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. …”

Table_1_Effect of Dosage Reduction of Hypoglycemic Multidrug Regimens on the Incidences of Acute Glycemic Complications in People With Type 2 Diabetes Who Fast During Ramaḍān: A Ra... by Louay Y. Zaghlol (11087271)

“…Each 1% decrease in the baseline HbA_1c concentration was associated with a 19.9-fold (95% CI, 9.6–41.5; P < 0.001) increase in the odds of hypoglycemia, and each 1% increase in the baseline HbA_1c concentration was associated with a 15.7-fold (95% CI, 10.0–24.6; P < 0.001) increase in the odds of hyperglycemia.…”

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). …”

Supporting datasets for all figures. by Matthew Drill (22258391)

“…Multiplex mRNA analysis demonstrated changes in genes associated with both apoptosis and pyroptosis, whilst a decrease in receptor-interacting serine/threonine-protein kinase 1 (RIPK1) expression along with an increase in TNFR1-associated death domain protein (TRADD) expression suggests potential involvement of the TRADD mediated RIPK1-independent necroptosis pathway. …”

Table_2_Changes in attitudes towards smoking during smoking cessation courses for Turkish- and Albanian-speaking migrants in Switzerland and its association with smoking behavior:... by Raquel Paz Castro (9167806)

“…</p>Results<p>Participation in an adapted smoking cessation course led to a decrease of positive attitudes toward smoking (T: β = −0.65, p < 0.001; A: β = −0.68, p < 0.001) and a decrease of cigarettes smoked per day in the short-term (T: β = −0.58, p < 0.001; A: β = −0.43, p < 0.001) with only Turkish-speaking migrants further reducing their smoking in the long-term (T: β = −0.59, p < 0.001; A: β = −0.14, p = 0.57). …”

Table_1_Changes in attitudes towards smoking during smoking cessation courses for Turkish- and Albanian-speaking migrants in Switzerland and its association with smoking behavior:... by Raquel Paz Castro (9167806)

“…</p>Results<p>Participation in an adapted smoking cessation course led to a decrease of positive attitudes toward smoking (T: β = −0.65, p < 0.001; A: β = −0.68, p < 0.001) and a decrease of cigarettes smoked per day in the short-term (T: β = −0.58, p < 0.001; A: β = −0.43, p < 0.001) with only Turkish-speaking migrants further reducing their smoking in the long-term (T: β = −0.59, p < 0.001; A: β = −0.14, p = 0.57). …”