Effects of finerenone on the urinary β2-MG levels in all patients. by Jia Song (495396)

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Conceptual model being tested in the current study. by K. Kanoho Hosoda (19929050)

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Baseline characteristics of the participants. by Jia Song (495396)

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Summary of sample descriptive statistics. by K. Kanoho Hosoda (19929050)

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Vertebral cancellous tissueμCT parameters are not significantly affected by aging from 16 to 21 weeks, housing type, or microgravity. by Rukmani Cahill (20939813)

Comparison of the mean number of unique diagnoses per hospital admission, between 2000 and 2023, stratified by chapters of ICD-10. The table shows absolute and relative changes in... by Stuart A. Bowyer (21253319)

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. …”

Model-averaged coefficients, significance, and relative importance (RI) of the predictor variables for the GLMMs to evaluate effects on: (A) overall pollinator abundance; (B) fruit... by Carmelo Gómez-Martínez (9384385)

S1 File - by Luqiong Liu (11537092)

“…Compared with LPS treatment alone, BA significantly mitigated the reduction in the TEER, decreased FD-4 flux permeability, increased the mRNA expression of ZO-1 and Occludin, and normalized the distribution of ZO-1 and Occludin in Caco2 cells. …”

Experimental treatments and groups. by Luqiong Liu (11537092)

“…Compared with LPS treatment alone, BA significantly mitigated the reduction in the TEER, decreased FD-4 flux permeability, increased the mRNA expression of ZO-1 and Occludin, and normalized the distribution of ZO-1 and Occludin in Caco2 cells. …”

Primers for RT-qPCR. by Luqiong Liu (11537092)

“…Compared with LPS treatment alone, BA significantly mitigated the reduction in the TEER, decreased FD-4 flux permeability, increased the mRNA expression of ZO-1 and Occludin, and normalized the distribution of ZO-1 and Occludin in Caco2 cells. …”

Mean offspring production of the aphid population treated with different fungal isolates at a concentration of 3.2×10⁶. by Aime Cheoh Enoh (20141758)

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Lethal time (mean LT₅₀ and LT₉₀ with 95% confidence limits) for aphid populations from different agroecological zones exposed to six entomopathogenic fungal i... by Aime Cheoh Enoh (20141758)

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Percent mortality (mean± SE) of aphid populations from four agroecological zones after ten days of exposure to six entomopathogenic fungal isolates in the laboratory at a concentra... by Aime Cheoh Enoh (20141758)

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Cumulative mortality rates of aphids treated with four concentrations of BIITAC10.3.3, BIITAC6.2.2, and MIITAC6.2.2. by Aime Cheoh Enoh (20141758)

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Mean conidial viability of <i>Beauveria</i> and <i>Metarhizium</i> isolates. by Aime Cheoh Enoh (20141758)

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