Prisma flow diagram of study selection. by Hattapark Dejakaisaya (22238613)

“…Additionally, watching ≥6 hours of television per day was associated with a significant decrease in cognitive score (standardized beta coefficient = −0.09; 95% CI: −0.17, −0.003; I² = 71.8%; seven studies). …”

Variations in slope utilization by a female red panda across different time periods and in comparison with their wild individuals. Different capital letters indicated significant d... by Yanshan Zhou (14131359)

Variations in altitude utilization by a female red panda across different time periods and in comparison with wild individuals. Different capital letters indicated significant diff... by Yanshan Zhou (14131359)

Variations in aspect utilization by a female red panda across different time periods and in comparison with wild individuals. Different capital letters indicated significant differ... by Yanshan Zhou (14131359)

ZLN005 increases PGC-1α and TFAM expression. by Joel R. Frandsen (20685133)

Medicare clozapine data analysis. by Luke R. Cavanah (19022435)

“…We observed a steady decrease in clozapine use adjusted for population (−18.0%) and spending (−24.9%) over time. …”

PRCC values (with <i>p</i>-values in parentheses) for the median final population size and difference in mean MSI at the final time, without weevil predation and with weevil predat... by Alexandra M. Campbell (3284094)

Subjects:

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. …”

Overexpression of TNFAIP9 alleviated the renal fibrosis caused by 5/6 Nx in C57 mice. by Ying Chen (9697)

Overexpression of TNFAIP9 remitted the renal inflammation caused by 5/6 Nx in C57 mice. by Ying Chen (9697)

Overexpression of TNFAIP9 suppressed inflammation in mouse peritoneal macrophages caused by 5/6 Nx in C57 mice. by Ying Chen (9697)

Overexpression of TNFAIP9 inhibited the TGF-β-induced fibrosis in HK-2 cells. by Ying Chen (9697)

TNFAIP9 expression was increased in mice with chronic kidney disease (CKD) induced by 5/6 nephrectomy (Nx). by Ying Chen (9697)

Flowchart of analysis. by Fanqiang Meng (4786071)

Subjects:

Overexpression of TNFAIP9 counteracted the renal damage caused by 5/6 Nx in C57 mice. by Ying Chen (9697)

SBP3 KD decreases the surface localization of VESA-1. by Atefeh Fathi (7370876)