AI-driven chatbots in urology has the potential to revolutionize the discipline by enhancing patient care, raising physician productivity, and lowering healthcare costs. Chatbots can offer patients individualized assistance, information, and guidance throug... | [
"<title>Purpose of Review</title>",
"<p id=\"Par1\">Artificial intelligence (AI) chatbots have emerged as a potential tool to transform urology by improving patient care and physician efficiency. With an emphasis on their potential advantages and drawbacks, this literature review offers a thorough assessment of t... | [] | [
"<title>Author contributions</title>",
"<p>AT- conception, data collection, data analysis, writing main manuscript NN - conception, data collection, data analysis, writing main manuscript BZH - conception, data collection, data analysis, writing main manuscript PJJ - data analysis, writing and editing, supervisio... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Flow diagram of included sources</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Examples of clinical applications of chatbot</p></caption></fig>",
"<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Example of chatbot mechanism of actio... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Review of published urological literature on the use of chatbots</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Authors</th><th align=\"left\">Type</th><th align=\"left\">Area</th><th align=\"left\">Function</th><th alig... | [] | [] | [] | [] | [] | [] | [
"<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"11934_2023_1184_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"11934_2023_1184_Fig2_HTML\" id=\"MO2\"/>",
"<graphic xlink:href=\"11934_2023_1184_Fig3_HTML\" id=\"MO3\"/>"
] | [] | [] | {
"acronym": [],
"definition": []
} | 0 | CC BY | no | 2024-01-15 23:41:53 | Curr Urol Rep. 2024 Sep 19; 25(1):9-18 | oa_package/b2/0c/PMC10787686.tar.gz |
PMC10787687 | 37833451 | [
"<title>Introduction</title>",
"<p id=\"Par5\">Human epidermal growth factor 2 (HER2) is a transmembrane receptor that plays an integral role in the control of epithelial cell growth and differentiation [##REF##31548601##1##]. Amplification of this receptor has been reported in many forms of cancer and is general... | [
"<title>Methods</title>",
"<p id=\"Par7\">This study was conducted under a MSK institutional review board-approved protocol #16–458. A retrospective chart review of patients receiving both trastuzumab and pertuzumab for the treatment of HER2 positive breast cancer (SEER category 1) from 11/23/2011 to 6/21/2021 wa... | [
"<title>Results</title>",
"<title>Demographics</title>",
"<p id=\"Par11\">A total of 122 female patients (median age at pruritus onset 54.5, range 28–88) with a diagnosis of HER2 + breast cancer who experienced pruritus attributed to HP from 11/23/2011 to 6/21/2021 were included in this study (Table ##TAB##0##1... | [
"<title>Discussion</title>",
"<p id=\"Par25\">In this single center study, we found a reported pruritus incidence of 4.72% among patients treated with HP. This is lower than what has been reported in clinical trials, which have ranged from 11 to 17.6% [##REF##32171426##8##, ##REF##20124182##11##, ##UREF##0##12##]... | [] | [
"<title>Purpose</title>",
"<p id=\"Par1\">The combination of trastuzumab and pertuzumab (HP) as part of a taxane-based regimen has shown benefit in the adjuvant and metastatic HER2 + breast cancer setting. In the CLEOPATRA trial, pruritus was reported in 11-17.6% of patients. The clinical phenotype and potential ... | [] | [
"<title>Acknowledgements</title>",
"<p>We would like to thank Joseph Schmeltz for performing the database inquiry for this study.</p>",
"<title>Author contributions</title>",
"<p>Sarah Noor and Mario E Lacouture contributed to the study conception and design. Data collection was performed by Stephanie Gu. Dat... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Study schema</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Anatomic distribution of pruritus</p></caption></fig>",
"<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Histopathologic and clinical presentation of excoriations in patient... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of study population (n = 122)</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\"/><th align=\"left\">n (%)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Sex</td><td align=\"... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>Table key:</p><p>ACTHP: doxorubicin, cyclophosphamide → paclitaxel, trastuzumab, pertuzumab.</p><p>THP: paclitaxel, trastuzumab, pertuzumab.</p><p>TCHP: docetaxel, carboplatin, trastuzumab, and pertuzumab.</p><p>VHP: vinorelbine, trastuzumab, pertuzumab.</p><p>GHP: gemcitabine, trastuzumab, per... | [
"<graphic xlink:href=\"10549_2023_7143_Fig1_HTML\" id=\"d32e301\"/>",
"<graphic xlink:href=\"10549_2023_7143_Fig2_HTML\" id=\"d32e529\"/>",
"<graphic xlink:href=\"10549_2023_7143_Fig3_HTML\" id=\"d32e802\"/>"
] | [] | [{"label": ["12."], "surname": ["Miles", "Lacasia", "Lee", "Dana", "Heeson", "Knott", "Portera", "Swain"], "given-names": ["D", "A", "L", "K", "S", "A", "CC", "SM"], "article-title": ["Skin and subcutaneous tissue disorders (SSTDs) in patients (pts) with HER2-positive metastatic breast cancer (MBC) in the phase III trial CLEOPATRA of pertuzumab or placebo with trastuzumab and docetaxel"], "source": ["J Clin Oncol"], "year": ["2015"], "volume": ["33"], "fpage": ["598"], "lpage": ["598"], "pub-id": ["10.1200/jco.2015.33.15_suppl.598"]}, {"label": ["13."], "mixed-citation": ["Common Terminology Criteria for Adverse Events (CTCAE) Version 5.0. In"]}] | {
"acronym": [],
"definition": []
} | 27 | CC BY | no | 2024-01-15 23:41:53 | Breast Cancer Res Treat. 2024 Oct 13; 203(2):271-280 | oa_package/fa/58/PMC10787687.tar.gz |
PMC10787689 | 37450232 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Language is a principle characteristic of humanity (Staats ##UREF##32##2012##). As a minimally meaningful unit in language, a word comprises an association between perceptually linguistic (i.e., written and spoken word forms, Braille, fingerspelling, and so on) and p... | [
"<title>Methods</title>",
"<p id=\"Par3\">We examined whether subjective evaluations of written Japanese pseudowords were conditioned to those of spoken Japanese words. First, we conducted a survey study (Survey 1) in which participants evaluated whether each presented word was positive or negative, active or pas... | [
"<title>Results</title>",
"<p id=\"Par9\">Cronbach’s alphas of negative (<italic>α</italic> = 0.91), positive (<italic>α</italic> = 0.79), passive (<italic>α</italic> = 0.90), and active words (<italic>α</italic> = 0.80) were greater than 0.70 (Cortina ##UREF##6##1993##); however, we could not detect the sufficie... | [
"<title>Discussion</title>",
"<p id=\"Par19\">Experiment 1 investigated whether written Japanese pseudowords are conditioned to spoken Japanese positive or negative words. Two findings emerged: first, there was no significant difference among evaluative responses of conditions (evaluative responses of pseudowords... | [] | [
"<p id=\"Par1\">This study aimed to examine whether Japanese participants condition spoken words’ meanings to written pseudowords. In Survey 1, we selected spoken words associated with negative (<italic>α</italic> = .91) and positive (<italic>α</italic> = .79) features for Experiment 1 and passive (<italic>α</itali... | [
"<title>Experiment 1</title>",
"<title>Participants</title>",
"<p id=\"Par10\">There were 37 participants (21 females; <italic>M</italic><sub><italic>age</italic></sub> = 36.24; <italic>SD</italic><sub><italic>age</italic></sub> = 7.35); all of them were native Japanese speakers. They were recruited from a crow... | [
"<title>Appendix A Words in Survey 1</title>",
"<p id=\"Par56\">\n</p>",
"<p id=\"Par57\">Category means word categories used in this study. Positive: positive words; Negative: negative words; Active: active words; Passive: passive words; Strong: strong words; Weak: weak words. We translated English words of pr... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Means and 95% confidence intervals in Experiment 1. The values were calculated in an R package “Rmisc” (Hope ##UREF##13##2022##) on R (R Core Team ##UREF##30##2022##). We used “ggplot2” to create this figure (Wickham ##UREF##39##2016##) in R</p></caption></fig>",
... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Medians and quartiles of evaluative responses in Experiment 1</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Conditions</th><th align=\"left\" colspan=\"3\">Before conditioning</th><th align=\"left\" colspa... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p><italic>Mdn</italic>: Medians; <italic>Q1</italic>: first quartiles; <italic>Q3</italic>: third quartiles. In Experiment 1, participants evaluated whether each pseudoword was positive or negative using a 7-point semantic differential scale ranging from 1 (<italic>negative</italic>) to 7 (<itali... | [
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An experiment using 5-point, 7-point and 10-point scales"], "source": ["Int J Mark Res"], "year": ["2008"], "volume": ["50"], "issue": ["1"], "fpage": ["61"], "lpage": ["104"], "pub-id": ["10.1177/147078530805000106"]}, {"surname": ["de Saussure"], "given-names": ["F"], "source": ["Course in general linguistics"], "year": ["1959"], "publisher-loc": ["New York"], "publisher-name": ["McGraw-Hill"]}, {"surname": ["Dedonder", "Corneille", "Bertinchamps", "Yzerbyt"], "given-names": ["J", "O", "D", "V"], "article-title": ["Overcoming correlational pitfalls: Experimental evidence suggests that evaluative conditioning occurs for explicit but not implicit encoding of CS\u2212US pairings"], "source": ["Soc Psychol Pers Sci"], "year": ["2014"], "volume": ["5"], "issue": ["2"], "fpage": ["250"], "lpage": ["257"], "pub-id": ["10.1177/1948550613490969"]}, {"surname": ["F\u00f6rderer", "Unkelbach"], "given-names": ["S", "C"], "article-title": ["On the stability of evaluative conditioning effects: the role of identity memory, valence memory, and evaluative consolidation"], "source": ["Soc Psychol"], "year": ["2013"], "volume": ["44"], "fpage": ["380"], "lpage": ["389"], "pub-id": ["10.1027/1864-9335/a000150"]}, {"surname": ["Galli", "Gorn"], "given-names": ["M", "G"], "article-title": ["Unconscious transfer of meaning to brands"], "source": ["J Con Psychol"], "year": ["2011"], "volume": ["21"], "issue": ["3"], "fpage": ["215"], "lpage": ["225"], "pub-id": ["10.1016/j.jcps.2010.12.004"]}, {"surname": ["Higami", "Fujita", "Kaneko", "Suyama", "Ito", "Sato", "Matsumoto", "Suzuki"], "given-names": ["K", "C", "Y", "H", "R", "H", "T", "S"], "article-title": ["Affective valence and emotionality of two-kanji-compound words [Kanji niji jukugo niokeru kanjoka oyobi jodosei no chosa]"], "source": ["Acad Humanit Nat Sci [nanzan Daigaku Kiyo: Academia. Jimbun Shizenkagakuhen]"], "year": ["2015"], "volume": ["10"], "fpage": ["195"], "lpage": ["204"], "pub-id": ["10.15119/00000529"]}, {"mixed-citation": ["Hope RM (2022) Rmisc: ryan miscellaneous. R package version 1.5.1. 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R Foundation for Statistical Computing, Vienna, Austria. "], "ext-link": ["https://www.R-project.org/"]}, {"surname": ["Spence", "Gallace"], "given-names": ["C", "A"], "article-title": ["Tasting shapes and words"], "source": ["Food Qual Pref"], "year": ["2011"], "volume": ["22"], "issue": ["3"], "fpage": ["290"], "lpage": ["295"], "pub-id": ["10.1016/j.foodqual.2010.11.005"]}, {"surname": ["Staats"], "given-names": ["AW"], "source": ["The marvelous learning animal: what makes human nature unique"], "year": ["2012"], "publisher-loc": ["New York"], "publisher-name": ["Prometheus Books"]}, {"surname": ["Staats", "Staats", "Heard"], "given-names": ["AW", "CK", "WG"], "article-title": ["Denotative meaning established by classical conditioning"], "source": ["J Exp Psychol"], "year": ["1961"], "volume": ["61"], "issue": ["4"], "fpage": ["300"], "lpage": ["303"], "pub-id": ["10.1037/h0046306"]}, {"surname": ["Stahl", "Heycke"], "given-names": ["C", "T"], "article-title": ["Evaluative conditioning with simultaneous and sequential pairings under incidental and intentional learning conditions"], "source": ["Soc Cogn"], "year": ["2016"], "volume": ["34"], "issue": ["5"], "fpage": ["382"], "lpage": ["412"], "pub-id": ["10.1521/soco.2016.34.5.382"]}, {"surname": ["Till", "Priluck"], "given-names": ["BD", "RL"], "article-title": ["Stimulus generalization in classical conditioning: an initial investigation and extension"], "source": ["Psychol Mark"], "year": ["2000"], "volume": ["17"], "issue": ["1"], "fpage": ["55"], "lpage": ["72"], "pub-id": ["10.1002/(SICI)1520-6793(200001)17:1<55::AID-MAR4>3.0.CO;2-C"]}, {"surname": ["Till", "Priluck"], "given-names": ["BD", "RL"], "article-title": ["Conditioning of meaning in advertising: brand gender perception effects"], "source": ["J Curr Issues Res Advertising"], "year": ["2001"], "volume": ["23"], "issue": ["2"], "fpage": ["1"], "lpage": ["8"], "pub-id": ["10.1080/10641734.2001.10505116"]}, {"surname": ["Umemoto", "Morikawa", "Ibuki"], "given-names": ["T", "Y", "M"], "article-title": ["The non-association values and meaningfulness of 1892 Japanese two-letter syllables and words"], "source": ["Jpn J Psychol"], "year": ["1955"], "volume": ["26"], "issue": ["3"], "fpage": ["148"], "lpage": ["155"], "pub-id": ["10.4992/jjpsy.26.148"]}, {"surname": ["Vidal", "Costa", "Foucart"], "given-names": ["A", "A", "A"], "article-title": ["Are our preferences and evaluations conditioned by the language context?"], "source": ["J Multiling Multicul Dev"], "year": ["2021"], "pub-id": ["10.1080/01434632.2021.1896530"]}, {"surname": ["Wickham"], "given-names": ["H"], "source": ["ggplot2: elegant graphics for data analysis"], "year": ["2016"], "publisher-loc": ["New York"], "publisher-name": ["Springer-Verlag"]}, {"surname": ["Winter"], "given-names": ["B"], "source": ["Statistics for linguists: an introduction using R"], "year": ["2020"], "publisher-loc": ["New York"], "publisher-name": ["Routledge"]}, {"surname": ["Yamaguchi"], "given-names": ["T"], "source": ["Japanese linguistics: an introduction"], "year": ["2007"], "publisher-loc": ["London"], "publisher-name": ["Continuum"]}, {"surname": ["Zajonc"], "given-names": ["RB"], "article-title": ["Mere exposure: a gateway to the subliminal"], "source": ["Curr Dir Psychol Sci"], "year": ["2001"], "volume": ["10"], "issue": ["6"], "fpage": ["224"], "lpage": ["228"], "pub-id": ["10.1111/1467-8721.00154"]}] | {
"acronym": [],
"definition": []
} | 98 | CC BY | no | 2024-01-15 23:41:53 | Cogn Process. 2023 Jul 14; 24(3):387-413 | oa_package/3d/7f/PMC10787689.tar.gz |
PMC10787690 | 38189953 | [
"<title>Introduction</title>",
"<p id=\"Par5\">Apoptosis, one type of programmed cell death (PCD), represents one of the highly conserved cellular eukaryotic suicide programs triggered by extrinsic or intrinsic cellular stimuli. It has been described and investigated in detail in mammalian cells (Hamann et al. ##... | [
"<title>Materials and methods</title>",
"<title>Fungi and culture media</title>",
"<p id=\"Par9\"><italic>C. cinerea</italic> Okayama 7 (#130; <italic>A43</italic>, <italic>B43</italic>, <italic>ade8</italic>) (ATCC No. MYA-4618™) and <italic>Gongronella</italic> sp. w5 (China Center for Type Culture Collection... | [
"<title>Results</title>",
"<title>Apoptotic-like PCD and increased transcription of a potential AIF are observed in <italic>C. cinerea </italic>during interspecific interaction with <italic>Gongronella</italic> sp. w5</title>",
"<p id=\"Par30\">Previous comparative transcriptomic analysis using <italic>C. ciner... | [
"<title>Discussion</title>",
"<p id=\"Par42\">Several criteria have been reported for the consensus definition of apoptotic-like PCD in fungi (Shlezinger et al. ##REF##22891165##2012##; Hardwick ##UREF##4##2018##; Herrmann and Riemer ##REF##32769219##2021##), such as the PS externalization on the outer leaflet of... | [] | [
"<title>Abstract</title>",
"<p id=\"Par1\">Apoptotic-like programmed cell death (PCD) is one of the main strategies for fungi to resist environmental stresses and maintain homeostasis. The apoptosis-inducing factor (AIF) has been shown in different fungi to trigger PCD through upregulating reactive oxygen species... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Author contribution</title>",
"<p>JF: methodology, investigation. GZ: methodology, software. HZ, DX, JZ: investigation. UK: writing — review and editing. YX: resources. ZF: conceptualization, supervision, writing — review and editing, funding acquisition. JL: conceptualization, supervision, methodology, w... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Apoptotic-like cell death is induced in <italic>C. cinerea</italic> (<bold>a</bold>, <bold>b</bold>) accompanied by upregulation of one identified AIF (<bold>c</bold>, <bold>d</bold>) in coculture of <italic>C. cinerea</italic> and <italic>Gongronella</italic> sp. ... | [] | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>Junnan Fang and Gang Zhou contributed equally.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"253_2023_12988_Fig1_HTML\" id=\"MO1\"/>",
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"<media xlink:href=\"253_2023_12988_MOESM1_ESM.pdf\"><caption><p>Supplementary file1 (PDF 419 KB)</p></caption></media>"
] | [{"mixed-citation": ["Bourbonnais R, Paice MG (1990) Oxidation of non-phenolic substrates. An expanded role for laccase in lignin biodegradation. FEBS Lett 267(1):99\u2013102. 10.1016/0014-5793(90)80298-w"]}, {"mixed-citation": ["Chen L, Ma Y, Peng M, Chen W, Xia H, Zhao J, Zhang Y, Fan Z, Xing X, Li H (2021) Analysis of apoptosis-related genes reveals that apoptosis functions in conidiation and pathogenesis of "], "italic": ["Fusarium pseudograminearum"]}, {"mixed-citation": ["Daskalov A, Heller J, Herzog S, Flei\u00dfner A, Glass NL (2017) Molecular mechanisms regulating cell fusion and heterokaryon formation in filamentous fungi. Microbiol Spectr 5(2). 10.1128/microbiolspec.FUNK-0015-2016"]}, {"surname": ["D\u00f6rnte", "K\u00fces"], "given-names": ["B", "U"], "article-title": ["Reliability in transformation of the basidiomycete "], "italic": ["Coprinopsis cinerea"], "source": ["Curr Trends Biotechnol Pharm"], "year": ["2012"], "volume": ["6"], "issue": ["3"], "fpage": ["340"], "lpage": ["355"]}, {"mixed-citation": ["Hardwick JM (2018) Do fungi undergo apoptosis-like programmed cell death? mBio 9(4). 10.1128/mBio.00948-18"]}, {"mixed-citation": ["Hu J, Zhang Y, Xu Y, Sun Q, Liu J, Fang W, Xiao Y, K\u00fces U, Fang Z (2019) "], "italic": ["Gongronella", "Coprinopsis cinerea"]}, {"mixed-citation": ["Kumar S, Stecher G, Tamura K (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33(7):1870\u20134. 10.1093/molbev/msw054"]}, {"mixed-citation": ["Lu BC, Sakaguchi K (1991) An endo-exonuclease from meiotic tissues of the basidiomycete "], "italic": ["Coprinus cinereus"]}, {"surname": ["Umar", "Van Griensven"], "given-names": ["MH", "LJ"], "article-title": ["Morphogenetic cell death in developing primordia of "], "italic": ["Agaricus bisporus"], "source": ["Mycologia"], "year": ["1997"], "volume": ["89"], "issue": ["2"], "fpage": ["274"], "lpage": ["277"], "pub-id": ["10.1080/00275514.1997.12026781"]}, {"mixed-citation": ["Zhu J, Wu F, Yue S, Chen C, Song S, Wang H, Zhao M (2019) Functions of reactive oxygen species in apoptosis and ganoderic acid biosynthesis in "], "italic": ["Ganoderma lucidum"]}] | {
"acronym": [],
"definition": []
} | 74 | CC BY | no | 2024-01-15 23:41:53 | Appl Microbiol Biotechnol. 2024 Jan 13; 108(1):1-20 | oa_package/92/c3/PMC10787690.tar.gz |
PMC10787691 | 38217728 | [
"<title>Introduction</title>",
"<p id=\"Par5\">Paediatric urology arguably begins with the study of childhood stone disease which was much more prevalent in the last two centuries than today. This may have been as a result of dehydration, diarrhoeal illness, or diets consisting of predominantly single grains [##R... | [
"<title>Methods</title>",
"<p id=\"Par9\">An 18-item cross-sectional survey was compiled through the EAU Young Academic Urologists (YAU) office (Appendix ##SUPPL##0##1##) and disseminated to a trans-Atlantic convenience sample of current practising paediatric urologists. This Google Forms questionnaire was approv... | [
"<title>Results</title>",
"<p id=\"Par11\">A total of 228 respondents completed the survey. Due to broad dissemination on specific social media channels, it was not possible to ascertain how many viewed or partially completed and then failed to submit the survey. As the survey was anonymised, it was also not feas... | [
"<title>Discussion</title>",
"<p id=\"Par15\">The study has demonstrated a number of similarities and differences between paediatric urologists trained in adult urology and those trained in paediatric general surgery. The demographics and practice types were broadly similar between both groups. It is unclear as t... | [
"<title>Conclusion</title>",
"<p id=\"Par20\">This study represents the first time that a cross-sectional cohort of paediatric urologists from different training backgrounds were compared to assess their casemix, practice patterns and attitudes. Paediatric urology is in a unique position to have two specialities ... | [
"<title>Objective</title>",
"<p id=\"Par1\">To identify any self-reported differences or attitudes towards certification, publication, or practice patterns between adult urology and paediatric general surgery-trained paediatric urology providers. There are no known published differences in clinical/operative/rese... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Author contributions</title>",
"<p>OF protocol/project development; data collection or management; data analysis; Manuscript writing/editing. tHLA protocol/project development; data collection or management; data analysis; manuscript writing/editing. BMB protocol/project development; data analysis; manusc... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Clinical and operative casemix of paediatric urology respondents broken down by initial training pathway (adult urology and paediatric general surgery). The <italic>Y</italic>-axis indicates the percentage of paediatric urologists actively and regularly engaged in ... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Clinical and Occupational Characteristics between Urologists and Paediatric General Surgeons practising Paediatric Urology</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Comparator</th><th align=\"left\">Urological surge... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>Bold values indicate statistical significance</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"345_2023_4743_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"345_2023_4743_Fig2_HTML\" id=\"MO2\"/>"
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"<media xlink:href=\"345_2023_4743_MOESM1_ESM.docx\"><caption><p>Supplementary file1 (DOCX 37 KB)</p></caption></media>"
] | [{"label": ["2."], "surname": ["Hinman"], "given-names": ["FH"], "suffix": ["Jr"], "source": ["American pediatric urology"], "year": ["1991"], "publisher-loc": ["San Francisco p"], "publisher-name": ["Norman Publishing"], "fpage": ["21"]}] | {
"acronym": [],
"definition": []
} | 14 | CC BY | no | 2024-01-15 23:41:53 | World J Urol. 2024 Jan 13; 42(1):34 | oa_package/aa/ec/PMC10787691.tar.gz |
PMC10787692 | 0 | [
"<title>Introduction</title>",
"<p id=\"Par5\">Influenza A virus (IAV) is a respiratory pathogen, which contains a negative-sense, single-stranded RNA genome with eight viral RNA (vRNA) segments (Krammer et al. ##REF##29955068##2018##). Each year, IAV infections lead to a high disease burden with up to 290,000–65... | [
"<title>Materials and methods</title>",
"<title>Cells and viruses</title>",
"<p id=\"Par9\">MDCK cells growing in suspension culture (Lohr et al. ##REF##20638458##2010##) and stably expressing PB2 (encoded by Seg 1) (Bdeir et al. ##REF##30822349##2019##; Hein et al. ##REF##34519855##2021b##), referred to as MDC... | [
"<title>Results</title>",
"<title>Medium dilution impairs yields, whereas infection at 32 °C increases OP7 chimera DIP titers and fractions in shake flasks</title>",
"<p id=\"Par25\">Previously, a CME prior to infection has been performed for cell culture-based production of OP7 chimera DIPs (Dogra et al. ##URE... | [
"<title>Discussion</title>",
"<p id=\"Par40\">IAV DIPs are regarded as a highly interesting option for future broad-spectrum antiviral therapy (Dimmock et al. ##REF##18579602##2008##, ##REF##23251341##2012b##; Easton et al. ##REF##21320545##2011##; Huo et al. ##REF##33250863##2020##; Kupke et al. ##REF##30463972#... | [] | [
"<title>Abstract</title>",
"<p id=\"Par1\">Defective interfering particles (DIPs) of influenza A virus (IAV) are suggested for use as broad-spectrum antivirals. We discovered a new type of IAV DIP named “OP7” that carries point mutations in its genome segment (Seg) 7 instead of a deletion as in conventional DIPs ... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Acknowledgements</title>",
"<p>The authors thank Claudia Best and Nancy Wynserski for their excellent technical assistance. We appreciate the supply of the Xeno<sup>TM</sup> medium from Shanghai BioEngine Sci-Tech and Prof. Tan from the East China University of Science and Technology. Moreover, we thank D... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Scheme of OP7 chimera DIPs and Seg 1 cDIPs. Reverse genetics was used to generate a mixture of <bold>a)</bold> OP7 chimera DIPs and <bold>b)</bold> Seg 1 cDIPs. Both together replicate without addition of infectious standard virus as the missing PB2 is provided by ... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Summary of OP7 chimera DIP production in a 1 L STR in batch mode (with medium dilution) and perfusion mode</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Spent medium<sup>a</sup></th><th align=\"left\... | [
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"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p><sup>a</sup>Batch: V<sub>W</sub> at TOI. Perfusion: V<sub>W</sub> plus spent medium volume until time of optimal harvest (45 hpi)</p><p><sup>b</sup>Maximum VCC (VCC<sub>max</sub>) after TOI</p><p><sup>c</sup>Accumulated HA titer (HA<sub>acc</sub>) according to Eq. ##FORMU##0##1##</p><p><sup>d</... | [
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... | [
"<media xlink:href=\"253_2023_12959_MOESM1_ESM.pdf\"><caption><p>Supplementary file1 (PDF 441 KB)</p></caption></media>"
] | [{"mixed-citation": ["Chen J, Wang J, Zhang J, Ly H (2021) Advances in development and application of influenza vaccines. Front Immunol 12:711997. 10.3389/fimmu.2021.711997"]}, {"mixed-citation": ["Dogra T, Pelz L, Boehme JD, Kuechler J, Kershaw O, Marichal-Gallardo P, Baelkner M, Hein MD, Gruber AD, Benndorf D, Genzel Y, Bruder D, Kupke SY, Reichl U (2023) Generation of \u201cOP7 chimera\u201d defective interfering influenza A particle preparations free of infectious virus that show antiviral efficacy in mice. Sci Rep 13(1):20936. 10.1038/s41598-023-47547-1"]}, {"surname": ["Genzel", "Reichl", "Portner"], "given-names": ["Y", "U", "R"], "article-title": ["Vaccine Production"], "source": ["Animal Cell Biotechnology"], "year": ["2007"], "publisher-loc": ["Methods in Biotechnology"], "publisher-name": ["Humana Press, Totowa, NJ"], "fpage": ["457"], "lpage": ["473"]}, {"mixed-citation": ["G\u00f6bel S, Pelz L, Reichl U, Genzel Y (2022) Upstream processing for viral vaccines-Process intensification. In: Kamen A, Cervera L (eds) Bioprocessing of Viral Vaccines. CRC Press, Boca Raton"]}, {"mixed-citation": ["G\u00f6bel S, Ja\u00e9n KE, Dorn M, Neumeyer V, Jordan I, Sandig V, Reichl U, Altomonte J, Genzel Y (2023) Process intensification strategies toward cell culture-based high-yield production of a fusogenic oncolytic virus. Biotechnol Bioeng 120:2639\u20132657. 10.1002/bit.28353"]}, {"surname": ["Jug", "Hosta", "Tajnik", "Strancar", "Brodesser", "Tisch", "Heger", "Wolschek", "Seipelt", "Reiter"], "given-names": ["H", "N", "M", "A", "D", "B", "T", "M", "J", "M"], "article-title": ["Production and Purification of Newcastle Disease Virus"], "source": ["BioProcess Int"], "year": ["2023"], "volume": ["21"], "fpage": ["18"]}, {"surname": ["Karki", "Bull", "Krone"], "given-names": ["B", "JJ", "SM"], "article-title": ["Modeling the therapeutic potential of defective interfering particles in the presence of immunity"], "source": ["Virus Evol"], "year": ["2022"], "volume": ["8"], "issue": ["2"], "fpage": ["1"], "lpage": ["11"], "pub-id": ["10.1093/ve/veac047"]}, {"mixed-citation": ["Kupke SY, Ly LH, Borno ST, Ruff A, Timmermann B, Vingron M, Haas S, Reichl U (2020) Single-cell analysis uncovers a vast diversity in intracellular viral defective interfering RNA content affecting the large cell-to-cell heterogeneity in influenza a virus replication. Viruses 12(1):71. 10.3390/v12010071"]}, {"surname": ["Nayak", "Chambers", "Akkina", "Cooper", "Eisen", "Goebel", "Hofschneider", "Koprowski", "Melchers", "Oldstone", "Rott", "Schweiger", "Vogt", "Wilson"], "given-names": ["DP", "TM", "RK", "M", "H", "W", "PH", "H", "F", "M", "R", "HG", "PK", "I"], "article-title": ["Defective-Interfering (DI) RNAs of Influenza Viruses: Origin, Structure, Expression, and Interference"], "source": ["Current Topics in Microbiology and Immunology"], "year": ["1985"], "publisher-loc": ["Berlin Heidelberg, Berlin, Heidelberg"], "publisher-name": ["Springer"], "fpage": ["103"], "lpage": ["151"]}, {"mixed-citation": ["Rand U, Kupke SY, Shkarlet H, Hein MD, Hirsch T, Marichal-Gallardo P, Cicin-Sain L, Reichl U, Bruder D (2021) Antiviral activity of influenza a virus defective interfering particles against SARS-CoV-2 replication in vitro through stimulation of innate immunity. Cells 10(7):1756. 10.3390/cells10071756"]}, {"mixed-citation": ["Smither SJ, Garcia-Dorival I, Eastaugh L, Findlay JS, O'Brien LM, Carruthers J, Williamson ED, Molina-Par\u00eds C, Hiscox JA, Laws TR (2020) An investigation of the effect of transfected defective, ebola virus genomes on ebola replication. Front Cell Infect Microbiol 10:159. 10.3389/fcimb.2020.00159"]}, {"mixed-citation": ["WHO (2023) Influenza (Seasonal). "], "ext-link": ["https://www.who.int/news-room/fact-sheets/detail/influenza-(seasonal)#:~:text=There%20are%20around%20a%20billion,650%20000%20respiratory%20deaths%20annually"]}] | {
"acronym": [],
"definition": []
} | 68 | CC BY | no | 2024-01-15 23:41:53 | Appl Microbiol Biotechnol. 2024 Jan 13; 108(1):1-15 | oa_package/89/28/PMC10787692.tar.gz |
PMC10787693 | 38217690 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Methicillin-resistant <italic>Staphylococcus aureus</italic> (MRSA) is a global health threat with high morbidity and mortality rates [##REF##35065702##1##]. Colonization with MRSA leads to increased infection rates of up to 25% [##REF##15472807##2##, ##REF##30737488... | [
"<title>Methods</title>",
"<p id=\"Par6\">This cohort study was conducted at the University Medical Center Groningen, a tertiary hospital in the Northern part of the Netherlands, between 2017 and 2022. The prevalence of MRSA carriage in the Netherlands during this time was < 1%. During these years, genetic ana... | [
"<title>Results</title>",
"<p id=\"Par15\">During the study period, 181 patients visited the MRSA outpatient clinic. WGS was performed in 56/181 (31%) patients and these were included in the study (Fig. ##FIG##0##1##). As shown in Fig. ##FIG##0##1##, there were 12 patients with treatment failure (i.e., one in the... | [
"<title>Discussion</title>",
"<p id=\"Par19\">In this study, we explored associations between MRSA isolate characteristics, genetic determinants, and decolonization outcomes in a Dutch population of MRSA carriers in a tertiary hospital. We found an association of eradication failure with carriage of ciprofloxacin... | [] | [
"<p id=\"Par1\">Methicillin-resistant <italic>Staphylococcus aureus</italic> (MRSA) colonization increases the risk of infection. Response to decolonization treatment is highly variable and determinants for successful decolonization or failure of eradication treatment are largely unknown. Insight into genetic predi... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Acknowledgements</title>",
"<p>The authors would like to thank Kees Swenne and Karin Wold for their invaluable help with data collection.</p>",
"<title>Funding</title>",
"<p>This project was partially funded by the Antibiotic Resistance Network Holland West.</p>",
"<title>Data Availability</title>",... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Flow chart. Flow chart of inclusions. MRSA carriage was defined as complicated in 44/56 (79%) patients, of whom 34/44 (77%) received systemic antibiotics as eradication treatment. The other 10/44 (23%) patients with complicated carriage were spontaneously cleared o... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline and treatment characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Characteristic</th><th align=\"left\">Treatment failure</th><th align=\"left\">Successful decolonization</th><th align=\... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>*This percentage represents the percentage of the patients who were treated with systemic antibiotics. Patients with uncomplicated carriage, MRSA infection without subsequent carriage, or spontaneous decolonization were not treated with systemic antibiotics</p><p><italic>n.a.</italic> not appli... | [
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"<graphic xlink:href=\"284_2023_3581_Fig3_HTML\" id=\"MO3\"/>"
] | [
"<media xlink:href=\"284_2023_3581_MOESM1_ESM.docx\"><caption><p>Supplementary file1 (DOCX 72 KB)</p></caption></media>"
] | [{"label": ["4."], "collab": ["WHO Regional Office for Europe, European Centre for Disease Prevention and Control"], "source": ["Antimicrobial resistance surveillance in Europe 2022\u20132020 data"], "year": ["2022"], "publisher-loc": ["Copenhagen"], "publisher-name": ["WHO Regional Office for Europe"]}, {"label": ["16."], "mixed-citation": ["Dutch Working Party on Antibiotic Policy (Stichting Werkgroep Antibiotica Beleid [SWAB]) (2012) "], "italic": ["Guideline for the treatment of MRSA carriage"]}, {"label": ["17."], "mixed-citation": ["Bernards AT, Bonten MJM, Cohen Stuart J, Diederen BMW, Goessens WHF, Grundmann H, Kluytmans JAJW, Kluytmans-van den Bergh MFQ, Leversteinvan Hall MA, Mouton JW, Al Naiemi N, Troelstra A, Vandenbroucke-Grauls CMJE, Vos MC, Voss A (2012)"], "italic": [" NVMM Guideline Laboratory detection of highly resistant microorganisms (HRMO), version 2.0"], "ext-link": ["https://www.nvmm.nl/media/1051/2012_hrmo_mrsa_esbl.pdf"]}, {"label": ["18."], "mixed-citation": ["The European Committee on Antimicrobial Susceptibility Testing."], "italic": [" Breakpoint tables for interpretation of MICs and zone diameters"], "ext-link": ["http://www.eucast.org"]}, {"label": ["27."], "surname": ["J\u00f6rgensen"], "given-names": ["J"], "article-title": ["The majority of MRSA colonized children not given eradication treatment are still colonized one year later: systemic antibiotics improve the eradication rate"], "source": ["Infect Dis"], "year": ["2018"], "volume": ["50"], "issue": ["9"], "fpage": ["687"], "lpage": ["696"], "pub-id": ["10.1080/23744235.2018.1459828"]}] | {
"acronym": [],
"definition": []
} | 28 | CC BY | no | 2024-01-15 23:41:53 | Curr Microbiol. 2024 Jan 13; 81(2):63 | oa_package/29/e8/PMC10787693.tar.gz |
PMC10787694 | 0 | [
"<title>Introduction</title>",
"<p id=\"Par5\"><italic>Goal Recognition</italic> is the task of recognizing the intentions of autonomous agents or humans by observing their interactions in an environment. Existing work on goal and plan recognition addresses this task over several different types of domain setting... | [] | [
"<title>Offline keyhole recognition results</title>",
"<p id=\"Par115\">We now assess how accurate our recognition approach is in the <italic>Keyhole Recognition</italic> setting. Table ##TAB##0##1## shows three inner tables that summarize and aggregate the average results of all the six datasets for four differe... | [
"<title>Related work and discussion</title>",
"<p id=\"Par123\">To the best of our knowledge, existing approaches to <italic>Goal and Plan Recognition as Planning</italic> cannot explicitly recognize temporally extended goals in non-deterministic environments. Seminal and recent work on <italic>Goal Recognition a... | [
"<title>Conclusions</title>",
"<p id=\"Par126\">This article introduced a novel problem formalization for recognizing <italic>temporally extended goals</italic>, specified in either <sc>ltl</sc> or <sc>ppltl</sc>, in <sc>fond</sc> planning domain models. It also developed a novel probabilistic framework for goal ... | [
"<title>Ramon Fraga Pereira</title>",
"<p id=\"Par1\">is a researcher in the field of Artificial Intelligence, particularly in the area of Automated Planning. Hisresearch interests include Goal and Plan Recognition and Heuristic Search for Sequential Decision-Making. He iscurrently a Lecturer in Symbolic AI and A... | [
"<title>Preliminaries</title>",
"<p id=\"Par10\">This section briefly recalls the syntax and semantics of <italic>Linear-time Temporal Logics</italic> on finite traces (<sc>ltl</sc> and <sc>ppltl</sc>) and revises the concept and terminology of <sc>fond</sc> planning.</p>",
"<title><sc>ltl</sc> and <sc>PPLTL</s... | [
"<title>Acknowledgements</title>",
"<p>This work has been partially supported by the ERC-ADGWhiteMech (No. 834228), the EU ICT-48 2020 project TAILOR (No. 952215), the PRIN project RIPER (No. 20203FFYLK),and the PNRR MUR project FAIR (No. PE0000013).</p>"
] | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p><sc>Triangle-Tireworld</sc> domain and policy</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>DFA and PDFA for </p></caption></fig>",
"<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Overview of our solution approach</p></caption></fi... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Offline Recognition results for Conjunctive, <sc>ltl</sc>, and <sc>ppltl</sc> goals</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\"></th><th align=\"left\">|<italic>Obs</italic>|</th><th align=\"left\"... | [
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"<fn-group><fn id=\"Fn1\"><label>1</label><p id=\"Par25\">The fairness assumption defines that all action outcomes in a given state have a non-zero probability.</p></fn><fn id=\"Fn2\"><label>2</label><p id=\"Par83\"></p></fn><fn id=\"Fn3\"><label>3</label><p id=\"Par88\"><ext-link ext-link-type=\"uri\" xlink:href=\... | [
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} | 39 | CC BY | no | 2024-01-15 23:41:53 | Appl Intell (Dordr). 2024 Dec 14; 54(1):470-489 | oa_package/bc/1b/PMC10787694.tar.gz |
PMC10787695 | 0 | [
"<title>Introduction</title>",
"<p id=\"Par6\">High-rate anaerobic wastewater treatment applies granular biofilms to achieve stable and efficient production of biogas (Lettinga et al. ##REF##18551476##1983##). Comprised of tightly interconnected bacteria and archaea of diverse metabolic functionalities, granular ... | [
"<title>Materials and methods</title>",
"<title>Cultivation of microorganisms</title>",
"<p id=\"Par11\">Pure cultures of <italic>Methanospirillum hungatei</italic> strain JF1 (DSM 864<sup>T</sup>), <italic>Methanobacterium formicicum</italic> strain MF (DSM 1535) and <italic>Syntrophobacterium fumaroxidans</it... | [
"<title>Results</title>",
"<title>Physiological and morphological analysis of early- and late-aggregation state co-cultures</title>",
"<p id=\"Par23\">In this study, we successfully achieved aggregation of two syntrophic propionate-oxidizing microbial co-cultures comprised of (1) <italic>Syntrophobacterium fuma... | [
"<title>Discussion</title>",
"<p id=\"Par32\">In this investigation of the aggregate-forming ability of syntrophic propionate-oxidizing co-cultures, we compare aggregate morphology and biochemical make-up of early- and late-aggregation state co-cultures of <italic>S. fumaroxidans</italic> and methanogens. Althoug... | [] | [
"<title>Abstract</title>",
"<p id=\"Par1\">For several decades, the formation of microbial self-aggregates, known as granules, has been extensively documented in the context of anaerobic digestion. However, current understanding of the underlying microbial-associated mechanisms responsible for this phenomenon rem... | [
"<title>Supplementary information</title>",
"<p>\n</p>"
] | [
"<title>Acknowledgements</title>",
"<p>Authors would like to thank Dr Caroline Plugge for the insightful discussions on the formation of aggregates in the syntrophic co-cultures.</p>",
"<title>Author contribution</title>",
"<p>AD and DS conceived and designed research. AD conducted experiments. MB and MS cont... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Propionate (green), methane (orange) and acetate (grey) measured in the co-cultures of <italic>S. fumaroxidans</italic> and <italic>M. formicicum</italic> (left panel) or <italic>M. hungatei</italic> (right panel). Cycle 1 corresponds to the early-aggregation state... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Concentration and type of the identified AHL compounds in the extracts from late exponential growth phase pure cultures of <italic>M. formicicum</italic> and <italic>M. hungatei</italic> and their correspondent co-cultures with <italic>S. fumaroxidans</ital... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
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] | [
"<table-wrap-foot><p>*Incomplete dissolution of the C12-HSL compound might lead to an overestimation of the actual quantity in the co-culture extracts, since full dissolution was assumed for calibration curves</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Publisher’s Note</bold></p><p>Springer Nature remains ne... | [
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"acronym": [],
"definition": []
} | 58 | CC BY | no | 2024-01-15 23:41:53 | Appl Microbiol Biotechnol. 2024 Jan 13; 108(1):1-15 | oa_package/77/2b/PMC10787695.tar.gz |
PMC10787696 | 38217830 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Prostate cancer is the most common<bold>ly</bold> diagnosed tumor in men worldwide, with the highest incidence in Northern Europe [##REF##34716119##1##]. However, mortality rates do not align with incidence rates, thanks to its early diagnosis and treatment in most c... | [
"<title>Methods</title>",
"<title>Data collection</title>",
"<p id=\"Par7\">A retrospective review of medical records of all patients who underwent LRRP between October 2019 and July 2021 was conducted. A single experienced robotic surgeon performed all procedures. Patients with a pathological diagnosis of pros... | [
"<title>Results</title>",
"<p id=\"Par20\">Overall, the study included 70 patients. Table ##TAB##0##1## summarizes the baseline data and demographic characteristics. The median age was 64 (61–68) years, and the median CCI was 6 (5–6,5).</p>",
"<p id=\"Par21\">Intraoperative, perioperative and pathology outcomes... | [
"<title>Discussion</title>",
"<p id=\"Par22\">Robotic surgery has gained acceptance and spread globally due to its ability to provide enhanced visualization and great precision in hard-to-reach areas. The magnification of the surgical field has allowed for the development of various techniques to preserve the per... | [
"<title>Conclusion</title>",
"<p id=\"Par30\">Our study shows that our technique was feasible in experienced hands and associated with a low rate of early complications and PSM. Our lateral approach demonstrated similar rates of continence recovery compared to other techniques while showing promising results in e... | [
"<p id=\"Par1\">In the era of robotic prostate surgery, various techniques have been developed to improve functional outcomes. Urinary continence has shown satisfactory results, but the preservation of lateral nerves to the periprostatic capsule is only achievable by sparing the pubovesical complex. This study aims... | [] | [
"<title>Author contributions</title>",
"<p>CG: Methodology, Writing – original draft, Writing – review and editing. DC: Conceptualization, Methodology, Writing – review, and editing. NSV: Writing – review and editing. JR: Supervision, JPK: Writing – original draft. LHL: Writing – original draft TP: Writing – orig... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>The Location for Port Placement in Lateral Approach in Robot-assisted Radical Prostatectomy</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Surgical steps of lateral-approach robot-assisted radical prostatectomy: <bold>a</bold> Peritoneum... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline data of patients related to Overall group</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variable</th><th align=\"left\">Overall (<italic>n</italic>=70)</th></tr></thead><tbody><tr><td align=\"left\">Age, <itali... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>Data are presented as medians (interquartile range) and frequencies (proportions). <italic>BMI</italic> Body Mass Index, <italic>ASA</italic> American Society of Anesthesiologists, <italic>MRI</italic> Magnetic resonance imaging, <italic>PSA</italic> Prostate-Specific Antigen</p></table-wrap-fo... | [
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"<media xlink:href=\"11701_2023_1772_MOESM1_ESM.mp4\" id=\"MOESM1\"><caption><p>Supplementary file1 (MP4 335599 KB)</p></caption></media>"
] | [{"label": ["2."], "mixed-citation": ["EAU Guidelines. Edn. presented at the EAU Annual Congress Amsterdam 2022. ISBN 978-94-92671-16-5."]}, {"label": ["9."], "mixed-citation": ["Brierley JD, Gospodarowicz MK, Wittekind C (2017) TNM classification of malignant tumors. UICC International Union Against Cancer. 8th edn"]}, {"label": ["23."], "mixed-citation": ["Rosenberg JE, Jung JH, Edgerton Z, et al. (2020) Retzius-sparing versus standard robotic-assisted laparoscopic prostatectomy for the treatment of clinically localized prostate cancer.\u00a0Cochrane Database Syst Rev 8(8):CD013641. 10.1002/14651858.CD013641.pub2"]}] | {
"acronym": [],
"definition": []
} | 26 | CC BY | no | 2024-01-15 23:41:53 | J Robot Surg. 2024 Jan 13; 18(1):24 | oa_package/1c/ea/PMC10787696.tar.gz |
PMC10787697 | 38064042 | [
"<title>Introduction</title>",
"<p id=\"Par2\"><sc>d</sc>-Glucaric acid (<sc>d</sc>-saccharic acid) is a di-carboxylic acid that can be used for example to produce furan dicarboxylic acid (van Strien et al. ##UREF##10##2020##) or various polyamides, and polyesters (Sakuta and Nakamura ##UREF##8##2019##). Biotechn... | [
"<title>Materials and methods</title>",
"<title>Strains and strain construction</title>",
"<p id=\"Par6\"><italic>E. coli</italic> strains DH5alpha or TOP10 were used for cloning steps, plasmid propagation and storage, and the <italic>Saccharomyces cerevisiae</italic> strain FY834 for recombination cloning. <it... | [
"<title>Results</title>",
"<title><sc>d</sc>-Glucose conversion to <sc>d</sc>-glucaric acid with <italic>pgi1-</italic>deficient <italic>S. cerevisiae</italic></title>",
"<p id=\"Par17\">The <italic>pgi1</italic>-deficient <italic>S. cerevisiae</italic> CEN.PK2-1D strains expressed <italic>INO1</italic>, <itali... | [
"<title>Discussion</title>",
"<p id=\"Par28\">In <italic>S. cerevisiae</italic> the major flux of <sc>d</sc>-glucose is through glycolysis, and only a small fraction, approximately 1–4%, is channelled to the PPP, depending on strain and culture conditions (Blank et al. ##REF##15780654##2005##; Fiaux et al. ##REF#... | [] | [
"<p id=\"Par1\"><sc>d</sc>-Glucaric acid is a potential biobased platform chemical. Previously mainly <italic>Escherichia coli,</italic> but also the yeast <italic>Saccharomyces cerevisiae,</italic> and <italic>Pichia pastoris,</italic> have been engineered for conversion of <sc>d</sc>-glucose to <sc>d</sc>-glucari... | [] | [
"<title>Acknowledgements</title>",
"<p>This study was financially supported by the Academy of Finland through the grants Centre of Excellence in White Biotechnology—Green Chemistry (grant 118573), and SA SBio (grant 310191), and by the European Commission through the Seventh Framework Programme (FP7/2007-2013) un... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Schematic outline for conversion of <sc>d</sc>-glucose to <sc>d</sc>-glucaric acid with Pgi1p-deficient <italic>S. cerevisiae</italic>. In the Pgi1p-deficient strain, <sc>d</sc>-glucose should be channeled to myo-inositol, while <sc>d</sc>-fructose and ethanol woul... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Production of <sc>d</sc>-glucaric acid by <italic>E. coli</italic>, yeast <italic>S. cerevisiae</italic> or <italic>P. pastoris</italic> in vivo. The highest extracellular volumetric titer and corresponding yield of a study are presented. Partly adapted fro... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p><sup><bold>a</bold></sup>Originally reported in mol/mol</p><p><sup>b</sup>Reported only per steam exploded corn stover</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and insti... | [
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"<graphic xlink:href=\"10529_2023_3443_Fig5_... | [] | [{"surname": ["Fang", "Deng", "Pan", "Li", "Yu"], "given-names": ["H", "Y", "Y", "C", "L"], "article-title": ["Distributive and collaborative push-and-pull in an artificial microbial consortium for improved consolidated bioprocessing"], "source": ["AIChE J"], "year": ["2022"], "volume": ["68"], "fpage": ["e17844"], "pub-id": ["10.1002/AIC.17844"]}, {"surname": ["Fang", "Zhao", "Li", "Song", "Yu", "Song", "Wu", "Yang"], "given-names": ["H", "C", "C", "Y", "L", "X", "J", "L"], "article-title": ["Direct consolidated bioprocessing for "], "sc": ["d"], "source": ["J Chem Eng"], "year": ["2023"], "volume": ["454"], "fpage": ["140339"], "pub-id": ["10.1016/J.CEJ.2022.140339"]}, {"surname": ["Gancedo", "Lagunas"], "given-names": ["JM", "R"], "article-title": ["Contribution of the pentose-phosphate pathway to glucose metabolism in "], "italic": ["Saccharomyces cerevisiae"], "source": ["Plant Sci Lett"], "year": ["1973"], "volume": ["1"], "fpage": ["193"], "lpage": ["200"], "pub-id": ["10.1016/0304-4211(73)90044-8"]}, {"surname": ["Gancedo", "Serrano", "Rose", "Harrison"], "given-names": ["C", "R", "AH", "JS"], "article-title": ["Energy yielding metabolism"], "source": ["The yeasts"], "year": ["1989"], "publisher-loc": ["Cambridge"], "publisher-name": ["Academic press"], "fpage": ["205"], "lpage": ["259"]}, {"surname": ["Lien"], "given-names": ["OG"], "article-title": ["Determination of gluconolactone, galactonolactone and their free acids by the hydroxamate method"], "source": ["Anal Chem"], "year": ["1959"], "volume": ["31"], "fpage": ["1363"], "lpage": ["1366"], "pub-id": ["10.1021/ac60152a035"]}, {"surname": ["Liu", "Gong", "Wang", "Du", "Chen", "Kang"], "given-names": ["Y", "X", "C", "G", "J", "Z"], "article-title": ["Production of glucaric acid from myo-inositol in engineered "], "italic": ["Pichia pastoris"], "source": ["Enzyme Microb"], "year": ["2016"], "volume": ["91"], "fpage": ["8"], "lpage": ["16"], "pub-id": ["10.1016/j.enzmictec.2016.05.009"]}, {"surname": ["Marques", "Anderson", "Sandoval", "Hicks", "Prather"], "given-names": ["WL", "LA", "L", "MA", "KLJ"], "article-title": ["Sequence-based bioprospecting of myo-inositol oxygenase (Miox) reveals new homologues that increase glucaric acid production in "], "italic": ["Saccharomyces cerevisiae"], "source": ["Enzyme Microb"], "year": ["2020"], "volume": ["140"], "fpage": ["109623"], "pub-id": ["10.1016/J.ENZMICTEC.2020.109623"]}, {"surname": ["Moon", "Yoon", "Lanza", "Roy-Mayhew", "Prather"], "given-names": ["TS", "SH", "AM", "JD", "KL"], "article-title": ["Production of glucaric acid from a synthetic pathway in recombinant "], "italic": ["Escherichia coli"], "source": ["AEM"], "year": ["2009"], "volume": ["75"], "fpage": ["589"], "lpage": ["595"], "pub-id": ["10.1128/AEM.00973-08"]}, {"surname": ["Sakuta", "Nakamura"], "given-names": ["R", "N"], "article-title": ["Production of hexaric acids from biomass"], "source": ["International J Mol Sci"], "year": ["2019"], "volume": ["20"], "fpage": ["3660"], "pub-id": ["10.3390/IJMS20153660"]}, {"surname": ["Sherman", "Fink", "Hicks"], "given-names": ["F", "G", "JB"], "source": ["Methods in yeast genetics: A laboratory manual"], "year": ["1983"], "publisher-loc": ["Cold Springs Harbor"], "publisher-name": ["Cold Springs Harbor Laboratory"]}, {"surname": ["van Strien", "Rautiainen", "Asikainen", "Thomas", "Linnekoski", "Niemel\u00e4", "Harlin"], "given-names": ["N", "S", "M", "DA", "J", "K", "A"], "article-title": ["A unique pathway to platform chemicals: aldaric acids as stable intermediates for the synthesis of furandicarboxylic acid esters"], "source": ["Green Chem"], "year": ["2020"], "volume": ["22"], "fpage": ["8271"], "lpage": ["8277"], "pub-id": ["10.1039/d0gc02293d"]}, {"surname": ["Ye", "Bandara", "Greenberg"], "given-names": ["C", "WMMS", "ML"], "article-title": ["Regulation of inositol metabolism is fine-tuned by inositol pyrophosphates in "], "italic": ["Saccharomyces cerevisiae"], "source": ["JBC"], "year": ["2013"], "volume": ["288"], "fpage": ["24898"], "lpage": ["24908"], "pub-id": ["10.1074/jbc.M113.493353"]}, {"surname": ["Zhang", "Wan", "Yu", "Tsang"], "given-names": ["Q", "Z", "IKM", "DCW"], "article-title": ["Sustainable production of high-value gluconic acid and glucaric acid through oxidation of biomass-derived glucose: a critical review"], "source": ["J Clean Prod"], "year": ["2021"], "volume": ["312"], "fpage": ["127745"], "pub-id": ["10.1016/J.JCLEPRO.2021.127745"]}, {"surname": ["Zhao", "Zuo", "Shu", "Yang", "Deng"], "given-names": ["Y", "F", "Q", "X", "Y"], "article-title": ["Efficient production of glucaric acid by engineered "], "italic": ["Saccharomyces cerevisiae"], "source": ["AEM"], "year": ["2023"], "pub-id": ["10.1128/aem.00535-23"]}] | {
"acronym": [],
"definition": []
} | 60 | CC BY | no | 2024-01-15 23:41:53 | Biotechnol Lett. 2024 Dec 8; 46(1):69-83 | oa_package/0a/9a/PMC10787697.tar.gz |
PMC10787698 | 0 | [
"<title>Introduction</title>",
"<p id=\"Par5\">Imine reducing enzymes produce a chiral amine often with excellent stereoselectivity. Therefore, there is a constant interest towards them in the pharmaceutical industry. This interest is driven by the high number of active pharmaceutical ingredients (APIs) bearing a... | [
"<title>Materials and methods</title>",
"<p id=\"Par8\">All reagents and solvents, amino acids, and ketoacids were commercially available and used without further purification.</p>",
"<title>HPLC methods</title>",
"<p id=\"Par9\">Analytical HPLC measurements were performed on Agilent Technologies 1260 LC syst... | [
"<title>Results</title>",
"<title>Metagenome mining of ODHs</title>",
"<p id=\"Par41\">We started with building a database containing ODH sequences that can be used as templates for metagenome search. We mined the literature for enzymes with known primary structure and confirmed activity to avoid falsely annota... | [
"<title>Discussion</title>",
"<p id=\"Par52\">Opine dehydrogenases are a diverse class of enzymes specialized to perform wide ranging physiological roles in organisms across the tree of life. Substrate specificities of each enzyme have evolved to perform a certain physiological function in the given organism. Min... | [] | [
"<title>Abstract</title>",
"<p id=\"Par1\">Enzymatic processes play an increasing role in synthetic organic chemistry which requires the access to a broad and diverse set of enzymes. Metagenome mining is a valuable and efficient way to discover novel enzymes with unique properties for biotechnological application... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Acknowledgements</title>",
"<p>The authors thank the Analytical Department of SRIMC for the analytical development and the characterization of the compounds.</p>",
"<title>Author contribution</title>",
"<p>AT, ZM, GT, and BGV conceived and designed research, analyzed data, and wrote the paper. KT, BV,... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p><bold>A</bold> Sequence similarity network (SSN) of ODHs with a connection threshold of 30% sequence identity. <bold>B</bold> Phylogenetic tree of ODHs. In both cases, template sequences used for the metagenome search are colored according to their origin: marine i... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Kinetic parameters of mODHs. The cofactor preference is indicated in the last column as the ratio of catalytic efficiencies (<italic>k</italic><sub>cat</sub>/<italic>K</italic><sub>M</sub>) with NADH and NADPH; thus, a value > 1 indicates NADH preference... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"253_2023_12871_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"253_2023_12871_Sch1_HTML\" id=\"MO2\"/>",
"<graphic xlink:href=\"253_2023_12871_Fig2_HTML\" id=\"MO3\"/>",
"<graphic xlink:href=\"253_2023_12871_Fig3_HTML\" id=\"MO4\"/>",
"<graphic xlink:href=\"253_2023_12871_Fig4_HTML\... | [
"<media xlink:href=\"253_2023_12871_MOESM1_ESM.pdf\"><caption><p>Supplementary file1 (PDF 2.56 MB)</p></caption></media>"
] | [{"surname": ["Baud", "Jeffries", "Moody", "Ward", "Hailes"], "given-names": ["D", "JWE", "TS", "JM", "HC"], "article-title": ["A metagenomics approach for new biocatalyst discovery: application to transaminases and the synthesis of allylic amines"], "source": ["Green Chem"], "year": ["2017"], "volume": ["19"], "fpage": ["1134"], "lpage": ["1143"], "pub-id": ["10.1039/c6gc02769e"]}, {"surname": ["Caparco", "Pelletier", "Petit", "Jouenne", "Bommarius", "Berardinis", "Zaparucha", "Champion", "Bommarius", "Vergne-Vaxelaire"], "given-names": ["AA", "E", "JL", "A", "BR", "V", "A", "JA", "AS", "C"], "article-title": ["Metagenomic mining for amine dehydrogenase discovery"], "source": ["Adv Synth Catal"], "year": ["2020"], "volume": ["362"], "fpage": ["2427"], "lpage": ["2436"], "pub-id": ["10.1002/adsc.202000094"]}, {"mixed-citation": ["Chen H, Collier SJ, Nazor J, Sukumaran J, Smith D, Moore JC, Hughes G, Janey J, Huisman G, Novick S, Agard N, Alvizo O, Cope G (2013) Engineered imine reductases and methods for the reductive amination of ketone and amine compounds. US Pat. 2013/0302859 1:102pp."]}, {"mixed-citation": ["Cosgrove SC, Brzezniak A, France SP, Ramsden JI, Mangas-Sanchez J, Montgomery SL, Heath RS, Turner NJ (2018) Imine reductases, reductive aminases, and amine oxidases for the synthesis of chiral amines: discovery, characterization, and synthetic applications. In: Methods in Enzymology, 1st edn. Elsevier Inc., pp 131\u2013149"]}, {"surname": ["Ducrot", "Bennett", "Grogan", "Vergne-Vaxelaire"], "given-names": ["L", "M", "G", "C"], "article-title": ["NAD(P)H-dependent enzymes for reductive amination: active site description and carbonyl-containing compound spectrum"], "source": ["Adv Synth Catal"], "year": ["2021"], "volume": ["363"], "fpage": ["328"], "lpage": ["351"], "pub-id": ["10.1002/adsc.202000870"]}, {"mixed-citation": ["Kali\u010danin N, Bala\u017e AM, Prodanovi\u0107 O, Prodanovi\u0107 R (2023) Heterologous expression and partial characterization of a putative opine dehydrogenase from a metagenomic sequence of "], "italic": ["Desulfohalobium retbaense"]}, {"surname": ["Kato", "Asano"], "given-names": ["Y", "Y"], "article-title": ["Opine dehydrogenase, secondary amine dicarboxylic acids"], "source": ["Encyclopedia of Industrial Biotechnology: Fermentation, Biocatalysis, and Bioseparation"], "year": ["2002"], "publisher-loc": ["Hoboken, NJ, USA"], "publisher-name": ["John Wiley & Sons Inc"], "fpage": ["1851"], "lpage": ["1858"]}, {"surname": ["Kato", "Yamada", "Asano"], "given-names": ["Y", "H", "Y"], "article-title": ["Stereoselective synthesis of opine-type secondary amine carboxylic acids by a new enzyme opine dehydrogenase use of recombinant enzymes"], "source": ["J Mol Catal B Enzym"], "year": ["1996"], "volume": ["1"], "fpage": ["151"], "lpage": ["160"], "pub-id": ["10.1016/1381-1177(95)00011-9"]}, {"mixed-citation": ["McFarlane JS, Lamb AL (2020) Opine metallophore biosynthesis. In: Comprehensive natural products III, 3rd edn. Elsevier, pp 395\u2013414"]}, {"mixed-citation": ["Niesen FH, Berglund H, Vedadi M (2007) The use of differential scanning fluorimetry to detect ligand interactions that promote protein stability. Nat Protoc 2007 29 2:2212\u20132221. 10.1038/nprot.2007.321"]}, {"surname": ["Patil", "Grogan", "Bommarius", "Yun"], "given-names": ["MD", "G", "A", "H"], "article-title": ["Oxidoreductase-catalyzed synthesis of chiral amines"], "source": ["ACS Catal"], "year": ["2018"], "volume": ["8"], "fpage": ["10985"], "lpage": ["11015"], "pub-id": ["10.1021/ACSCATAL.8B02924"]}, {"surname": ["Schober", "MacDermaid", "Ollis", "Chang", "Khan", "Hosford", "Latham", "Ihnken", "Brown", "Fuerst", "Sanganee", "Roiban"], "given-names": ["M", "C", "AA", "S", "D", "J", "J", "LAF", "MJB", "D", "MJ", "G-D"], "article-title": ["Chiral synthesis of LSD1 inhibitor GSK2879552 enabled by directed evolution of an imine reductase"], "source": ["Nat Catal"], "year": ["2019"], "volume": ["2"], "fpage": ["909"], "lpage": ["915"], "pub-id": ["10.1038/s41929-019-0341-4"]}, {"mixed-citation": ["Smits SHJ, Meyer T, Mueller A, van Os N, Stoldt M, Willbold D, Schmitt L, Grieshaber MK (2010) Insights into the mechanism of ligand binding to octopine dehydrogenase from "], "italic": ["Pecten maximus"]}, {"surname": ["Tassano", "Moore", "Dussauge", "Vargas", "Snajdrova"], "given-names": ["E", "C", "S", "A", "R"], "article-title": ["Discovery of new Fe(II)/\u03b1-ketoglutarate-dependent dioxygenases for oxidation of l -proline"], "source": ["Org Process Res Dev"], "year": ["2022"], "volume": ["26"], "fpage": ["1996"], "lpage": ["2003"], "pub-id": ["10.1021/acs.oprd.1c00405"]}] | {
"acronym": [],
"definition": []
} | 45 | CC BY | no | 2024-01-15 23:41:53 | Appl Microbiol Biotechnol. 2024 Jan 13; 108(1):1-16 | oa_package/86/58/PMC10787698.tar.gz |
PMC10787699 | 37855866 | [
"<title>Introduction</title>",
"<p id=\"Par2\">The brain is metabolically highly active and uses around 20% of the glucose and oxygen consumed by the body [##REF##22152301##1##, ##UREF##0##2##]. The oxygen consumed in brain is needed mainly for ATP regeneration by mitochondrial oxidative phosphorylation as this p... | [
"<title>Materials and Methods</title>",
"<title>Materials</title>",
"<p id=\"Par7\">Sterile cell culture materials, unsterile 96-well plates and black microtiter plates were purchased from Sarstedt (Nümbrecht, Germany). Fetal calf serum (FCS), adenylate kinase, AMP, antimycin A, creatine, creatine kinase and 2-... | [
"<title>Results</title>",
"<title>Culture Age Dependency of the Cellular Contents of Adenosine Phosphates and CrP in Cultured Astrocytes</title>",
"<p id=\"Par17\">To determine the basal contents of adenosine phosphates and CrP in cultured astrocytes and to investigate a potential dependency of these contents o... | [
"<title>Discussion</title>",
"<p id=\"Par27\">To investigate the interplay of ATP and CrP metabolism of astrocytes, we have used confluent astrocyte cultures of an age between 14 and 28 days. The average specific content of ATP in untreated astrocyte cultures was with 36.0 ± 6.4 nmol/mg in the range between 20 an... | [] | [
"<p id=\"Par1\">Adenosine triphosphate (ATP) is the main energy currency of all cells, while creatine phosphate (CrP) is considered as a buffer of high energy-bond phosphate that facilitates rapid regeneration of ATP from adenosine diphosphate (ADP). Astrocyte-rich primary cultures contain ATP, ADP and adenosine mo... | [] | [
"<title>Acknowledgements</title>",
"<p>The authors would like to acknowledge the basal financial support of the University of Bremen for the project presented here.</p>",
"<title>Author Contributions</title>",
"<p>GK and RD designed the study concept. GK performed all experiments, analysed the data obtained a... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Basal levels of adenosine phosphates and CrP in cultured astrocytes. The cellular protein content (<bold>a</bold>) as well as the specific contents of ATP (<bold>b</bold>), CrP (<bold>c</bold>), ADP (<bold>e</bold>) and AMP (<bold>f</bold>) were determined for untr... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Basal levels of adenosine phosphates and CrP in cultured astrocytes after incubation for 24 h in culture medium without (control) or with 1 mM creatine</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">C... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>The data presented are means ± SD of values that were obtained in 22 (control) or 13 (creatine) individual measurements on cell lysates that had been obtained from 16 (control) or 10 (creatine) independently prepared cultures. The significance of differences (unpaired t test) between data for i... | [
"<graphic xlink:href=\"11064_2023_4039_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"11064_2023_4039_Fig2_HTML\" id=\"MO2\"/>",
"<graphic xlink:href=\"11064_2023_4039_Fig3_HTML\" id=\"MO3\"/>",
"<graphic xlink:href=\"11064_2023_4039_Fig4_HTML\" id=\"MO4\"/>",
"<graphic xlink:href=\"11064_2023_4039_Fig5_... | [] | [{"label": ["2."], "surname": ["Fern\u00e1ndez-Gonz\u00e1lez", "Galea"], "given-names": ["I", "E"], "article-title": ["Astrocyte strategies in the energy-efficient brain"], "source": ["Essays Biochem"], "year": ["2022"], "volume": ["67"], "fpage": ["3"], "lpage": ["16"], "pub-id": ["10.1042/EBC20220077"]}, {"label": ["4."], "surname": ["Verkhratsky", "Arranz", "Ciuba", "P\u0119kowska"], "given-names": ["A", "AM", "K", "A"], "article-title": ["Evolution of neuroglia"], "source": ["Ann N Y Acad Sci"], "year": ["2018"], "volume": ["1518"], "fpage": ["120"], "lpage": ["130"], "pub-id": ["10.1111/nyas.14917"]}, {"label": ["23."], "surname": ["Hori", "Matsumoto", "Maeda"], "given-names": ["O", "M", "Y"], "article-title": ["Metabolic and biosynthetic alterations in cultured astrocytes exposed to hypoxia/reoxygenation"], "source": ["J Neurochem"], "year": ["2008"], "volume": ["62"], "fpage": ["1489"], "lpage": ["1495"], "pub-id": ["10.1046/j.1471-4159.1994.62041489.x"]}, {"label": ["27."], "surname": ["Bhatt", "Chen", "Geiger", "Rosenberger"], "given-names": ["DP", "X", "JD", "TA"], "article-title": ["A sensitive HPLC-based method to quantify adenine nucleotides in primary astrocyte cell cultures"], "source": ["J Chromatogr B"], "year": ["2012"], "volume": ["889\u2013890"], "fpage": ["110"], "lpage": ["115"], "pub-id": ["10.1016/j.jchromb.2012.02.005"]}, {"label": ["43."], "surname": ["Tulpule", "Hohnholt", "Hirrlinger", "Dringen", "Hirrlinger", "Waagepetersen"], "given-names": ["K", "MC", "J", "R", "J", "HS"], "article-title": ["Primary cultures of astrocytes and neurons as model systems to study the metabolism and metabolite export from brain cells"], "source": ["Brain energy metabolism"], "year": ["2014"], "publisher-loc": ["New York, USA"], "publisher-name": ["Springer"], "fpage": ["45"], "lpage": ["72"]}, {"label": ["48."], "surname": ["Bergmeyer", "Bergmeyer", "Gra\u00dfl"], "given-names": ["HU", "J", "M"], "article-title": ["Tri- and dicarboxylic acids, purines, pyrimidines and derivatives, coenzymes, inorganic compounds"], "source": ["Methods of enzymatic analysis"], "year": ["1985"], "edition": ["3"], "publisher-loc": ["Germany"], "publisher-name": ["VCH Verlagsgesellschaft"], "fpage": ["370"], "lpage": ["379"]}, {"label": ["52."], "surname": ["Bryla", "Kaniuga", "Slater"], "given-names": ["J", "Z", "EC"], "article-title": ["Studies on the mechanism of inhibition of the mitochondrial electron transport by antimycin III. Binding of antimycin to sub-mitochondrial particles and to complex III"], "source": ["Biochim Biophys Acta (BBA) Bioenerg"], "year": ["1969"], "volume": ["189"], "fpage": ["327"], "lpage": ["336"], "pub-id": ["10.1016/0005-2728(69)90163-7"]}, {"label": ["53."], "surname": ["M\u00f6ller", "Bergstr\u00f6m", "F\u00fcrst", "Hellstr\u00f6m"], "given-names": ["P", "J", "P", "K"], "article-title": ["Effect of aging on energy-rich phosphagens in human skeletal muscles"], "source": ["Clin Sci"], "year": ["1980"], "volume": ["58"], "fpage": ["553"], "lpage": ["555"], "pub-id": ["10.1042/cs0580553"]}] | {
"acronym": [],
"definition": []
} | 74 | CC BY | no | 2024-01-15 23:41:53 | Neurochem Res. 2024 Oct 19; 49(2):402-414 | oa_package/b5/9d/PMC10787699.tar.gz |
PMC10787700 | 0 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Anthropogenic climatic warming is driving increases in the volume and extent of oxygen minimum zones (OMZs), as well as increases in sea surface temperatures (SST), the combination of which is likely to threaten the functioning of marine ecosystems (Diaz and Rosenber... | [
"<title>Methods</title>",
"<title>Summary</title>",
"<p id=\"Par9\">This study used high-resolution depth time-series data from 92 BET and 175 YFT tagged in the Eastern Pacific Ocean (EPO) in 2000 and between 2003 and 2005 (BET) and 2002 and 2011 (YFT). Light-based geolocation estimates were modelled with the u... | [
"<title>Results</title>",
"<title>Geographic distribution of tagging data</title>",
"<p id=\"Par21\">The most probable tracks by individuals of the two species occupied two distinct areas of the EPO. The tracks of both species extended over large areas and a broad range of depths, although it was evident that s... | [
"<title>Discussion</title>",
"<p id=\"Par42\">Here, we investigated the extent to which DO concentrations at depth act as a limiting factor in BET and YFT daytime depth distributions by analysing observed vertical distributions (time-at-depth) with DO and temperature at a range of depths. Determining the role of ... | [
"<title>Conclusions</title>",
"<p id=\"Par54\">Both species respond to low DO in different ways. BET do not significantly adjust their depths in response to lower DO; however, they do increase the number of upward vertical excursions they perform, which reduces the time available for foraging. YFT, on the other h... | [
"<p>Responsible Editor: H.-O. Pörtner.</p>",
"<p id=\"Par1\">Oxygen minimum zones in the open ocean are predicted to significantly increase in volume over the coming decades as a result of anthropogenic climatic warming. The resulting reduction in dissolved oxygen (DO) in the pelagic realm is likely to have detri... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Acknowledgements</title>",
"<p>The bigeye tuna archival tag data sets utilized in this study were obtained from tagging experiments made possible by the generous financial support of Japan Fisheries Agency and the Taiwan Fisheries Agency. The yellowfin tuna archival tag data sets utilized in this study we... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Distribution of depth time-series locations in the study area. Orange is YFT, blue is BET, and DO is the average for 2005 (µmol/l) at 100 m. A 5-degree grid is superimposed</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>TAD for daylight ... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Summary of GAM analysis results</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Detailed response variable</th><th align=\"left\">Best distribution</th><th align=\"left\">Best model</th><th align=\"left\">% deviance</th><... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>Detailed response results are those derived from analysis of the depth time-series data. Spatial response results are those derived from the data selected from the 1-degree grid cells. Daily response variables were derived for each individual track using locations between the hours of 06:00 to ... | [
"<graphic xlink:href=\"227_2023_4366_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"227_2023_4366_Fig2_HTML\" id=\"MO2\"/>",
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"<graphic xlink:href=\"227_2023_4366_Fig4_HTML\" id=\"MO4\"/>",
"<graphic xlink:href=\"227_2023_4366_Fig5_HTML\" id=... | [
"<media xlink:href=\"227_2023_4366_MOESM1_ESM.pdf\"><caption><p>Supplementary file1 (PDF 2997 KB)</p></caption></media>"
] | [{"surname": ["Baez", "Pascual-Alayon", "Ramos", "Abascal"], "given-names": ["JC", "P", "ML", "FJ"], "article-title": ["Tropical tunas: global warming and food security, an overview"], "source": ["Rev Biol Mar Oceanogr"], "year": ["2018"], "volume": ["53"], "fpage": ["1"], "lpage": ["8"]}, {"surname": ["Bernal", "Bernal", "Sepulveda", "Brill", "Domenici", "Kapoor"], "given-names": ["D", "D", "C", "R", "PK", "BG"], "article-title": ["The eco-physiology of swimming and movement patterns of tunas, bill fishes, and large pelagic sharks"], "source": ["Fish locomotion, an etho-ecological perspective"], "year": ["2010"], "publisher-loc": ["Enfield, USA"], "publisher-name": ["Science Publishers"]}, {"surname": ["Bernal", "Brill", "Dickson", "Shiels"], "given-names": ["D", "RW", "KA", "HA"], "article-title": ["Sharing the water column: physiological mechanisms underlying species-specific habitat use in tunas"], "source": ["Rev Fish Biol Fish"], "year": ["2017"], "volume": ["27"], "fpage": ["843"], "lpage": ["880"], "pub-id": ["10.1007/s11160-017-9497-7"]}, {"surname": ["Bertrand", "Bard", "Josse"], "given-names": ["A", "FX", "E"], "article-title": ["Tuna food habits related to the micronekton distribution in French Polynesia"], "source": ["Mar Biol"], "year": ["2002"], "volume": ["140"], "fpage": ["1023"], "lpage": ["1037"], "pub-id": ["10.1007/s00227-001-0776-3"]}, {"surname": ["Bertrand", "Josse", "Bach", "Gros", "Dagorn"], "given-names": ["A", "E", "P", "P", "L"], "article-title": ["Hydrological and trophic characteristics of tuna habitat: consequences on tuna distribution and longline catchability"], "source": ["Can J Fish Aquat Sci"], "year": ["2002"], "volume": ["59"], "fpage": ["1002"], "lpage": ["1013"], "pub-id": ["10.1139/f02-073"]}, {"surname": ["Breitburg", "Levin", "Oschlies", "Gr\u00e9goire", "Chavez", "Conley", "Gar\u00e7on", "Gilbert", "Guti\u00e9rrez", "Isensee", "Jacinto", "Limburg", "Montes", "Naqvi", "Pitcher", "Rabalais", "Roman", "Rose", "Seibel", "Telszewski", "Yasuhara", "Zhang"], "given-names": ["D", "LA", "A", "M", "FP", "DJ", "V", "D", "D", "K", "GS", "KE", "I", "SWA", "GC", "NN", "MR", "KA", "BA", "M", "M", "J"], "article-title": ["Declining oxygen in the global ocean and coastal waters"], "source": ["Science"], "year": ["2018"], "volume": ["359"], "fpage": ["7240"], "pub-id": ["10.1126/science.aam7240"]}, {"surname": ["Brill"], "given-names": ["R"], "article-title": ["A review of temperature and oxygen tolerance studies of tunas pertinent to fisheries oceanography, movement models and stock assessments"], "source": ["Fish Oceanogr"], "year": ["2007"], "volume": ["3"], "fpage": ["204"], "lpage": ["216"], "pub-id": ["10.1111/j.1365-2419.1994.tb00098.x"]}, {"surname": ["Czeschel", "Stramma", "Johnson"], "given-names": ["R", "L", "GC"], "article-title": ["Oxygen decreases and variability in the eastern equatorial Pacific"], "source": ["J Geophys Res-Oceans"], "year": ["2012"], "volume": ["117"], "fpage": ["0148"], "lpage": ["0227"], "pub-id": ["10.1029/2012jc008043"]}, {"surname": ["Fuller", "Schaefer", "Hampton", "Caillot", "Leroy", "Itano"], "given-names": ["DW", "KM", "J", "S", "BM", "DG"], "article-title": ["Vertical movements, behavior, and habitat of bigeye tuna ("], "italic": ["Thunnus obesus"], "source": ["Fish Res"], "year": ["2015"], "volume": ["172"], "fpage": ["57"], "lpage": ["70"], "pub-id": ["10.1016/j.fishres.2015.06.024"]}, {"surname": ["Gilly", "Beman", "Litvin", "Robison", "Carlson", "Giovannoni"], "given-names": ["WF", "JM", "SY", "BH", "CA", "SJ"], "article-title": ["Oceanographic and biological effects of shoaling of the oxygen minimum zone"], "source": ["Annual review of marine science"], "year": ["2013"], "publisher-loc": ["Palo Alto"], "publisher-name": ["Annual Reviews"], "fpage": ["393"], "lpage": ["420"]}, {"surname": ["Gruber"], "given-names": ["N"], "article-title": ["Warming up, turning sour, losing breath: ocean biogeochemistry under global change"], "source": ["Philos Trans R Soc Math Phys Eng Sci"], "year": ["2011"], "volume": ["369"], "fpage": ["1980"]}, {"surname": ["Gu", "Kenney", "Zhu"], "given-names": ["H", "T", "M"], "article-title": ["Partial generalized additive models: an information-theoretic approach for dealing with concurvity and selecting variables"], "source": ["J Comput Graph Stat"], "year": ["2010"], "volume": ["19"], "fpage": ["531"], "lpage": ["551"], "pub-id": ["10.1198/jcgs.2010.07139"]}, {"mixed-citation": ["ISSF (2020) Status of the world fisheries for tuna. 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"year": ["2003"], "volume": ["12"], "fpage": ["152"], "lpage": ["169"], "pub-id": ["10.1046/j.1365-2419.2003.00229.x"]}, {"collab": ["NIH"], "source": ["International guiding principles for biomedical research involving animals"], "year": ["2012"], "publisher-name": ["National Institutes of Health"]}, {"surname": ["Olivar", "Hulley", "Castellon", "Emelianov", "Lopez", "Tuset", "Contreras", "Moli"], "given-names": ["MP", "PA", "A", "M", "C", "VM", "T", "B"], "article-title": ["Mesopelagic fishes across the tropical and equatorial Atlantic: biogeographical and vertical patterns"], "source": ["Prog Oceanogr"], "year": ["2017"], "volume": ["151"], "fpage": ["116"], "lpage": ["137"], "pub-id": ["10.1016/j.pocean.2016.12.001"]}, {"surname": ["Prince", "Goodyear"], "given-names": ["ED", "CP"], "article-title": ["Hypoxia-based habitat compression of tropical pelagic fishes"], "source": ["Fish Oceanogr"], "year": ["2006"], "volume": ["15"], "fpage": ["451"], "lpage": ["464"], "pub-id": ["10.1111/j.1365-2419.2005.00393.x"]}, {"surname": ["Prince", "Luo", "Phillip Goodyear", "Hoolihan", "Snodgrass", "Orbesen", "Serafy", "Ortiz", "Schirripa"], "given-names": ["ED", "J", "C", "JP", "D", "ES", "JE", "M", "MJ"], "article-title": ["Ocean scale hypoxia-based habitat compression of Atlantic istiophorid billfishes"], "source": ["Fish Oceanogr"], "year": ["2010"], "volume": ["19"], "fpage": ["448"], "lpage": ["462"], "pub-id": ["10.1111/j.1365-2419.2010.00556.x"]}, {"surname": ["Schaefer", "Fuller"], "given-names": ["KM", "DW"], "article-title": ["Movements, behavior, and habitat selection of bigeye tuna ("], "italic": ["Thunnus obesus"], "source": ["Fish Bull"], "year": ["2002"], "volume": ["100"], "fpage": ["765"], "lpage": ["788"]}, {"surname": ["Schaefer", "Fuller"], "given-names": ["KM", "DW"], "article-title": ["Horizontal movements of bigeye tuna ("], "italic": ["Thunnus obesus"], "source": ["Inter-Am Trop Tuna Commis Bull"], "year": ["2009"], "volume": ["24"], "fpage": ["191"], "lpage": ["247"]}, {"surname": ["Schaefer", "Fuller"], "given-names": ["KM", "DW"], "article-title": ["Vertical movements, behavior, and habitat of bigeye tuna ("], "italic": ["Thunnus obesus"], "source": ["Mar Biol"], "year": ["2010"], "volume": ["157"], "fpage": ["2625"], "lpage": ["2642"], "pub-id": ["10.1007/s00227-010-1524-3"]}, {"surname": ["Schaefer", "Fuller", "Morais", "Daverat"], "given-names": ["KM", "DW", "P", "F"], "article-title": ["An introduction to fish migration"], "source": ["Methodologies for investigating oceanodromous fish movements: archival and pop-up satellite archival tags"], "year": ["2016"], "publisher-loc": ["Boca Raton, USA"], "publisher-name": ["CRC Press"], "fpage": ["315"]}, {"surname": ["Schaefer", "Fuller", "Block"], "given-names": ["KM", "DW", "BA"], "article-title": ["Movements, behavior, and habitat utilization of yellowfin tuna ("], "italic": ["Thunnus albacares"], "source": ["Mar Biol"], "year": ["2007"], "volume": ["152"], "fpage": 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"Hoolihan", "Visbeck", "Wallace", "Brandt", "K\u00f6rtzinger"], "given-names": ["L", "ED", "S", "J", "JP", "M", "DWR", "P", "A"], "article-title": ["Expansion of oxygen minimum zones may reduce available habitat for tropical pelagic fishes"], "source": ["Nat Clim Chang"], "year": ["2011"], "volume": ["2"], "fpage": ["33"], "lpage": ["37"], "pub-id": ["10.1038/nclimate1304"]}, {"surname": ["Vedor", "Mucientes", "Hern\u00e1ndez-Chan", "Rosa", "Humphries", "Sims", "Queiroz"], "given-names": ["M", "G", "S", "R", "N", "DW", "N"], "article-title": ["Oceanic diel vertical movement patterns of blue sharks vary with water temperature and productivity to change vulnerability to fishing"], "source": ["Front Mar Sci"], "year": ["2021"], "pub-id": ["10.3389/fmars.2021.688076"]}, {"surname": ["Vetter", "Kohin", "Preti", "McClatchie", "Dewar"], "given-names": ["R", "S", "A", "S", "H"], "article-title": ["Predatory interactions and niche overlap between mako shark, "], "italic": ["Isurus oxyrinchus", "Dosidicus gigas"], "source": ["Calif Coop Ocean Fish Invest Rep"], "year": ["2008"], "volume": ["49"], "fpage": ["142"], "lpage": ["156"]}, {"surname": ["Wood"], "given-names": ["SN"], "article-title": ["Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models"], "source": ["J R Stat Soc Ser B-Stat Methodol"], "year": ["2011"], "volume": ["73"], "fpage": ["3"], "lpage": ["36"], "pub-id": ["10.1111/j.1467-9868.2010.00749.x"]}] | {
"acronym": [],
"definition": []
} | 56 | CC BY | no | 2024-01-15 23:41:53 | Mar Biol. 2024 Jan 13; 171(2):55 | oa_package/85/90/PMC10787700.tar.gz |
PMC10787706 | 38217782 | [
"<title>Background</title>",
"<title>Subjective views of ageing and informal caregiving</title>",
"<p id=\"Par2\">Subjective views of ageing can include perceptions of age in general or of one’s own ageing process (Chasteen and Cary ##UREF##7##2015##; Wurm and Westerhof ##UREF##19##2015##). In this study, we an... | [
"<title>Method</title>",
"<title>Sample</title>",
"<p id=\"Par11\">Data from wave 2014 and 2017 of the population-based German Ageing Survey from the German Centre for Gerontology were used (DZA, 2014, 2017). This is a cohort-sequential panel representing community-dwelling adults aged 40 years and older in Ger... | [
"<title>Results</title>",
"<title>Description of the sample</title>",
"<p id=\"Par18\">The complete sample included 2162 informal caregivers (49.07% caring for parents, 23.59% for spouses or partners, and 26.97% for other related or non-related adults). They were on average aged 64.25 (± 10.25) years and 59.02%... | [
"<title>Discussion</title>",
"<p id=\"Par23\">This study explored if specific aspects of the care situation could affect informal caregivers’ views of ageing and if this differed as a function of caregiver’s age and gender. To answer these research questions, the number of caregiving hours, range of care tasks an... | [
"<title>Conclusion</title>",
"<p id=\"Par33\">In sum, this study’s findings provide new insight into views of ageing among informal caregivers, as well as age and gender differences, which highlight the need for different strategies to modify care performance to prevent a deterioration of views of ageing.</p>",
... | [
"<p>Responsible editor: Morten Wahrendorf.</p>",
"<p id=\"Par1\">We analysed whether care time, burden and range of caregiving tasks were associated with informal caregivers’ subjective views of ageing (measured as attitudes towards own age (ATOA), subjective age (SA), and onset of old age (OOA)), and whether the... | [
"<title>Supplementary Information</title>",
"<p>Below is the link to the electronic supplementary material.</p>"
] | [
"<title>Acknowledgements</title>",
"<p>The study was not preregistered. Data from the German Ageing Survey were used. It is available for scientific, noncommercial use for researchers free of charge and can be applied for via the website of the German Centre of Gerontology (<ext-link ext-link-type=\"uri\" xlink:h... | [] | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Results of fixed effects regression analysis</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Variables</th><th align=\"left\" colspan=\"3\">ATOA</th><th align=\"left\" colspan=\"3\">Subjective age</th><th al... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>Fixed Effects regression analysis adjusted for age (continuous variable), employment status, self-rated health and number of chronic diseases; unstandardized regression coefficients and robust standard errors are given. ATOA refers to attitudes towards own ageing (Range: 1–4), subjective age (R... | [] | [
"<media xlink:href=\"10433_2023_797_MOESM1_ESM.docx\"><caption><p><bold>Additional file 1.</bold></p></caption></media>"
] | [{"surname": ["Allison"], "given-names": ["PD"], "source": ["Missing data"], "year": ["2001"], "publisher-loc": ["London"], "publisher-name": ["Sage publications"]}, {"surname": ["Alonso Debreczeni", "Bailey"], "given-names": ["F", "PE"], "article-title": ["A systematic review and meta-analysis of subjective age and the association with cognition, subjective well-being, and depression"], "source": ["J Gerontol Ser B"], "year": ["2020"], "pub-id": ["10.1093/geronb/gbaa069"]}, {"surname": ["Ayalon"], "given-names": ["L"], "article-title": ["Perceived age, gender, and racial/ethnic discrimination in Europe: results from the European social survey"], "source": ["Educ Gerontol"], "year": ["2014"], "volume": ["40"], "issue": ["7"], "fpage": ["499"], "lpage": ["517"], "pub-id": ["10.1080/03601277.2013.845490"]}, {"surname": ["Bai"], "given-names": ["X"], "article-title": ["Images of ageing in society: a literature review"], "source": ["J Popul Age"], "year": ["2014"], "volume": ["7"], "issue": ["3"], "fpage": ["231"], "lpage": ["253"], "pub-id": ["10.1007/s12062-014-9103-x"]}, {"surname": ["Br\u00fcderl", "Wolf", "Best"], "given-names": ["J", "C", "H"], "article-title": ["Kausalanalyse mit Paneldaten"], "source": ["Handbuch der sozialwissenschaftlichen Datenanalyse"], "year": ["2010"], "publisher-loc": ["Wiesbaden"], "publisher-name": ["VS Verlag f\u00fcr Sozialwissenschaften"], "fpage": ["963"], "lpage": ["994"]}, {"surname": ["Br\u00fcderl", "Ludwig", "Best", "Wolf"], "given-names": ["J", "V", "H", "C"], "article-title": ["Fixed-effects panel regression"], "source": ["The SAGE handbook of regression analysis and causal inference"], "year": ["2015"], "publisher-loc": ["London"], "publisher-name": ["SAGE Publications Ltd"], "fpage": ["327"], "lpage": ["357"]}, {"surname": ["Cameron", "Trivedi"], "given-names": ["AC", "PK"], "source": ["Microeconometrics using stata"], "year": ["2009"], "publisher-loc": ["College Station"], "publisher-name": ["Stata Press"]}, {"surname": ["Chasteen", "Cary"], "given-names": ["A", "L"], "article-title": ["Age stereotypes and age stigma: connections to research on subjective aging"], "source": ["Annu Rev Gerontol Geriatr"], "year": ["2015"], "pub-id": ["10.1891/0198-8794.35.99"]}, {"mixed-citation": ["DZA, Deutsches Zentrum f\u00fcr Altersforschung (2014) Scientific use file German Ageing Survey (SUF DEAS) 2014, Version 4.1. 10.5156/DEAS.2014.M.007"]}, {"mixed-citation": ["DZA, Deutsches Zentrum f\u00fcr Altersforschung (2017) Scientific Use File Deutscher Alterssurvey (SUF DEAS) 2017, Version 2.2. 10.5156/DEAS.2017.M.005"]}, {"mixed-citation": ["Hamama-Raz Y, Nissanholtz Gannot R, Michaelis M, Beloosesky Y, Nissanholtz A (2022) Informal caregivers\u2019 negative affect: the interplay of caregivers\u2019 resilience, aging anxiety and burden. Aging Ment Health 1\u20137"]}, {"mixed-citation": ["Hess TM (2006) Attitudes toward aging and their effects on behavior. In: Handbook of the psychology of aging. Elsevier, pp 379\u2013406"]}, {"surname": ["Kim", "Kim", "Jopp", "Boerner"], "given-names": ["YK", "K", "DS", "K"], "article-title": ["Dyadic analysis of very old parents\u2019 and their children\u2019s interdependence and self-perceptions of aging"], "source": ["J Soc Person Relation"], "year": ["2023"], "volume": ["40"], "issue": ["1"], "fpage": ["311"], "lpage": ["332"], "pub-id": ["10.1177/02654075221118032"]}, {"surname": ["Martens", "Goldenberg", "Greenberg"], "given-names": ["A", "JL", "J"], "article-title": ["A terror management perspective on ageism"], "source": ["J Soc Issues"], "year": ["2005"], "volume": ["61"], "issue": ["2"], "fpage": ["223"], "lpage": ["239"], "pub-id": ["10.1111/j.1540-4560.2005.00403.x"]}, {"surname": ["Pinquart", "S\u00f6rensen"], "given-names": ["M", "S"], "article-title": ["Gender differences in caregiver stressors, social resources, and health: an updated meta-analysis"], "source": ["J Gerontol Ser B"], "year": ["2006"], "volume": ["61"], "issue": ["1"], "fpage": ["P33"], "lpage": ["P45"], "pub-id": ["10.1093/geronb/61.1.P33"]}, {"surname": ["Russell"], "given-names": ["R"], "article-title": ["Men doing \"women's work:\" elderly men caregivers and the gendered construction of care work"], "source": ["J Men's Stud"], "year": ["2007"], "volume": ["15"], "fpage": ["1"], "lpage": ["18"], "pub-id": ["10.3149/jms.1501.1"]}, {"mixed-citation": ["Schaffer M, Stillman S (2016) XTOVERID: Stata module to calculate tests of overidentifying restrictions after xtreg, xtivreg, xtivreg2, xthtaylor. In "], "ext-link": ["http://EconPapers.repec.org/RePEc:boc:bocode:s456779"]}, {"mixed-citation": ["Schiel S, Ruiz Marcos J, Dickmann C, Aust F, Middendorf L (2018) Methodenbericht Deutscher Alterssurvey (DEAS): Durchf\u00fchrung der 6. Erhebungswelle 2017"]}, {"surname": ["Shrira", "Palgi", "Diehl", "Palgi", "Shrira", "Diehl"], "given-names": ["A", "Y", "M", "Y", "A", "M"], "article-title": ["Advancing the field of subjective views of aging: an overview of recent achievements"], "source": ["Subjective views of aging: theory, research, and practice"], "year": ["2022"], "publisher-loc": ["Berlin"], "publisher-name": ["Springer"], "fpage": ["11"], "lpage": ["37"]}, {"surname": ["Wooldridge"], "given-names": ["JM"], "source": ["Econometric analysis of cross section and panel data"], "year": ["2010"], "publisher-loc": ["Cambridge"], "publisher-name": ["MIT press"]}, {"surname": ["Wurm", "Westerhof"], "given-names": ["S", "GJ"], "article-title": ["Longitudinal research on subjective aging, health, and longevity: current evidence and new directions for research"], "source": ["Annu Rev Gerontol Geriatr"], "year": ["2015"], "volume": ["35"], "issue": ["1"], "fpage": ["145"], "lpage": ["165"], "pub-id": ["10.1891/0198-8794.35.145"]}, {"surname": ["Zwar", "Konig", "Hajek"], "given-names": ["L", "HH", "A"], "article-title": ["Can informal caregiving be perceived as rejuvenating? Changes in perceptions of ageing at onset and end of informal caregiving during different stages of life"], "source": ["Gerontology"], "year": ["2022"], "pub-id": ["10.1159/000527745"]}, {"surname": ["Zygouri", "Cowdell", "Ploumis", "Gouva", "Mantzoukas"], "given-names": ["I", "F", "A", "M", "S"], "article-title": ["Gendered experiences of providing informal care for older people: a systematic review and thematic synthesis"], "source": ["BMC Health Serv Res"], "year": ["2021"], "pub-id": ["10.1186/s12913-021-06736-2"]}] | {
"acronym": [],
"definition": []
} | 49 | CC BY | no | 2024-01-15 23:41:53 | Eur J Ageing. 2024 Jan 13; 21(1):4 | oa_package/be/c2/PMC10787706.tar.gz |
PMC10787715 | 38217669 | [
"<title>Background</title>",
"<p id=\"Par6\">Frailty is a medical condition characterized by decreased physiological reserve. Recent studies have found that frail patients have a reduced ability to cope with stress, including surgery, which has a strong correlation with postoperative morbidity and mortality in ol... | [
"<title>Methods</title>",
"<title>Ethical approval</title>",
"<p id=\"Par9\">This study is a secondary analysis of a prospective observational study, which focused on the effects of 3-month postoperative recovery as measured by the quality of recovery-15 in hospital on disability-free survival. This study was a... | [
"<title>Results</title>",
"<p id=\"Par17\">Out of the initial 230 patients, a total of 210 patients were included in this study (Fig. ##FIG##0##1##). Among them, 165 patients were classified as robust and 45 as frail. There were no statistically significant differences in preoperative characteristics between the ... | [
"<title>Discussion</title>",
"<p id=\"Par20\">This secondary analysis involving 210 patients undergoing abdominal cancer surgery, revealed that frail patients had a lower SAS. Furthermore, patients with a SAS ≤ 7 exhibited a higher rate of postoperative severe complications and a longer duration of hospital stay ... | [
"<title>Conclusions</title>",
"<p id=\"Par24\">This study demonstrated that frail patients have lower SAS, and patients with lower SAS have higher postoperative complication rates and longer hospital stays in patients who underwent cancer surgery.</p>"
] | [
"<title>Introduction</title>",
"<p id=\"Par1\">The surgical Apgar score is useful for predicting postoperative morbidity and mortality. However, its applicability in frail patients with minimal hemodynamic variation remains unknown. This study aimed to investigate the association between frailty and surgical Apga... | [
"<title>Supplementary Information</title>",
"<p>\n</p>"
] | [
"<title>Acknowledgements</title>",
"<p>None</p>",
"<title>Patient consent statement</title>",
"<p id=\"Par25\">We obtained patient consent by verbal explanation.</p>",
"<title>Permission to reproduce material from other sources</title>",
"<p id=\"Par26\">Not applicable</p>",
"<title>Authors’ contributio... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Patient flowchart</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Distribution of surgical Apgar score</p></caption></fig>"
] | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Preoperative and intraoperative characteristics</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>Total (<italic>n</italic>=210)</th><th>Robust (<italic>n</italic>=165)</th><th>Frailty (<italic>n</italic>=45)</th><th>P-value</th></tr... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>"
] | [
"<table-wrap-foot><p>Median [interquartile range] or number (%) <italic>ASA-PS</italic> American Society of Anesthesiologists-physical status: <italic>PCEA</italic> patient-controlled epidural analgesia: <italic>IV-PCA</italic> intravenous patient-controlled analgesia</p></table-wrap-foot>",
"<table-wrap-foot><p>... | [
"<graphic xlink:href=\"40981_2024_687_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"40981_2024_687_Fig2_HTML\" id=\"MO2\"/>"
] | [
"<media xlink:href=\"40981_2024_687_MOESM1_ESM.pptx\"><caption><p><bold>Additional file 1.</bold>\n</p></caption></media>",
"<media xlink:href=\"40981_2024_687_MOESM2_ESM.docx\"><caption><p><bold>Additional file 2.</bold>\n</p></caption></media>"
] | [{"label": ["8."], "surname": ["Lone", "Campbell", "Corrigan", "Ramkumar", "Hegde", "Rahmy"], "given-names": ["Z", "RA", "D", "R", "P", "A"], "article-title": ["Ability of the surgical Apgar score to predict acute kidney injury following radical cystectomy"], "source": ["Urol Oncol."], "year": ["2022"], "volume": ["40"], "issue": ["194"], "fpage": ["e1"], "lpage": ["e6"]}, {"label": ["9."], "surname": ["Sugimoto", "Fukuoka", "Nagahara", "Shibutani", "Iseki", "Wang"], "given-names": ["A", "T", "H", "M", "Y", "E"], "article-title": ["The surgical Apgar score predicts postoperative complications in elderly patients after surgery for colorectal cancer"], "source": ["Ann Med Surg."], "year": ["2021"], "volume": ["16"], "fpage": ["31348211038576"]}, {"label": ["10."], "mixed-citation": ["Kinugasa Y, Ida M, Nakatani S, Uyama K, Kawaguchi M. Quality of recovery in hospital and disability-free survival at 3 months after major abdominal surgery. Korean J Anesthesiol. 2023; 10.4097/kja.23082."]}] | {
"acronym": [
"SAS"
],
"definition": [
"surgical Apgar score"
]
} | 17 | CC BY | no | 2024-01-15 23:41:54 | JA Clin Rep. 2024 Jan 13; 10:2 | oa_package/55/72/PMC10787715.tar.gz |
PMC10787724 | 38217751 | [
"<title>Introduction</title>",
"<p id=\"Par24\">Spinal fusion surgery has been a standard of care for lumbar degenerative diseases refractory to conservative treatment and can produce satisfactory clinical results [##REF##29524706##1##]. However, lumbar arthrodesis may increase biomechanical stress on the levels ... | [
"<title>Materials and methods</title>",
"<title>Patients</title>",
"<p id=\"Par27\">After the approval of Institutional Review Board, a retrospective review of one database comprising patients with lumbar degenerative diseases between January 2009 and January 2018 was performed. The patient enrollment criteria ... | [
"<title>Results</title>",
"<title>Patients</title>",
"<p id=\"Par35\">A total of 234 consecutive patients were enrolled in this study. The average age at the index surgery was 60.1 years (range, 41–78 years). The fusion level was L4–5 in 118 and L3–5 in 116 cases, respectively. With a mean follow-up duration of... | [
"<title>Discussion</title>",
"<p id=\"Par41\">Although the importance of spinopelvic alignment and its correlation with ASD have been validated in many studies, the “normal” alignment remains poorly defined. Previous studies have investigated the relationship between PI − LL mismatch and the occurrence of ASD. In... | [
"<title>Conclusion</title>",
"<p id=\"Par46\">In summary, loss of DL and mismatched Roussouly type were significant risk factors affecting the occurrence of ASD after short-level fusion surgery for lumbar degenerative diseases. In pathologic patients, PI is a reliable index for classifying sagittal types, rather ... | [
"<title>Background</title>",
"<p id=\"Par1\">Recent studies demonstrated that restoring sagittal alignment to the original Roussouly type can remarkably reduce complication rates after adult spinal deformity surgery. However, there is still no data proving the benefit of maintaining ideal Roussouly shape in the l... | [] | [
"<title>Acknowledgements</title>",
"<p>Not applicable</p>",
"<title>Author contributions</title>",
"<p>All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by M.W., X.W., and H.W. The first draft of the manuscript was written by M.W., an... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Linear regression between lumbar sagittal parameters (LDI and PI − LL) and PI. <italic>LDI</italic> lordosis distribution index, <italic>PI</italic> pelvic incidence, <italic>LL</italic> lumbar lordosis</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Comparison of demographic and clinical characteristics between the non-ASD and ASD groups</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Non-ASD</th><th align=\"left\">ASD</th><th align=\"left\"><ital... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>Values are presented as number (%) or mean ± standard deviation unless otherwise indicated</p><p><italic>ASD</italic> adjacent segment disease, <italic>BMI</italic> body mass index</p></table-wrap-foot>",
"<table-wrap-foot><p>Values are presented as number (%) or mean ± standard deviation unl... | [
"<graphic xlink:href=\"10195_2023_744_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"10195_2023_744_Fig2_HTML\" id=\"MO2\"/>",
"<graphic xlink:href=\"10195_2023_744_Fig3_HTML\" id=\"MO3\"/>",
"<graphic xlink:href=\"10195_2023_744_Fig4_HTML\" id=\"MO4\"/>"
] | [] | [] | {
"acronym": [
"ASD",
"BMI",
"PI",
"PT",
"SS",
"LL",
"DL",
"SL",
"LDI",
"SVA",
"NVL",
"TLIF",
"CT",
"MRI",
"OR",
"CI",
"ICC",
"ANOVA"
],
"definition": [
"Adjacent segment disease",
"Body mass index",
"Pelvic incidence",
"Pelvic tilt",
"Sacral slope",
"Lumbar lordosis",
"Distal lordosis",
"Segmental lordosis",
"Lordosis distribution index",
"Sagittal vertical axis",
"Number of vertebrae in lordosis",
"Transforaminal lumbar interbody fusion",
"Computed tomography",
"Magnetic resonance imaging",
"Odds ratio",
"Confidence interval",
"Intraclass correlation coefficient",
"One-way analysis of variance"
]
} | 33 | CC BY | no | 2024-01-15 23:41:54 | J Orthop Traumatol. 2024 Dec 13; 25:2 | oa_package/ad/cd/PMC10787724.tar.gz |
PMC10787732 | 38218867 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Potato is the third most important commodity in the world and represents an essential energy source for human consumption. Potato tubers are processed to provide food, starch, crisps, food additives, and beverages and used for some pharmaceutical products<sup>##UREF#... | [
"<title>Materials and methods</title>",
"<title>Plant material</title>",
"<p id=\"Par8\">Samples of potato tuber were collected from two sources: (1) from Graminor’s core collection grown in field experiments at Ridabu, Norway, from 2019 to 2022, and (2) from a greenhouse inoculation experiment in which 840 int... | [
"<title>Results</title>",
"<p id=\"Par33\">ScabyNet is a user-friendly application that contains two main modules and a home tab dedicated to providing information on how to use the application. Modules 1 and 2 were designed to process images for morphology traits and CS severity. In both cases, an individual ima... | [
"<title>Discussion</title>",
"<p id=\"Par50\">In plant breeding, tuber quality, in terms of shape, size, and severity level of CS, still relies on manual measurements and low-throughput visual assessments. These approaches are known to suffer from a lack of accuracy and reproducibility on top of being time-consum... | [
"<title>Conclusion</title>",
"<p id=\"Par56\">This study proposes a novel application named ScabyNet that combines traditional image processing techniques and deep learning algorithms to estimate potato tuber morphology features and the detection and severity scoring of CS disease. This approach demonstrated oper... | [
"<p id=\"Par1\">Common scab (CS) is a major bacterial disease causing lesions on potato tubers, degrading their appearance and reducing their market value. To accurately grade scab-infected potato tubers, this study introduces “ScabyNet”, an image processing approach combining color-morphology analysis with deep le... | [
"<title>Supplementary Information</title>",
"<p>\n</p>"
] | [
"<title>Supplementary Information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41598-023-51074-4.</p>",
"<title>Acknowledgements</title>",
"<p>We would like to thank Anja Haneberg (Graminor) for providing significant technical and laboratory assistance throughout this... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Tile images were used as a training set for the deep learning process. The tiles correspond to a whole potato tuber. (<bold>A</bold>) and (<bold>C</bold>) correspond to a yellow and a red tuber respectively with no symptoms of common scab (CS); (<bold>B</bold>) a... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Comparison of the six CNN architectures used in the benchmark.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">CNN Architectures</th><th align=\"left\">Description</th><th align=\"left\">References</th></tr></thead><tbody... | [
"<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepack... | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"41598_2023_51074_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"41598_2023_51074_Fig2_HTML\" id=\"MO2\"/>",
"<graphic xlink:href=\"41598_2023_51074_Fig3_HTML\" id=\"MO3\"/>",
"<graphic xlink:href=\"41598_2023_51074_Fig4_HTML\" id=\"MO4\"/>",
"<graphic xlink:href=\"41598_2023_51074_... | [
"<media xlink:href=\"41598_2023_51074_MOESM1_ESM.docx\"><caption><p>Supplementary Figure 1.</p></caption></media>"
] | [{"label": ["1."], "surname": ["Rady", "Guyer"], "given-names": ["AM", "DE"], "article-title": ["Rapid and/or nondestructive quality evaluation methods for potatoes: A review"], "source": ["Comput. Electron. Agric."], "year": ["2015"], "volume": ["117"], "fpage": ["31"], "lpage": ["48"], "pub-id": ["10.1016/j.compag.2015.07.002"]}, {"label": ["2."], "mixed-citation": ["Carputo, D., R. Aversano, and L. Frusciante. "], "italic": ["Breeding potato for quality traits", "Meeting of the Physiology Section of the European Association for Potato Research 684"]}, {"label": ["3."], "surname": ["Storey"], "given-names": ["M"], "article-title": ["The harvested crop"], "source": ["Potato Biology and Biotechnology"], "year": ["2007"], "publisher-name": ["Elsevier"], "fpage": ["441"], "lpage": ["470"]}, {"label": ["6."], "surname": ["Braun"], "given-names": ["S"], "article-title": ["Potato common scab: A review of the causal pathogens, management practices, varietal resistance screening methods, and host resistance"], "source": ["Am. J. Potato Res."], "year": ["2017"], "volume": ["94"], "fpage": ["283"], "lpage": ["296"], "pub-id": ["10.1007/s12230-017-9575-3"]}, {"label": ["7."], "mixed-citation": ["Zitter, T.A. and R. Loria, "], "italic": ["Detection of potato tuber diseases and defects."]}, {"label": ["8."], "surname": ["Buhrig"], "given-names": ["W"], "article-title": ["The influence of ethephon application timing and rate on plant growth, yield, tuber size distribution and skin color of red LaSoda potatoes"], "source": ["Am. J. Potato Res."], "year": ["2015"], "volume": ["92"], "fpage": ["100"], "lpage": ["108"], "pub-id": ["10.1007/s12230-014-9417-5"]}, {"label": ["11."], "surname": ["Neilson"], "given-names": ["JA"], "article-title": ["Potato tuber shape phenotyping using RGB imaging"], "source": ["Agronomy"], "year": ["2021"], "volume": ["11"], "issue": ["9"], "fpage": ["1781"], "pub-id": ["10.3390/agronomy11091781"]}, {"label": ["12."], "surname": ["Miller"], "given-names": ["MD"], "article-title": ["TubAR: An R package for quantifying tuber shape and skin traits from images"], "source": ["Am. J. Potato Res."], "year": ["2022"], "volume": ["100"], "fpage": ["52"], "pub-id": ["10.1007/s12230-022-09894-z"]}, {"label": ["13."], "surname": ["Si"], "given-names": ["Y"], "article-title": ["Potato tuber length-width ratio assessment using image analysis"], "source": ["Am. J. Potato Res."], "year": ["2017"], "volume": ["94"], "fpage": ["88"], "lpage": ["93"], "pub-id": ["10.1007/s12230-016-9545-1"]}, {"label": ["14."], "surname": ["Caraza-Harter", "Endelman"], "given-names": ["MV", "JB"], "article-title": ["Image-based phenotyping and genetic analysis of potato skin set and color"], "source": ["Crop Sci."], "year": ["2020"], "volume": ["60"], "issue": ["1"], "fpage": ["202"], "lpage": ["210"], "pub-id": ["10.1002/csc2.20093"]}, {"label": ["15."], "surname": ["Barbedo"], "given-names": ["JGA"], "article-title": ["A review on the main challenges in automatic plant disease identification based on visible range images"], "source": ["Biosyst. Eng."], "year": ["2016"], "volume": ["144"], "fpage": ["52"], "lpage": ["60"], "pub-id": ["10.1016/j.biosystemseng.2016.01.017"]}, {"label": ["16."], "mixed-citation": ["Kool, J., T. Been, and A. Evenhuis. "], "italic": ["Detection of Latent Potato Late Blight by Hyperspectral Imaging", "2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)"]}, {"label": ["18."], "surname": ["Samanta", "Chaudhury", "Ghosh"], "given-names": ["D", "PP", "A"], "article-title": ["Scab diseases detection of potato using image processing"], "source": ["Int. J. Comput. Trends Technol."], "year": ["2012"], "volume": ["3"], "issue": ["1"], "fpage": ["109"], "lpage": ["113"]}, {"label": ["19."], "surname": ["Khan", "Muselet", "Tr\u00e9meau"], "given-names": ["R", "D", "A"], "article-title": ["Texture classification across illumination color variations"], "source": ["Int. J. Comput. Theory Eng."], "year": ["2013"], "volume": ["5"], "issue": ["1"], "fpage": ["65"], "pub-id": ["10.7763/IJCTE.2013.V5.648"]}, {"label": ["20."], "mixed-citation": ["Dacal-Nieto, A., et al. "], "italic": ["Common scab detection on potatoes using an infrared hyperspectral imaging system", "Image Analysis and Processing\u2013ICIAP 2011: 16th International Conference, Ravenna, Italy, September 14\u201316, 2011, Proceedings, Part II 16"]}, {"label": ["25."], "surname": ["Breiman"], "given-names": ["L"], "article-title": ["Random forests"], "source": ["Mach. Learn."], "year": ["2001"], "volume": ["45"], "fpage": ["5"], "lpage": ["32"], "pub-id": ["10.1023/A:1010933404324"]}, {"label": ["26."], "mixed-citation": ["Van Rossum, G. and F.L. Drake, "], "italic": ["Python reference manual"]}, {"label": ["27."], "mixed-citation": ["Bradski, G. and A. Kaehler, "], "italic": ["OpenCV."], "bold": ["3"]}, {"label": ["28."], "mixed-citation": ["Abadi, M., et al., "], "italic": ["Tensorflow: Large-scale machine learning on heterogeneous distributed systems."], "ext-link": ["arXiv:1603.04467"]}, {"label": ["29."], "mixed-citation": ["Shipman, J.W., "], "italic": ["Tkinter 8.5 reference: A GUI for Python."], "bold": ["54"]}, {"label": ["30."], "mixed-citation": ["Schimansky, T. "], "italic": ["CustomTkinter"], "ext-link": ["https://github.com/TomSchimansky/CustomTkinter"]}, {"label": ["31."], "mixed-citation": ["Luo, M.R., "], "italic": ["CIELAB", "Encyclopedia of Color Science and Technology"]}, {"label": ["32."], "surname": ["Burtsev", "Kuzmin"], "given-names": ["S", "YP"], "article-title": ["An efficient flood-filling algorithm"], "source": ["Comput. Graph."], "year": ["1993"], "volume": ["17"], "issue": ["5"], "fpage": ["549"], "lpage": ["561"], "pub-id": ["10.1016/0097-8493(93)90006-U"]}, {"label": ["33."], "mixed-citation": ["Serra, J.P.F. "], "italic": ["Image Analysis and Mathematical Morphology"]}, {"label": ["34."], "surname": ["Sklansky"], "given-names": ["J"], "article-title": ["Finding the convex hull of a simple polygon"], "source": ["Pattern Recognit. Lett."], "year": ["1982"], "volume": ["1"], "issue": ["2"], "fpage": ["79"], "lpage": ["83"], "pub-id": ["10.1016/0167-8655(82)90016-2"]}, {"label": ["35."], "surname": ["Beucher"], "given-names": ["S"], "article-title": ["The watershed transformation applied to image segmentation"], "source": ["Scann. Microsc."], "year": ["1992"], "volume": ["1992"], "issue": ["6"], "fpage": ["28"]}, {"label": ["36."], "surname": ["Najman", "Couprie", "Algorithms", "Preservation"], "given-names": ["L", "M", "W", "C"], "source": ["Watershed Algorithms and Contrast Preservation"], "year": ["2003"], "publisher-name": ["Springer"]}, {"label": ["37."], "surname": ["Kasuga", "Yamamoto", "Okamoto"], "given-names": ["H", "H", "M"], "article-title": ["Color quantization using the fast K-means algorithm"], "source": ["Syst. Comput. Jpn."], "year": ["2000"], "volume": ["31"], "issue": ["8"], "fpage": ["33"], "lpage": ["40"], "pub-id": ["10.1002/1520-684X(200007)31:8<33::AID-SCJ4>3.0.CO;2-C"]}, {"label": ["38."], "mixed-citation": ["Muhammed, M.A.E., A.A. Ahmed, and T.A. Khalid. "], "italic": ["Benchmark analysis of popular imagenet classification deep cnn architectures", "2017 International Conference on Smart Technologies for (SmartTechCon)"]}, {"label": ["39."], "mixed-citation": ["Deng, J., et al., "], "italic": ["Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"]}, {"label": ["40."], "mixed-citation": ["Simonyan, K. and A. Zisserman, "], "italic": ["Very deep convolutional networks for large-scale image recognition."], "ext-link": ["arXiv:1409.1556"]}, {"label": ["41."], "mixed-citation": ["He, K., et al. "], "italic": ["Deep residual learning for image recognition", "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"]}, {"label": ["42."], "mixed-citation": ["Szegedy, C., et al. "], "italic": ["Rethinking the inception architecture for computer vision", "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"]}, {"label": ["43."], "mixed-citation": ["Chollet, F. "], "italic": ["Xception: Deep learning with depthwise separable convolutions", "Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"]}, {"label": ["44."], "mixed-citation": ["Guo, Y., et al. "], "italic": ["Spottune: Transfer learning through adaptive fine-tuning", "Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition"]}, {"label": ["45."], "mixed-citation": ["Peng, P. and J. Wang, "], "italic": ["How to fine-tune deep neural networks in few-shot learning?"], "ext-link": ["arXiv:2012.00204"]}, {"label": ["46."], "mixed-citation": ["Team, R.C., "], "italic": ["R: A language and environment for statistical computing."]}, {"label": ["47."], "mixed-citation": ["Van Rossum, G. & F.L. Drake Jr, "], "italic": ["Python tutorial"]}, {"label": ["48."], "surname": ["Pedregosa"], "given-names": ["F"], "article-title": ["Scikit-learn: Machine learning in python"], "source": ["J. Mach. Learn. Res."], "year": ["2011"], "volume": ["12"], "fpage": ["2825"], "lpage": ["2830"]}, {"label": ["49."], "surname": ["Si"], "given-names": ["Y"], "article-title": ["Image-based automated potato tuber shape evaluation"], "source": ["J. Food Measur. Charact."], "year": ["2018"], "volume": ["12"], "fpage": ["702"], "lpage": ["709"], "pub-id": ["10.1007/s11694-017-9683-2"]}, {"label": ["50."], "surname": ["Leiva"], "given-names": ["F"], "article-title": ["Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging"], "source": ["Innov. Imag. Tech. Plant Sci."], "year": ["2022"], "volume": ["16648714"], "fpage": ["142"]}, {"label": ["51."], "mixed-citation": ["Cgrain_AB. "], "italic": ["Cgrain Value TM, The new standard for analysis grain quality"], "ext-link": ["www.cgrain.se"]}, {"label": ["52."], "mixed-citation": ["Hughes, D. and M. Salath\u00e9, "], "italic": ["An open access repository of images on plant health to enable the development of mobile disease diagnostics."], "ext-link": ["arXiv:1511.08060"]}, {"label": ["53."], "surname": ["Pound"], "given-names": ["MP"], "article-title": ["Deep machine learning provides state-of-the-art performance in image-based plant phenotyping"], "source": ["Gigascience"], "year": ["2017"], "volume": ["6"], "issue": ["10"], "fpage": ["gix083"], "pub-id": ["10.1093/gigascience/gix083"]}] | {
"acronym": [],
"definition": []
} | 53 | CC BY | no | 2024-01-15 23:41:54 | Sci Rep. 2024 Jan 13; 14:1277 | oa_package/c6/3f/PMC10787732.tar.gz |
PMC10787733 | 38218751 | [] | [
"<title>Methods</title>",
"<title>Deep convolutional neural networks for face memorability prediction</title>",
"<p id=\"Par22\">It is common knowledge that deep convolutional neural networks have great power in different machine learning tasks like regression and classification. In this work, we trained two mo... | [
"<title>Results</title>",
"<p id=\"Par9\">This section presents the key findings and discoveries obtained from the experimental analysis of training models for face photographs memorability prediction. First, we demonstarte that the current memorability models that are trained on LaMem fail in predicting face mem... | [
"<title>Discussion</title>",
"<p id=\"Par18\">Focusing on face images, we showed the existing models used in predicting image memorability fail to predict face memorability scores. We specifically first evaluated MemNet<sup>##UREF##7##11##</sup>, which is trained on LaMem<sup>##UREF##7##11##</sup> dataset. Moreov... | [] | [
"<p id=\"Par1\">With the advent of social media in our daily life, we are exposed to a plethora of images, particularly face photographs, every day. Recent behavioural studies have shown that some of these photographs stick in the mind better than others. Previous research have shown that memorability is an intrins... | [
"<p id=\"Par2\">Every day, we meet new people or encounter new faces on social media. Some of these faces stick in our minds, while others are forgotten quickly. Image memorability is the probability that an observer will detect the repetition of an image after a single exposure to that image when presented amidst ... | [
"<title>Acknowledgements</title>",
"<p>This study was supported by the Canada First Research Excellence Fund (CFREF) through Western’s BrainsCAN Initiative and a Vector Institute Research Grant to YM. The Computational modeling was conducted on Compute Canada resources. MY was supported by a Vector Institute Mast... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Architecture of MemNet.</p></caption></fig>",
"<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Architecture of MemVGG.</p></caption></fig>",
"<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Architecture of IncResMem. The input image goes through two p... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Spearman’s correlation score between predicted memorability scores and ground-truth memorability scores is reported in two categories-hit rate and true hit rate (corrected hit rate) obtained by the three proposed Memorability models on 10k US Faces Database... | [
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"<table-wrap-foot><p>Fine-tuned MemNet model (FaceMemNet) performed better than FaceMemVGG and FaceInceptionResMem in predicting face memorability scores. The best performance is highlighted in bold text.</p></table-wrap-foot>",
"<table-wrap-foot><p>The results are close to each other, however we can observe that... | [
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} | 41 | CC BY | no | 2024-01-15 23:41:54 | Sci Rep. 2024 Jan 13; 14:1246 | oa_package/bb/1d/PMC10787733.tar.gz |
PMC10787734 | 38218969 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Correct and cost-effective species identification is crucial in various research areas, including biodiversity assessments, where obtaining reliable information on species’ occurrences and distributions is pivotal. If species cannot be morphologically identified to t... | [
"<title>Methods</title>",
"<title>Sample material</title>",
"<p id=\"Par28\">Tissue for measurements was taken from the marine organisms tissue bank of the Senckenberg am Meer, German Centre for Marine Biodiversity Research, which was established using samples from numerous studies<sup>##UREF##11##30##,##UREF##... | [
"<title>Results</title>",
"<p id=\"Par6\">The data set contained 1246 specimens from 198 taxa including echinoderms (Asteroidea, Echinoidea and Ophiuroidea), molluscs (Bivalvia, Gastropoda, Polyplacophora and Cephalopoda), arthropods (Crustacea, Pantopoda) and chordates (Tunicata, Vertebrata: Teleostei, Elasmobra... | [
"<title>Discussion</title>",
"<p id=\"Par18\">The aims of this study were (1) to evaluate the wide applicability of proteomic fingerprinting for species identification in marine science across different metazoan phyla and classes, (2) to identify critical steps in sample preparation and data processing, and (3) t... | [
"<title>Conclusion</title>",
"<p id=\"Par27\">MALDI-TOF MS was proven an easy to apply, cost-effective and time-saving tool for identification across taxa. It is especially feasible in applications where mere species identification is desired, for example in biodiversity assessments By the standardized workflow b... | [
"<p id=\"Par1\">Proteomic fingerprinting using MALDI-TOF mass spectrometry is a well-established tool for identifying microorganisms and has shown promising results for identification of animal species, particularly disease vectors and marine organisms. And thus can be a vital tool for biodiversity assessments in e... | [
"<title>Supplementary Information</title>",
"<p>\n</p>"
] | [
"<title>Supplementary Information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41598-024-51235-z.</p>",
"<title>Acknowledgements</title>",
"<p>The authors thank Michael Raupach, Thomas Knebelsberger, Andrea Barco and their collaborators, students and technicians for b... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Results of the sample preparation test. All graphs show peak intensity on the y-axis. In (<bold>A</bold>), x-axis represents tissue:matrix ratio in µg per µl. In (<bold>B–D</bold>) m/z values (ratio of molecule mass and loading) are depicted on the x-axis. (<bold... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Results of the GAM analyses to detect the most important variable for data processing optimization.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Estimate</th><th align=\"left\">Std.error</th><th ali... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>",
"<supplementary-material ... | [
"<table-wrap-foot><p>Asterisks indicate levels of significance. * indicates P ≤ 0.05; ** indicates P ≤ 0.01; *** indicates P ≤ 0.001.</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institution... | [
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"<media xlink:href=\"41598_2024_51235_MOESM1_ESM.docx\"><caption><p>Supplementary Legends.</p></caption></media>",
"<media xlink:href=\"41598_2024_51235_MOESM2_ESM.png\"><caption><p>Supplementary Figure 1.</p></caption></media>",
"<media xlink:href=\"41598_2024_51235_MOESM3_ESM.png\"><caption><p>Supplementary F... | [{"label": ["1."], "surname": ["Bailey", "Norris", "Reynoldson"], "given-names": ["RC", "RH", "TB"], "article-title": ["Taxonomic resolution of benthic macroinvertebrate communities in bioassessments"], "source": ["J. N. Am. Benthol. Soc."], "year": ["2001"], "volume": ["20"], "fpage": ["280"], "lpage": ["286"], "pub-id": ["10.2307/1468322"]}, {"label": ["2."], "surname": ["Timms", "Bowden", "Summerville", "Buddle"], "given-names": ["LL", "JJ", "KS", "CM"], "article-title": ["Does species-level resolution matter? Taxonomic sufficiency in terrestrial arthropod biodiversity studies"], "source": ["Insect Conserv. 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"acronym": [],
"definition": []
} | 66 | CC BY | no | 2024-01-15 23:41:54 | Sci Rep. 2024 Jan 13; 14:1280 | oa_package/ce/70/PMC10787734.tar.gz |
PMC10787735 | 38218913 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Cementless Oxford mobile-bearing unicompartmental knee arthroplasty (OUKA) reportedly achieves comparable clinical results compared with cemented OUKA, with markedly reduced radiolucent lines<sup>##REF##23925740##1##</sup>. However, medial tibial plateau fracture is ... | [
"<title>Materials and methods</title>",
"<p id=\"Par4\">This research has been approved by the institutional review board of Takatsuki general hospital (No. 2020-14). All methods were carried out in accordance with relevant guidelines and regulations. We studied 51 unilateral lower limbs in 51 consecutive patient... | [
"<title>Results</title>",
"<p id=\"Par11\">For all subjects, significantly shorter KCDs and larger over-coverage in valgus3 were found compared with the others (neutral, varus3, and varus6) (<italic>P</italic> < 0.0083 after Bonferroni correction; Table ##TAB##0##1##). Posterior KCDs showed lower values in neu... | [
"<title>Discussion</title>",
"<p id=\"Par18\">In this 3D simulation study, the KCD was longer and the over-coverage was smaller in accordance with varus implantation. Meanwhile, a valgus implantation shortened the KCD and increased the amount of over-coverage. Based on these results, a varus implantation seems to... | [
"<title>Conclusions</title>",
"<p id=\"Par23\">In OUKA, varus implantation increased the KCD and this may decrease the risk of fracture, even in knees with overhanging medial condyle. By contrast, the KCD is shortened by valgus alignment of the tibial component, which increases over-coverage, so this alignment sh... | [
"<p id=\"Par1\">A short keel-cortex distance (KCD), especially to the posterior cortex, is a potential risk factor for tibial plateau fracture after Oxford mobile-bearing unicompartmental knee arthroplasty (OUKA). This study aimed to evaluate the effect of tibial component alignment in the coronal plane and tibial ... | [] | [
"<title>Acknowledgements</title>",
"<p>The authors would like to thank Mr. Benjamin Phillis at the Clinical Study Support Center, Wakayama Medical University for proofreading and editing.</p>",
"<title>Author contributions</title>",
"<p>T.H. and R.K. conceived of the research idea. T.K. and T.H. designed the ... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Images showing the setting of the tibial component coronal alignment. The tibial component neutral position was set perpendicular to the tibial mechanical axis (<bold>A</bold>). The tibial component varus (<bold>B</bold>)/valgus (<bold>C</bold>) alignment was adj... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Reliability of all measurements when the component was set in neutral position.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">ICC Intra-rater (95% CI)</th><th align=\"left\">ICC Inter-rater (95% CI)<... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p><italic>KCD</italic> keel-cortex distance, <italic>ICC</italic> intraclass correlation coefficient, <italic>CI</italic> confidence interval.</p></table-wrap-foot>",
"<table-wrap-foot><p>Date presented as mean (standard deviation).</p><p><italic>KCD</italic> keel-cortex distance.</p><p>*Signif... | [
"<graphic xlink:href=\"41598_2023_48659_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"41598_2023_48659_Fig2_HTML\" id=\"MO2\"/>",
"<graphic xlink:href=\"41598_2023_48659_Fig3_HTML\" id=\"MO3\"/>",
"<graphic xlink:href=\"41598_2023_48659_Fig4_HTML\" id=\"MO4\"/>",
"<graphic xlink:href=\"41598_2023_48659_... | [] | [{"label": ["2."], "surname": ["Rudol", "Jackson", "James"], "given-names": ["G", "MP", "SE"], "article-title": ["Medial tibial plateau fracture complicating unicompartmental knee arthroplasty"], "source": ["J. Arthroplast."], "year": ["2007"], "volume": ["22"], "fpage": ["148"], "lpage": ["150"], "pub-id": ["10.1016/j.arth.2006.01.005"]}, {"label": ["6."], "surname": ["Song", "Kim", "Ahn", "Yoo", "Lee"], "given-names": ["MH", "BH", "SJ", "SH", "MS"], "article-title": ["Early complications after minimally invasive mobile-bearing medial unicompartmental knee arthroplasty"], "source": ["J. Arthroplast."], "year": ["2009"], "volume": ["24"], "fpage": ["1281"], "lpage": ["1284"], "pub-id": ["10.1016/j.arth.2009.07.012"]}, {"label": ["20."], "surname": ["Akagi"], "given-names": ["M"], "article-title": ["An anteroposterior axis of the tibia for total knee arthroplasty"], "source": ["Clin. Orthop. Relat. Res"], "year": ["2004"], "volume": ["420"], "fpage": ["213"], "lpage": ["219"], "pub-id": ["10.1097/00003086-200403000-00030"]}, {"label": ["25."], "surname": ["Inoue"], "given-names": ["S"], "article-title": ["The valgus inclination of the tibial component increases the risk of medial tibial condylar fractures in unicompartmental knee arthroplasty"], "source": ["J. Arthroplast."], "year": ["2016"], "volume": ["31"], "fpage": ["2025"], "lpage": ["2030"], "pub-id": ["10.1016/j.arth.2016.02.043"]}, {"label": ["30."], "surname": ["Nagamine"], "given-names": ["R"], "article-title": ["Medial torsion of the tibia in Japanese patients with osteoarthritis of the knee"], "source": ["Clin. Orthop. Relat. Res."], "year": ["2003"], "volume": ["408"], "fpage": ["218"], "lpage": ["224"], "pub-id": ["10.1097/00003086-200303000-00028"]}, {"label": ["31."], "surname": ["Matsuda"], "given-names": ["S"], "article-title": ["Tibial shaft axis does not always serve as a correct coronal landmark in total knee arthroplasty for varus knees"], "source": ["J. Arthroplast."], "year": ["2003"], "volume": ["18"], "fpage": ["56"], "lpage": ["62"], "pub-id": ["10.1054/arth.2003.50002"]}] | {
"acronym": [],
"definition": []
} | 31 | CC BY | no | 2024-01-15 23:41:54 | Sci Rep. 2024 Jan 13; 14:1274 | oa_package/75/94/PMC10787735.tar.gz |
PMC10787736 | 38218987 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Recently, there has been an increase in the sales and consumption of herbal supplements for complementary health<sup>##UREF##0##1##</sup>. However, there are gaps in the current understanding of the safety concerns from the use of herbal or natural products (NPs), in... | [
"<title>Methods</title>",
"<title>Data collection</title>",
"<p id=\"Par13\">The first data source was the Center for Excellence for Natural Product-Drug Interaction Research (NaPDI) Database, from which we collected the known product names of several NPs, some of the previously identified spelling variations, ... | [
"<title>Results</title>",
"<title>Model training results</title>",
"<p id=\"Par25\">After training the SM for up to five hundred (500) epochs, the model terminated early at seventeen (17) epochs (Fig. ##FIG##3##4##). The best-performing epoch in this training run achieved a validation accuracy of 0.97 (validati... | [
"<title>Discussion</title>",
"<p id=\"Par30\">This study combined fuzzy string-matching and Siamese neural network approaches to identify NP names in adverse event reports in the FAERS database and successfully broadened the capture of NP reports by approximately 7.5%. Prior work in using string matching methods ... | [
"<title>Conclusion</title>",
"<p id=\"Par36\">A SM was trained to identify potential spelling variations of NP names. The SM model training terminated early at seventeen (17) epochs, achieving a validation accuracy of 0.97. In MRR evaluation, the SM performance was, at most, comparable to that of the fuzzy string... | [
"<p id=\"Par1\">Increased sales of natural products (NPs) in the US and growing safety concerns highlight the need for NP pharmacovigilance. A challenge for NP pharmacovigilance is ambiguity when referring to NPs in spontaneous reporting systems. We used a combination of fuzzy string-matching and a neural network t... | [
"<title>Supplementary Information</title>",
"<p>\n</p>"
] | [
"<title>Supplementary Information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41598-023-51004-4.</p>",
"<title>Acknowledgements</title>",
"<p>We take this opportunity to acknowledge the excellent work from our collaborators from the University of Pittsburgh School of... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Example of Fuzzy String-Matching (GPM): Finding the similarity between the terms S<sub>1</sub>: “ZINGIBER” and S<sub>2</sub>: “GINGER” using the Gestalt Pattern-Matching approach. The longest matching substring K<sub>1</sub>: “ING” servers as an anchor to align t... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Train-validation data summary.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Matching*</th><th align=\"left\">Non-Matching*</th><th align=\"left\">Total</th></tr></thead><tbody><tr><td align=\"left\"... | [
"<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n... | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>*In the columns for Matching and Non-Matching, the first number represents the actual count number, and the second number represents what percentage of set that first number accounts for.</p></table-wrap-foot>",
"<table-wrap-foot><p>Significant values are in bold.</p></table-wrap-foot>",
"<... | [
"<inline-graphic xlink:href=\"41598_2023_51004_Article_IEq1.gif\"/>",
"<inline-graphic xlink:href=\"41598_2023_51004_Article_IEq2.gif\"/>",
"<inline-graphic xlink:href=\"41598_2023_51004_Article_IEq3.gif\"/>",
"<inline-graphic xlink:href=\"41598_2023_51004_Article_IEq4.gif\"/>",
"<inline-graphic xlink:href=... | [
"<media xlink:href=\"41598_2023_51004_MOESM1_ESM.docx\"><caption><p>Supplementary Information.</p></caption></media>"
] | [{"label": ["1."], "mixed-citation": ["Smith, T., Resetar, H. & Morton, C. US Sales of Herbal Supplements Increase by 9.7% in 2021. "], "italic": ["J. Am. Botanical Council"], "bold": ["19"]}, {"label": ["2."], "surname": ["Sharma", "Sarkar"], "given-names": ["V", "IN"], "article-title": ["Identifying natural health product and dietary supplement information within adverse event reporting systems"], "source": ["Biocomputing"], "year": ["2017"], "volume": ["2018"], "fpage": ["268"], "lpage": ["279"], "pub-id": ["10.1142/9789813235533_0025"]}, {"label": ["3."], "surname": ["Leite", "Martins", "de Gra\u00e7asCarvalho", "Castilho"], "given-names": ["PM", "MAP", "M", "RO"], "article-title": ["Mechanisms and interactions in concomitant use of herbs and warfarin therapy: An updated review"], "source": ["Biomed. Pharmacotherapy"], "year": ["2021"], "volume": ["143"], "fpage": ["112103"], "pub-id": ["10.1016/j.biopha.2021.112103"]}, {"label": ["4."], "surname": ["Gouws", "Hamman"], "given-names": ["C", "JH"], "article-title": ["What are the dangers of drug interactions with herbal medicines?"], "source": ["Expert Opinion Drug Metabolism Toxicol."], "year": ["2020"], "volume": ["16"], "fpage": ["165"], "lpage": ["167"], "pub-id": ["10.1080/17425255.2020.1733969"]}, {"label": ["6."], "mixed-citation": ["Natural Products Research\u2014Information for Researchers. "], "italic": ["NCCIH"], "ext-link": ["https://www.nccih.nih.gov/grants/natural-products-research-information-for-researchers"]}, {"label": ["7."], "mixed-citation": ["Research, C. FDA Adverse Event Reporting System (FAERS) Public Dashboard. "], "italic": ["FDA"]}, {"label": ["11."], "mixed-citation": ["difflib \u2014 Helpers for computing deltas. "], "italic": ["Python documentation"], "ext-link": ["https://docs.python.org/3/library/difflib.html"]}, {"label": ["12."], "surname": ["Chicco", "Cartwright"], "given-names": ["D", "H"], "article-title": ["Siamese Neural Networks: An Overview"], "source": ["Artificial Neural Networks"], "year": ["2021"], "publisher-name": ["Springer US"], "fpage": ["73"], "lpage": ["94"]}, {"label": ["13."], "mixed-citation": ["Koch, G., Zemel, R. & Salakhutdinov, R. Siamese Neural Networks for One-shot Image Recognition. 8."]}, {"label": ["15."], "mixed-citation": ["Team, K. Keras documentation: Embedding layer. "], "ext-link": ["https://keras.io/api/layers/core_layers/embedding/"]}, {"label": ["17."], "mixed-citation": ["tf.edit_distance | TensorFlow v2.11.0. "], "ext-link": ["https://www.tensorflow.org/api_docs/python/tf/edit_distance"]}, {"label": ["18."], "surname": ["Craswell", "Liu", "\u00d6zsu"], "given-names": ["N", "L", "MT"], "article-title": ["Mean Reciprocal Rank"], "source": ["Encyclopedia of Database Systems"], "year": ["2009"], "publisher-name": ["Springer, US"], "fpage": ["1703"], "lpage": ["1703"]}, {"label": ["19."], "mixed-citation": ["Smith, T., Majid, F., Eckl, V. & Reynolds, C. M. Herbal Supplement Sales in US Increase by Record-Breaking 17.3% in 2020. 14."]}] | {
"acronym": [],
"definition": []
} | 21 | CC BY | no | 2024-01-15 23:41:55 | Sci Rep. 2024 Jan 13; 14:1272 | oa_package/f6/07/PMC10787736.tar.gz |
PMC10787737 | 38218750 | [
"<title>Background</title>",
"<p id=\"Par20\">In competition, physique athletes are subjectively judged and ranked on muscle size, proportion, symmetry, bodyfat levels, and posing ability on the day. Accordingly, stronger performers maximise these variables by implementing appropriate pre-competition nutrition an... | [] | [] | [] | [
"<title>Conclusions</title>",
"<p id=\"Par64\">Despite the extent of its effect on physique performance being largely unexplored, CHO manipulation strategies are widely employed by physique athletes [##REF##30352979##6##]. Only one quasi-experimental trial, one limited experimental trial, and few observational st... | [
"<title>Background</title>",
"<p id=\"Par1\">Physique athletes are ranked by a panel of judges against the judging criteria of the corresponding division. To enhance on-stage presentation and performance, competitors in certain categories (i.e. bodybuilding and classic physique) achieve extreme muscle size and de... | [
"<title>Carbohydrate Manipulation Practices in Endurance Athletes and Application to Physique Athletes</title>",
"<title>Carbohydrate Loading Studies in the Endurance Literature</title>",
"<p id=\"Par23\">The study of interactions between muscle glycogen content, diet, and exercise performance began with a seri... | [
"<title>Acknowledgements</title>",
"<p>Not applicable.</p>",
"<title>Author contributions</title>",
"<p>KH and EH conceived and designed this review. KH performed database searches and compiled the relevant information from the included studies. KH wrote the first draft of the manuscript. All authors critical... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Schematic representation of the glycogenesis pathway in skeletal muscle. ADP = adenosine diphosphate; AMPK = adenosine monophosphate-activated protein kinase; ATP = adenosine triphosphate; Ca<sup>2+</sup> = calcium ions; G1P = glucose-1-phosphate; G6P = glucose-6-... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Summary of reviewed cross-sectional studies relating to peak week CHO manipulation in physique athletes</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">References</th><th align=\"left\">Sample</th><th align=\"left\">Metho... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p><italic>CL</italic> carbohydrate loading, <italic>CHO</italic> carbohydrate, <italic>GI</italic> glycaemic index, <italic>M</italic> male, <italic>F</italic> female</p></table-wrap-foot>",
"<table-wrap-foot><p>CHO = carbohydrate, 4CM = four compartment model, SF = skinfold, ↑ = increased, FFM... | [
"<graphic xlink:href=\"40798_2024_674_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"40798_2024_674_Fig2_HTML\" id=\"MO2\"/>"
] | [] | [{"label": ["12."], "surname": ["Burke", "van Loon", "Hawley"], "given-names": ["LM", "LJC", "JA"], "article-title": ["Postexercise muscle glycogen resynthesis in humans"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["2017"], "issue": ["122"], "fpage": ["1055"], "lpage": ["1067"]}, {"label": ["14."], "surname": ["McCoy", "Proietto", "Hargreaves"], "given-names": ["M", "J", "M"], "article-title": ["Skeletal muscle GLUT-4 and postexercise muscle glycogen storage in humans"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["1996"], "issue": ["80"], "fpage": ["411"], "lpage": ["415"]}, {"label": ["15."], "surname": ["Tarnopolsky", "MacDougall", "Atkinson", "Tarnopolsky", "Sutton"], "given-names": ["LJ", "JD", "SA", "MA", "JR"], "article-title": ["Gender differences in substrate for endurance exercise"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["1990"], "issue": ["68"], "fpage": ["302"], "lpage": ["308"]}, {"label": ["25."], "surname": ["Adamo", "Graham"], "given-names": ["KB", "TE"], "article-title": ["Comparison of traditional measurements with macroglycogen and proglycogen analysis of muscle glycogen"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["1998"], "issue": ["84"], "fpage": ["908"], "lpage": ["913"]}, {"label": ["26."], "surname": ["Alonso", "Lomako", "Lomako", "Whelan"], "given-names": ["MD", "J", "WM", "WJ"], "article-title": ["A new look at the biogenesis of glycogen"], "source": ["FASEB J Off Publ Fed Am Soc Exp Biol"], "year": ["1995"], "volume": ["9"], "fpage": ["1126"], "lpage": ["1137"]}, {"label": ["28."], "surname": ["Marchand", "Chorneyko", "Tarnopolsky", "Hamilton", "Shearer", "Potvin"], "given-names": ["I", "K", "M", "S", "J", "J"], "article-title": ["Quantification of subcellular glycogen in resting human muscle: granule size, number, and location"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["2002"], "issue": ["93"], "fpage": ["1598"], "lpage": ["1607"]}, {"label": ["29."], "surname": ["Bamman", "Hunter", "Newton", "Roney", "Khaled"], "given-names": ["MM", "GR", "LE", "RK", "MA"], "article-title": ["Changes in body composition, diet, and strength of bodybuilders during the 12 weeks prior to competition"], "source": ["J Sports Med Phys Fit"], "year": ["1993"], "volume": ["33"], "fpage": ["383"], "lpage": ["391"]}, {"label": ["35."], "surname": ["Ahlborg", "Bergstr\u00f6m", "Ekelund", "Hultman"], "given-names": ["B", "J", "L-G", "E"], "article-title": ["Muscle glycogen and muscle electrolytes during prolonged physical exercise1"], "source": ["Acta Physiol Scand"], "year": ["1967"], "volume": ["70"], "fpage": ["129"], "lpage": ["142"], "pub-id": ["10.1111/j.1748-1716.1967.tb03608.x"]}, {"label": ["44."], "surname": ["Hawley", "Palmer", "Noakes"], "given-names": ["JA", "GS", "TD"], "article-title": ["Effects of 3 days of carbohydrate supplementation on muscle glycogen content and utilisation during a 1-h cycling performance"], "source": ["Eur J Appl Physiol"], "year": ["1997"], "volume": ["75"], "fpage": ["407"], "lpage": ["412"], "pub-id": ["10.1007/s004210050180"]}, {"label": ["48."], "surname": ["Shiose", "Yamada", "Motonaga", "Sagayama", "Higaki", "Tanaka"], "given-names": ["K", "Y", "K", "H", "Y", "H"], "article-title": ["Segmental extracellular and intracellular water distribution and muscle glycogen after 72-h carbohydrate loading using spectroscopic techniques"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["2016"], "issue": ["121"], "fpage": ["205"], "lpage": ["211"]}, {"label": ["49."], "surname": ["Goforth", "Arnall", "Bennett", "Law"], "given-names": ["HW", "DA", "BL", "PG"], "article-title": ["Persistence of supercompensated muscle glycogen in trained subjects after carbohydrate loading"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["1997"], "issue": ["82"], "fpage": ["342"], "lpage": ["347"]}, {"label": ["54."], "surname": ["Neufer", "Sawka", "Young", "Quigley", "Latzka", "Levine"], "given-names": ["PD", "MN", "AJ", "MD", "WA", "L"], "article-title": ["Hypohydration does not impair skeletal muscle glycogen resynthesis after exercise"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["1991"], "issue": ["70"], "fpage": ["1490"], "lpage": ["1494"]}, {"label": ["65."], "surname": ["Achten", "Halson", "Moseley", "Rayson", "Casey", "Jeukendrup"], "given-names": ["J", "SL", "L", "MP", "A", "AE"], "article-title": ["Higher dietary carbohydrate content during intensified running training results in better maintenance of performance and mood state"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["2004"], "issue": ["96"], "fpage": ["1331"], "lpage": ["1340"]}, {"label": ["71."], "surname": ["Walker", "Heigenhauser", "Hultman", "Spriet"], "given-names": ["JL", "GJ", "E", "LL"], "article-title": ["Dietary carbohydrate, muscle glycogen content, and endurance performance in well-trained women"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["2000"], "issue": ["88"], "fpage": ["2151"], "lpage": ["2158"]}, {"label": ["83."], "mixed-citation": ["de Moraes WMAM, de Moura FC, da Costa Moraes TC, Oliveira de Sousa LG, Dos Santos Rosa T, Schoenfeld BJ, et al. Oxidative stress, inflammation, psychological status, and severity of respiratory infections are negatively affected during the pre-contest period in amateur bodybuilders. Appl Physiol Nutr Metab. 2019;44:468\u201376."]}, {"label": ["87."], "surname": ["van Loon"], "given-names": ["LJC"], "article-title": ["Use of intramuscular triacylglycerol as a substrate source during exercise in humans"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["2004"], "issue": ["97"], "fpage": ["1170"], "lpage": ["1187"]}, {"label": ["105."], "mixed-citation": ["Crane R, Miller D, Bihler I. Membrane transport and metabolism. In: Kleinzeller A, editor. 1961. p. 439\u201349."]}, {"label": ["112."], "surname": ["Sims", "Rehrer", "Bell", "Cotter"], "given-names": ["ST", "NJ", "ML", "JD"], "article-title": ["Preexercise sodium loading aids fluid balance and endurance for women exercising in the heat"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["2007"], "issue": ["103"], "fpage": ["534"], "lpage": ["541"]}, {"label": ["119."], "surname": ["Hackney", "Cook", "Fairchild", "Ploutz-Snyder"], "given-names": ["KJ", "SB", "TJ", "LL"], "article-title": ["Skeletal muscle volume following dehydration induced by exercise in heat"], "source": ["Extreme Physiol Med"], "year": ["2012"], "volume": ["1"], "fpage": ["3"], "pub-id": ["10.1186/2046-7648-1-3"]}, {"label": ["120."], "surname": ["Brinkman", "Dorius", "Sharma"], "given-names": ["JE", "B", "S"], "source": ["Physiology"], "year": ["2023"], "publisher-loc": ["Treasure Island"], "publisher-name": ["Body Fluids. StatPearls"]}, {"label": ["123."], "surname": ["Hill"], "given-names": ["LL"], "article-title": ["Body composition, normal electrolyte concentrations, and the maintenance of normal volume, tonicity, and acid-base metabolism"], "source": ["Pediatr Clin N Am"], "year": ["1990"], "volume": ["37"], "fpage": ["241"], "lpage": ["256"], "pub-id": ["10.1016/S0031-3955(16)36865-1"]}, {"label": ["125."], "surname": ["Escalante", "Barakat", "Tinsley", "Schoenfeld"], "given-names": ["G", "C", "GM", "BJ"], "article-title": ["Nutrition, training, supplementation, and performance-enhancing drug practices of male and female physique athletes peaking for competition"], "source": ["J Strength Cond Res"], "year": ["2022"], "pub-id": ["10.1519/JSC.0000000000004462"]}, {"label": ["129."], "mixed-citation": ["Castro APA de, Damasceno V de O, Miranda JA de, Lima JRP de, Vianna JM. Photo silhouettes for assessment of body image of bodybuilders. Rev Bras Med Esporte. 2011;17:250\u20133."]}, {"label": ["138."], "surname": ["O\u2019Reilly", "Warhol", "Fielding", "Frontera", "Meredith", "Evans"], "given-names": ["KP", "MJ", "RA", "WR", "CN", "WJ"], "article-title": ["Eccentric exercise-induced muscle damage impairs muscle glycogen repletion"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["1987"], "issue": ["63"], "fpage": ["252"], "lpage": ["256"]}, {"label": ["140."], "surname": ["Costill", "Pascoe", "Fink", "Robergs", "Barr", "Pearson"], "given-names": ["DL", "DD", "WJ", "RA", "SI", "D"], "article-title": ["Impaired muscle glycogen resynthesis after eccentric exercise"], "source": ["J Appl Physiol"], "year": ["1985"], "volume": ["1990"], "issue": ["69"], "fpage": ["46"], "lpage": ["50"]}] | {
"acronym": [
"CHO",
"DW",
"MT",
"ST",
"ICW",
"ECW",
"BIA",
"BIS",
"SGLT1",
"GLUT"
],
"definition": [
"Carbohydrate",
"Dry weight",
"Muscle thickness",
"Subcutaneous tissue thickness",
"Intracellular water",
"Extracellular water",
"Bioelectrical impedance analysis",
"Bioelectrical impedance spectroscopy",
"Sodium–glucose cotransporter one",
"Glucose transporter"
]
} | 146 | CC BY | no | 2024-01-15 23:41:55 | Sports Med Open. 2024 Jan 13; 10:8 | oa_package/84/fd/PMC10787737.tar.gz |
PMC10787738 | 38218856 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Depression is a prevalent mental health disorder affecting millions worldwide<sup>##REF##32427307##1##</sup>. Persistent sadness, hopelessness, and loss of interest in daily activities<sup>##REF##35152089##2##</sup> characterize it. Despite multiple treatment options... | [
"<title>Methods</title>",
"<title>Ethics statement</title>",
"<p id=\"Par39\">The current study was performed with the approval of the Ethics Committee of Youjiang Medical University for Nationalities and performed strictly with the Guide for the Care and Use of Laboratory Animals published by the US National I... | [
"<title>Results</title>",
"<title>Longya Lilium combined with fluoxetine has a more significant antidepressant effect and fewer adverse reactions</title>",
"<p id=\"Par6\">A total of 462 related documents were retrieved. After screening, 8 documents met the criteria and were finally included. All of them were r... | [
"<title>Discussion</title>",
"<p id=\"Par30\">In this study, the literature screening process was employed to define the research question and scope, ensuring the inclusion of high-quality studies to guarantee accurate analysis results. In addition, it aimed to enhance the homogeneity of the research, enabling a ... | [] | [
"<p id=\"Par1\">Traditional Chinese medicine is one of the most commonly used complementary and alternative medicine therapies for depression. Integrated Chinese-western therapies have been extensively applied in numerous diseases due to their superior efficiency in individual treatment. We used the meta-analysis, ... | [
"<title>Supplementary information</title>",
"<p>\n\n\n</p>"
] | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41540-024-00329-5.</p>",
"<title>Acknowledgements</title>",
"<p>This study was supported by 2022 Baise City Scientific Research and Technology Development Plan (Bai Ke 20224141) and 2020 Gu... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Main results of meta-analysis.</title><p><bold>A</bold> Forest plot comparing HAMD score between the fluoxetine group and the Longya Lilium + fluoxetine group. <italic>I</italic><sup>2</sup> = 91%, <italic>p</italic> < 0.01, with a random effect model used. ... | [] | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>"
] | [
"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Huina Ma, Hehua Huang.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"41540_2024_329_Fig1_HTML\" id=\"d32e426\"/>",
"<graphic xlink:href=\"41540_2024_329_Fig2_HTML\" id=\"d32e489\"/>",
"<graphic xlink:href=\"41540_2024_329_Fig3_HTML\" id=\"d32e556\"/>",
"<graphic xlink:href=\"41540_2024_329_Fig4_HTML\" id=\"d32e602\"/>",
"<graphic xlink:href=\"41540_202... | [
"<media xlink:href=\"41540_2024_329_MOESM1_ESM.pdf\"><caption><p>Supplementary information</p></caption></media>",
"<media xlink:href=\"41540_2024_329_MOESM2_ESM.pdf\"><caption><p>Reporting summary</p></caption></media>"
] | [{"label": ["5."], "surname": ["Mifsud", "Cilia", "Mifsud", "Gruppetta"], "given-names": ["S", "K", "EL", "M"], "article-title": ["Lithium-associated hyperparathyroidism"], "source": ["Br. J. Hosp. Med."], "year": ["2020"], "volume": ["81"], "fpage": ["1"], "lpage": ["9"], "pub-id": ["10.12968/hmed.2020.0457"]}, {"label": ["10."], "surname": ["Guo"], "given-names": ["B"], "article-title": ["Elucidation of the anti-inflammatory mechanism of Er Miao San by integrative approach of network pharmacology and experimental verification"], "source": ["Pharm. Res."], "year": ["2022"], "volume": ["175"], "fpage": ["106000"], "pub-id": ["10.1016/j.phrs.2021.106000"]}, {"label": ["15."], "surname": ["Yang", "Zhang"], "given-names": ["M", "H"], "article-title": ["Effect of Baihe Dihuang decoction in the treatment of post-stroke depression"], "source": ["Chin. J. Mod. Drug"], "year": ["2020"], "volume": ["14"], "fpage": ["3"]}, {"label": ["23."], "surname": ["Zhang"], "given-names": ["M"], "article-title": ["Shuxie-1 decoction alleviated CUMS -induced liver injury via IL-6/JAK2/STAT3 signaling"], "source": ["Front. Pharm."], "year": ["2022"], "volume": ["13"], "fpage": ["848355"], "pub-id": ["10.3389/fphar.2022.848355"]}, {"label": ["25."], "mixed-citation": ["Alberter, A. A., Chambers, A. J. & Wills, B. K. "], "italic": ["Bupropion Toxicity", "StatPearls"]}, {"label": ["54."], "mixed-citation": ["Tran, K. & Ford, C. In "], "italic": ["Lurasidone Hydrochloride for Bipolar Disorder: A Review of Clinical Effectiveness, Cost-Effectiveness, and Guidelines"]}, {"label": ["67."], "surname": ["Ji", "Yan"], "given-names": ["X", "Y"], "article-title": ["Effect of using the active cycle of breathing technique combined with Watson\u2019s theory of human caring in rapid patient rehabilitation following lung cancer surgery"], "source": ["Alter. Ther. Health Med."], "year": ["2023"], "volume": ["29"], "fpage": ["14"], "lpage": ["20"]}, {"label": ["81."], "mixed-citation": ["Zhang, X.-Y., Han, X.-L. & Qian, Z.-B. J. A. J. o. C. Study on optimization of extraction process of saponins from Bulbus Lilii and their antidepressant effects. "], "italic": ["Asian J. Chem."], "bold": ["26"]}, {"label": ["84."], "surname": ["Ayyub", "Najmi", "Akhtar"], "given-names": ["M", "AK", "M"], "article-title": ["Protective effect of irbesartan an angiotensin (AT1) receptor antagonist in unpredictable chronic mild stress induced depression in mice"], "source": ["Drug Res."], "year": ["2017"], "volume": ["67"], "fpage": ["59"], "lpage": ["64"]}, {"label": ["88."], "surname": ["Wang", "Huang", "Lu", "Wei", "Xu"], "given-names": ["Y", "M", "X", "R", "J"], "article-title": ["Ziziphi spinosae lily powder suspension in the treatment of depression-like behaviors in rats"], "source": ["BMC Complement Alter. Med."], "year": ["2017"], "volume": ["17"], "fpage": ["238"], "pub-id": ["10.1186/s12906-017-1749-5"]}] | {
"acronym": [],
"definition": []
} | 89 | CC BY | no | 2024-01-15 23:41:55 | NPJ Syst Biol Appl. 2024 Jan 13; 10:5 | oa_package/ff/74/PMC10787738.tar.gz |
PMC10787740 | 38218971 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Uranium may infiltrate the environment via uranium mining, production, and usage, posing risks to human health and the natural environment related to its toxicity and radioactivity<sup>##UREF##0##1##</sup>. To eliminate uranium from an aqueous solution, a multitude o... | [
"<title>Materials and methods</title>",
"<p id=\"Par7\">Graphite Powder (99% Purity, Chemical Reagent from China) was utilized as a precursor to create graphene oxide (GO). Carbon nanotubes with multiple walls were used to create graphene oxide nanoribbons (GONRs) (MWCNTs, Cheap Tube Inc., USA). In the synthesis ... | [
"<title>Results and discussion</title>",
"<title>Characterization of GO/GONRS/SA aerogels</title>",
"<p id=\"Par13\">Figure ##FIG##0##1## displays GO, GONRs, and GO/GONRs/SA aerogels FT-IR spectra. GO's FT-IR Fig. ##FIG##0##1##a spectrum reveals distinctive peaks at 1619 cm<sup>−1</sup> (graphitic C = C) and 13... | [
"<title>Results and discussion</title>",
"<title>Characterization of GO/GONRS/SA aerogels</title>",
"<p id=\"Par13\">Figure ##FIG##0##1## displays GO, GONRs, and GO/GONRs/SA aerogels FT-IR spectra. GO's FT-IR Fig. ##FIG##0##1##a spectrum reveals distinctive peaks at 1619 cm<sup>−1</sup> (graphitic C = C) and 13... | [
"<title>Conclusions</title>",
"<p id=\"Par32\">Aerogel materials as adsorbents are very appealing because of their diverse chemical composition, high porosity, and a variety of pore sizes, including micro-, meso–, and macropores. These features lead to efficient and affordable processes, and frequently also to se... | [
"<p id=\"Par1\">Waste-water pollution by radioactive elements such as uranium has emerged as a major issue that might seriously harm human health. Graphene oxide, graphene oxide nanoribbons, and sodium alginate nanocomposite aerogels (GO/GONRs/SA) were combined to create a novel nanocomposite using a modified Humme... | [
"<title>Contact time effect and studies of adsorption kinetics</title>",
"<p id=\"Par19\">Contact time is another crucial factor that might reveal the adsorption kinetics. As demonstrated in Fig. ##FIG##5##6##, the sorption capacity of U(VI) changes as a function of contact time. The quantity of U(VI) adsorbate r... | [
"<title>Acknowledgements</title>",
"<p>The authors are thankful to the Deanship of Scientific Research at the University of Bisha for supporting this work through the Fast-Track Research Support Program.</p>",
"<title>Author contributions</title>",
"<p>A.A.J., D.H.H., K.H.L., S.A., A.K.J., G.M.S., M.M.A. conc... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Spectrum FT-IR of GO (<bold>a</bold>), GONRs (<bold>b</bold>), GO/ GONRs /SA (<bold>c</bold>) aerogels.</p></caption></fig>",
"<fig id=\"Fig2\"><label>Figure 2</label><caption><p>The XRD patterns of GO (<bold>a</bold>), GONRs (<bold>b</bold>), and GO/GONRs/SA (... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>The kinetic parameters of U(VI) sorption on GO/GONRs/SA aerogel.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">q<sub>e,exp</sub>/(mg/ g)</th><th align=\"left\" colspan=\"3\">Pseudo-first-order kinetic</th>... | [
"<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepack... | [] | [] | [] | [] | [] | [
"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
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"<graphic xlink:href=\"41598_2024_52043_Article_Equ1.gif\" position=\"anchor\"/>",
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"<graphi... | [] | [{"label": ["1."], "surname": ["Srivastava", "Pathak", "Perween", "Gupta", "Walther"], "given-names": ["RR", "P", "M", "DK", "C"], "article-title": ["Environmental and health impact due to uranium mining"], "source": ["Uranium in Plants and the Environment"], "year": ["2020"], "publisher-name": ["Springer Nature"], "fpage": ["69"], "lpage": ["89"]}, {"label": ["3."], "surname": ["Gunathilake", "G\u00f3rka", "Dai", "Jaroniec"], "given-names": ["C", "J", "S", "M"], "article-title": ["Amidoxime-modified mesoporous silica for uranium adsorption under seawater conditions"], "source": ["J. Mater. Chem. A"], "year": ["2015"], "volume": ["3"], "fpage": ["11650"], "lpage": ["11659"], "pub-id": ["10.1039/C5TA02863A"]}, {"label": ["4."], "surname": ["Crini", "Lichtfouse", "Wilson"], "given-names": ["G", "E", "LD"], "article-title": ["Conventional and non-conventional adsorbents for wastewater treatment"], "source": ["Environ. Chem. Lett."], "year": ["2019"], "volume": ["17"], "fpage": ["195"], "lpage": ["213"], "pub-id": ["10.1007/s10311-018-0786-8"]}, {"label": ["7."], "surname": ["Bai"], "given-names": ["J"], "article-title": ["Synthesis of amidoximated polyacrylonitrile nanoparticle/graphene composite hydrogel for selective uranium sorption from saline lake brine"], "source": ["Chem. Eng. J."], "year": ["2020"], "volume": ["391"], "fpage": ["123553"], "pub-id": ["10.1016/j.cej.2019.123553"]}, {"label": ["9."], "surname": ["Wei", "Mao"], "given-names": ["L", "Y"], "article-title": ["Enhanced hydrogen storage performance of reduced graphene oxide hybrids with nickel or its metallic mixtures based on spillover mechanism"], "source": ["Int. J. Hydrogen Energy"], "year": ["2016"], "volume": ["41"], "issue": ["27"], "fpage": ["11692"], "lpage": ["11699"], "pub-id": ["10.1016/j.ijhydene.2016.04.030"]}, {"label": ["13."], "surname": ["Wu", "Wang", "Hu"], "given-names": ["P", "Y", "X"], "article-title": ["Synthesis of magnetic graphene oxide nanoribbons composite for the removal of Th(IV) from aqueous solutions"], "source": ["J. Radioanal. Nucl. Chem."], "year": ["2019"], "volume": ["319"], "fpage": ["1111"], "lpage": ["1118"], "pub-id": ["10.1007/s10967-018-6375-2"]}, {"label": ["14."], "surname": ["Lim", "Lee", "Lee"], "given-names": ["J", "GY", "HJ"], "article-title": ["Open porous graphene nanoribbon hydrogel via additive-free interfacial self-assembly: fast mass transport electrodes for high-performance biosensing and energy storage"], "source": ["Energy Storage Mater."], "year": ["2019"], "volume": ["16"], "fpage": ["251"], "lpage": ["258"], "pub-id": ["10.1016/j.ensm.2018.06.004"]}, {"label": ["16."], "surname": ["Zia", "Sobhani", "Mohammadi", "Athar", "Afzal", "Sultana", "Shchipunov", "Zia"], "given-names": ["F", "H", "M", "M", "M", "T", "Y", "F"], "article-title": ["Alginate-based hybrid nanocomposite materials"], "source": ["Algae Based Polymers, Blends, and Composites Chemistry, Biotechnology and Materials Science"], "year": ["2017"], "publisher-name": ["Elsevier"], "fpage": ["603"], "lpage": ["648"]}, {"label": ["18."], "surname": ["Mura", "Jiang", "Vassalini", "Gianoncelli", "Alessandri", "Granozzi", "Calvillo", "Senes", "Enzo", "Innocenzi", "Malfatti"], "given-names": ["S", "Y", "I", "A", "I", "G", "L", "N", "S", "P", "L"], "article-title": ["Graphene oxide/iron oxide nanocomposites for water remediation"], "source": ["ACS Appl. 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"acronym": [],
"definition": []
} | 38 | CC BY | no | 2024-01-15 23:41:55 | Sci Rep. 2024 Jan 13; 14:1285 | oa_package/7f/46/PMC10787740.tar.gz |
PMC10787741 | 38218970 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Globally, bladder cancer (BLCA) was estimated to account for 573,000 new cases and 213,000 deaths in 2020 [##REF##33538338##1##]. Surgery in combination with adjuvant chemotherapy has improved the survival rates of patients with BLCA. However, the recurrence and aggr... | [
"<title>Materials and methods</title>",
"<title>Cell culture</title>",
"<p id=\"Par6\">T24 and 5637 cells were cultured in 1640 medium, UM-UC-3 cells were cultured in MEM, and 293 T cells were cultured in DMEM. The cells used in this study were obtained from the Chinese Academy of Sciences Cell Bank and were fr... | [
"<title>Results</title>",
"<title>siRNA screening reveals USP43 as a key deubiquitinase for glycolysis and c-Myc transcriptional activity</title>",
"<p id=\"Par29\">To identify deubiquitinases (DUBs) that play roles in both glycolysis and c-Myc transcriptional activity, we performed further screening of seven D... | [
"<title>Discussion</title>",
"<p id=\"Par42\">The initiation and progression of cancer are characterized by distinct metabolic reprogramming, which maintains the high proliferation rate of cancer cells [##REF##32273439##33##]. In BLCA, various metabolic pathways are altered and contribute to tumorigenesis. Notabl... | [] | [
"<p id=\"Par1\">A hallmark of tumor cells, including bladder cancer (BLCA) cells, is metabolic reprogramming toward aerobic glycolysis (Warburg effect). The classical oncogene MYC, which is crucial in regulating glycolysis, is amplified and activated in BLCA. However, direct targeting of the c-Myc oncoprotein, whic... | [
"<title>Supplementary information</title>",
"<p>\n\n\n\n</p>"
] | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41419-024-06446-7.</p>",
"<title>Acknowledgements</title>",
"<p>The excellent technical assistance of Ms. Mengxue Yu, Ms. Danni Shan, Ms. Cong Zou, Mr. Wen Chen and Mr. Zongning Zhou is gra... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>siRNA screening reveals USP43 as a key deubiquitinase for glycolysis and c-Myc transcriptional activity.</title><p><bold>A</bold> 293 T cells were transfected with siRNA pools targeting seven deubiquitinase candidates for 24 h, transfected with 5× E-box lucifer... | [] | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>"
] | [
"<fn-group><fn><p>Edited by Quan Chen</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p><bold>Statement of animal rights</bold> The animal study was approved by the Experimental Animal We... | [
"<graphic xlink:href=\"41419_2024_6446_Fig1_HTML\" id=\"d32e795\"/>",
"<graphic xlink:href=\"41419_2024_6446_Fig2_HTML\" id=\"d32e940\"/>",
"<graphic xlink:href=\"41419_2024_6446_Fig3_HTML\" id=\"d32e1077\"/>",
"<graphic xlink:href=\"41419_2024_6446_Fig4_HTML\" id=\"d32e1155\"/>",
"<graphic xlink:href=\"414... | [
"<media xlink:href=\"41419_2024_6446_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>",
"<media xlink:href=\"41419_2024_6446_MOESM2_ESM.pdf\"><caption><p>Original full and uncropped Western blots</p></caption></media>",
"<media xlink:href=\"41419_2024_6446_MOESM3_ESM.pdf\"><caption><... | [] | {
"acronym": [],
"definition": []
} | 50 | CC BY | no | 2024-01-15 23:41:55 | Cell Death Dis. 2024 Jan 13; 15(1):44 | oa_package/90/ad/PMC10787741.tar.gz |
PMC10787742 | 38218902 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Advanced glycation end products (AGEs) are the final products of proteins or lipids that become glycated and oxidized<sup>##REF##11270668##1##,##REF##7598459##2##</sup>. They are primarily formed in environments of hyperglycemia and oxidative stress but also accumula... | [
"<title>Methods and materials</title>",
"<title>Study design</title>",
"<p id=\"Par5\">This study is embedded within the Rotterdam Study, a prospective population-based cohort designed to study the occurrence and determinants of diseases in the older population, as described previously<sup>##REF##32367290##24##... | [
"<title>Results</title>",
"<p id=\"Par16\">Among the 2922 included participants the mean age was 72.6 years (SD 9.4) and 57% were women (Table ##TAB##0##1##). Skin autofluorescence levels were normally distributed with values ranging from 1.1 to 4.4 arbitrary units and a mean of 2.4 (SD 0.5). Levels increased wit... | [
"<title>Discussion</title>",
"<p id=\"Par22\">We found that higher skin autofluorescence, reflecting long-term accumulation of AGEs, is associated with a higher risk of all-cause dementia and of AD, independently of age and several other potential confounders. These associations were more pronounced in <italic>AP... | [] | [
"<p id=\"Par1\">Conditions such as hyperglycemia and oxidative stress lead to the formation of advanced glycation end products (AGEs), which are harmful compounds that have been implicated in dementia. Within the Rotterdam Study, we measured skin AGEs as skin autofluorescence, reflecting long-term accumulation of A... | [
"<title>Supplementary Information</title>",
"<p>\n</p>"
] | [
"<title>Supplementary Information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41598-024-51703-6.</p>",
"<title>Author contributions</title>",
"<p>S.S.M. designed the study, analyzed and interpreted the data and wrote the manuscript. T.L. contributed to the analysis a... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Skin autofluorescence in association with the risk of dementia and Alzheimer’s disease in subgroups of the study population. <italic>CKD</italic> chronic kidney disease, <italic>CI</italic> confidence interval, <italic>HR</italic> hazard ratio, <italic>SD</italic... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline characteristics of the study population.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\">Characteristic</th><th align=\"left\" colspan=\"4\">All participants</th><th align=\"left\" rowspan=\"3\">MRI... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>Numbers are means (standard deviation) or numbers (percentages).</p><p>Missing values (% in total study population) per variable: education (1.4%), smoking (0.4%), <italic>APOE</italic> ε4 genotype (6.3%), systolic blood pressure (0.3%), diastolic blood pressure (0.3%), glucose (3.9%), total ch... | [
"<graphic xlink:href=\"41598_2024_51703_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"41598_2024_51703_Fig2_HTML\" id=\"MO2\"/>"
] | [
"<media xlink:href=\"41598_2024_51703_MOESM1_ESM.pdf\"><caption><p>Supplementary Information.</p></caption></media>"
] | [{"label": ["47."], "surname": ["Rajaobelina"], "given-names": ["K"], "article-title": ["Autofluorescence of skin advanced glycation end products: Marker of metabolic memory in elderly population"], "source": ["J. Gerontol. Ser. A"], "year": ["2015"], "volume": ["70"], "fpage": ["841"], "lpage": ["846"], "pub-id": ["10.1093/gerona/glu243"]}] | {
"acronym": [],
"definition": []
} | 48 | CC BY | no | 2024-01-15 23:41:55 | Sci Rep. 2024 Jan 13; 14:1256 | oa_package/34/fc/PMC10787742.tar.gz |
PMC10787743 | 38218991 | [
"<title>Introduction</title>",
"<p id=\"Par2\">The vast majority of computer game users have not and will not experience gaming disorder<sup>##REF##28956935##1##</sup>. The prevalence of gaming disorder (GD) varies<sup>##REF##28816494##2##</sup>, but it can be assumed that in general populations it is approximate... | [
"<title>Methods</title>",
"<title>Participants</title>",
"<p id=\"Par6\">A survey was conducted among Polish gamers over 18 years of age who play video games on any device (computer, smartphone, console, tablet, or other). In total, 595 people participated. We assumed data elimination in two cases: when partici... | [
"<title>Results</title>",
"<title>Descriptive statistics</title>",
"<p id=\"Par17\">The number of participants who met the binary GD criteria in our sample was 3 (0.52%). Data on gender, place of residence, age, level of education, and relationship status are summarized in Table ##TAB##0##1##.</p>",
"<p id=\"... | [
"<title>Discussion</title>",
"<p id=\"Par24\">The present study attempted to test the hypothesis that some mental health conditions, more specifically depression and anxiety, can play the role of not only a mediator, but also a moderator of the relationship between the dose of gaming and the risk of GD. This pred... | [
"<title>Conclusions</title>",
"<p id=\"Par28\">In line with our hypotheses and the results of our predecessors, not only anxiety, but also depression, turned out to play the role of dosage effect moderators. Our findings were found to be consistent with the I-PACE model<sup>##REF##27590829##21##</sup>. This resul... | [
"<p id=\"Par1\">The relationship between gaming time and gaming disorder can be moderated by other variables. This study aimed to test the moderating role of mental health. Participants (N = 461) were recruited online. Gaming time was a statistically significant predictor of gaming disorder risk, with an explained ... | [
"<title>Limitations</title>",
"<p id=\"Par27\">The present findings are subject to some limitations. The present study used data from a snowball procedure, which could be improved using a representative population sample. When recruiting respondents, we did not inform them in any way that the purpose of the study... | [
"<title>Acknowledgements</title>",
"<p>This research has been supported by a grant from the Faculty of Management and Social Communication (AS), a grant from the Priority Research Area Digiworld under the Strategic Programme Excellence Initiative at the Jagiellonian University in Kraków (PS; U1U/P06/NO/02.34), an... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Gaming disorder risk as a function of the interaction between gaming time and psychopathology symptoms; depression (<bold>a</bold>) and anxiety (<bold>b</bold>). Note: *: <italic>p</italic> < .05; ***: <italic>p</italic> < 0.001; ns: non-significant.</p></c... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Demographic data of the respondents (N = 461). Note: the percentages for preferred genres do not add up to 100 because participants had the opportunity to mark all genres they played.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th alig... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>Pearson’s correlation coefficients are presented; N = 459 ** <italic>p</italic> < .01.</p></table-wrap-foot>",
"<table-wrap-foot><p>N = 459 * <italic>p</italic> < .05, ** <italic>p</italic> < .01, *** <italic>p</italic> < .001.</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Pub... | [
"<graphic xlink:href=\"41598_2024_51568_Fig1_HTML\" id=\"MO1\"/>"
] | [] | [{"label": ["5."], "mixed-citation": ["Strojny, P. "], "italic": ["et al.", "Front. Psychiatry"], "bold": ["14"]}, {"label": ["11."], "surname": ["Koncz"], "given-names": ["P"], "article-title": ["The moderating role of mental health on the association between gaming time and gaming disorder symtoms"], "source": ["Eur. Psychiatr."], "year": ["2022"], "volume": ["65"], "fpage": ["S81"], "lpage": ["S81"], "pub-id": ["10.1192/j.eurpsy.2022.246"]}, {"label": ["12."], "surname": ["Baron", "Kenny"], "given-names": ["RM", "DA"], "article-title": ["The moderator\u2013mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations"], "source": ["J. Personal. Soc. Psychol."], "year": ["1986"], "volume": ["51"], "fpage": ["1173"], "lpage": ["1182"], "pub-id": ["10.1037/0022-3514.51.6.1173"]}, {"label": ["13."], "surname": ["Kim"], "given-names": ["NR"], "article-title": ["Characteristics and psychiatric symptoms of internet gaming disorder among adults using self-reported DSM-5 criteria"], "source": ["Psychiatry Invest."], "year": ["2016"], "volume": ["13"], "fpage": ["58"], "pub-id": ["10.4306/pi.2016.13.1.58"]}, {"label": ["25."], "mixed-citation": ["Liu, L. "], "italic": ["et al.", "Front. Psychiatry"], "bold": ["9"]}, {"label": ["26."], "surname": ["Pontes"], "given-names": ["HM"], "article-title": ["Measurement and conceptualization of gaming disorder according to the world health organization framework: the development of the gaming disorder test"], "source": ["Int. J. Ment. Health Addict."], "year": ["2021"], "volume": ["19"], "fpage": ["508"], "lpage": ["528"], "pub-id": ["10.1007/s11469-019-00088-z"]}, {"label": ["28."], "mixed-citation": ["Kokoszka, Andrzej, Jastrz\u0119bski, Adam & Obr\u0119bski, Marcin. Ocena psychometrycznych w\u0142a\u015bciwo\u015bci polskiej wersji Kwestionariusza Zdrowia Pacjenta-9 dla os\u00f3b doros\u0142ych. "], "bold": ["13"]}, {"label": ["32."], "mixed-citation": ["Hayes, Andrew. "], "italic": ["Introduction to Mediation, Moderation, and Conditional Process Analysis: Third Edition: A Regression-Based Approach"]}, {"label": ["33."], "mixed-citation": ["Koban, K., Biehl, J., Bornemeier, J. & Ohler, P. Compensatory video gaming. Gaming behaviours and adverse outcomes and the moderating role of stress, social interaction anxiety, and loneliness. "], "italic": ["Behav. Inf. Technol."], "bold": ["41"]}] | {
"acronym": [],
"definition": []
} | 35 | CC BY | no | 2024-01-15 23:41:55 | Sci Rep. 2024 Jan 13; 14:1257 | oa_package/ef/f1/PMC10787743.tar.gz |
PMC10787744 | 38218900 | [
"<title>Introduction</title>",
"<p id=\"Par4\">Sleep is a natural behavioral process involving reduced responses to external stimuli and changes in the activity of the cerebral cortex and muscle strength<sup>##REF##26833831##1##</sup>. Every human spends about 27 years of his life sleeping, which alone expresses ... | [
"<title>Methods</title>",
"<p id=\"Par8\">This study is based on a cross-sectional study design and included all women working at Shiraz University of Medical Sciences, Shiraz, Iran, who were randomly selected through cluster sampling from January to April in 2021. In the absence of any similar prior research, wh... | [
"<title>Results</title>",
"<p id=\"Par17\">In Table ##TAB##0##1##, demographic features of the sample are listed, and their correlation with sleep quality has been examined to identify the confounding variable. This study included 150 female participants with an average age of 51.44 ± 1.3 years. The average years... | [
"<title>Discussion</title>",
"<p id=\"Par21\">The present study revealed that the total marital satisfaction score does not predict the sleep quality score of premenopausal working women. In explaining this relationship, one can say daily occupational status and not safe physical activity of these women make it d... | [
"<title>Conclusion</title>",
"<p id=\"Par26\">The study results showed that more than half of the working women during the climacteric period had undesirable sleep quality. At the same time, they reported high marital satisfaction scores. Although the marital satisfaction score could not predict the sleep quality... | [
"<p id=\"Par1\">Sleep disorders can adversely affect physical, sexual, and marital health, particularly among middle-aged women. This study aimed to determine the relationship between sleep quality and marital satisfaction of working women during the premenopausal period. In this cross-sectional study, we selected ... | [] | [
"<title>Acknowledgements</title>",
"<p>At this moment, we would like to thank the Vice Chancellor for Research of Shiraz University of Medical Sciences, and the employee premenopause women in Shiraz University of Medical Sciences in for their non-stop support in the study.</p>",
"<title>Classification</title>",... | [] | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Sample characteristics and test with sleep quality.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristics</th><th align=\"left\">Levels</th><th align=\"left\">N (%)</th><th align=\"left\">Mean ± SD</th><th align... | [
"<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t... | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>N, number of cases in total; SD, standard deviation; ANOVA, One-way analysis of variance; T-Test, Independent Samples t-Test; r, Correlation coefficient.</p></table-wrap-foot>",
"<table-wrap-foot><p>SD, standard deviation.</p></table-wrap-foot>",
"<table-wrap-foot><p>N, number of cases in t... | [
"<graphic xlink:href=\"41598_2024_51440_Article_Equa.gif\" position=\"anchor\"/>",
"<inline-graphic xlink:href=\"41598_2024_51440_Article_IEq1.gif\"/>",
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"<inline-graphi... | [] | [{"label": ["2."], "surname": ["Ohayon"], "given-names": ["MM"], "article-title": ["Epidemiological overview of sleep disorders in the general population"], "source": ["Sleep Med. Res."], "year": ["2011"], "volume": ["2"], "issue": ["1"], "fpage": ["1"], "lpage": ["9"], "pub-id": ["10.17241/smr.2011.2.1.1"]}, {"label": ["3."], "surname": ["Mendoza-Mel\u00e9ndez", "Jimenez-Correa", "Gallegos-Cari", "Ayala-Guerrero", "Jim\u00e9nez-Anguiano"], "given-names": ["M\u00c1", "U", "A", "F", "A"], "article-title": ["Prevalence of sleep disorders, daytime sleepiness and clinical symptomatology in older adults"], "source": ["Rev. M\u00e9d. Hosp. Gen. M\u00e9x."], "year": ["2016"], "volume": ["79"], "issue": ["3"], "fpage": ["136"], "lpage": ["143"]}, {"label": ["5."], "surname": ["Afsahri", "Salehnejad", "Hekmat", "Abedi", "Fakhri", "Haghighizadeh"], "given-names": ["P", "Z", "K", "P", "A", "M"], "article-title": ["Do sleeping disorders impair sexual function in married iranian women of reproductive age? Results from a cross-sectional study"], "source": ["Psychiatry J."], "year": ["2018"], "volume": ["2018"], "fpage": ["1"], "lpage": ["7"], "pub-id": ["10.1155/2018/1045738"]}, {"label": ["7."], "surname": ["Kravitz", "Joffe"], "given-names": ["HM", "H"], "article-title": ["Sleep during the perimenopause: A SWAN story"], "source": ["Obstet. Gynecol. Clin. N. Am."], "year": ["2011"], "volume": ["38"], "issue": ["3"], "fpage": ["567"], "lpage": ["586"], "pub-id": ["10.1016/j.ogc.2011.06.002"]}, {"label": ["11."], "surname": ["Naderi", "Molavi", "Nory"], "given-names": ["L", "H", "A"], "article-title": ["The prediction of marital satisfaction of couples in isfahan based on empathy and forgiveness"], "source": ["Knowl. Res. Appl. Psychol."], "year": ["2017"], "volume": ["16"], "issue": ["4"], "fpage": ["69"], "lpage": ["75"]}, {"label": ["12."], "mixed-citation": ["Holt, M., Broady, T., Callander, D., Pony, M., Duck-Chong, L., Cook, T., "], "italic": ["et al", "Int. J. Transgender Health"]}, {"label": ["13."], "surname": ["Setoudeh", "Motaghi", "Mousavi"], "given-names": ["S", "M", "M"], "article-title": ["Survey of sexual satisfaction in women referred to public health centers of Mashhad in 2017"], "source": ["J. Sabzevar Univ. Med. Sci."], "year": ["2019"], "volume": ["26"], "issue": ["1"], "fpage": ["73"], "lpage": ["80"]}, {"label": ["15."], "mixed-citation": ["Ness, T.E.B., Saksvik-Lehouillier, I., editors. The Relationships between Life Satisfaction and Sleep Quality, Sleep Duration and Variability of Sleep in University Students (2018)."]}, {"label": ["16."], "surname": ["Nazifi", "Mokarami", "Akbaritabar", "Kalte", "Rahi"], "given-names": ["M", "H", "A", "HO", "A"], "article-title": ["Psychometric properties of the persian translation of Pittsburgh sleep quality index"], "source": ["Health Scope"], "year": ["2014"], "volume": ["3"], "fpage": ["1"], "lpage": ["4"], "pub-id": ["10.17795/jhealthscope-15547"]}, {"label": ["18."], "mixed-citation": ["Olson, D. H., Druckman, J. M., Fournier, D. G., Prepare, Enrich: Counselor\u2019s Manual: Prepare-Enrich Incorporated (1982)."]}, {"label": ["19."], "mixed-citation": ["Soleimanian, A., Surveying the relationship of irrational beliefs with marital satisfaction among married university students [Tesis]. Tehran: Tarbiat Moallem University (1994)."]}, {"label": ["20."], "surname": ["Ahadi", "Heydarali", "Koshki", "Shahnazari"], "given-names": ["H", "H", "S", "M"], "article-title": ["Psychometric properties of marital satisfaction questionnaire and its relationship with attachment style"], "source": ["Psychol. Res."], "year": ["2012"], "volume": ["4"], "issue": ["14"], "fpage": ["101"], "lpage": ["114"]}, {"label": ["25."], "surname": ["Cibelle de Sousae", "Oliveira", "Souza", "Silva", "Ykeda"], "given-names": ["S", "BCD", "SMDO", "HGN", "DS"], "article-title": ["Comparative study of sleep quality and insomnia among women in the climax and with regular menstrual cycle"], "source": ["J. Physiother. Res."], "year": ["2020"], "volume": ["10"], "issue": ["2"], "fpage": ["163"], "lpage": ["171"]}, {"label": ["27."], "mixed-citation": ["Lampio, L. "], "italic": ["et al.", "Sleep"], "bold": ["40"]}, {"label": ["29."], "surname": ["Jahangiry", "Parviz", "Mirghafourvand", "Khazaee-Pool", "Ponnet"], "given-names": ["L", "R", "M", "M", "K"], "article-title": ["The psychometric properties of the Persian menopause rating scale"], "source": ["BMC Women\u2019s Health"], "year": ["2020"], "volume": ["2020"], "fpage": ["172"], "pub-id": ["10.1186/s12905-020-01027-0"]}, {"label": ["30."], "surname": ["Shareh", "Eshaghi"], "given-names": ["H", "SM"], "article-title": ["Predictive role of morningness-eveningness personality, cognitive flexibility and cognitive emotion regulation in marital satisfaction in middle-aged women"], "source": ["Iran. J. Psychiatry Clin. Psychol."], "year": ["2019"], "volume": ["24"], "issue": ["4"], "fpage": ["384"], "lpage": ["399"], "pub-id": ["10.32598/ijpcp.24.4.384"]}, {"label": ["31."], "surname": ["Talayizadeh", "Bakhtiyarpour"], "given-names": ["F", "S"], "article-title": ["The relationship between marital satisfaction and sexual satisfaction with couple mental health"], "source": ["Thoughts Behav. Clin. Psychol."], "year": ["2016"], "volume": ["11"], "issue": ["40"], "fpage": ["37"], "lpage": ["46"]}] | {
"acronym": [
"PSQI",
"ENRICH"
],
"definition": [
"Pittsburgh Sleep Quality Index",
"Evaluation and Nurturing Relationship Issues, Communication, and Happiness"
]
} | 31 | CC BY | no | 2024-01-15 23:41:55 | Sci Rep. 2024 Jan 13; 14:1248 | oa_package/11/67/PMC10787744.tar.gz |
PMC10787745 | 38218977 | [
"<title>Introduction</title>",
"<p id=\"Par2\">The total reflection X-ray fluorescence (TXRF) spectroscopy is a variant of conventional energy dispersive X-ray fluorescence and belongs to the instrumental methods of multi-elemental analysis. The source-sample-detector geometry used in TXRF method leads to signifi... | [
"<title>Materials and methods</title>",
"<title>Experimental animals</title>",
"<p id=\"Par30\">The animals used in this study were male Wistar rats originating from the husbandry of the Department of Experimental Neuropathology of the Institute of Zoology and Biomedical Research, Jagiellonian University in Kra... | [
"<title>Results</title>",
"<p id=\"Par8\">As a result of the element analysis, for each of the examined 15 animals (5 animals × 3 study groups), the information on concentrations of P, S, K, Ca, Fe, Cu, Zn and Se in the 4 examined organs (kidney, heart, spleen and lung) was obtained. The content of elements was m... | [
"<title>Discussion and conclusions</title>",
"<p id=\"Par18\">The cancerogenesis process occurring in the brain may influence the response of the immune system also in other parts of the body and it may manifest itself with the changes in the elemental composition of tissues<sup>##REF##31315034##36##–##REF##34248... | [
"<title>Discussion and conclusions</title>",
"<p id=\"Par18\">The cancerogenesis process occurring in the brain may influence the response of the immune system also in other parts of the body and it may manifest itself with the changes in the elemental composition of tissues<sup>##REF##31315034##36##–##REF##34248... | [
"<p id=\"Par1\">Glioblastoma (GBM) is a fast-growing and aggressive brain tumor which invades the nearby brain tissue but generally does not spread to the distant organs. Nonetheless, if untreated, GBM can result in patient death in time even less than few months from the diagnosis. The influence of the tumor progr... | [
"<title>Supplementary Information</title>",
"<p>\n</p>"
] | [
"<title>Supplementary Information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41598-024-51731-2.</p>",
"<title>Acknowledgements</title>",
"<p>The authors would like to acknowledge the contribution of the COST ACTION CA18130. This work was also partially financed by t... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Box-and-whiskers plots presenting the ranges of P and S concentrations in kidneys, hearts, spleens and lungs taken from animals representing N (green), T (blue) and U (violet) groups obtained through the cooperating laboratories (from left to right: L1, L2 and L3... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Statistically significant differences (Mann–Whitney <italic>U</italic> test, significance level of 5%) determined between experimental animals (groups T and U implanted with T98G and U87MG cells) and normal rats in cooperating laboratories (L1, L2, L3) toge... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>*Double down/up arrows indicate statistically significant (<italic>p</italic>-value < 0.05) decrease (blue arrows)/increase (red arrows) of the concentration of element comparing to control rats;</p><p>**Observed differences in elemental composition were divided into fully (table cell marked... | [
"<graphic xlink:href=\"41598_2024_51731_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"41598_2024_51731_Fig2_HTML\" id=\"MO2\"/>",
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"<graphic xlink:href=\"41598_2024_51731_Fig4_HTML\" id=\"MO4\"/>",
"<graphic xlink:href=\"41598_2024_51731_... | [
"<media xlink:href=\"41598_2024_51731_MOESM1_ESM.pdf\"><caption><p>Supplementary Information.</p></caption></media>"
] | [{"label": ["1."], "mixed-citation": ["Klockenk\u00e4mper, R. & von Bohlen, A. "], "italic": ["Total-Reflection X-Ray Fluorescence Analysis and Related Methods"]}, {"label": ["2."], "mixed-citation": ["Streli, C., Kregsamer, P.& Wobrauschek, P. Total reflection XRF (TXRF). 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} | 102 | CC BY | no | 2024-01-15 23:41:55 | Sci Rep. 2024 Jan 13; 14:1254 | oa_package/04/29/PMC10787745.tar.gz |
PMC10787746 | 38218852 | [
"<title>Introduction</title>",
"<p id=\"Par2\">A tumor of the sympathetic nervous system, neuroblastoma (NB) is one of the most common tumors in children [##REF##29378002##1##, ##REF##30305231##2##]. The incidence of NB is estimated at 1.2 cases per 100,000 people, accounting for about 15% of all cancer deaths in... | [
"<title>Materials and methods</title>",
"<title>Reagents</title>",
"<p id=\"Par21\">The following reagents were used: Minimum Essential Medium (Invitrogen, Carlsbad, CA, USA), fetal calf serum (Invitrogen, USA), Nonessential Amino Acid Solution (Invitrogen, USA), TRIzol reagent (15596018, Invitrogen, USA), Tris... | [
"<title>Results</title>",
"<title>Overexpression of Tmprss6 in neuro-2a cells</title>",
"<p id=\"Par6\">To explore the relationship between Tmprss6 and NB, we manipulated the levels of Tmprss6 in the NB cell line, neuro-2a [##REF##35531888##25##]. We confirmed increases in Tmprss6 and FLAG expression in the cel... | [
"<title>Discussion</title>",
"<p id=\"Par15\">Proteolytic enzymes have long been thought to be involved in carcinogenesis, since proteolytic enzymes can hydrolyze the extracellular matrix (ECM), allowing cancer cells to escape the basement membrane and invade surrounding tissues [##REF##27870503##22##]. Notably, ... | [] | [
"<p id=\"Par1\">Transmembrane serine protease 6 (Tmprss6) has been correlated with the occurrence and progression of tumors, but any specific molecular mechanism linking the enzyme to oncogenesis has remained elusive thus far. In the present study, we found that Tmprss6 markedly inhibited mouse neuroblastoma N2a (n... | [
"<title>Supplementary information</title>",
"<p>\n\n\n\n</p>"
] | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41419-024-06442-x.</p>",
"<title>Acknowledgements</title>",
"<p>This study was funded by the National Natural Science Foundation of China (grant number 31471035), Foundations of the Key Lab... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Overexpression of Tmprss6 in neuro-2a cells.</title><p><bold>A</bold> qRT-PCR detection of Tmprss6 mRNA expression in neuro-2a cells. Western blot detection of FLAG <bold>B</bold> and Tmprss6 <bold>C</bold> expression in neuro-2a cells. A representative blot im... | [] | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>"
] | [
"<fn-group><fn><p>Edited by Professor Massimiliano Agostini</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
] | [
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"<graphic xlink:href=\"41419... | [
"<media xlink:href=\"41419_2024_6442_MOESM1_ESM.docx\"><caption><p>SUPPLEMENTAL MATERIAL</p></caption></media>",
"<media xlink:href=\"41419_2024_6442_MOESM2_ESM.pdf\"><caption><p>Original Data File</p></caption></media>",
"<media xlink:href=\"41419_2024_6442_MOESM3_ESM.pdf\"><caption><p>The reproducibility chec... | [{"label": ["5."], "surname": ["Ward", "DeSantis", "Robbins", "Kohler", "Jemal"], "given-names": ["E", "C", "A", "B", "A"], "article-title": ["Childhood and adolescent cancer statistics, 2014"], "source": ["Cancer J Clin"], "year": ["2014"], "volume": ["64"], "fpage": ["83"], "lpage": ["103"], "pub-id": ["10.3322/caac.21219"]}, {"label": ["17."], "surname": ["Rohini", "Haritha Menon", "Selvamurugan"], "given-names": ["M", "A", "N"], "article-title": ["Role of activating transcription factor 3 and its interacting proteins under physiological and pathological conditions"], "source": ["Int J Biol macromolecules"], "year": ["2018"], "volume": ["120"], "fpage": ["310"], "lpage": ["7"], "pub-id": ["10.1016/j.ijbiomac.2018.08.107"]}, {"label": ["24."], "surname": ["Gitlin-Domagalska", "Mangold", "Debowski", "Ptaszynska", "Legowska", "Gutschow"], "given-names": ["A", "M", "D", "N", "A", "M"], "article-title": ["Matriptase-2: monitoring and inhibiting its proteolytic activity"], "source": ["Future Med Chem"], "year": ["2018"], "volume": ["23"], "fpage": ["1328"]}] | {
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"definition": []
} | 42 | CC BY | no | 2024-01-15 23:41:55 | Cell Death Dis. 2024 Jan 13; 15(1):49 | oa_package/96/d1/PMC10787746.tar.gz |
PMC10787747 | 38218732 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Wafer bonding technology has evolved significantly, particularly in the field of electronic applications manufactured at the wafer level to achieve high throughput. This advancement is especially critical for the packaging and assembly of MEMS devices, optical system... | [
"<title>Methods</title>",
"<p id=\"Par32\">4 inch Si wafers, with a thickness of 525 m, were utilized for the wafer bonding experiments. A 20 wt% solution of PHPS in dibutyl ether solvent was purchased.</p>",
"<p id=\"Par33\">In the case of the wafer bonding via both side PHPS layers, first, the wafers were coa... | [
"<title>Results</title>",
"<p id=\"Par12\">In order to investigate the PHPS conversion into at room temperature by plasma hydrophilic treatment, chemical components of the PHPS coating on wafers were analyzed by x-ray photoelectron spectroscopy (XPS). Figure ##FIG##0##1## presents the XPS core spectra of the PHP... | [
"<title>Discussion</title>",
"<p id=\"Par26\">Based on the experimental results, we propose a room temperature bonding mechanism as illustrated in Fig. ##FIG##6##7##. For bonding with both-side PHPS layers, the plasma treatment initiates the conversion of one PHPS layer to , facilitated by adsorbed water, as conf... | [
"<title>Conclusions</title>",
"<p id=\"Par31\">In this paper, we investigated and demonstrated wafer bonding via PHPS at room temperature. Through the strategic use of plasma hydrophilic treatment on either the Si surface or the PHPS layer, we have effectively facilitated the conversion of PHPS into a robust bond... | [
"<p id=\"Par1\">Room temperature wafer bonding is a desirable approach for the packaging and assembly of diverse electronic devices. The formation of layer at the bonding interface is crucial for a reliable wafer bonding as represented by conventional bonding techniques such as hydrophilic bonding and glass frit b... | [] | [
"<title>Acknowledgements</title>",
"<p>This work is supported by the Amada foundation, Tokyo Ohka Foundation for The Promotion of Science and Technology, and JSPS KAKENHI Grant Number JP23K13554.</p>",
"<title>Author contributions</title>",
"<p>K.T. designed and carried out the experiments, analyzed the exper... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>XPS core spectra of the PHPS surfaces before and after the plasma hydrophilic treatment for (<bold>a</bold>) O1s, (<bold>b</bold>) N1s, and (<bold>c</bold>) Si2p peaks.</p></caption></fig>",
"<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Stitched IR imag... | [] | [
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"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
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} | 46 | CC BY | no | 2024-01-15 23:41:56 | Sci Rep. 2024 Jan 13; 14:1267 | oa_package/5f/94/PMC10787747.tar.gz |
PMC10787748 | 38218950 | [
"<title>Introduction</title>",
"<p id=\"Par3\">Reproduction in an environment suitable for genome stability is important for the preservation of species. This requirement is more imperative in poikilotherms animals, in which gametes are directly exposed to undesirable temperatures. As a result, they may have evol... | [
"<title>Methods</title>",
"<title>Fish</title>",
"<p id=\"Par35\">All experiments using animals in this study followed the guidelines of Osaka Medical and Pharmaceutical University. Adult <italic>zebrafish</italic> and <italic>medaka</italic> were maintained at 28.5 °C on a 10 h light:14 h darkness photoperiod.... | [
"<title>Results</title>",
"<title>High temperature affects Leydig cells prior to germ cells</title>",
"<p id=\"Par8\">To determine the appropriate condition for high-temperature stimulation in adult <italic>zebrafish</italic> whose optimal temperature is 28–30 °C, we first examined fish survival during 2 weeks ... | [
"<title>Discussion</title>",
"<p id=\"Par29\">Multiple reports in mammals showed that germ cells are the main target of detrimental high temperatures in testes<sup>##REF##12943702##32##</sup>. Our study using <italic>zebrafish</italic> revealed that high temperature induced apoptosis in Leydig cells prior to germ... | [] | [
"<p id=\"Par1\">Exposure of testes to high-temperature environment results in defective spermatogenesis. <italic>Zebrafish</italic> exposed to high temperature exhibited apoptosis not only in germ cells but also in Leydig cells, as expected from studies using mice or salmon. However, the role of testicular somatic ... | [
"<title>Supplementary information</title>",
"<p>\n\n\n\n\n\n\n\n\n\n</p>"
] | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s42003-023-05740-y.</p>",
"<title>Acknowledgements</title>",
"<p>We are grateful to Mrs. Natsuko Okuda at OMPU for her excellent care of <italic>zebrafish</italic> and <italic>medaka</italic... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>High-temperature treatment led to significant apoptosis of Leydig cells.</title><p><bold>a</bold> Top row: HE staining of testes. The black lines indicate areas containing E3 spermatid. The red lines indicate areas containing abnormal cell populations. Arrows i... | [] | [] | [] | [] | [] | [] | [
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"<supplementary-material ... | [
"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
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"<media xlink:href=\"42003_2023_5740_MOESM1_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>",
"<media xlink:href=\"42003_2023_5740_MOESM2_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>",
"<media xlink:href=\"42003_2023_5740_MOESM3_ESM.pdf\"><caption><p>Description of Addition... | [{"label": ["1."], "surname": ["Pankhurst", "Munday"], "given-names": ["NW", "PL"], "article-title": ["Effects of climate change on fish reproduction and early life history stages"], "source": ["Mar. Freshw. Res."], "year": ["2011"], "volume": ["62"], "fpage": ["1015"], "lpage": ["1026"], "pub-id": ["10.1071/MF10269"]}, {"label": ["3."], "mixed-citation": ["Taranger, G. L. et al. Effects of photoperiod, temperature and GnRHa treatment on the reproductive physiology of Atlantic salmon ("], "italic": ["Salmo salar", "Fish Physiol. Biochem."], "bold": ["28"]}, {"label": ["4."], "mixed-citation": ["Cobellis, G. et al. Spermatogenesis and cryptorchidism. "], "italic": ["Front. Endocrinol."], "bold": ["5"]}, {"label": ["11."], "mixed-citation": ["Munabi, A. K. et al. The effects of temperature on the activity of testicular steroidogenic enzymes. "], "italic": ["Steroids"], "bold": ["43"]}, {"label": ["19."], "surname": ["Peier"], "given-names": ["AM"], "article-title": ["A heat-sensitive TRP channel expressed in keratinocytes"], "source": ["Science (1979)"], "year": ["2002"], "volume": ["296"], "fpage": ["2046"], "lpage": ["2049"]}, {"label": ["34."], "mixed-citation": ["Kumar, P. G. & Shoeb, M. The role of TRP ion channels in testicular function. "], "italic": ["Adv Exp Med Biol."], "bold": ["704"]}, {"label": ["38."], "mixed-citation": ["Baratchi, S. et al. The TRPV4 agonist GSK1016790A regulates the membrane expression of TRPV4 channels. "], "italic": ["Front. Pharmacol."], "bold": ["9"]}, {"label": ["40."], "mixed-citation": ["Raina, V. B., Schoot Uiterkamp, M. & Vader, G. Checkpoint control in meiotic prophase: Idiosyncratic demands require unique characteristics. 281\u2013315 10.1016/bs.ctdb.2022.04.007 (2023)."]}, {"label": ["46."], "mixed-citation": ["Morgan, R. et al. Are model organisms representative for climate change research? Testing thermal tolerance in wild and laboratory zebrafish populations. "], "italic": ["Conserv. Physiol."], "bold": ["7"]}] | {
"acronym": [],
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} | 49 | CC BY | no | 2024-01-15 23:41:56 | Commun Biol. 2024 Jan 13; 7:96 | oa_package/7d/96/PMC10787748.tar.gz |
PMC10787749 | 38218990 | [
"<title>Introduction</title>",
"<p id=\"Par2\">A considerable body of evidence suggests that disparities in SES—reflecting education, income, occupational prestige, and subjective status—play a critical role in shaping the health trajectories of people, with lower SES associated with elevated morbidity and mortal... | [
"<title>Methods and materials</title>",
"<title>Add health and differential gene expression</title>",
"<p id=\"Par7\">The National Longitudinal Study of Adolescent to Adult Health (Add Health) is a representative study of adolescents in the Unites States who were followed into adulthood over five waves of data ... | [
"<title>Results</title>",
"<title>Transcriptional alterations with SES are characterized by organism wide dysregulation</title>",
"<p id=\"Par16\">We performed a differential gene expression analysis followed by an enrichment analysis of the resulting SES–DEG (see Supplementary Dataset ##SUPPL##1##S2## and Supp... | [
"<title>Discussions</title>",
"<p id=\"Par22\">The present analyses expand the scope of prior studies of SES-related alteration in transcriptomic profiles of human immune response genes by identifying new genomic functional impacts (e.g., ribosomal biology) and new features of the gene regulatory architecture of ... | [] | [
"<p id=\"Par1\">Disparities in socio-economic status (SES) predict many immune system-related diseases, and previous research documents relationships between SES and the immune cell transcriptome. Drawing on a bioinformatically-informed network approach, we situate these findings in a broader molecular framework by... | [
"<title>Limitations</title>",
"<p id=\"Par29\">Several limitations are noteworthy. First, the hypotheses and the subsequent results are driven by the mRNA abundance data collected once from every participating subject. The repeated collection of transcriptomic data would be essential to address their highly trans... | [
"<title>Supplementary Information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41598-024-51517-6.</p>",
"<title>Author contributions</title>",
"<p>S.R. conceived of paper, performed data analysis, and wrote paper. S.C., K.H., and M.S. collected the data. S.C., B.L., a... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Enrichment analysis of the WGCNA clusters exhibiting significant cluster-SES relationships. Eigengenes from individual clusters were identified using WGCNA and then examined for significant associations with SES (adjusted <italic>p</italic> < 0.05). Enriched b... | [] | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>",
"<supplementary-material ... | [
"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
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"<media xlink:href=\"41598_2024_51517_MOESM1_ESM.xlsx\"><caption><p>Supplementary Information 1.</p></caption></media>",
"<media xlink:href=\"41598_2024_51517_MOESM2_ESM.xlsx\"><caption><p>Supplementary Information 2.</p></caption></media>",
"<media xlink:href=\"41598_2024_51517_MOESM3_ESM.xlsx\"><caption><p>Su... | [{"label": ["1."], "surname": ["Mathews"], "given-names": ["L"], "article-title": ["Impact of socioeconomic status on mortality and readmission in patients with heart failure with reduced ejection fraction: The ARIC study"], "source": ["J. Am. Heart Assoc."], "year": ["2022"], "volume": ["11"], "fpage": ["24057"], "pub-id": ["10.1161/JAHA.121.024057"]}, {"label": ["16."], "mixed-citation": ["Klopack, E. T. "], "italic": ["et al."]}, {"label": ["19."], "surname": ["Azad"], "given-names": ["MB"], "article-title": ["Influence of socioeconomic status trajectories on innate immune responsiveness in children"], "source": ["PLoS One"], "year": ["2012"], "volume": ["7"], "fpage": ["38669"], "pub-id": ["10.1371/journal.pone.0038669"]}, {"label": ["30."], "surname": ["Robles"], "given-names": ["TF"], "article-title": ["Annual Research Review: Social relationships and the immune system during development"], "source": ["J. Child Psychol. Psychiatry Allied Disciplines."], "year": ["2021"], "pub-id": ["10.1111/jcpp.13350"]}, {"label": ["33."], "mixed-citation": ["Marbach, D. "], "italic": ["et al. ", "Nature Methods"], "bold": ["13"]}, {"label": ["37."], "surname": ["Bridger Staatz"], "given-names": ["C"], "article-title": ["Life course socioeconomic position and body composition in adulthood: A systematic review and narrative synthesis"], "source": ["Int. J. Obesity."], "year": ["2021"], "volume": ["45"], "fpage": ["2300"], "lpage": ["2315"], "pub-id": ["10.1038/s41366-021-00898-z"]}, {"label": ["43."], "mixed-citation": ["Adler, N. E. Health disparities: Taking on the challenge. "], "italic": ["Perspect. Psychol. Sci"], "bold": ["8"]}, {"label": ["44."], "mixed-citation": ["Hout, M., Smith, T. W. & Marsden, P. V. Prestige and Socioeconomic Scores for the 2010 Census Codes."]}, {"label": ["45."], "surname": ["Robinson", "Oshlack"], "given-names": ["MD", "A"], "article-title": ["A scaling normalization method for differential expression analysis of RNA-seq data"], "source": ["Genome Biol."], "year": ["2010"], "volume": ["11"], "fpage": ["1"], "lpage": ["9"], "pub-id": ["10.1186/gb-2010-11-3-r25"]}, {"label": ["48."], "surname": ["Law", "Chen", "Shi", "Smyth"], "given-names": ["CW", "Y", "W", "GK"], "article-title": ["Voom: Precision weights unlock linear model analysis tools for RNA-seq read counts"], "source": ["Genome Biol."], "year": ["2014"], "volume": ["15"], "fpage": ["1"], "lpage": ["17"], "pub-id": ["10.1186/gb-2014-15-2-r29"]}, {"label": ["50."], "surname": ["Langfelder", "Horvath"], "given-names": ["P", "S"], "article-title": ["WGCNA: An R package for weighted correlation network analysis"], "source": ["BMC Bioinform."], "year": ["2008"], "volume": ["9"], "fpage": ["1"], "lpage": ["13"], "pub-id": ["10.1186/1471-2105-9-559"]}] | {
"acronym": [],
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} | 63 | CC BY | no | 2024-01-15 23:41:56 | Sci Rep. 2024 Jan 13; 14:1255 | oa_package/7e/bc/PMC10787749.tar.gz |
PMC10787750 | 38218941 | [
"<title>Introduction</title>",
"<p id=\"Par10\">Atherosclerosis is a chronic, progressive disease characterized by the accumulation of lipids, inflammatory cells, and fibrous elements in the arterial wall, leading to the formation of atherosclerotic plaques [##REF##33883728##1##, ##REF##31420554##2##]. These plaq... | [] | [] | [] | [
"<title>Conclusions and future perspectives</title>",
"<p id=\"Par59\">In conclusion, the multifaceted role of copper in atherosclerosis involves complex cellular and systemic metabolic processes. Copper deficiency or excess promotes atherosclerosis by inducing oxidative stress, inflammation, endothelial dysfunct... | [
"<p id=\"Par1\">Copper is an essential micronutrient that plays a pivotal role in numerous physiological processes in virtually all cell types. Nevertheless, the dysregulation of copper homeostasis, whether towards excess or deficiency, can lead to pathological alterations, such as atherosclerosis. With the advent ... | [
"<title>Facts</title>",
"<p id=\"Par2\">\n<list list-type=\"order\"><list-item><p id=\"Par3\">Copper is an essential trace element required in various physiological processes in the human body.</p></list-item><list-item><p id=\"Par4\">Dysregulation of copper homeostasis, whether towards excess or deficiency, has ... | [
"<title>Author contributions</title>",
"<p>MW and LL provided the guidance for this study. SY prepared the original manuscript and the illustrations. YL, LZ, and XW assisted with manuscript review and revision. All the authors have read and approved the final version of the manuscript.</p>",
"<title>Funding</ti... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Schematic diagram of systemic copper metabolism process.</title><p>Copper absorption occurs primarily in the small intestine, a process mediated by SLC31A1. Copper is then transported and exported to the bloodstream through the action of ATP7A, combined with so... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Evidence linking copper excess and atherosclerosis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Country, author, year</th><th>Methods and study population</th><th>Result</th></tr></thead><tbody><tr><td>Finland, Reunanen et al. [##RE... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>CVDs, cardiovascular disease; CHD, coronary heart disease; AMI, acute myocardial infarction; IHD, ischemic heart disease; IMT, intima-media thickness.</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims i... | [
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] | [] | [{"label": ["9."], "surname": ["Uriu-Adams", "Keen"], "given-names": ["JY", "CL"], "article-title": ["Copper, oxidative stress, and human health"], "source": ["Mol Asp Med"], "year": ["2005"], "volume": ["26"], "fpage": ["268"], "lpage": ["98"], "pub-id": ["10.1016/j.mam.2005.07.015"]}, {"label": ["19."], "mixed-citation": ["Lutsenko S. Dynamic and cell-specific transport networks for intracellular copper ions. J Cell Sci. 2021;134:96\u2013114."]}, {"label": ["27."], "surname": ["Liang", "Tsai", "Lee", "Savaraj", "Kuo"], "given-names": ["ZD", "WB", "MY", "N", "MT"], "article-title": ["Specificity protein 1 (sp1) oscillation is involved in copper homeostasis maintenance by regulating human high-affinity copper transporter 1 expression"], "source": ["Mol Pharm"], "year": ["2012"], "volume": ["81"], "fpage": ["455"], "lpage": ["64"], "pub-id": ["10.1124/mol.111.076422"]}, {"label": ["41."], "mixed-citation": ["Horn N, Wittung SP. et al. ATP7A-regulated enzyme metalation and trafficking in the menkes disease puzzle. Biomedicines. 2021;9:123\u2013142."]}, {"label": ["42."], "surname": ["Chen", "Jiang", "Shi", "Peng", "Fan", "Li"], "given-names": ["J", "Y", "H", "Y", "X", "C"], "article-title": ["The molecular mechanisms of copper metabolism and its roles in human diseases"], "source": ["Pflug Arch"], "year": ["2020"], "volume": ["472"], "fpage": ["1415"], "lpage": ["29"], "pub-id": ["10.1007/s00424-020-02412-2"]}, {"label": ["52."], "surname": ["Begum", "Sultana", "Faysal", "Alam", "Tasnim", "Akter"], "given-names": ["S", "I", "MR", "S", "J", "T"], "article-title": ["Study of changes in serum copper level in patients with acute myocardial infarction"], "source": ["Mymens Med J."], "year": ["2023"], "volume": ["32"], "fpage": ["39"], "lpage": ["43"]}, {"label": ["53."], "surname": ["Nowicki", "\u015alusarska", "Prystupa", "Blicharska", "Adamczuk", "Czernecki"], "given-names": ["GJ", "B", "A", "E", "A", "T"], "article-title": ["Assessment of concentrations of heavy metals in postmyocardial infarction patients and patients free from cardiovascular event"], "source": ["Cardiol Res Pr"], "year": ["2021"], "volume": ["2021"], "fpage": ["9546358"]}, {"label": ["94."], "mixed-citation": ["Ye J, DeBose RA. Regulation of cholesterol and fatty acid synthesis. Cold Spring Harb Perspect Biol. 2011;3:96\u2013105."]}, {"label": ["105."], "surname": ["Nunes", "de Souza"], "given-names": ["T", "HS"], "article-title": ["Inflammasome in intestinal inflammation and cancer"], "source": ["Mediat Inflamm"], "year": ["2013"], "volume": ["2013"], "fpage": ["654963"], "pub-id": ["10.1155/2013/654963"]}, {"label": ["116."], "mixed-citation": ["Kuriakose T, Man SM, Malireddi RK, Karki R, Kesavardhana S, Place DE, et al. ZBP1/DAI is an innate sensor of influenza virus triggering the NLRP3 inflammasome and programmed cell death pathways. Sci Immunol. 2016;1:52\u201368."]}, {"label": ["121."], "surname": ["Yang", "Pei", "Zhang", "Liao", "Yu", "Qiao"], "given-names": ["F", "R", "Z", "J", "W", "N"], "article-title": ["Copper induces oxidative stress and apoptosis through mitochondria-mediated pathway in chicken hepatocytes"], "source": ["Toxicol Vitr"], "year": ["2019"], "volume": ["54"], "fpage": ["310"], "lpage": ["6"], "pub-id": ["10.1016/j.tiv.2018.10.017"]}, {"label": ["128."], "mixed-citation": ["Li F, Wu X, Liu H, Liu M, Yue Z, Wu Z, et al. Copper depletion strongly enhances ferroptosis via mitochondrial perturbation and reduction in antioxidative mechanisms. Antioxid Basel. 2022;11:652\u201398."]}, {"label": ["137."], "mixed-citation": ["Ciccarelli G, Conte S, Cimmino G, Maiorano P, Morrione A, Giordano A, et al. Mitochondrial dysfunction: the hidden player in the pathogenesis of atherosclerosis? Int J Mol Sci. 2023;24:56\u201368."]}, {"label": ["144."], "mixed-citation": ["Sudhahar V, Shi Y, Kaplan JH, Ushio-Fukai M, Fukai T, et al. Whole-transcriptome sequencing analyses of nuclear antixoxidant-1 in endothelial cells: role in inflammation and atherosclerosis. Cells. 2022;11:881\u201396."]}, {"label": ["147."], "mixed-citation": ["Xue Q, Kang R, Klionsky DJ, Tang D, Liu J, Chen X, et al. Copper metabolism in cell death and autophagy. Autophagy. 2023;12:65\u2013104."]}] | {
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} | 149 | CC BY | no | 2024-01-15 23:41:56 | Cell Death Discov. 2024 Jan 13; 10:25 | oa_package/75/58/PMC10787750.tar.gz |
PMC10787751 | 38218988 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Fish skin has been used as a biological dressing in the management of burns<sup>##UREF##0##1##–##REF##34332229##9##</sup> and wounds<sup>##REF##33426019##10##</sup>. It has also been used as a biological graft in neovaginoplasty in cases of vaginal agenesis<sup>##REF... | [
"<title>Materials and methods</title>",
"<title>Ethics statement</title>",
"<p id=\"Par8\">The Research Ethics Committee (REC) of the Faculty of Veterinary Medicine, Assiut University, Assiut, Egypt, has approved all the procedures in this study in accordance with the Egyptian bylaws and OIE animal welfare stan... | [
"<title>Results</title>",
"<title>Microbiological evaluation of the lyophilized fish skin after gamma irradiation</title>",
"<p id=\"Par17\">Gamma irradiation exhibited an efficient sterilizing effect on fish skin surface microbiota. Different lengths of gamma irradiation (5, 10, and 25 KGy) were applied to the... | [
"<title>Discussion</title>",
"<p id=\"Par25\">Recently, biological dressings have become indispensable in modern strategies of burn and wound management. The current study investigated the use of lyophilization as a method for the long-term preservation of fish skin grafts in a manner that maintains the integrity... | [
"<title>Conclusion</title>",
"<p id=\"Par38\">This study established an optimized method for tilapia skin lyophilization. It defined the standard length of gamma irradiation for sterilizing lyophilized tilapia skin, considering the collagen integrity and microbial count of fish skin. These processed, sterilized, ... | [
"<p id=\"Par1\">The introduction of fish skin as a biological dressing for treating burns and wounds holds great promise, offering an alternative to existing management strategies. However, the risk of disease transmission is a significant concern. Therefore, this study aimed to examine how established sterilizatio... | [] | [
"<title>Author contributions</title>",
"<p>A.I.: Conceptualization, Methodology, Validation, Data curation, Visualization, Investigation, Supervision, Writing- Original draft preparation, Writing- Reviewing and Editing. H.F.: Conceptualization, Methodology, Data curation, Investigation, Writing- Original draft pr... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>(<bold>A</bold>) and (<bold>B</bold>) Gross micrographs of the fish skin after the lyophilization, (<bold>C</bold>) vacuum- packaging.</p></caption></fig>",
"<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Total accounts (CFU/cm<sup>2</sup> ± S.D) of aerobi... | [] | [] | [] | [] | [] | [] | [] | [
"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
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"<graphic xlink:href=\"41598_2024_51608_... | [] | [{"label": ["1."], "surname": ["Costa", "Lima J\u00fanior", "de Moraes Filho", "Fechine", "de Moraes", "Silva J\u00fanior", "do Nascimento Soares", "Rocha"], "given-names": ["BA", "EM", "MO", "FV", "MEA", "FR", "MFA", "MBS"], "article-title": ["Use of Tilapia skin as a Xenograft for pediatric burn treatment: A case report"], "source": ["J. Burn Care Res. Off. Publ. Am. Burn Assoc."], "year": ["2019"], "volume": ["40"], "issue": ["5"], "fpage": ["714"], "lpage": ["717"], "pub-id": ["10.1093/jbcr/irz085"]}, {"label": ["7."], "surname": ["Lima-Junior", "de Moraes Filho", "Costa", "Fechine", "de Moraes", "Silva-Junior", "Soares", "Rocha", "Leontsinis"], "given-names": ["EM", "MO", "BA", "FV", "MEA", "FR", "MFAN", "MBS", "CMP"], "article-title": ["Innovative treatment using tilapia skin as a xenograft for partial thickness burns after a gunpowder explosion"], "source": ["J. Surg. Rep."], "year": ["2019"], "volume": ["6"], "fpage": ["181"], "pub-id": ["10.1093/jscr/rjz181"]}, {"label": ["8."], "surname": ["Lordello", "Bianco", "Ribas"], "given-names": ["A", "A", "A"], "article-title": ["Leather of the tilapia, how healing in the treatment of burns"], "source": ["Nurse Care Open Access J."], "year": ["2019"], "volume": ["6"], "issue": ["4"], "fpage": ["147"], "lpage": ["153"], "pub-id": ["10.15406/ncoaj.2019.06.00199"]}, {"label": ["16."], "surname": ["Je\u017eek", "Buchtov\u00e1"], "given-names": ["F", "H"], "article-title": ["Physical and chemical changes in fresh chilled muscle tissue of common carp ("], "italic": ["Cyprinus carpio"], "source": ["Acta Vet. Brno"], "year": ["2007"], "volume": ["76"], "issue": ["8"], "fpage": ["83"], "lpage": ["92"], "pub-id": ["10.2754/avb200776S8S083"]}, {"label": ["17."], "surname": ["Ghaly", "Dave", "Budge", "Brooks"], "given-names": ["AE", "D", "S", "M"], "article-title": ["Fish spoilage mechanisms and preservation techniques"], "source": ["Am. J. Appl. Sci."], "year": ["2010"], "volume": ["7"], "issue": ["7"], "fpage": ["859"], "pub-id": ["10.3844/ajassp.2010.859.877"]}, {"label": ["18."], "surname": ["Haseley", "Oetjen"], "given-names": ["P", "G-W"], "source": ["Freeze-Drying"], "year": ["2017"], "publisher-name": ["Wiley"]}, {"label": ["22."], "mixed-citation": ["Morton, R. D. Aerobic plate count, compendium of methods for the microbiological examination of foods "], "bold": ["4"]}, {"label": ["23."], "surname": ["Atlas"], "given-names": ["R"], "source": ["Handbook of Microbiology Media"], "year": ["1993"], "publisher-name": ["CRC Press"]}, {"label": ["24."], "surname": ["Barnett", "Payne", "Yarrow"], "given-names": ["JA", "RW", "D"], "source": ["Yeasts: Characteristics and Identification"], "year": ["1990"], "publisher-name": ["Cambridge University Press"]}, {"label": ["25."], "surname": ["Leslie", "Summerell"], "given-names": ["JF", "BA"], "source": ["The Fusarium Laboratory Manual"], "year": ["2008"], "publisher-name": ["Wiley"]}, {"label": ["26."], "surname": ["Bergey"], "given-names": ["DH"], "source": ["Bergey's Manual of Determinative Bacteriology"], "year": ["1994"], "publisher-name": ["Lippincott Williams & Wilkins"]}, {"label": ["27."], "surname": ["Moubasher"], "given-names": ["A"], "source": ["Soil Fungi in Qatar and Other Arab Countries"], "year": ["1993"], "publisher-name": ["University of Qatar"]}, {"label": ["32."], "surname": ["Rogers", "Converse"], "given-names": ["BO", "JM"], "article-title": ["Bovine embryo skin zoografts as temporary biologic dressings for burns and other skin defects"], "source": ["Plast. Reconstr. Surg."], "year": ["1958"], "volume": ["22"], "issue": ["5"], "fpage": ["471"], "lpage": ["485"], "pub-id": ["10.1097/00006534-195811000-00007"]}, {"label": ["33."], "surname": ["Zhu", "Xu", "Zhao"], "given-names": ["Z", "G", "J"], "article-title": ["Clinical study on application of bovine amnion on burn wounds"], "source": ["Chin. J. Repar. Reconstr. Surg."], "year": ["2006"], "volume": ["20"], "issue": ["7"], "fpage": ["735"], "lpage": ["738"]}, {"label": ["34."], "surname": ["Switzek", "Moncrief", "Mills", "Lindberg"], "given-names": ["WE", "JA", "W", "RB"], "suffix": ["Jr"], "article-title": ["The use of canine heterografts in the therapy of thermal injury"], "source": ["J. Trauma Acute Care Surg."], "year": ["1966"], "volume": ["6"], "issue": ["3"], "fpage": ["391"], "lpage": ["398"], "pub-id": ["10.1097/00005373-196605000-00008"]}, {"label": ["35."], "surname": ["Sarto Piccolo", "Sarto Piccolo", "Sarto Piccolo"], "given-names": ["N", "M", "M"], "source": ["The Use of Frogskin as a Biological Dressing for Temporary Cover of Burn Wounds. Innovations in Plastic and Aesthetic Surgery"], "year": ["2008"], "publisher-name": ["Springer"], "fpage": ["129"], "lpage": ["137"]}, {"label": ["41."], "surname": ["Rangaraj", "Harding", "Leaper"], "given-names": ["A", "K", "D"], "article-title": ["Role of collagen in wound management"], "source": ["Wounds uk"], "year": ["2011"], "volume": ["7"], "issue": ["2"], "fpage": ["54"], "lpage": ["63"]}, {"label": ["42."], "surname": ["Yamamoto", "Igawa", "Sugimoto", "Yoshizawa", "Yanagiguchi", "Ikeda", "Yamada", "Hayashi"], "given-names": ["K", "K", "K", "Y", "K", "T", "S", "Y"], "article-title": ["Biological safety of fish (tilapia) collagen"], "source": ["BioMed Res. Int."], "year": ["2014"], "volume": ["1"], "fpage": ["1"], "pub-id": ["10.1155/2014/630757"]}, {"label": ["54."], "surname": ["Fissore", "Pisano"], "given-names": ["D", "R"], "article-title": ["Computer-aided framework for the design of freeze-drying cycles: Optimization of the operating conditions of the primary drying stage"], "source": ["Processes"], "year": ["2015"], "volume": ["3"], "issue": ["2"], "fpage": ["406"], "lpage": ["421"], "pub-id": ["10.3390/pr3020406"]}, {"label": ["59."], "surname": ["Willey", "Sherwood", "Woolverton"], "given-names": ["JM", "L", "CJ"], "source": ["Prescott's Microbiology"], "year": ["2011"], "publisher-name": ["McGraw-Hill"]}, {"label": ["62."], "surname": ["Hall", "Giaccia"], "given-names": ["EJ", "AJ"], "source": ["Radiobiology for the Radiologist"], "year": ["2006"], "publisher-name": ["Lippincott Williams and Wilkins"]}, {"label": ["64."], "surname": ["Rubin", "Neligan"], "given-names": ["JP", "PC"], "source": ["Plastic Surgery. Aesthetic Surgery"], "year": ["2017"], "publisher-name": ["Elsevier"]}, {"label": ["66."], "surname": ["Geethabanu", "Vanaja"], "given-names": ["S", "R"], "article-title": ["A study to analyse the influence of bacterial bio-burden on the success rate of split thickness skin grafting"], "source": ["J. Clin. Diagn. Res."], "year": ["2018"], "volume": ["12"], "issue": ["6"], "fpage": ["23"]}, {"label": ["67."], "mixed-citation": ["Nsaful, K. O., Paintsil, A. B., Dakubo, J. C., Nsaful, J., Appiah-Labi, K., Nartey, E. An evaluation of bacterial infection of split thickness skin grafts: At the Korle Bu Teaching Hospital. LAP LAMBERT Academic Publishing (2020)."]}] | {
"acronym": [],
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} | 67 | CC BY | no | 2024-01-15 23:41:56 | Sci Rep. 2024 Jan 13; 14:1253 | oa_package/80/57/PMC10787751.tar.gz |
PMC10787752 | 38218958 | [
"<title>Introduction</title>",
"<p id=\"Par2\">When matter vibrates, it transmits energy waves into our ears and produces sound. The sound changes depending on two factors: the material of the object and how slow or fast the object vibrates, making different sound waves. In other words, the object’s material affe... | [
"<title>Methodology</title>",
"<title>Pipeline overview</title>",
"<p id=\"Par14\">Figure ##FIG##1##2## shows the pipeline of the classifier of unknown events. Our system can divide into two stages, the self-supervised training stage, and the fine-tuning stage.</p>",
"<p id=\"Par15\">In the self-supervised tr... | [
"<title>Experiment and result</title>",
"<title>Evaluation metrics</title>",
"<p id=\"Par39\">We evaluate the performance of the open-set sound event recognition model by two evaluation metrics:</p>",
"<title>AUROC (area under the receiver operating characteristic)</title>",
"<p id=\"Par40\">It is a thresho... | [
"<title>Conclusion and discussion</title>",
"<p id=\"Par56\">In this paper, we investigated the self-supervised learning approach to open-set sound event classification. The experiment result on DCASE2019 Subtask 1C, GTZAN and Tori dataset demonstrated that the self-supervised pre-trained model improves the robus... | [
"<title>Conclusion and discussion</title>",
"<p id=\"Par56\">In this paper, we investigated the self-supervised learning approach to open-set sound event classification. The experiment result on DCASE2019 Subtask 1C, GTZAN and Tori dataset demonstrated that the self-supervised pre-trained model improves the robus... | [
"<p id=\"Par1\">Sound is one of the primary forms of sensory information that we use to perceive our surroundings. Usually, a sound event is a sequence of an audio clip obtained from an action. The action can be rhythm patterns, music genre, people speaking for a few seconds, etc. The sound event classification add... | [
"<title>Related work</title>",
"<title>Open set recognition</title>",
"<p id=\"Par9\">The open set recognition task aims to detect an unexpected sample but learns only among known samples. It has primarily been applied to the image classification problem<sup>##UREF##19##21##</sup>, and extended to other fields.... | [
"<title>Supplementary Information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41598-023-50639-7.</p>",
"<title>Acknowledgements</title>",
"<p>This work was funded by the National Research Foundation of Korea (NRF) under the Development of AI for Analysis and Synthesi... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>The category definition of the open set recognition task.</p></caption></fig>",
"<fig id=\"Fig2\"><label>Figure 2</label><caption><p>The pipeline of the open set sound event detection model.</p></caption></fig>",
"<fig id=\"Fig3\"><label>Figure 3</label><capt... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>The performance result of the open set acoustic scene classification dataset.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Model</th><th align=\"left\">ACC<sub>k</sub></th><th align=\"left\">ACC<sub>u</sub></th><th ali... | [
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] | [
"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
] | [
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"<inline-graphic xlink:href=\"4159... | [
"<media xlink:href=\"41598_2023_50639_MOESM1_ESM.pdf\"><caption><p>Supplementary Tables.</p></caption></media>"
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"definition": []
} | 65 | CC BY | no | 2024-01-15 23:41:56 | Sci Rep. 2024 Jan 13; 14:1282 | oa_package/76/1f/PMC10787752.tar.gz |
PMC10787753 | 38218989 | [
"<title>Introduction</title>",
"<p id=\"Par2\">There is no need to dwell on the global obesity epidemic in 2023 and its many connections with environmental issues. It has been estimated that the economic impact of overweight and obesity will grow from 2.19 to 3.3% of gross domestic product in > 160 countries b... | [
"<title>Material and methods</title>",
"<p id=\"Par13\">Following the steps described in previous studies<sup>##REF##29851650##2##–##UREF##1##5##</sup> and taking into account local possibilities, we have identified several measures that could be implemented in our center and its bariatric component, with a combi... | [
"<title>Results</title>",
"<title>Difficulties encountered to implement the study</title>",
"<p id=\"Par25\">\n<list list-type=\"bullet\"><list-item><p>Sparing GHGe is a dynamic and challenging process that currently requires finesse and local negotiation. Not everything is possible in terms of packaging, steri... | [
"<title>Discussion</title>",
"<title>Obesity, obesity surgery, and environment</title>",
"<p id=\"Par42\">Obesity per se is a major contributor to GHGe, in relation with the carbon footprint of food production and associated supply chain. The genesis of obesity thus significantly impacts GHGe worldwide. Regardi... | [
"<title>Conclusion</title>",
"<p id=\"Par52\">Is it cost-effective to try and diminish GHGe (and other items) in an operative setting? Does it affect surgical outcomes? Basically, the efforts we can make without further delay are mostly cheap or affordable, more strategic ones (e.g. shifting to less consuming ope... | [
"<p id=\"Par1\">Obesity is a growing issue worldwide, whose causes and consequences are linked to the environment and which therefore has a high carbon footprint. On the other hand, obesity surgery, along with other procedures in surgical suites, entails environmental consequences and responsibilities. We conducted... | [
"<title>Operating room and environment</title>",
"<p id=\"Par3\">Depending on calculations, the healthcare sector is responsible of up to 10% of total GHGe (USA, UK)<sup>##REF##29851650##2##,##REF##32516230##3##</sup>, which should raise awareness and call for urgent action<sup>##UREF##0##4##</sup>. It has been s... | [
"<title>Acknowledgements</title>",
"<p>Nisrine Nait, VEOLIA Lyon, France. ONYX Auvergne Rhône Alpes, 5 Rue des Frères Lumière, 69680 Chassieu.</p>",
"<title>Author contributions</title>",
"<p>J.D. wrote the manuscript text and reviewed it.</p>",
"<title>Data availability</title>",
"<p>Data will be made av... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Items to be recycled (disposable instruments), it may be published under a CC BY open access license.</p></caption></fig>",
"<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Separation of plastic parts and metallic parts, it may be published under a CC BY op... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Calculations.</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr><td align=\"left\" colspan=\"2\">Calculation methods: General<sup>##UREF##2##7##</sup></td></tr><tr><td align=\"left\" colspan=\"2\"> 1 kwH electricity = 0.0217 kg/ plastic produ... | [] | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>Calculation: A from 20 to 24, B from 16 to 20, etc. + and – accounted for.</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
... | [
"<graphic xlink:href=\"41598_2024_51441_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"41598_2024_51441_Fig2_HTML\" id=\"MO2\"/>"
] | [] | [{"label": ["4."], "surname": ["Tuech"], "given-names": ["JJ"], "article-title": ["Surgeons must get on the train of reducing greenhouse gas emissions for their operating theatre, their hospital and their university"], "source": ["J. Chir. Visc."], "year": ["2020"], "volume": ["157"], "fpage": ["273"], "lpage": ["275"]}, {"label": ["5."], "surname": ["Thiel", "Woods", "Bilec"], "given-names": ["CL", "NC", "MM"], "article-title": ["Strategies to reduce greenhouse gas emissions from laparoscopic surgery"], "source": ["AJPH Res."], "year": ["2021"], "volume": ["108"], "issue": ["S2"], "fpage": ["158"], "lpage": ["164"], "pub-id": ["10.2105/AJPH.2018.304397"]}, {"label": ["7."], "italic": ["UK Government GHG Factors for Company Reporting"]}, {"label": ["8."], "collab": ["Data on file"], "source": ["Efficiency Test Canister"], "year": ["2020"], "publisher-name": ["ZeoSys Medical"]}, {"label": ["9."], "ext-link": ["https://world.openfoodfacts.org/data"]}, {"label": ["13."], "surname": ["Neve", "Isaacs"], "given-names": ["K", "A"], "article-title": ["How does the food environment influence people engaged in weight management? A systematic review and thematic synthesis of the qualitative literature"], "source": ["Ob Rev."], "year": ["2020"], "volume": ["2"], "fpage": ["e13398"]}, {"label": ["19."], "surname": ["Aldoori", "Hartley", "MacFie"], "given-names": ["J", "J", "J"], "article-title": ["Sustainable surgery in and out of the operative theatre"], "source": ["Br. J. Surg"], "year": ["2022"], "volume": ["108"], "issue": ["6"], "fpage": ["e219"], "lpage": ["e220"], "pub-id": ["10.1093/bjs/znab073"]}, {"label": ["20."], "surname": ["Qin", "Velin", "Yates", "El Omrani", "Mc Leod", "Tudravu"], "given-names": ["RX", "L", "EF", "O", "E", "J"], "article-title": ["Building sustainable and resilient surgical systems: A narrative review of opportunities to integrate climate change into national surgical planning in the Western Pacific region"], "source": ["Lancet Region. Health-Western Pac."], "year": ["2022"], "volume": ["22"], "fpage": ["100407"], "pub-id": ["10.1016/j.lanwpc.2022.100407"]}] | {
"acronym": [],
"definition": []
} | 23 | CC BY | no | 2024-01-15 23:41:56 | Sci Rep. 2024 Jan 13; 14:1252 | oa_package/c5/20/PMC10787753.tar.gz |
PMC10787754 | 38218992 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Hot water balneotherapy in water rich in minerals and bicarbonate has traditionally been used around the world<sup>##UREF##0##1##</sup>, and various studies have been performed to investigate the effectiveness of balneotherapy in trauma<sup>##REF##32875343##2##</sup>... | [
"<title>Materials and methods</title>",
"<title>Participant eligibility and recruitment</title>",
"<p id=\"Par26\">This randomized clinical trial was conducted and reported in compliance with the Declaration of Helsinki and the CONSORT Statement, respectively. It was approved by the Chiyoda Paramedical Care Cli... | [
"<title>Results</title>",
"<title>Study population</title>",
"<p id=\"Par7\">A flow diagram of the study population is shown in Fig. ##FIG##0##1##. Of 41 potential participants who understood the content of the study and provided written informed consent, 39 completed the screening test, which comprised a simpl... | [
"<title>Discussion</title>",
"<p id=\"Par14\">Balneotherapy, i.e., the treatment of diseases by bathing, has been practiced since ancient times for therapeutic and medical purposes, and its therapeutic effects on cardiovascular and dermatological diseases have been documented<sup>##REF##30478742##25##–##REF##1291... | [] | [
"<p id=\"Par1\">We previously demonstrated that neutral bicarbonate ionized water (NBIW) bathing enhances blood flow by bicarbonate ions and described the underlying mechanism. However, additional clinical investigation was warranted to investigate the efficacy of NBIW bathing. Hence, we performed a randomized, ope... | [
"<title>Supplementary Information</title>",
"<p>\n</p>"
] | [
"<title>Supplementary Information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41598-024-51851-9.</p>",
"<title>Acknowledgements</title>",
"<p>The study was funded by Cranescience Co., Ltd., Tokyo, Japan. Editing assistance was provided by Jacquie Klesing, Board-certi... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Flow diagram of the randomized crossover-controlled trial. NBIW, neutral bicarbonate ionized water.</p></caption></fig>",
"<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Assessment of stress with the Brief Job Stress Questionnaire. The Brief Job Stress Que... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Background characteristics of the participants.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Items</th><th align=\"left\" colspan=\"2\">Mean (± SD)</th><th align=\"left\" rowspan=\"2\">Between-group compa... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>Between-group comparisons of age, mean hours of sleep, body mass index, and bathing time during the intervention were tested by Wilcoxon rank sum test; height, weight, diastolic blood pressure, pulse rate, Brief Job Stress Questionnaire, and the Pittsburgh Sleep Quality Index Japanese version, ... | [
"<graphic xlink:href=\"41598_2024_51851_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"41598_2024_51851_Fig2_HTML\" id=\"MO2\"/>",
"<graphic xlink:href=\"41598_2024_51851_Fig3_HTML\" id=\"MO3\"/>",
"<graphic xlink:href=\"41598_2024_51851_Fig4_HTML\" id=\"MO4\"/>"
] | [
"<media xlink:href=\"41598_2024_51851_MOESM1_ESM.pptx\"><caption><p>Supplementary Information.</p></caption></media>"
] | [{"label": ["1."], "surname": ["Nghargbu", "Siegel", "Selinus", "Finkelman"], "given-names": ["K", "M", "O", "R"], "article-title": ["Balneotherapy: An overview of healing with natural waters"], "source": ["Practical Applications of Medical Geology"], "year": ["2021"], "publisher-name": ["Springer"]}, {"label": ["7."], "surname": ["Cheleschi"], "given-names": ["S"], "article-title": ["Balneotherapy year in review 2021: Focus on the mechanisms of action of balneotherapy in rheumatic diseases"], "source": ["Environ. Sci. Pollut. Res."], "year": ["2022"], "volume": ["29"], "fpage": ["8054"], "lpage": ["8073"], "pub-id": ["10.1007/s11356-021-17780-0"]}, {"label": ["20."], "surname": ["Bucci", "Schwannauer", "Berry"], "given-names": ["S", "M", "N"], "article-title": ["The digital revolution and its impact on mental health care"], "source": ["Psychol. Psychother. Theory Res. Pract."], "year": ["2019"], "volume": ["92"], "fpage": ["277"], "lpage": ["297"], "pub-id": ["10.1111/papt.12222"]}, {"label": ["23."], "mixed-citation": ["Shimomitsu, T. The final development of the Brief Job Stress Questionnaire mainly used for assessment of the individuals. "], "italic": ["Ministry of Labour sponsored grant for the prevention of work-related illness: The 1999 report"]}, {"label": ["58."], "surname": ["Enomoto"], "given-names": ["M"], "article-title": ["Newly developed waist actigraphy and its sleep/wake scoring algorithm"], "source": ["Sleep Biol Rhythms"], "year": ["2009"], "volume": ["7"], "fpage": ["17"], "lpage": ["22"], "pub-id": ["10.1111/j.1479-8425.2008.00377.x"]}, {"label": ["60."], "surname": ["Curran", "Andrykowski", "Studts"], "given-names": ["SL", "MA", "JL"], "article-title": ["Short form of the Profile of Mood States (POMS-SF): Psychometric information"], "source": ["Psychol. Assess."], "year": ["1995"], "volume": ["7"], "fpage": ["80"], "lpage": ["83"], "pub-id": ["10.1037/1040-3590.7.1.80"]}, {"label": ["61."], "surname": ["Yokoyama", "Araki", "Kawakami", "Takeshita"], "given-names": ["K", "S", "N", "T"], "article-title": ["Production of the Japanese edition of profile of mood states (POMS): Assessment of reliability and validity"], "source": ["Nihon Kosyu Eisei Zasshi"], "year": ["1990"], "volume": ["37"], "fpage": ["913"], "lpage": ["918"]}] | {
"acronym": [],
"definition": []
} | 62 | CC BY | no | 2024-01-15 23:41:56 | Sci Rep. 2024 Jan 13; 14:1261 | oa_package/40/14/PMC10787754.tar.gz |
PMC10787755 | 38218892 | [
"<title>Introduction</title>",
"<p id=\"Par2\">The need for digitization in the German healthcare system is undisputed. But in a globalized world, where various sectors of the economy can internationally interact without problems, the innovation and modification of healthcare systems specifically in Germany, have... | [] | [] | [] | [] | [
"<p id=\"Par1\">With the advent of artificial intelligence and Big Data - projects, the necessity for a transition from analog medicine to modern-day solutions such as cloud computing becomes unavoidable. Even though this need is now common knowledge, the process is not always easy to start. Legislative changes, fo... | [
"<title>Supplementary information</title>",
"<p>\n\n</p>"
] | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41746-024-01000-3.</p>",
"<title>Author contributions</title>",
"<p>Conceptualization, M.P.; N.T.; methodology M.P., T.K., S.T., M.D.; F.T., N.T.; resources, M.P.; writing—original draft M.... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Example of UNITY platform and RayView DICOM viewer.</p></caption></fig>",
"<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Dataflow and systematic overview of the AIQNET architecture.</p></caption></fig>"
] | [] | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"41746_2024_1000_Fig1_HTML\" id=\"d32e448\"/>",
"<graphic xlink:href=\"41746_2024_1000_Fig2_HTML\" id=\"d32e464\"/>"
] | [
"<media xlink:href=\"41746_2024_1000_MOESM1_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>"
] | [{"label": ["1."], "surname": ["Caumanns"], "given-names": ["J"], "article-title": ["Zur Diskussion: Stand der Digitalisierung im deutschen Gesundheitswesen [Fordiscussion: The state of digitization of the German healthcaresystem]"], "source": ["Z. EvidFortbild Qual. Gesundhwes."], "year": ["2019"], "volume": ["143"], "fpage": ["22"], "lpage": ["29"], "pub-id": ["10.1016/j.zefq.2019.04.002"]}, {"label": ["4."], "mixed-citation": ["Klauber J., Geraedts M., Friedrich J. & Wasem J. Krankenhaus-Report 2019 \u2013 das digitale Krankenhaus. Springer Verlag. (2019)."]}, {"label": ["5."], "surname": ["Wirth", "Meurers", "Johns", "Prasser"], "given-names": ["FN", "T", "M", "F"], "article-title": ["Privacy-preserving data sharing infrastructures for medical research: systematization and comparison"], "source": ["BMC Med Inf. 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(2011)."]}, {"label": ["12."], "surname": ["Armbrust"], "given-names": ["M"], "article-title": ["A View of Cloud Computing"], "source": ["Commun. ACM"], "year": ["2010"], "volume": ["53"], "fpage": ["50"], "lpage": ["58"], "pub-id": ["10.1145/1721654.1721672"]}, {"label": ["14."], "mixed-citation": ["Avila-Garcia, M. S., Trefethen, A. E., Brady, M., Gleeson, F. & Goodman, D. Lowering the Barriers to Cancer Imaging,\u201d 2008 IEEE Fourth International Conference on eScience, Indianapolis, IN, USA, pp. 63\u201370, 10.1109/eScience.2008.33 (2008)."]}, {"label": ["22."], "mixed-citation": ["Schwanke-Seer/Helling: Cloud-Networking im Gesundheitswesen; 24.11.202."]}, {"label": ["23."], "mixed-citation": ["Extreme Networks: Welcome to 2020! Survey says: Healthcare is ready for Cloud Networking."]}, {"label": ["24."], "mixed-citation": ["IDG Research Services: Studie Cloud Native (2020)."]}, {"label": ["25."], "surname": ["Hassan"], "given-names": ["J"], "article-title": ["The Rise of Cloud Computing: Data Protection, Privacy, and Open Research Challenges-A Systematic Literature Review (SLR)"], "source": ["ComputIntellNeurosci"], "year": ["2022"], "volume": ["2022"], "fpage": ["8303504"]}, {"label": ["27."], "surname": ["Ogiela", "Ogiela", "Ko"], "given-names": ["L", "MR", "H"], "article-title": ["Intelligent Data Management and Security in Cloud Computing"], "source": ["Sens. (Basel)."], "year": ["2020"], "volume": ["20"], "fpage": ["3458"], "pub-id": ["10.3390/s20123458"]}, {"label": ["28."], "mixed-citation": ["Schneider, Einrichtungs\u00fcbergreifende elektronische Patientenakten, S. 415 ff. m.w.N.)."]}, {"label": ["29."], "mixed-citation": ["Verordnung (EU) 2016/679 des Europ\u00e4ischen Parlaments und des Rates vom 27. April 2016 zum Schutz nat\u00fcrlicher Personen bei der Verarbeitung personenbezogener Daten, zum freien Datenverkehr und zur Aufhebung der Richtlinie 95/46/EG, ABl. EU L 119 v. 04.05.2016, S. 1-88, mit Berichtigungen der deutschen Sprachfassung v. 22.11.2016 (ABl. L 314, S. 72) und v. 23.05.2018 (ABl. EU L 127, S.2."]}, {"label": ["30."], "mixed-citation": ["Vgl. Kurzpapier Nr. 13 der Datenschutzkonferenz (DSK) vom 17.12.2018."]}, {"label": ["31."], "ext-link": ["https://dsgvo-gesetz.de/art-28-dsgvo/"]}, {"label": ["32."], "mixed-citation": ["(Muster-)Berufsordnung f\u00fcr die in Deutschland t\u00e4tigen \u00c4rztinnen und \u00c4rzte."]}, {"label": ["33."], "mixed-citation": ["Artikel 1 Nr. 2 Buchst. c des Gesetzes zur Neuregelung des Schutzes von Geheimnissen bei der Mitwirkung Dritter an der Berufsaus\u00fcbung schweigepflichtiger Personen vom 30. Oktober, BGBl (2017)."]}, {"label": ["34."], "mixed-citation": ["Cornelius NJW, 3571, 3572 (2017)."]}, {"label": ["35."], "ext-link": ["https://dsgvo-gesetz.de/blndsg/"]}, {"label": ["36."], "ext-link": ["https://gesetze.berlin.de/bsbe/document/jlr-KHGBE2011V6P24"]}, {"label": ["37."], "ext-link": ["https://www.privacy-regulation.eu/en/article-44-general-principle-for-transfers-GDPR.htm"]}, {"label": ["39."], "ext-link": ["https://hapifhir.io/hapi-fhir/"]}, {"label": ["40."], "ext-link": ["https://www.datenschutzzentrum.de/uploads/projekte/its-apt/ITS.APT_D2.3_DatenschutzrechtlicheBetrachtung.pdf"]}, {"label": ["41."], "ext-link": ["https://gesundheitsdatenschutz.org/download/datenschutz_klin-studien.pdf"]}] | {
"acronym": [],
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} | 44 | CC BY | no | 2024-01-15 23:41:56 | NPJ Digit Med. 2024 Jan 13; 7:12 | oa_package/36/19/PMC10787755.tar.gz |
PMC10787756 | 38218993 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Groundwater is a crucial source of drinking water, and its availability is essential for economic growth in urban and rural areas worldwide<sup>##UREF##0##1##,##REF##37775015##2##</sup>. Groundwater is less vulnerable to contamination and pollution than surface water... | [
"<title>Materials and methods</title>",
"<title>Methodology</title>",
"<p id=\"Par8\">In this study, the following methodological steps have been followed to fulfill the current research objectives:<list list-type=\"bullet\"><list-item><p id=\"Par9\">In the initial stage, 352 water samples were collected from t... | [
"<title>Result</title>",
"<title>Statistical measures of selected hydrochemical parameters</title>",
"<p id=\"Par29\">In this study, three statistical tests were conducted on the selected hydrochemical dataset: MC, correlation coefficient, and PCA. The MC test (Table ##TAB##0##1##) showed that all factors were ... | [
"<title>Discussion</title>",
"<p id=\"Par34\">The fundamental reasons for spatio-temporal fluctuations in the groundwater supply are increasing water demand across all sectors and changing climatic conditions<sup>##UREF##46##57##</sup>. These factors present a significant challenge to water resource planners. Thi... | [
"<title>Conclusion</title>",
"<p id=\"Par39\">Globally, assessing susceptibility to groundwater contamination is crucial for proactive management aimed at safeguarding groundwater resources for various uses. Creating more effective sustainable development policies regarding potential groundwater pollution by util... | [
"<p id=\"Par1\">Determining the degree of high groundwater arsenic (As) and fluoride (F<sup>−</sup>) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point s... | [
"<title>Study area</title>",
"<p id=\"Par7\">The Ganges and Brahmaputra delta, known as the Ganges delta, is one of the mega-deltas in the world, covering an area of approximately 105,000 km<sup>2</sup>. It consists of Bangladesh and parts of India’s state of West Bengal, formed by sedimentation of the Ganga, Meg... | [
"<title>Acknowledgements</title>",
"<p>This publication was supported by the Deanship of Scientific Research at the King Faisal University, Saudi Arabia (Grant: 3422).</p>",
"<title>Author contributions</title>",
"<p>A.S.: conceptualization, methodology, investigation, formal analysis, visualization, writing-... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Details about the study area: (<bold>a</bold>) Ganges delta in a transnational boundary of Indo-Bangladesh region, (<bold>b</bold>) Ganges delta and its morphological types and (<bold>c</bold>) litholog profile of some selected points (this map was generated usin... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Multi-collinearity analysis of selected factors.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Factors</th><th align=\"left\">VIF</th><th align=\"left\">TOL</th></tr></thead><tbody><tr><td align=\"left\">Depth (m)</td><... | [
"<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepack... | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>**Correlation is significant at the 0.01 level (2-tailed).</p><p>*Correlation is significant at the 0.05 level (2-tailed).</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and i... | [
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"acronym": [],
"definition": []
} | 71 | CC BY | no | 2024-01-15 23:41:56 | Sci Rep. 2024 Jan 13; 14:1265 | oa_package/30/f4/PMC10787756.tar.gz |
PMC10787757 | 38218904 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Burning biomass, including wood, leaves, grass, and other materials, is likely to be a contributor to air pollution in many parts of the world<sup>##REF##27908624##1##</sup>. The transition from a natural to an agricultural ecosystem resulted in the loss of 20–80 ton... | [
"<title>Materials and methods</title>",
"<title>Synthesis of materials</title>",
"<title>Synthesis of kaolinite-biochar composite</title>",
"<p id=\"Par5\">Conocarpus waste was gathered from the King Saud University Campus in Riyadh, Saudi Arabia. The conocarpus waste was then washed, air-dried, ground, passe... | [
"<title>Results and discussion</title>",
"<title>Characterization</title>",
"<title>Proximate and chemical analyses</title>",
"<p id=\"Par25\">The results of the chemical and proximate analyses of the fabricated materials are presented in Table ##TAB##0##1##. The results revealed that the HC materials (HC, HC... | [
"<title>Results and discussion</title>",
"<title>Characterization</title>",
"<title>Proximate and chemical analyses</title>",
"<p id=\"Par25\">The results of the chemical and proximate analyses of the fabricated materials are presented in Table ##TAB##0##1##. The results revealed that the HC materials (HC, HC... | [
"<title>Conclusion</title>",
"<p id=\"Par46\">Conocarpus waste-derived BC and HC were composited with kaolinite deposits and characterized for their chemical, proximate, elemental, and structural properties. Moreover, the potential toxicity of the synthesized materials was assessed for their implications as soil ... | [
"<p id=\"Par1\">In this study, biochar (BC) and hydrochar (HC) composites were synthesized with natural kaolinite clay and their properties, stability, carbon (C) sequestration potential, polycyclic aromatic hydrocarbons (PAHs) toxicity, and impacts on maize germination and growth were explored. Conocarpus waste wa... | [] | [
"<title>Acknowledgements</title>",
"<p>The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project No. (IFKSUOR3-603-1).</p>",
"<title>Author contributions</title>",
"<p>Conceptualization, H.A.A.-... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>X-ray diffraction of (<bold>a</bold>) kaolinite and BM, (<bold>b</bold>) BC materials, and (<bold>c</bold>) HC materials. (<italic>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolin... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Chemical and proximate analyses of the biomass and synthesized materials.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Material</th><th align=\"left\">Moisture (%)</th><th align=\"left\">Volatiles (%)</th><th align=\"l... | [
"<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepack... | [] | [] | [] | [] | [] | [
"<table-wrap-foot><p>Biochar (BC), BC with 10% kaolinite enrichment (BCK10), BC with 20% kaolinite enrichment (BCK20), Hydrochar (HC), HC with 10% kaolinite enrichment (HCK10), HC with 20% kaolinite enrichment (HCK20), BM: biomass.</p></table-wrap-foot>",
"<table-wrap-foot><p>Biochar (BC), BC with 10% kaolinite e... | [
"<graphic xlink:href=\"41598_2024_51786_Article_Equ1.gif\" position=\"anchor\"/>",
"<graphic xlink:href=\"41598_2024_51786_Article_Equ2.gif\" position=\"anchor\"/>",
"<graphic xlink:href=\"41598_2024_51786_Article_Equ3.gif\" position=\"anchor\"/>",
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"acronym": [],
"definition": []
} | 130 | CC BY | no | 2024-01-15 23:41:56 | Sci Rep. 2024 Jan 13; 14:1259 | oa_package/e7/aa/PMC10787757.tar.gz |
PMC10787758 | 38218951 | [
"<title>Introduction</title>",
"<p id=\"Par2\">For poultry farm owners globally, detecting infertile eggs before incubation is an essential economic concern. Since the embryo at the initial stages of development is too small, fertility detection cannot be carried out by the traditional candling method. On the oth... | [
"<title>Materials and methods</title>",
"<title>Sample preparation</title>",
"<p id=\"Par13\">A total of 227 clean, white-shell, fresh, unwashed eggs were prepared including 131 fertile and 96 infertile eggs. The eggs were collected from a flock of 60 Leghorn laying breeder hens (Hy-Line W-36). The birds were p... | [
"<title>Results and discussion</title>",
"<title>Overview of spectral data</title>",
"<p id=\"Par36\">The raw and 1st derivative of average transmission spectra of fertile and infertile eggs are shown in Fig. ##FIG##3##4##. As shown, the fertile samples had higher raw transmission values than the infertile ones... | [
"<title>Results and discussion</title>",
"<title>Overview of spectral data</title>",
"<p id=\"Par36\">The raw and 1st derivative of average transmission spectra of fertile and infertile eggs are shown in Fig. ##FIG##3##4##. As shown, the fertile samples had higher raw transmission values than the infertile ones... | [
"<title>Conclusion</title>",
"<p id=\"Par53\">In this study, the feasibility of a line-scan hyperspectral imaging system in the Vis-SWNIR region was assessed for early detection of non-fertile eggs on day 0 before incubation. After capturing hyperspectral images of eggs, the edge detection method was used to segm... | [
"<p id=\"Par1\">Detection of infertile eggs prior to incubation can lead to an increase in the hatchability rate and prevent the wastage of billions of non-fertile eggs ended up by failed incubation. In this study, the feasibility of a line-scan hyperspectral imaging system in the visible and short-wavelength near-... | [] | [
"<title>Acknowledgements</title>",
"<p>The authors gratefully appreciate the financial support from Isfahan University of Technology, Isfahan, Iran.</p>",
"<title>Author contributions</title>",
"<p>M.G.: Methodology, validation, formal analysis, investigation, data curation, writing—original draft, visualizat... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Hyperspectral imaging system for acquiring the egg pictures in full-transmittance mode (<bold>a</bold>) system set-up with main and secondary incubators, (<bold>b</bold>) The schematic of the hyperspectral imaging system, (<bold>c</bold>) light sources, and (<bol... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Test set validation results of SIMCA analysis for the detection of infertile and fertile eggs before incubation or day 0.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Preprocess</th><th align=\"left\" row... | [
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"<table-wrap-foot><p>1st Der.: First derivative; 2nd Der.: Second derivative; Acc.: Accuracy; BC: Baseline correction; MSC: Multiplicative scatter correction; Norm.: Normalization; PCs: Principal components; Percis.: Precision; Preprocess.: Preprocessing; Sen.: Sensitivity; SNV: Standard normal variate; Spe.: Speci... | [
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Agric."], "year": ["2019"], "volume": ["157"], "fpage": ["471"], "lpage": ["478"], "pub-id": ["10.1016/j.compag.2019.01.021"]}, {"label": ["4."], "mixed-citation": ["Adegbenjo, A. O., Liu, L. & Ngadi, M. O. Non-destructive assessment of chicken egg fertility. "], "italic": ["Sensors (Basel)"], "bold": ["20"]}, {"label": ["5."], "surname": ["Das", "Evans"], "given-names": ["K", "M"], "article-title": ["Detecting fertility of hatching eggs using machine vision II: Neural network classifiers"], "source": ["Trans. ASAE"], "year": ["1992"], "volume": ["35"], "fpage": ["2035"], "lpage": ["2041"], "pub-id": ["10.13031/2013.28832"]}, {"label": ["6."], "surname": ["Lin", "Yeh", "Chen", "Chiou", "Lee"], "given-names": ["CS", "PT", "DC", "YC", "CH"], "article-title": ["The identification and filtering of fertilized eggs with a thermal imaging system"], "source": ["Comput. Electron. 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"acronym": [],
"definition": []
} | 35 | CC BY | no | 2024-01-15 23:41:56 | Sci Rep. 2024 Jan 14; 14:1289 | oa_package/fe/21/PMC10787758.tar.gz |
PMC10787759 | 38218949 | [
"<title>Introduction</title>",
"<p id=\"Par3\">The combustion of fossil fuels for industrialization and transportation, which accompanies the rapid growth of cities, has resulted in the release of massive volumes of CO<sub>2</sub> gas into the atmosphere<sup>##REF##24886955##1##</sup>. However, the excessive emis... | [
"<title>Methods</title>",
"<title>Catalyst preparation</title>",
"<title>Graphene oxide (GO)</title>",
"<p id=\"Par34\">K<sub>2</sub>S<sub>2</sub>O<sub>8</sub> (7.5 g, Rgent Chemical Reagent Co.) and P<sub>2</sub>O<sub>5</sub> (7.5 g, Damao Chemical Reagent Co.) were added to a round-bottomed flask under the ... | [
"<title>Results</title>",
"<title>Adjustable spatial distribution of multiple sites</title>",
"<p id=\"Par8\">A series of graphene-supported Fe-Co bimetallic catalysts with identical total contents of Fe, Co, and K were synthesized by varying the addition order of Fe and Co during the hydrothermal and impregnat... | [
"<title>Discussion</title>",
"<p id=\"Par33\">In summary, graphene-fence-regulated Fe-Co bimetallic catalysts with homogeneous active sites or scattered spatial dual active sites were successfully prepared and employed in the CO<sub>2</sub> hydrogenation reaction for the selective production of light olefins or L... | [] | [
"<p id=\"Par1\">Tuning CO<sub>2</sub> hydrogenation product distribution to obtain high-selectivity target products is of great significance. However, due to the imprecise regulation of chain propagation and hydrogenation reactions, the oriented synthesis of a single product is challenging. Herein, we report an app... | [
"<title>Supplementary information</title>",
"<p>\n\n\n</p>",
"<title>Source data</title>",
"<p>\n\n</p>"
] | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41467-024-44763-9.</p>",
"<title>Acknowledgements</title>",
"<p>This work was supported by National Natural Science Foundation of China (22102001) (L.G.), NEDO (New energy and industrial Te... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>The structure and morphology investigation of graphene-supported Fe-Co catalysts.</title><p><bold>a</bold> XRD patterns of fresh catalysts. <bold>b</bold> XRD patterns of GO and rGO. <bold>c</bold> SEM images of GO and rGO; the bars stand for 40 μm. <bold>d</bo... | [] | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>"
] | [
"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Jiaming Liang, Jiangtao Liu.</p></fn></fn-group>"
] | [
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"<media xlink:href=\"41467_2024_44763_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>",
"<media xlink:href=\"41467_2024_44763_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>",
"<media xlink:href=\"41467_2024_44763_MOESM3_ESM.zip\"><caption><p>Source data</p></cap... | [{"label": ["2."], "surname": ["Liang"], "given-names": ["J"], "article-title": ["Direct conversion of CO"], "sub": ["2"], "source": ["Ind. Eng. Chem. Res."], "year": ["2022"], "volume": ["61"], "fpage": ["10336"], "lpage": ["10346"], "pub-id": ["10.1021/acs.iecr.1c05061"]}, {"label": ["5."], "surname": ["Sachs"], "given-names": ["JD"], "article-title": ["Six transformations to achieve the sustainable development goals"], "source": ["Nat. Sustain."], "year": ["2019"], "volume": ["2"], "fpage": ["805"], "lpage": ["814"], "pub-id": ["10.1038/s41893-019-0352-9"]}, {"label": ["7."], "surname": ["Dong"], "given-names": ["X"], "article-title": ["CO"], "sub": ["2", "2"], "source": ["Appl. Catal. 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"acronym": [],
"definition": []
} | 68 | CC BY | no | 2024-01-15 23:41:57 | Nat Commun. 2024 Jan 13; 15:512 | oa_package/39/28/PMC10787759.tar.gz |
PMC10787760 | 38218848 | [
"<title>Introduction</title>",
"<p id=\"Par3\">Host resistance is the most effective way to reduce yield loss caused by crop diseases. Unfortunately, deployed resistance (R) genes frequently become ineffective due to genetic changes in the pathogens. Breeding for resistance is costly and introduction of resistanc... | [
"<title>Methods</title>",
"<title>Plant materials and field trials</title>",
"<p id=\"Par32\"><italic>Dasypyrum villosum</italic> accessions and two panels of wheat-<italic>D. villosum</italic> introgression lines listed in Supplementary Table ##SUPPL##0##1## are maintained at the Cytogenetics Institute, Nanjin... | [
"<title>Results</title>",
"<title>High-resolution mapping reveals an inhibitor linked with <italic>Pm55</italic></title>",
"<p id=\"Par8\">To accurately evaluate the response of the 5VS-translocation lines to powdery mildew, the translocated chromosomes T5AL·5 V#4 S, T5DL·5 V#4 S and T5DL·5 V#5 S were transferr... | [
"<title>Discussion</title>",
"<p id=\"Par27\">Alien introgressions have greatly enriched the otherwise limited gene pool in bread wheat. However, the successful map-based cloning of introgressed resistance genes has often been hampered by insufficient recombination in the alien chromatin regions<sup>##REF##279098... | [] | [
"<p id=\"Par1\">Powdery mildew poses a significant threat to wheat crops worldwide, emphasizing the need for durable disease control strategies. The wheat-<italic>Dasypyrum villosum</italic> T5AL·5 V#4 S and T5DL·5 V#4 S translocation lines carrying powdery mildew resistant gene <italic>Pm55</italic> shows developm... | [
"<title>Supplementary information</title>",
"<p>\n\n\n\n\n\n\n</p>",
"<title>Source data</title>",
"<p>\n\n</p>"
] | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41467-024-44796-0.</p>",
"<title>Acknowledgements</title>",
"<p>We thank Prof. Robert McIntosh, University of Sydney, for reviewing the manuscript. We also thank Zdeňka Dubská, Romana Šperk... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Map-based cloning of the candidates in both inhibitor and <italic>Pm55/Pm5V</italic> intervals on the chromosome arm 5VS of <italic>D. villosum</italic>.</title><p><bold>a</bold> Seedling responses of three 5VS-introgression lines inoculated with <italic>Bgt</i... | [] | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>",
"<supplementary-material ... | [
"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Chuntian Lu, Jie Du.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"41467_2024_44796_Fig1_HTML\" id=\"d32e750\"/>",
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"<media xlink:href=\"41467_2024_44796_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>",
"<media xlink:href=\"41467_2024_44796_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>",
"<media xlink:href=\"41467_2024_44796_MOESM3_ESM.pdf\"><caption><p>Description of Addit... | [{"label": ["1."], "surname": ["Tian", "Traw", "Chen", "Kreitman", "Bergelson"], "given-names": ["D", "MB", "JQ", "M", "J"], "article-title": ["Fitness costs of R-gene-mediated resistance in "], "italic": ["Arabidopsis thaliana"], "source": ["Nature"], "year": ["2013"], "volume": ["423"], "fpage": ["74"], "lpage": ["77"], "pub-id": ["10.1038/nature01588"]}, {"label": ["6."], "surname": ["Johnson"], "given-names": ["R"], "article-title": ["A critical analysis of durable resistance"], "source": ["Annu. Rev. Phytopathol."], "year": ["1984"], "volume": ["22"], "fpage": ["309"], "lpage": ["330"], "pub-id": ["10.1146/annurev.py.22.090184.001521"]}, {"label": ["12."], "surname": ["Zhang"], "given-names": ["RQ"], "italic": ["Pm55", "Dasypyrum villosum"], "source": ["Theor. Appl. Genet."], "year": ["2016"], "volume": ["129"], "fpage": ["975"], "lpage": ["1984"], "pub-id": ["10.1007/s00122-016-2753-8"]}, {"label": ["13."], "surname": ["Zhang"], "given-names": ["RQ"], "italic": ["Pm67", "Dasypyrum villosum", "Triticum aestivum"], "source": ["Crop J."], "year": ["2021"], "volume": ["9"], "fpage": ["882"], "lpage": ["888"], "pub-id": ["10.1016/j.cj.2020.09.012"]}, {"label": ["16."], "surname": ["Lu"], "given-names": ["X"], "article-title": ["Allelic barley MLA immune receptors recognize sequence unrelated avirulence effectors of the powdery mildew pathogen"], "source": ["Proc. Natl Acad. Sci. USA"], "year": ["2016"], "volume": ["113"], "fpage": ["6486"], "lpage": ["6495"], "pub-id": ["10.1073/pnas.1612947113"]}, {"label": ["25."], "surname": ["Zhang"], "given-names": ["RQ"], "article-title": ["The genetic effect of wheat-"], "italic": ["D. villosum"], "source": ["Sci. Agric. Sin."], "year": ["2015"], "volume": ["48"], "fpage": ["1041"], "lpage": ["1051"]}, {"label": ["26."], "surname": ["Fu"], "given-names": ["BS"], "article-title": ["Development of molecular markers specific to 5VS chromosome arm of "], "italic": ["Dasypyrum villosum"], "source": ["J. Plant Gene. Res."], "year": ["2022"], "volume": ["23"], "fpage": ["195"], "lpage": ["208"]}, {"label": ["31."], "surname": ["Zhu"], "given-names": ["SY"], "article-title": ["Orthologous genes "], "italic": ["Pm12", "Pm21"], "source": ["Plant Commun."], "year": ["2022"], "volume": ["9"], "fpage": ["100472"]}, {"label": ["40."], "surname": ["Said"], "given-names": ["M"], "article-title": ["Dissecting the complex genome of crested wheatgrass by chromosome flow sorting"], "source": ["Plant Genome"], "year": ["2019"], "volume": ["12"], "fpage": ["180096"], "pub-id": ["10.3835/plantgenome2018.12.0096"]}, {"label": ["43."], "mixed-citation": ["Zeng, Q. D. et al. Novel high-accuracy genome assembly method utilizing a high-throughput workflow. Preprint 10.1101/2020.11.26.400507 (2020)."]}, {"label": ["49."], "surname": ["Li"], "given-names": ["JH"], "article-title": ["Dissecting conserved cis-regulatory modules of "], "italic": ["Glu-1"], "source": ["Crop J."], "year": ["2019"], "volume": ["7"], "fpage": ["8"], "lpage": ["18"], "pub-id": ["10.1016/j.cj.2018.08.003"]}] | {
"acronym": [],
"definition": []
} | 56 | CC BY | no | 2024-01-15 23:41:57 | Nat Commun. 2024 Jan 13; 15:503 | oa_package/a3/7b/PMC10787760.tar.gz |
PMC10787761 | 38218959 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Accumulation of evidence regarding molecular interactions in biological processes has paved the way for the construction of various biological networks, including signaling, Protein-Protein Interaction (PPI), metabolic, and gene regulatory networks, among others. The... | [
"<title>Methods</title>",
"<title>Construction of the Global Integrative Network from ten databases</title>",
"<p id=\"Par30\">The owl files of BioPAX level 3 prepared by PathwayCommons were downloaded from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.pathwaycommons.org/archives/PC2/v12/\">https://w... | [
"<title>Results</title>",
"<title>Conversion of BioPAX to SIFI</title>",
"<p id=\"Par9\">In our efforts to tackle the challenges of different knowledge base languages, we developed a R package named “SIFItools” to efficiently convert BioPAX level 3 owl files from various databases into SIFI format. OWL (Web Ont... | [
"<title>Discussion</title>",
"<p id=\"Par20\">In this work, we compiled a much more comprehensive GIN for human compared with the previous version. The previous GIN<sup>##REF##36533264##22##</sup> for human was built only upon KEGG, which includes 5145 genes and 1501 metabolites. In the present work, we compiled ... | [] | [
"<p id=\"Par1\">Knowledge bases have been instrumental in advancing biological research, facilitating pathway analysis and data visualization, which are now widely employed in the scientific community. Despite the establishment of several prominent knowledge bases focusing on signaling, metabolic networks, or both,... | [
"<title>Supplementary information</title>",
"<p>\n\n\n</p>"
] | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41540-024-00330-y.</p>",
"<title>Acknowledgements</title>",
"<p>We thank Yan Yan for her insightful discussion on SIFItools, and Siyi Su for his help on the glycolysis illustration.</p>",
... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Construction of global integrative network for human.</title><p>The workflow consists of (<bold>a</bold>, <bold>b</bold>), the extraction of reactions from the BioPAX level3 (owl) files of 10 databases, (<bold>c</bold>) the conversion of the data into meta-path... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Top 20 nodes with highest degrees.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Id</th><th>Name</th><th>Degree</th></tr></thead><tbody><tr><td>CID962</td><td>Water</td><td>3179</td></tr><tr><td>CID1038</td><td>H+</td><td>2925</td></t... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>",
"<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>"
] | [
"<table-wrap-foot><p>The IDs in bold are protein kinases.</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Xiao Chang, She... | [
"<graphic xlink:href=\"41540_2024_330_Fig1_HTML\" id=\"d32e410\"/>",
"<graphic xlink:href=\"41540_2024_330_Fig2_HTML\" id=\"d32e461\"/>",
"<graphic xlink:href=\"41540_2024_330_Fig3_HTML\" id=\"d32e515\"/>",
"<graphic xlink:href=\"41540_2024_330_Fig4_HTML\" id=\"d32e725\"/>"
] | [
"<media xlink:href=\"41540_2024_330_MOESM1_ESM.pdf\"><caption><p>Supplementary Figures</p></caption></media>",
"<media xlink:href=\"41540_2024_330_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>"
] | [{"label": ["4."], "surname": ["Loers", "Vermeirssen"], "given-names": ["JU", "V"], "article-title": ["SUBATOMIC: a SUbgraph BAsed mulTi-OMIcs clustering framework to analyze integrated multi-edge networks"], "source": ["BMC Bioinforma."], "year": ["2022"], "volume": ["23"], "fpage": ["363"], "lpage": ["363"], "pub-id": ["10.1186/s12859-022-04908-3"]}, {"label": ["5."], "surname": ["Catlett"], "given-names": ["NL"], "article-title": ["Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data"], "source": ["BMC Bioinforma."], "year": ["2013"], "volume": ["14"], "fpage": ["340"], "pub-id": ["10.1186/1471-2105-14-340"]}, {"label": ["17."], "surname": ["van Iersel"], "given-names": ["MP"], "article-title": ["Presenting and exploring biological pathways with PathVisio"], "source": ["BMC Bioinforma."], "year": ["2008"], "volume": ["9"], "fpage": ["399"], "pub-id": ["10.1186/1471-2105-9-399"]}, {"label": ["18."], "mixed-citation": ["Hucka, M. in "], "italic": ["Encyclopedia of Systems Biology"]}, {"label": ["20."], "surname": ["Sompairac"], "given-names": ["N"], "article-title": ["Metabolic and signalling network maps integration: application to cross-talk studies and omics data analysis in cancer"], "source": ["BMC Bioinforma."], "year": ["2019"], "volume": ["20"], "fpage": ["140"], "pub-id": ["10.1186/s12859-019-2682-z"]}, {"label": ["38."], "surname": ["Weber", "Schreiber", "Daub"], "given-names": ["C", "TB", "H"], "article-title": ["Dual phosphoproteomics and chemical proteomics analysis of erlotinib and gefitinib interference in acute myeloid leukemia cells"], "source": ["J. Proteom."], "year": ["2012"], "volume": ["75"], "fpage": ["1343"], "lpage": ["1356"], "pub-id": ["10.1016/j.jprot.2011.11.004"]}, {"label": ["59."], "mixed-citation": ["Canzler, S. metaboliteIDmapping: mapping of metabolite IDs from different sources. "], "italic": ["R package version 0.99.10"], "ext-link": ["https://github.com/yigbt/metaboliteIDmapping"]}] | {
"acronym": [],
"definition": []
} | 60 | CC BY | no | 2024-01-15 23:41:57 | NPJ Syst Biol Appl. 2024 Jan 13; 10:4 | oa_package/88/16/PMC10787761.tar.gz |
PMC10787762 | 38218942 | [
"<title>Introduction</title>",
"<p id=\"Par10\">The ‘use of opportunistic modes of nutrient acquisition’ was recently described as a hallmark of cancer cells [##REF##26771115##1##], living in a nutrient-poor microenvironment. Cancer cells must adapt their metabolism to support biomass production, ATP generation a... | [] | [] | [] | [] | [
"<p id=\"Par1\">Amino acid metabolism plays important roles in tumor biology and tumor therapy. Accumulating evidence has shown that amino acids contribute to tumorigenesis and tumor immunity by acting as nutrients, signaling molecules, and could also regulate gene transcription and epigenetic modification. Therefo... | [
"<title>Facts</title>",
"<p id=\"Par2\">\n<list list-type=\"bullet\"><list-item><p id=\"Par3\">Altered amino acid metabolism in tumors challenges the traditional classification of essential and nonessential amino acids.</p></list-item><list-item><p id=\"Par4\">Amino acids have emerged as pivotal regulators in tum... | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41419-024-06435-w.</p>",
"<title>Author contributions</title>",
"<p>Conceptualization: SX Compilation of literature: JC and LC. Article writing and editing: JC and LC. Figures: JC and LC. S... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Amino acid metabolism in tumors.</title><p>ASCT2 serves as the primary glutamine transporter, importing glutamine to generate purines and pyrimidines. GLS1 catalyzes glutaminolysis, generating glutamate which synthesizes GSH to maintain redox balance. Glutamate... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Key enzymes/transporters and their drugs in cancer amino acid metabolism.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>Target</th><th>Drug</th><th>Cancer type</th><th>Clinical phases</th></tr></thead><tbody><tr><td>Amino acid tr... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<fn-group><fn><p>Edited by Professor Anastasis Stephanou</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Jie Chen, Likun Cui.</p></fn></fn-group>"
] | [
"<graphic xlink:href=\"41419_2024_6435_Fig1_HTML\" id=\"d32e312\"/>",
"<graphic xlink:href=\"41419_2024_6435_Fig2_HTML\" id=\"d32e708\"/>",
"<graphic xlink:href=\"41419_2024_6435_Fig3_HTML\" id=\"d32e917\"/>",
"<graphic xlink:href=\"41419_2024_6435_Fig4_HTML\" id=\"d32e1084\"/>"
] | [
"<media xlink:href=\"41419_2024_6435_MOESM1_ESM.pdf\"><caption><p>checklist</p></caption></media>"
] | [{"label": ["128."], "surname": ["Xie", "Pei"], "given-names": ["M", "D-S"], "article-title": ["Serine hydroxymethyltransferase 2: a novel target for human cancer therapy"], "source": ["Investig N. Drugs"], "year": ["2021"], "volume": ["39"], "fpage": ["1671"], "lpage": ["81"], "pub-id": ["10.1007/s10637-021-01144-z"]}, {"label": ["132."], "surname": ["Chakrabarti", "Gerber", "Boothman"], "given-names": ["G", "DE", "DA"], "article-title": ["Expanding antitumor therapeutic windows by targeting cancer-specific nicotinamide adenine dinucleotide phosphate-biogenesis pathways"], "source": ["Clin Pharm"], "year": ["2015"], "volume": ["7"], "fpage": ["57"], "lpage": ["68"]}, {"label": ["137."], "surname": ["Recouvreux", "Commisso"], "given-names": ["MV", "C"], "article-title": ["Macropinocytosis: a metabolic adaptation to nutrient stress in cancer"], "source": ["Front Endocrinol"], "year": ["2017"], "volume": ["8"], "fpage": ["261"], "pub-id": ["10.3389/fendo.2017.00261"]}, {"label": ["217."], "surname": ["Avramis", "Tiwari"], "given-names": ["VI", "PN"], "article-title": ["Asparaginase (native ASNase or pegylated ASNase) in the treatment of acute lymphoblastic leukemia"], "source": ["Int J Nanomed"], "year": ["2006"], "volume": ["1"], "fpage": ["241"], "lpage": ["54"]}, {"label": ["222."], "surname": ["Holtsberg", "Ensor", "Steiner", "Bomalaski", "Clark"], "given-names": ["FW", "CM", "MR", "JS", "MA"], "article-title": ["Poly(ethylene glycol) (PEG) conjugated arginine deiminase: effects of PEG formulations on its pharmacological properties"], "source": ["J Controlled Release"], "year": ["2002"], "volume": ["80"], "fpage": ["259"], "lpage": ["71"], "pub-id": ["10.1016/S0168-3659(02)00042-1"]}] | {
"acronym": [],
"definition": []
} | 235 | CC BY | no | 2024-01-15 23:41:57 | Cell Death Dis. 2024 Jan 13; 15(1):42 | oa_package/c5/18/PMC10787762.tar.gz |
PMC10787763 | 38218974 | [
"<title>Introduction</title>",
"<p id=\"Par2\">Biocatalysts have long promised to play a major role in synthetic organic chemistry, offering potentially greener routes to high-value chemicals<sup>##UREF##0##1##,##REF##11196655##2##</sup>. However, their highly-valued specificity and selectivity also leads to limi... | [
"<title>Materials and methods</title>",
"<title>Materials</title>",
"<p id=\"Par12\">All chemicals were obtained from Sigma-Aldrich, Merck, Germany. Non-natural amino acid, p-Amino <sc>l</sc>-phenylalanine (pAMF) and p-Cyano-<sc>l</sc>-Phenylalanine (pCNF) were obtained from Chem Cruz (Texas, USA).</p>",
"<p ... | [
"<title>Results and discussion</title>",
"<p id=\"Par22\">Our goal was to find <italic>E. coli</italic> TK variants with improved activity towards aromatic acceptor aldehydes 3-FBA, 4-FBA and 3-HBA and with pyruvate as the donor, starting from the best variant to date, 6M (H192P/A282P/I365L/G506A/D469T/H100L). Th... | [
"<title>Results and discussion</title>",
"<p id=\"Par22\">Our goal was to find <italic>E. coli</italic> TK variants with improved activity towards aromatic acceptor aldehydes 3-FBA, 4-FBA and 3-HBA and with pyruvate as the donor, starting from the best variant to date, 6M (H192P/A282P/I365L/G506A/D469T/H100L). Th... | [
"<title>Conclusions</title>",
"<p id=\"Par40\">While earlier attempts to combine aromatic aldehyde-accepting mutations (S385Y, R520Q) into pyruvate accepting variants led to losses in activity, the current small library of S385X and R520Q mutations within the “6M” scaffold resulted in variants with up to 630× inc... | [
"<p id=\"Par1\">Improving the range of substrates accepted by enzymes with high catalytic activity remains an important goal for the industrialisation of biocatalysis. Many enzymes catalyse two-substrate reactions which increases the complexity in engineering them for the synthesis of alternative products. Often mu... | [
"<title>Supplementary Information</title>",
"<p>\n</p>"
] | [
"<title>Supplementary Information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41598-024-51831-z.</p>",
"<title>Acknowledgements</title>",
"<p>We gratefully acknowledge the Engineering and Physical Sciences Research Council (EPSRC) for funding (EP/P006485/1) and also ... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Lineages of <italic>E. coli</italic> TK variants. Multiple phases of rational, and semi-rational design guided by structure, bioinformatics and molecular dynamics simulations have introduced mutations that altered enzyme stability and the specificity for both ald... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Specific activities of previous variants towards aromatic aldehydes.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Variant</th><th align=\"left\">Donor</th><th align=\"left\">Acceptor</th><th align=\"left\"><italic>k</i... | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<table-wrap-foot><p>6M: H100L/H192P/A282P/I365L/D469T/G506A.</p><p>All enzymes purified. Reactions at 25 °C with 0.1 mg/mL TK, 50 mM Tris–HCl, 2.4 mM ThDP, 9 mM MgCl<sub>2</sub>, pH 7.0, and final concentrations of 50 mM aromatic aldehydes and 50 mM sodium pyruvate. 6 M reaction was at 0.07 mg/mL enzyme<sup>##REF#... | [
"<graphic xlink:href=\"41598_2024_51831_Fig1_HTML\" id=\"MO1\"/>",
"<graphic xlink:href=\"41598_2024_51831_Sch1_HTML\" id=\"MO2\"/>",
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"<graphic xlink:href=\"41598_2024_51831_Fig3_HTML\" id=\"MO4\"/>",
"<graphic xlink:href=\"41598_2024_51831_... | [
"<media xlink:href=\"41598_2024_51831_MOESM1_ESM.docx\"><caption><p>Supplementary Information.</p></caption></media>"
] | [{"label": ["1."], "surname": ["Albarr\u00e1n-Velo", "Gonz\u00e1lez-Mart\u00ednez", "Gotor-Fern\u00e1ndez"], "given-names": ["J", "D", "V"], "article-title": ["Stereoselective biocatalysis: A mature technology for the asymmetric synthesis of pharmaceutical building blocks"], "source": ["Biocatal. Biotrans."], "year": ["2018"], "volume": ["36"], "fpage": ["102"], "lpage": ["130"], "pub-id": ["10.1080/10242422.2017.1340457"]}, {"label": ["12."], "surname": ["Draths"], "given-names": ["KM"], "article-title": ["Biocatalytic synthesis of aromatics from D-glucose: The role of transketolase"], "source": ["J. Am. Chem. Soc."], "year": ["1992"], "volume": ["114"], "fpage": ["3956"], "lpage": ["3962"], "pub-id": ["10.1021/ja00036a050"]}, {"label": ["13."], "surname": ["\u015awi\u0119tochowska", "\u0141ochowicz", "Ocal", "Pollegioni", "Charmantray", "Hecquet", "Szyma\u0144ska"], "given-names": ["D", "A", "N", "L", "F", "L", "K"], "article-title": ["Co-immobilization of D-amino acid oxidase, catalase, and transketolase for one-pot, two-step synthesis of L-erythrulose"], "source": ["Catalysts"], "year": ["2023"], "volume": ["13"], "fpage": ["95"], "pub-id": ["10.3390/catal13010095"]}, {"label": ["14."], "surname": ["Ocal", "Arbia", "Lagarde", "Joly", "Gittings", "Graham", "Charmantray", "Hecquet"], "given-names": ["N", "G", "A", "M", "S", "KM", "F", "L"], "article-title": ["Synthesis of deoxysugars from aliphatic \u03b1-ketoacids and aldoses catalyzed by thermostable transketolase variants from "], "italic": ["Geobacillus", "stearothermophilus"], "source": ["Adv. Syn. Catal."], "year": ["2023"], "volume": ["365"], "fpage": ["78"], "lpage": ["87"], "pub-id": ["10.1002/adsc.202201190"]}, {"label": ["15."], "surname": ["F\u00faster-Fern\u00e1ndez", "Hecquet", "Fessner"], "given-names": ["I", "L", "WD"], "article-title": ["Transketolase catalyzed synthesis of N-aryl hydroxamic acids"], "source": ["Adv. Syn. Catal."], "year": ["2022"], "volume": ["364"], "fpage": ["612"], "lpage": ["621"], "pub-id": ["10.1002/adsc.202101100"]}, {"label": ["22."], "surname": ["Sprenger", "Pohl"], "given-names": ["GA", "M"], "article-title": ["Synthetic potential of thiamin diphosphate-dependent enzymes"], "source": ["J. Mol. Catal. B: Enzym."], "year": ["1999"], "volume": ["6"], "fpage": ["145"], "lpage": ["159"], "pub-id": ["10.1016/S1381-1177(98)00107-6"]}, {"label": ["27."], "surname": ["Zabar", "Lorilliere", "Yi", "Thangavelu", "Devamani", "Nauton", "Charmantray", "Helaine", "Fessner", "Hecquet"], "given-names": ["JA", "M", "D", "S", "T", "L", "F", "V", "WD", "L"], "article-title": ["Engineering a thermostable transketolase for unnatural conversion of (2S)-hydroxyaldehydes"], "source": ["Adv. Syn. Catal."], "year": ["2015"], "volume": ["357"], "fpage": ["1715"], "lpage": ["1720"], "pub-id": ["10.1002/adsc.201500207"]}, {"label": ["28."], "surname": ["Yi", "Saravanan", "Devamani", "Charmantray", "Hecquet", "Fessner"], "given-names": ["D", "T", "T", "F", "L", "WD"], "article-title": ["A thermostable transketolase evolved for aliphatic aldehyde acceptors"], "source": ["Chem. Commun."], "year": ["2015"], "volume": ["51"], "fpage": ["480"], "lpage": ["483"], "pub-id": ["10.1039/C4CC08436E"]}, {"label": ["29."], "surname": ["Saravanan", "Reif", "Yi", "Lorilliere", "Charmantray", "Hecquet", "Fessner"], "given-names": ["T", "ML", "D", "M", "F", "L", "WD"], "article-title": ["Engineering a thermostable transketolase for arylated substrates"], "source": ["Green Chem."], "year": ["2017"], "volume": ["19"], "fpage": ["481"], "lpage": ["489"], "pub-id": ["10.1039/C6GC02017H"]}, {"label": ["32."], "surname": ["Liu", "Xie", "Tang", "Tao", "Shi", "Zhang", "Huang", "Zhao", "Deng", "Lin"], "given-names": ["Q", "X", "M", "W", "T", "Y", "T", "Y", "Z", "S"], "article-title": ["One-pot asymmetric synthesis of an aminodiol intermediate of florfenicol using engineered transketolase and transaminase"], "source": ["ACS Catal."], "year": ["2021"], "volume": ["11"], "fpage": ["7477"], "lpage": ["7488"], "pub-id": ["10.1021/acscatal.1c01229"]}, {"label": ["36."], "surname": ["Saravanan", "Junker", "Kickstein", "Hein", "Link", "Ranglack", "Witt", "Lorilliere", "Hecquet", "Fessner"], "given-names": ["T", "S", "M", "S", "M-K", "J", "S", "M", "L", "WD"], "article-title": ["Donor promiscuity of a thermostable transketolase by directed evolution: Efficient complementation of 1-deoxy-d-xylulose-5-phosphate synthase activity"], "source": ["Angew. Intl. Ed."], "year": ["2017"], "volume": ["56"], "fpage": ["5358"], "lpage": ["5362"], "pub-id": ["10.1002/anie.201701169"]}, {"label": ["37."], "surname": ["Casajus", "Lagarde", "Leremboure", "Miguel", "Nauton", "Thery", "Fessner", "Duguet", "Charmantray", "Hecquet"], "given-names": ["H", "A", "M", "TD", "L", "V", "WD", "N", "F", "L"], "article-title": ["Enzymatic synthesis of aliphatic acyloins catalyzed by thermostable transketolase"], "source": ["Chem. Cat. Chem."], "year": ["2020"], "volume": ["12"], "fpage": ["5772"], "lpage": ["5779"]}, {"label": ["39."], "surname": ["Morris", "Rios-Solis", "Garc\u00eda-Arrazola", "Lye", "Dalby"], "given-names": ["P", "L", "R", "GJ", "PA"], "article-title": ["Impact of cofactor-binding loop mutations on thermotolerance and activity of "], "italic": ["E. coli"], "source": ["Enzym. Microb. Technol."], "year": ["2016"], "volume": ["89"], "fpage": ["85"], "lpage": ["91"], "pub-id": ["10.1016/j.enzmictec.2016.04.003"]}, {"label": ["45."], "surname": ["Burgi", "Dunitz", "Lehn", "Wipff"], "given-names": ["HB", "JD", "JM", "G"], "article-title": ["Stereochemistry of reaction paths at carbonyl centres"], "source": ["Tetrahedron"], "year": ["1974"], "volume": ["30"], "fpage": ["1563"], "lpage": ["1572"], "pub-id": ["10.1016/S0040-4020(01)90678-7"]}] | {
"acronym": [],
"definition": []
} | 45 | CC BY | no | 2024-01-15 23:41:57 | Sci Rep. 2024 Jan 14; 14:1287 | oa_package/1e/64/PMC10787763.tar.gz |
PMC10787764 | 38218910 | [
"<title>Introduction</title>",
"<p id=\"Par3\">The contribution of multiple individuals to group decision-making can bring substantial benefits<sup>##REF##12520299##1##</sup>. Shared decisions can be more accurate<sup>##UREF##0##2##,##REF##16701304##3##</sup>, for example homing pigeons (<italic>Columbia livia</i... | [
"<title>Methods</title>",
"<title>Data collection</title>",
"<p id=\"Par27\">Our study population of vulturine guineafowl resides in a savannah-woodland ecosystem of approximately 12 km<sup>2</sup> in the southern part of the Mpala Research Centre (MRC) in Laikipia, Kenya. Vulturine guineafowls are large (~1.5 ... | [
"<title>Results</title>",
"<title>Sex but not dominance determine who has influence</title>",
"<p id=\"Par8\">Decisions by animal groups can be despotic, where one individual decides<sup>##REF##19026539##28##</sup>, partially shared (or graded), where some individuals contribute to decisions more than others<su... | [
"<title>Discussion</title>",
"<p id=\"Par21\">Our study shows that the movements of vulturine guineafowl are consistent with the predictions from a classic theoretical model of leadership and collective decision-making, and have striking similarities to the movements described in taxonomically distant but sympatr... | [] | [
"<p id=\"Par1\">Shared-decision making is beneficial for the maintenance of group-living. However, little is known about whether consensus decision-making follows similar processes across different species. Addressing this question requires robust quantification of how individuals move relative to each other. Here ... | [
"<title>Supplementary information</title>",
"<p>\n\n\n\n\n\n\n\n\n\n\n</p>"
] | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s42003-024-05782-w.</p>",
"<title>Acknowledgements</title>",
"<p>We are grateful to Ariana Strandburg-Peshkin, Vivek Hari Sridhar, Margaret C. Crofoot, and Dora Biro for feedback on early ve... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>The distribution of distances (d, in metres) between initiators and potential followers, during pulls and anchors at three different times.</title><p>First, when the initiation starts (first column; <bold>A</bold>, <bold>D</bold>, <bold>G</bold>, <bold>J</bold>... | [] | [
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"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
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"<media xlink:href=\"42003_2024_5782_MOESM1_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>",
"<media xlink:href=\"42003_2024_5782_MOESM2_ESM.pdf\"><caption><p>Supplementary Material</p></caption></media>",
"<media xlink:href=\"42003_2024_5782_MOESM3_ESM.pdf\"><caption><p>Description of Additional ... | [{"label": ["2."], "surname": ["Galton"], "given-names": ["F"], "article-title": ["Vox populi (The wisdom of crowds)"], "source": ["Nature"], "year": ["1907"], "volume": ["75"], "fpage": ["450"], "lpage": ["451"], "pub-id": ["10.1038/075450a0"]}, {"label": ["5."], "surname": ["Conradt", "Roper"], "given-names": ["L", "TJ"], "article-title": ["Conflicts of interest and the evolution of decision sharing"], "source": ["Philos. Trans. R. Soc. B: Biol. Sci."], "year": ["2009"], "volume": ["364"], "fpage": ["807"], "lpage": ["819"], "pub-id": ["10.1098/rstb.2008.0257"]}, {"label": ["8."], "surname": ["Leca", "Gunst", "Thierry", "Petit"], "given-names": ["J", "N", "B", "O"], "article-title": ["Distributed leadership in semifree-ranging white-faced capuchin monkeys"], "source": ["Anim. Behav."], "year": ["2003"], "volume": ["66"], "fpage": ["1045"], "lpage": ["1052"], "pub-id": ["10.1006/anbe.2003.2276"]}, {"label": ["9."], "surname": ["Lewis", "Wartzok", "Heithaus"], "given-names": ["JS", "D", "MR"], "article-title": ["Highly dynamic fission\u2013fusion species can exhibit leadership when traveling"], "source": ["Behav. Ecol. Sociobiol."], "year": ["2011"], "volume": ["65"], "fpage": ["1061"], "lpage": ["1069"], "pub-id": ["10.1007/s00265-010-1113-y"]}, {"label": ["13."], "surname": ["Strandburg-Peshkin", "Farine", "Couzin", "Crofoot"], "given-names": ["A", "DR", "ID", "MC"], "article-title": ["Shared decision-making drives collective movement in wild baboons"], "source": ["Science (1979)"], "year": ["2015"], "volume": ["348"], "fpage": ["1358"], "lpage": ["1361"]}, {"label": ["14."], "mixed-citation": ["Byrne, R. How monkeys find their way. Leadership, coordination and cognitive maps of African baboons. In "], "italic": ["On the Move: How and Why Animals Travel", "in", "Groups"]}, {"label": ["25."], "surname": ["Connor", "Kr\u00fctzen"], "given-names": ["RC", "M"], "article-title": ["Male dolphin alliances in Shark Bay: changing perspectives in a 30-year study"], "source": ["Anim. Behav."], "year": ["2015"], "volume": ["103"], "fpage": ["223"], "lpage": ["235"], "pub-id": ["10.1016/j.anbehav.2015.02.019"]}, {"label": ["27."], "surname": ["Dehnen"], "given-names": ["T"], "article-title": ["Costs dictate strategic investment in dominance interactions"], "source": ["Philos. Trans. R. Soc. B: Biol. Sci."], "year": ["2022"], "volume": ["377"], "fpage": ["20200447"], "pub-id": ["10.1098/rstb.2020.0447"]}, {"label": ["29."], "surname": ["\u0160\u00e1rov\u00e1", "\u0160pinka", "Panam\u00e1", "\u0160ime\u010dek"], "given-names": ["R", "M", "JLA", "P"], "article-title": ["Graded leadership by dominant animals in a herd of female beef cattle on pasture"], "source": ["Anim. Behav."], "year": ["2010"], "volume": ["79"], "fpage": ["1037"], "lpage": ["1045"], "pub-id": ["10.1016/j.anbehav.2010.01.019"]}, {"label": ["34."], "surname": ["Kurvers"], "given-names": ["RHJM"], "article-title": ["Personality differences explain leadership in barnacle geese"], "source": ["Anim. Behav."], "year": ["2009"], "volume": ["78"], "fpage": ["447"], "lpage": ["453"], "pub-id": ["10.1016/j.anbehav.2009.06.002"]}, {"label": ["36."], "surname": ["Brandl", "Farine", "Funghi", "Schuett", "Griffith"], "given-names": ["HB", "DR", "C", "W", "SC"], "article-title": ["Early-life social environment predicts social network position in wild zebra finches"], "source": ["Proc. R. Soc. B: Biol. Sci."], "year": ["2019"], "volume": ["286"], "fpage": ["20182579"], "pub-id": ["10.1098/rspb.2018.2579"]}, {"label": ["37."], "surname": ["Strandburg-Peshkin", "Papageorgiou", "Crofoot", "Farine"], "given-names": ["A", "D", "MC", "DR"], "article-title": ["Inferring influence and leadership in moving animal groups"], "source": ["Philos. Trans. R. Soc. B: Biol. Sci."], "year": ["2018"], "volume": ["373"], "fpage": ["20170006"], "pub-id": ["10.1098/rstb.2017.0006"]}, {"label": ["38."], "surname": ["Sueur", "Petit"], "given-names": ["C", "O"], "article-title": ["Shared or unshared consensus decision in macaques?"], "source": ["Behav. Process."], "year": ["2008"], "volume": ["78"], "fpage": ["84"], "lpage": ["92"], "pub-id": ["10.1016/j.beproc.2008.01.004"]}, {"label": ["39."], "mixed-citation": ["Bracken, A. M., Christensen, C., O\u2019Riain, M. J., F\u00fcrtbauer, I. & King, A. J. Flexible group cohesion and coordination, but robust leader\u2013follower roles, in a wild social primate using urban space. "], "italic": ["Proc. R. Soc. B Biol. Sci."], "bold": ["289"]}, {"label": ["40."], "surname": ["Dal Pesco", "Trede", "Zinner", "Fischer"], "given-names": ["F", "F", "D", "J"], "article-title": ["Male\u2013male social bonding, coalitionary support and reproductive success in wild Guinea baboons"], "source": ["Proc. R. Soc. B Biol. Sci."], "year": ["2022"], "volume": ["289"], "fpage": ["20220347"], "pub-id": ["10.1098/rspb.2022.0347"]}, {"label": ["41."], "surname": ["Tokuyama", "Furuichi"], "given-names": ["N", "T"], "article-title": ["Leadership of old females in collective departures in wild bonobos ("], "italic": ["Pan paniscus"], "source": ["Behav. Ecol. Sociobiol."], "year": ["2017"], "volume": ["71"], "fpage": ["55"], "pub-id": ["10.1007/s00265-017-2277-5"]}, {"label": ["44."], "surname": ["McComb"], "given-names": ["K"], "article-title": ["Leadership in elephants: the adaptive value of age"], "source": ["Proc. R. Soc. B: Biol. Sci."], "year": ["2011"], "volume": ["278"], "fpage": ["3270"], "lpage": ["3276"], "pub-id": ["10.1098/rspb.2011.0168"]}, {"label": ["45."], "surname": ["Fischhoff"], "given-names": ["IR"], "article-title": ["Social relationships and reproductive state influence leadership roles in movements of plains zebra, "], "italic": ["Equus burchellii"], "source": ["Anim. Behav."], "year": ["2007"], "volume": ["73"], "fpage": ["825"], "lpage": ["831"], "pub-id": ["10.1016/j.anbehav.2006.10.012"]}, {"label": ["48."], "surname": ["Rowell"], "given-names": ["TE"], "article-title": ["Female reproduction cycles and social behavior in primates"], "source": ["Adv. Study Behav."], "year": ["1972"], "volume": ["4"], "fpage": ["69"], "lpage": ["105"], "pub-id": ["10.1016/S0065-3454(08)60007-8"]}, {"label": ["49."], "surname": ["Conradt", "Roper"], "given-names": ["L", "TJ"], "article-title": ["Deciding group movements: where and when to go"], "source": ["Behav. Process."], "year": ["2010"], "volume": ["84"], "fpage": ["675"], "lpage": ["677"], "pub-id": ["10.1016/j.beproc.2010.03.005"]}, {"label": ["53."], "mixed-citation": ["He, P. et al. A guide to sampling design for GPS\u2010based studies of animal societies. "], "italic": ["Methods Ecol. Evol."], "bold": ["14"]}, {"label": ["56."], "surname": ["Halekoh", "H\u00f8jsgaard", "Yan"], "given-names": ["U", "S", "J"], "article-title": ["The R package geepack for generalized estimating equations"], "source": ["J. Stat. Softw."], "year": ["2006"], "volume": ["15"], "fpage": ["1"], "lpage": ["11"], "pub-id": ["10.18637/jss.v015.i02"]}, {"label": ["57."], "surname": ["Hartigan", "Hartigan"], "given-names": ["JA", "PM"], "article-title": ["The dip test of unimodality"], "source": ["Ann. Stat."], "year": ["1985"], "volume": ["13"], "fpage": ["70"], "lpage": ["84"], "pub-id": ["10.1214/aos/1176346577"]}] | {
"acronym": [],
"definition": []
} | 57 | CC BY | no | 2024-01-15 23:41:57 | Commun Biol. 2024 Jan 13; 7:95 | oa_package/85/38/PMC10787764.tar.gz |
PMC10787765 | 38218947 | [
"<title>Introduction</title>",
"<p id=\"Par3\">Implantable bioelectronic devices play an increasingly important role in disease prevention, monitoring, and treatment<sup>##REF##32592227##1##–##UREF##0##3##</sup>, and it is one of the most rapidly emerging fields in medicine<sup>##REF##32895515##4##–##REF##3532428... | [
"<title>Methods</title>",
"<title>Fabrication of SICP</title>",
"<p id=\"Par19\">The enclosure of SICP was printed by a 3D printer (PHTON MONO) using UV-sensitive resins (ANYCUBIC Photon). Nanostructured PTFE film (50 μm) and POM pellets (diameter: 1.588 mm) were employed as triboelectric layers, processed by a... | [
"<title>Results</title>",
"<title>Design features and materials of SICP</title>",
"<p id=\"Par6\">The schematic illustration in Fig. ##FIG##0##1a## shows a battery-free and transcatheter SICP in the right ventricle. As shown in the partially enlarged drawing, this capsule-shaped device consists of EHU, PMU&... | [
"<title>Discussion</title>",
"<p id=\"Par17\">We develop a SICP and demonstrate its efficacy and safety in the cardiac pacing of large-animal models. This device provides a promising method to harvest biomechanical energy from cardiac motion for powering the pacemaker module with the significant advantages of bei... | [] | [
"<p id=\"Par1\">Harvesting biomechanical energy from cardiac motion is an attractive power source for implantable bioelectronic devices. Here, we report a battery-free, transcatheter, self-powered intracardiac pacemaker based on the coupled effect of triboelectrification and electrostatic induction for the treatmen... | [
"<title>Supplementary information</title>",
"<p>\n\n\n\n\n\n\n\n\n\n\n\n\n</p>",
"<title>Source data</title>",
"<p>\n\n</p>"
] | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41467-023-44510-6.</p>",
"<title>Acknowledgements</title>",
"<p>We are grateful to the laboratory members, Prof. Yubo Fan (Beihang University), Dr. Lingling Xu (National Center for Nanoscie... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Design features and materials of a self-powered intracardiac pacemaker (SICP).</title><p><bold>a</bold> Schematic illustration of the device in the right ventricle (RV) for converting biomechanical energy from cardiac motion to electricity and regulating arrhyt... | [] | [
"<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n... | [] | [] | [] | [] | [
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"<supplementary-material ... | [
"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Zhuo Liu, Yiran Hu, Xuecheng Qu, Ying Liu.</p></fn></fn-group>"
] | [
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"<media xlink:href=\"41467_2023_44510_MOESM1_ESM.pdf\"><caption><p>Peer review file</p></caption></media>",
"<media xlink:href=\"41467_2023_44510_MOESM2_ESM.pdf\"><caption><p>Supplementary information</p></caption></media>",
"<media xlink:href=\"41467_2023_44510_MOESM3_ESM.pdf\"><caption><p>Description of addit... | [{"label": ["3."], "surname": ["Zeng", "Rebscher", "Harrison", "Sun", "Feng"], "given-names": ["F-G", "S", "W", "X", "H"], "article-title": ["Cochlear implants: system design"], "source": ["Integr. Eval. IEEE Rev. Biomed. Eng."], "year": ["2008"], "volume": ["1"], "fpage": ["115"], "lpage": ["142"], "pub-id": ["10.1109/RBME.2008.2008250"]}, {"label": ["14."], "mixed-citation": ["Xiao, X. et al. Ultrasound\u2010driven injectable and fully biodegradable triboelectric nanogenerators. "], "italic": ["Small Methods"], "bold": ["7"]}, {"label": ["15."], "surname": ["Meng"], "given-names": ["X"], "article-title": ["An ultrasound\u2010driven bioadhesive triboelectric nanogenerator for instant wound sealing and electrically accelerated healing in emergencies"], "source": ["Adv. Mater."], "year": ["2023"], "volume": ["35"], "fpage": ["2209054"], "pub-id": ["10.1002/adma.202209054"]}, {"label": ["16."], "surname": ["Shi", "Li", "Fan"], "given-names": ["B", "Z", "Y"], "article-title": ["Implantable energy\u2010harvesting devices"], "source": ["Adv. Mater."], "year": ["2018"], "volume": ["30"], "fpage": ["1801511"], "pub-id": ["10.1002/adma.201801511"]}, {"label": ["19."], "surname": ["Gai"], "given-names": ["Y"], "article-title": ["A self\u2010powered wearable sensor for continuous wireless sweat monitoring"], "source": ["Small Methods"], "year": ["2022"], "volume": ["6"], "fpage": ["2200653"], "pub-id": ["10.1002/smtd.202200653"]}, {"label": ["21."], "surname": ["Gai"], "given-names": ["Y"], "article-title": ["A gyroscope nanogenerator with frequency up\u2010conversion effect for fitness and energy harvesting"], "source": ["Small"], "year": ["2022"], "volume": ["18"], "fpage": ["2108091"], "pub-id": ["10.1002/smll.202108091"]}, {"label": ["24."], "surname": ["Kim"], "given-names": ["DH"], "article-title": ["In vivo self\u2010powered wireless transmission using biocompatible flexible energy harvesters"], "source": ["Adv. Funct. Mater."], "year": ["2017"], "volume": ["27"], "fpage": ["1700341"], "pub-id": ["10.1002/adfm.201700341"]}, {"label": ["27."], "mixed-citation": ["Liu, Z. et al. A self-powered optogenetic system for implantable blood glucose control. "], "italic": ["Research"], "bold": ["2022"]}, {"label": ["30."], "surname": ["Dong"], "given-names": ["L"], "article-title": ["Piezoelectric buckled beam array on a pacemaker lead for energy harvesting"], "source": ["Adv. Mater. Technol."], "year": ["2019"], "volume": ["4"], "fpage": ["1800335"], "pub-id": ["10.1002/admt.201800335"]}, {"label": ["31."], "surname": ["Fan", "Tian", "Wang"], "given-names": ["F-R", "Z-Q", "ZL"], "article-title": ["Flexible triboelectric generator"], "source": ["Nano Energy"], "year": ["2012"], "volume": ["1"], "fpage": ["328"], "lpage": ["334"], "pub-id": ["10.1016/j.nanoen.2012.01.004"]}, {"label": ["32."], "surname": ["Liu"], "given-names": ["Z"], "article-title": ["Wearable and implantable triboelectric nanogenerators"], "source": ["Adv. Funct. Mater."], "year": ["2019"], "volume": ["29"], "fpage": ["1808820"], "pub-id": ["10.1002/adfm.201808820"]}, {"label": ["38."], "surname": ["Wang", "Wang"], "given-names": ["ZL", "AC"], "article-title": ["On the origin of contact-electrification"], "source": ["Mater. Today"], "year": ["2019"], "volume": ["30"], "fpage": ["34"], "lpage": ["51"], "pub-id": ["10.1016/j.mattod.2019.05.016"]}, {"label": ["39."], "surname": ["Xu"], "given-names": ["C"], "article-title": ["On the electron\u2010transfer mechanism in the contact\u2010electrification effect"], "source": ["Adv. Mater."], "year": ["2018"], "volume": ["30"], "fpage": ["1706790"], "pub-id": ["10.1002/adma.201706790"]}] | {
"acronym": [],
"definition": []
} | 41 | CC BY | no | 2024-01-15 23:41:57 | Nat Commun. 2024 Jan 13; 15:507 | oa_package/a6/83/PMC10787765.tar.gz |
PMC10787766 | 38218737 | [
"<title>Introduction</title>",
"<p id=\"Par2\"><italic>Plasmodium vivax</italic> malaria is a global health problem in many tropical and sub-tropical countries of the world, with > 70% of cases occurring in Asia and the Americas<sup>##REF##18165478##1##–##REF##11425182##3##</sup>. An effective vaccine that pro... | [
"<title>Materials and methods</title>",
"<title>Ethics statement</title>",
"<p id=\"Par16\">This study was approved by the Committee on Human Rights Related to Human Experimentation, Mahidol University Central Institutional Review Board (MU-IRB 2012/079.2408 and MU-CIRB 2021/281.2505), and the Committee on Use ... | [
"<title>Results</title>",
"<title>Naturally acquired antibodies to <italic>P. vivax</italic> PE antigens</title>",
"<p id=\"Par7\">Plasma samples from 51 Thai patients with acute <italic>P. vivax</italic> infections were screened for the presence of antigen-specific antibodies to selected PE antigens including ... | [
"<title>Discussion</title>",
"<p id=\"Par9\">Characterization of naturally acquired antibody responses to <italic>P. vivax</italic> antigens following <italic>vivax</italic> infections is an important step towards target selection and rational vaccine design to protect against vivax malaria. Naturally acquired Ig... | [] | [
"<p id=\"Par1\">In <italic>Plasmodium vivax</italic>, the most studied vaccine antigens are aimed at blocking merozoite invasion of erythrocytes and disease development. Very few studies have evaluated pre-erythrocytic (PE) stage antigens. The <italic>P. vivax</italic> circumsporozoite protein (CSP), is considered ... | [
"<title>Supplementary Information</title>",
"<p>\n</p>"
] | [
"<title>Supplementary Information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41598-024-51820-2.</p>",
"<title>Acknowledgements</title>",
"<p>We thank the residents of Ranong and Chumphon provinces of Thailand for providing the plasma samples that were used for this ... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Profile of naturally acquired IgG antibodies against PE antigens. (<bold>a</bold>) Antigen-specific antibody levels in plasma samples from Thai <italic>P. vivax</italic> infected patients (n = 51) were measured by indirect ELISA against recombinant CSP, SSP3, SPE... | [] | [] | [] | [] | [] | [] | [
"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
] | [
"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Francis Babila Ntumngia, Surendra Kumar Kolli, Pradeep Annamalai Subramani and Samantha J. Ba... | [
"<graphic xlink:href=\"41598_2024_51820_Fig1_HTML\" id=\"MO1\"/>",
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"<media xlink:href=\"41598_2024_51820_MOESM1_ESM.docx\"><caption><p>Supplementary Information.</p></caption></media>"
] | [{"label": ["66."], "surname": ["Yang"], "given-names": ["ASP"], "article-title": ["AMA1 and MAEBL are important for "], "italic": ["Plasmodium falciparum"], "source": ["Cell. Microbiol."], "year": ["2017"], "volume": ["19"], "fpage": ["e12745"], "pub-id": ["10.1111/cmi.12745"]}] | {
"acronym": [],
"definition": []
} | 78 | CC BY | no | 2024-01-15 23:41:57 | Sci Rep. 2024 Jan 13; 14:1260 | oa_package/1b/ad/PMC10787766.tar.gz |
PMC10787767 | 38218968 | [
"<title>Background & Summary</title>",
"<p id=\"Par2\">Repeat Terrestrial Laser Scanning (TLS) topography measurements were part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition, in which researchers aboard <italic>R/V Polarstern</italic><sup>##UREF##0##1##</su... | [
"<title>Methods</title>",
"<title>Terminology</title>",
"<p id=\"Par7\">We use the following terms throughout this manuscript. They are mostly drawn from their usage in RiSCAN (Riegl’s software for acquiring and processing TLS data):<list list-type=\"bullet\"><list-item><p id=\"Par8\">Scan Position: set-up the ... | [] | [] | [] | [
"<p id=\"Par1\">Snow and ice topography impact and are impacted by fluxes of mass, energy, and momentum in Arctic sea ice. We measured the topography on approximately a 0.5 km<sup>2</sup> drifting parcel of Arctic sea ice on 42 separate days from 18 October 2019 to 9 May 2020 via Terrestrial Laser Scanning (TLS). T... | [
"<title>Data Records</title>",
"<p id=\"Par35\">Repeat TLS data in Table ##TAB##0##1## are available at the Arctic Data Center<sup>##UREF##20##21##</sup>. The top level of the archive contains a directory for each Scan Area: Snow1, ROV (which includes the Snow2 projects since they are in the same reference frame)... | [
"<title>Acknowledgements</title>",
"<p>Data used in this manuscript were produced as part of the international MOSAiC project with the tag MOSAiC20192020 and the Project_ID: AWI_PS122_00. We thank all people involved in the expedition of the research vessel Polarstern<sup>##UREF##0##1##</sup> during MOSAiC in 201... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Photo showing data acquisition of a typical scan position. The Riegl VZ1000 terrestrial laser scanner is inside the custom heater case (metal cylinder) on top of the tripod. The researcher controls the scanner from the laptop via WiFi. To the right of the tripod is... | [
"<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Summary of repeat TLS measurements from October 2019 to May 2020 on MOSAiC.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Project Name</th><th>Date</th><th>Scan Area(s)</th><th># SingleScans</th><th>Location</th></tr></thead><tbody><t... | [
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"<table-wrap-foot><p><sup>a</sup>Some SingleScans were collected on the following day.</p></table-wrap-foot>",
"<table-wrap-foot><p>Pole Height is in the same reference frame as Fig. ##FIG##1##2##.</p></table-wrap-foot>",
"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regar... | [
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} | 43 | CC BY | no | 2024-01-15 23:41:58 | Sci Data. 2024 Jan 13; 11:70 | oa_package/2b/ae/PMC10787767.tar.gz |
PMC10787768 | 38218967 | [
"<title>Introduction</title>",
"<p id=\"Par3\">Skin provides convenient access for delivering most biotherapeutics and vaccines, typically via a hypodermic needle. However, ensuring patient compliance with long-term, repetitive pharmaceutical treatments remains a significant challenge. Hypodermic injection provid... | [
"<title>Methods</title>",
"<p id=\"Par34\">The sample size used in this study is based on the expected variations between animals and is comparable to many previous reports using similar techniques (cited in the corresponding sections). Sample size of each experiments can be found in the Figure legends. Data from... | [
"<title>Results and discussion</title>",
"<title>Configuration and characterization of the spatiotemporal on-demand patch (SOP)</title>",
"<p id=\"Par7\">Figure ##FIG##0##1a## highlights the design of an SOP, which consists of two primary parts: i) a drug-loaded microneedle (MN) patch protected with an electroc... | [
"<title>Results and discussion</title>",
"<title>Configuration and characterization of the spatiotemporal on-demand patch (SOP)</title>",
"<p id=\"Par7\">Figure ##FIG##0##1a## highlights the design of an SOP, which consists of two primary parts: i) a drug-loaded microneedle (MN) patch protected with an electroc... | [] | [
"<p id=\"Par1\">Transdermal drug delivery is of vital importance for medical treatments. However, user adherence to long-term repetitive drug delivery poses a grand challenge. Furthermore, the dynamic and unpredictable disease progression demands a pharmaceutical treatment that can be actively controlled in real-ti... | [
"<title>Supplementary information</title>",
"<p>\n\n\n\n\n\n</p>",
"<title>Source data</title>",
"<p>\n\n</p>"
] | [
"<title>Supplementary information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41467-023-44532-0.</p>",
"<title>Acknowledgements</title>",
"<p>This work was supported by the start-up funds from University of North Carolina at Chapel Hill and the fund from National Sci... | [
"<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Spatiotemporal on-demand patch for wireless, active control of drug delivery.</title><p><bold>a</bold> Schematic illustration highlighting the construction of a wirelessly controlled spatiotemporal on-demand patch (SOP) for high-precision drug delivery. The SOP... | [] | [
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"<supplementary-material ... | [
"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Yihang Wang, Zeka Chen.</p></fn></fn-group>"
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"<media xlink:href=\"41467_2023_44532_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>",
"<media xlink:href=\"41467_2023_44532_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>",
"<media xlink:href=\"41467_2023_44532_MOESM3_ESM.pdf\"><caption><p>Description of Addit... | [{"label": ["2."], "surname": ["Teo", "Seah"], "given-names": ["BM", "BC-Q"], "article-title": ["Recent advances in ultrasound-based transdermal drug delivery"], "source": ["Int. J. Nanomed."], "year": ["2018"], "volume": ["13"], "fpage": ["7749"], "lpage": ["7763"], "pub-id": ["10.2147/IJN.S174759"]}, {"label": ["13."], "surname": ["Zhu", "Zhang", "Zhang", "Hao", "Guo"], "given-names": ["DD", "XP", "BL", "YY", "XD"], "article-title": ["Safety assessment of microneedle technology for transdermal drug delivery: a review"], "source": ["Adv. Ther."], "year": ["2020"], "volume": ["3"], "fpage": ["2000033"], "pub-id": ["10.1002/adtp.202000033"]}, {"label": ["14."], "surname": ["Bilal"], "given-names": ["M"], "article-title": ["Microneedles in smart drug delivery"], "source": ["Adv. Wound Care"], "year": ["2021"], "volume": ["10"], "fpage": ["204"], "lpage": ["219"], "pub-id": ["10.1089/wound.2019.1122"]}, {"label": ["16."], "surname": ["Ning"], "given-names": ["T"], "article-title": ["Synergistically detachable microneedle dressing for programmed treatment of chronic wounds"], "source": ["Adv. Healthc Mater"], "year": ["2022"], "volume": ["11"], "fpage": ["2102180"], "pub-id": ["10.1002/adhm.202102180"]}, {"label": ["17."], "surname": ["Hsu"], "given-names": ["WL"], "article-title": ["On-skin glucose-biosensing and on-demand insulin-zinc hexamers delivery using microneedles for syringe-free diabetes management"], "source": ["Chem. Eng. J."], "year": ["2020"], "volume": ["398"], "fpage": ["125536"], "pub-id": ["10.1016/j.cej.2020.125536"]}, {"label": ["33."], "surname": ["Jamaledin"], "given-names": ["R"], "article-title": ["Engineered microneedle patches for controlled release of active compounds: recent advances in release profile tuning"], "source": ["Adv. 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} | 97 | CC BY | no | 2024-01-15 23:41:58 | Nat Commun. 2024 Jan 13; 15:511 | oa_package/eb/ad/PMC10787768.tar.gz |
PMC10787769 | 38218940 | [] | [
"<title>Materials and methods</title>",
"<title>Chemicals</title>",
"<p id=\"Par18\">All solvents for synthesis were purchased from VWR and used as received. Water (18.2 MΩ∙cm) was collected from an Arium (Sartorius) laboratory-grade water purification system. Gadolinium(III) nitrate hexahydrate (Gd(NO<sub>3</s... | [
"<title>Results and discussion</title>",
"<title>Immobilization strategy</title>",
"<p id=\"Par6\">To imprint POMs on surfaces (Fig. ##FIG##1##2##), we aimed to use MSPs whose pore wall is functionalized with amino groups (MSPs<sup>+</sup>), that are protonated and thus positively charged in an aqueous environm... | [
"<title>Results and discussion</title>",
"<title>Immobilization strategy</title>",
"<p id=\"Par6\">To imprint POMs on surfaces (Fig. ##FIG##1##2##), we aimed to use MSPs whose pore wall is functionalized with amino groups (MSPs<sup>+</sup>), that are protonated and thus positively charged in an aqueous environm... | [
"<title>Conclusions</title>",
"<p id=\"Par17\">Our proof-of-concept study shows, that the loading of MSPs with POM in conjunction with SPL techniques is a viable route to create surface-immobilized microarrays of MSPs-POM. Encapsulation of POM in MSPs increases stability and enables immobilization of a larger amo... | [
"<p id=\"Par1\">Polyoxometalates (POM) are anionic oxoclusters of early transition metals that are of great interest for a variety of applications, including the development of sensors and catalysts. A crucial step in the use of POM in functional materials is the production of composites that can be further process... | [
"<p id=\"Par2\">POMs are anionic oxo-clusters of early transition metals in their high oxidation states, which display unique chemical and physical properties on the basis of their structures, composition, sizes, rich redox chemistry and charges<sup>##UREF##0##1##,##UREF##1##2##</sup>. Prominent POM structures are ... | [
"<title>Supplementary Information</title>",
"<p>The online version contains supplementary material available at 10.1038/s41598-023-50846-2.</p>",
"<title>Acknowledgements</title>",
"<p>This work was partly carried out with the support of the Karlsruhe Nano Micro Facility (KNMFi, <ext-link ext-link-type=\"uri\... | [
"<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Structure of Preyssler-type POM [GdP<sub>5</sub>W<sub>30</sub>O<sub>110</sub>]<sup>##UREF##7##12##</sup> (<bold>A</bold>) Top view, (<bold>B</bold>) side view. Polyhedral color scheme: WO<sub>6</sub> green, PO<sub>4</sub> pink. Color scheme for balls: Gd = violet... | [] | [
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"<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>"
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"<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>"
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"<graphic xlink:href=\"41598_2023_50846_Fig1_HTML\" id=\"MO1\"/>",
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"<graphic xlink:href=\"41598_2023_50846_... | [
"<media xlink:href=\"41598_2023_50846_MOESM1_ESM.pdf\"><caption><p>Supplementary Figures.</p></caption></media>"
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"acronym": [],
"definition": []
} | 67 | CC BY | no | 2024-01-15 23:41:58 | Sci Rep. 2024 Jan 13; 14:1249 | oa_package/9c/58/PMC10787769.tar.gz |
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