Glucose/galactose with extended interferon-priming does not impact levels of ISG RNAs or non-ISG proteins levels. by Tyron Chang (9922533)

Time course of IRF1 and IFITM3 RNA and protein expression across conditions. by Tyron Chang (9922533)

Vaccinia virus infection (MOI = 3) of A549 cells in media supplemented with glucose/galactose. by Tyron Chang (9922533)

Vaccinia virus and VSV infections of <i>STAT1</i> and <i>IRF1</i> KO cells grown in glucose/galactose with and without IFN-γ priming. by Tyron Chang (9922533)

Glucose/galactose regulation of IFN-γ induced IRF1 protein is conserved in mammals. by Tyron Chang (9922533)

Characterization of the impact of glucose/galactose on infection outcomes. by Tyron Chang (9922533)

Glucose/galactose selectively alters ISG protein not RNA levels in interferon-primed cells. by Tyron Chang (9922533)

Glucose but not galactose culturing of IFN-γ primed cells reduces VACV and HSV-1 replication. by Tyron Chang (9922533)

<i>IRF1</i> impacts VACV replication in glucose/IFN-γ primed but not galactose conditions. by Tyron Chang (9922533)

Ingenuity pathway analysis of tonic ISG subclasses. by Tyron Chang (9922533)

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. …”

Participant characteristics by village. by Tyler M. Barrett (20846174)

“…Nearly all participants reported perceiving increases in temperature (94%) and decreases in rainfall (91%) in the previous five years, and most reported that they expect to have much less (57%) or somewhat less (35%) food to feed their families in the future due to changes in temperature and rainfall. …”

Ranking position of feature importance for each cluster in the post-vaccination period. by Jéssica Villar (21453137)

Ranking position of feature importance for each cluster in the pre-vaccination period. by Jéssica Villar (21453137)

SHAP Values per cluster in the pre-vaccination period show each feature’s importance on COVID-19 mortality. by Jéssica Villar (21453137)

SHAP Values per cluster in the post-vaccination period show each feature’s importance on COVID-19 mortality. by Jéssica Villar (21453137)