Table 7_Exploring Histoplasma species seroprevalence and risk factors for seropositivity in The Gambia’s working equid population: Baseline analysis of the Tackling Histoplasmosis... by Tessa Rose Cornell (15460726)

“…Investigation of risk factors, including equid husbandry and management strategies, as well as geoclimatic variations, is warranted. …”

AO/EtBr Staining of Cancer cell lines: <i>To check whether vitamin D3 promoted cell death in cancer cells, a dual staining protocol was implemented as detailed in the methods secti... by Vidya G. Bettada (22208808)

Table 1_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

“…Assessment of the proportion of HHD mortality and DALYs attributable to specific risk factors, quantified using population attributable fractions (PAFs) based on GBD risk-outcome associations and theoretical minimum risk exposure levels (TMRELs). …”

Table 4_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

“…Assessment of the proportion of HHD mortality and DALYs attributable to specific risk factors, quantified using population attributable fractions (PAFs) based on GBD risk-outcome associations and theoretical minimum risk exposure levels (TMRELs). …”

Image 1_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.tif by Ning An (618997)

“…Assessment of the proportion of HHD mortality and DALYs attributable to specific risk factors, quantified using population attributable fractions (PAFs) based on GBD risk-outcome associations and theoretical minimum risk exposure levels (TMRELs). …”

Table 3_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

“…Assessment of the proportion of HHD mortality and DALYs attributable to specific risk factors, quantified using population attributable fractions (PAFs) based on GBD risk-outcome associations and theoretical minimum risk exposure levels (TMRELs). …”

Table 2_Unveiling the global impact of hypertensive heart disease among individuals aged ≥ 65 years: metabolic risk factors and future projections for 2050.xlsx by Ning An (618997)

“…Assessment of the proportion of HHD mortality and DALYs attributable to specific risk factors, quantified using population attributable fractions (PAFs) based on GBD risk-outcome associations and theoretical minimum risk exposure levels (TMRELs). …”

SHAP interpretation of the optimal machine learning model. by Wentao Yang (205781)

Sociodemographic data of the sample. by María García-Arrabé (21156737)

“…Group-by-time interaction showed significant differences for the lunge test (p =  0.045), SLDJ height (p =  0.014), flight time (p =  0.019) and ground contact time (p =  0.035). …”

Flowchart of the study. by María García-Arrabé (21156737)

“…Group-by-time interaction showed significant differences for the lunge test (p =  0.045), SLDJ height (p =  0.014), flight time (p =  0.019) and ground contact time (p =  0.035). …”

Bandages: KT (3A) and RT (3B). by María García-Arrabé (21156737)

“…Group-by-time interaction showed significant differences for the lunge test (p =  0.045), SLDJ height (p =  0.014), flight time (p =  0.019) and ground contact time (p =  0.035). …”

ANOVA repeated measures of the variables. by María García-Arrabé (21156737)

“…Group-by-time interaction showed significant differences for the lunge test (p =  0.045), SLDJ height (p =  0.014), flight time (p =  0.019) and ground contact time (p =  0.035). …”

Dataset. by María García-Arrabé (21156737)

“…Group-by-time interaction showed significant differences for the lunge test (p =  0.045), SLDJ height (p =  0.014), flight time (p =  0.019) and ground contact time (p =  0.035). …”

CONSORT Flow Diagram. by María García-Arrabé (21156737)

“…Group-by-time interaction showed significant differences for the lunge test (p =  0.045), SLDJ height (p =  0.014), flight time (p =  0.019) and ground contact time (p =  0.035). …”

Excluded skewed studies. by Solima Sabeel (9545738)

Summary of non-randomized studies. by Solima Sabeel (9545738)

Summary table showing analysis of randomized controlled trials stratified based on age groups. by Solima Sabeel (9545738)

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. …”