S6 Table - by Yong-Rae Kim (19797278)

“…In addition, the vigor score was significantly increased while total mood disturbance was significantly decreased when viewing the wood-attached ECD. …”

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…For instance, the RF-MLPR model achieved a 3.7%–6.5% improvement in the Nash-Sutcliffe efficiency (NSE) metric across four hydrological stations compared to the RF-SVR model. (4) Prediction accuracy decreased with longer forecast periods, with the R² value dropping from 0.8886 for a 1-month forecast to 0.6358 for a 12-month forecast, indicating the increasing challenge of long-term predictions due to greater uncertainty and the accumulation of influencing factors over time. (5) The RF-MLPR model outperformed the RF-SVR model, demonstrating a superior ability to capture the complex, nonlinear relationships inherent in the data. …”

nsP4-C483Y or Q192L mutations decrease sensitivity to 4′-FlU. by Peiqi Yin (6533747)

<i>C</i>. <i>auris</i> induces IL-1Ra in macrophages to decrease the neutrophil function in the skin. by Abishek Balakumar (20189865)

3D reconstruction of a control PFC PV interneuron using Neurolucida showing the soma in light blue, the dendrites in green, and the axon in light blue. by Sara Hijazi (21656615)

The percentage of received patients who migrated for primary knee arthroplasty in different provinces. by Fanqiang Meng (4786071)

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Patient migration rates for primary knee arthroplasty from 2013 to 2018. by Fanqiang Meng (4786071)

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Comparison of postoperative outcomes and financial burden between migrated and non-migrated primary knee arthroplasty patients. by Fanqiang Meng (4786071)

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Sankey diagram of patient migration for primary knee arthroplasty in China. by Fanqiang Meng (4786071)

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Demographics of patients who underwent primary knee arthroplasty during 2013 to 2018. by Fanqiang Meng (4786071)

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Baseline characteristics of propensity score-matched migrated and non-migrated primary knee arthroplasty patients. by Fanqiang Meng (4786071)

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The percentage of patients who migrated to outside provinces for primary knee arthroplasty in different provinces. by Fanqiang Meng (4786071)

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The activity of representative Fe-S containing enzymes and mitochondrial membrane potential decreased in <i>ssr1-2</i> mutant, and were restored by <i>sus1</i> or <i>sus2</i>. by Xuanjun Feng (8768193)

Top 10 significant functional annotations of up-regulated DEGs. by Meitner Cadena (22216261)

“…Functional annotations are ordered by decreasing significance, with color indicating significance according to the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…”

Top 10 significant functional annotations of down-regulated DEGs. by Meitner Cadena (22216261)

“…Functional annotations are ordered by decreasing significance, with color indicating significance level based on the legend’s color scale, the ratio of genes on the horizontal axis, and DEG count represented by circle size.…”

The weighted UniFrac (A) and PCoA (B) analysis of bacterial samples in passion fruit rhizosphere soil. by Weiwei Lin (2442850)