Demographic and baseline characteristics. by Pitchaporn Sonuch (11785736)

“…</p><p>Conclusions</p><p>The combined intervention significantly decreased systolic blood pressure and showed a trend towards reduced urine sodium excretion in hypertensive patients. …”

The change of urine sodium excretion (Una). by Pitchaporn Sonuch (11785736)

“…</p><p>Conclusions</p><p>The combined intervention significantly decreased systolic blood pressure and showed a trend towards reduced urine sodium excretion in hypertensive patients. …”

The change of blood pressure. by Pitchaporn Sonuch (11785736)

“…</p><p>Conclusions</p><p>The combined intervention significantly decreased systolic blood pressure and showed a trend towards reduced urine sodium excretion in hypertensive patients. …”

Overview of all miRNA expression comparisons. by Barbara Mayr (7065950)

“…<p>Upward arrow = increased expression; downward arrow = decreased expression; dark arrows = significant alteration.…”

Dataset in. CSV. by Kare Chawicha Debessa (20660605)

“…Lower dropout likelihood was significantly associated with increased age (AOR = 0.93; 95% CI: 0.89–0.97; p < 0.001) and larger family size (AOR = 0.28; 95% CI: 0.17–0.50; p < 0.001).…”

Antibodies used in immunofluorescence staining. by Hiroharu Maegawa (8087543)

“…Moreover, the immunoreactivity of CGRP and D2 receptor in the Vc increased after ION ligation and decreased after antibody administration. …”

Supplementary Material for: Yoga Asana Increases Pre-Frontal Cortex Activity and Reduces Resting State Functional Connectivity by figshare admin karger (2628495)

“…Results: All three active asanas significantly increased PFC activity versus baseline, particularly in the right PFC. …”

Subject characteristics. by Barbara Mayr (7065950)

“…We found a significant increase both times for miR-103a (glycolysis, %change base: +12%, post +17%), miR-146a (inflammation, %change base: +20%, post +21%), and miR-222 (cardiac remodeling, %change base: +10%, post +21%), while miR-30a (inflammation, %change base: -27%, post: -38%) decreased significantly (all p≤0.043).…”

Raw data. by Barbara Mayr (7065950)

“…We found a significant increase both times for miR-103a (glycolysis, %change base: +12%, post +17%), miR-146a (inflammation, %change base: +20%, post +21%), and miR-222 (cardiac remodeling, %change base: +10%, post +21%), while miR-30a (inflammation, %change base: -27%, post: -38%) decreased significantly (all p≤0.043).…”

Flow diagram. by Barbara Mayr (7065950)

“…We found a significant increase both times for miR-103a (glycolysis, %change base: +12%, post +17%), miR-146a (inflammation, %change base: +20%, post +21%), and miR-222 (cardiac remodeling, %change base: +10%, post +21%), while miR-30a (inflammation, %change base: -27%, post: -38%) decreased significantly (all p≤0.043).…”

Data from: Decade-long bird trends in China: Stable species richness but increasing biotic homogenization by Jiekun He (20964269)

“…In contrast, functional richness increased significantly (Zr = 0.122, 95% CI = [0.016; 0.227]). …”

Ictogenic changes in input correlation and adaptation gains are associated with increased dynamical variability during baseline activity. by Scott Rich (9873638)

“…Plotted values are mean SD over 50 independent 200 s simulations. SLEs are significantly more frequent for each displayed <i>c</i> value in comparison to the red <i>c</i> = 0.01 case (<i>p</i> < 0.05, two sample t-test). …”

Descriptive data for the pBT-child dyads. by Madalina Ciobanu (2504683)

“…<div><p>Autism spectrum disorder (ASD) is a condition with growing prevalence that results in significant healthcare spending, reduced parent income, high levels of family stress, and decreased quality of life (QoL). …”

CFQL-2 descriptive statistics. by Madalina Ciobanu (2504683)

“…<div><p>Autism spectrum disorder (ASD) is a condition with growing prevalence that results in significant healthcare spending, reduced parent income, high levels of family stress, and decreased quality of life (QoL). …”

The clinical characteristic of patients. by Hai-bo Ai (20790405)

“…When the rate was between 18% and 59%, medical costs decreased as the rate increased, and when the rate was < 18% or ≥  59%, medical costs did not decrease as the rate increased.…”

The results of threshold effect analysis. by Hai-bo Ai (20790405)

“…When the rate was between 18% and 59%, medical costs decreased as the rate increased, and when the rate was < 18% or ≥  59%, medical costs did not decrease as the rate increased.…”

The forest plot of subgroup analysis. by Hai-bo Ai (20790405)

“…When the rate was between 18% and 59%, medical costs decreased as the rate increased, and when the rate was < 18% or ≥  59%, medical costs did not decrease as the rate increased.…”

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. …”