Crude rates in select US counties between 1999 and 2024. by Tony Wong (779773)

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Alcohol-related monthly crude rates for ages 15 and older between 2018 and 2024, stratified by gender and race. by Tony Wong (779773)

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Yearly crude rates normalized by their 1999 value. by Tony Wong (779773)

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Percent change in yearly crude rates (between 2019 and 2021 and between 2019 and 2024) by gender and state. by Tony Wong (779773)

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Yearly crude rates for alcohol-induced deaths among males by state in 2019, 2021, and 2024. by Tony Wong (779773)

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Antibodies used in this study. by Kevin J. Kokesh (19859571)

“…Western blot and densitometric analysis showed a significant increase in cathepsin B protein expression in <i>M</i>. …”

Time and flowrate used for proteomics. by Kevin J. Kokesh (19859571)

“…Western blot and densitometric analysis showed a significant increase in cathepsin B protein expression in <i>M</i>. …”

S1 Graphical abstract - by Kevin J. Kokesh (19859571)

“…Western blot and densitometric analysis showed a significant increase in cathepsin B protein expression in <i>M</i>. …”

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. …”

PGAM5 knockdown prevented rTsSPc from disrupting Caco-2 monolayer integrity. by Qi Qi Lu (17721401)

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Inhibition of PGAM5 and apoptosis alleviated the rTsSPc-damaged Caco-2 monolayer barrier integrity. by Qi Qi Lu (17721401)

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rTsSPc induced Caco-2 cell apoptosis. by Qi Qi Lu (17721401)

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Apoptosis inhibitor abolished and restored rTsSPc-disrupted Caco-2 monolayer barrier integrity. by Qi Qi Lu (17721401)

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Specific primer sequences of human gut epithelial TJs and apoptosis-related genes for qPCR. by Qi Qi Lu (17721401)

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Raw data of Figs 1–6 in this study. by Qi Qi Lu (17721401)

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Inhibition of PGAM5 and apoptosis prevented <i>T. spiralis</i> larval invasion of Caco-2 monolayer. by Qi Qi Lu (17721401)

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