The NMDS plots of bacterial (A) and fungal (B) communities of the basal portion and bacterial (C) and fungal (D) communities of the apical portion based on Bray-Curits distance (n ... by Bing-Nan Zhao (21553401)

Microbial alpha diversity of bacterial (A) and fungal (B) communities of the basal portion and bacterial (C) and fungal (D) communities of the apical portion under different treatm... by Bing-Nan Zhao (21553401)

Data Sheet 2_CD44 knockdown alters miRNA expression and their target genes in colon cancer.pdf by Diana Maltseva (11678641)

“…Introduction<p>Metastasis formation poses a significant challenge to oncologists, as it severely limits the survival of colorectal cancer (CRC) patients. …”

Data Sheet 4_CD44 knockdown alters miRNA expression and their target genes in colon cancer.pdf by Diana Maltseva (11678641)

“…Introduction<p>Metastasis formation poses a significant challenge to oncologists, as it severely limits the survival of colorectal cancer (CRC) patients. …”

Data Sheet 5_CD44 knockdown alters miRNA expression and their target genes in colon cancer.zip by Diana Maltseva (11678641)

“…Introduction<p>Metastasis formation poses a significant challenge to oncologists, as it severely limits the survival of colorectal cancer (CRC) patients. …”

Data Sheet 1_CD44 knockdown alters miRNA expression and their target genes in colon cancer.pdf by Diana Maltseva (11678641)

“…Introduction<p>Metastasis formation poses a significant challenge to oncologists, as it severely limits the survival of colorectal cancer (CRC) patients. …”

Data Sheet 3_CD44 knockdown alters miRNA expression and their target genes in colon cancer.pdf by Diana Maltseva (11678641)

“…Introduction<p>Metastasis formation poses a significant challenge to oncologists, as it severely limits the survival of colorectal cancer (CRC) patients. …”

Manifestations of Post-COVID-19 Conditions (PCC) three months post-acute illness, among those with PCC, by age group^a. by Chelsea S. Lutz (16001569)

Targeted analysis of cholesterol and cholesterol esters in epidermis reveals changes in specific cholesterol esters in CKO epidermis. by Kevin J. Mills (9270966)

Impact of Cold Plasma Treatment on the Shelf Life and Metabolite Profiles of Strawberries during Storage by D. Abouelenein (22365684)

“…Although ascorbic acid decreased after treatment, greater stability was noted during storage. …”

Flow chart for inclusion and exclusion criteria. by Maleda Tefera (9025154)

“…Similarly, children who breastfed until the second year of life had a greater chance of underweight and stunting. This study highlights a significant prevalence of undernutrition among children. …”

Median ± IQR <i>M. plutonius</i> bacterial load (CFU/bee) of caged adult summer worker bees over time after inoculation with A. low (1 × 10⁴ CFU/bee)B. Medium (1 × 10<su... by Midhun Sebastian Jose (19336846)

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. …”

Pneumococcal (black bars) and influenza (white bars) vaccine coverage in patients living with comorbidities (A), and immunosuppressive conditions (B) according to age of 65 years,... by Benjamin Wyplosz (22032756)

Pneumococcal (black bars) and influenza (white bars) vaccine coverage in patients living with comorbidities (A), and immunosuppressive conditions (B), who are at risk of pneumococc... by Benjamin Wyplosz (22032756)

<b>Data for s</b><b>easonal variations in coral lipids and their significance for energy maintenance in the </b><b>South China Sea</b> by Hongyan Mo (19721569)

“…<p dir="ltr">In recent years, the intensification of global warming and extreme climate have led to an increase in the frequency and severity of coral bleaching. Coral bleaching means a decrease in symbiotic zooxanthellae density (ZD). …”