Raw data for MaMsb2.xlsxSupplemental materials for MaMsb2, a signaling mucin, is involved in conidiation, stress tolerances, and virulence in the entomopathogenic fungus Metarhiziu... by Kai Jin (20368413)

Transmission of reduced levels of miR-34/449 from sperm to preimplantation embryos is a key step in the transgenerational epigenetic inheritance of the effects of paternal chronic... by Alexandre Champroux (18547075)

Changes in the concentration of lactate, bicarbonate (HCO), base excess (BE) and chloride (Cl) after a patient with septic shock was placed on high-volume hemofiltration (HVHF) and... by Rinaldo Bellomo (982)

Deficits and Disparities in Early Uptake of GLP-1 Receptor Agonists and SGLT2 Inhibitors among Medicare-insured Adults Following a New Diagnosis of Cardiovascular Disease or Heart... by Sara J. Cromer (13136821)

Effects of Low-Fat, Mediterranean, or Low-Carbohydrate Weight Loss Diets on Serum Urate and Cardiometabolic Risk Factors - A Secondary Analysis of the Dietary Intervention Randomiz... by Chio Yokose (9221485)

A Kinetic Model of CO₂ Absorption in Molten CaO-CaF₂-CaCl₂ (Supporting Information) by Sondre G. IVELAND (17999140)

Effects of Concurrent Training and a Multi-Ingredient Performance Supplement Containing <i>Rhodiola rosea</i> and <i>Cordyceps sinensis</i> on Body Composition, Performance, and He... by Vince C. Kreipke, PhD (9540472)

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. …”

Table 1_Global and regional burdens of opioid use disorder from 1990 to 2021, with future forecasts to 2050: a systematic analysis for the global burden of disease study 2021.docx by Juanzhao Cao (22651742)

Data_Sheet_1_The Associations of Plasma Carotenoids and Vitamins With Risk of Age-Related Macular Degeneration: Results From a Matched Case-Control Study in China and Meta-Analysis... by Hong Jiang (79543)

“…Nine studies were identified for the meta-analysis and calculated pooled risk estimates by means of a random-effects model.</p>Results<p>Plasma concentrations of examined carotenoids and vitamins were significantly lower in patients with AMD than those in controls. …”

Data_Sheet_1_The relationship between family diet consumption, family environment, parent anxiety and nutrition status children during the COVID-19 pandemic: a longitudinal study.P... by Lili Peng (626354)

Hypoxia-induced shedding of MICA and HIF1A-mediated immune escape of pancreatic cancer cells from NK cells: role of circ_0000977/miR-153 axis by Zheng-Lin Ou (7201604)

Data_Sheet_1_Association between oxidative balance score and metabolic syndrome and its components in US adults: a cross-sectional study from NHANES 2011–2018.CSV by Yi Lu (6211)

“…Our data indicated that a higher OBS score was correlated with a decreased risk of MetS and its components in a nonlinear manner. …”

Screening flowchart. by Xuechun Fan (5439470)

“…We plan to use the RevMan V.5.4 application and the random-effects model for conducting the meta-analysis. …”

Search strategy in PubMed. by Xuechun Fan (5439470)

“…We plan to use the RevMan V.5.4 application and the random-effects model for conducting the meta-analysis. …”

Abnormal labyrinth layer in <i>Pkd1^−/−</i> placentas. by Miguel A. Garcia-Gonzalez (74652)

“…<p>A. Low power (upper panels) and high power (lower panels) views of congenic C57BL/6, E12.5 placentas stained with haematoxylin-eosin (H&E). …”