Differentially expressed genes (DEGs)^a showing the greatest fold changes from each potato tissue: 10 with greatest increase in expression and 10 with greatest decrease in expression. by Margaret A. Carpenter (6104180)

“…<p>Differentially expressed genes (DEGs)^a showing the greatest fold changes from each potato tissue: 10 with greatest increase in expression and 10 with greatest decrease in expression.…”

Global Land Use Change Impacts on Soil Nitrogen Availability and Environmental Losses by Jing Wang (6206297)

Subjects:

Simulated regional mean wetland seasons. by Nic Gedney (19762301)

Projected decadal changes in seasonality in the hydrological cycle over the wetland regions. by Nic Gedney (19762301)

Contemporary mean inundation fraction in the Amazon basin. by Nic Gedney (19762301)

Decadal mean regional hydrology. by Nic Gedney (19762301)

Regional contemporary (1990–1999 mean) precipitation and JULES inundation fraction over the Amazon basin. by Nic Gedney (19762301)

Changes in decadal precipitation and JULES inundation between 1990–1999 and 2089–2098. by Nic Gedney (19762301)

The contemporary, long-term seasonality in inundation over the whole of the Amazon basin. by Nic Gedney (19762301)

Percentage regional changes in decadal regional hydrology. by Nic Gedney (19762301)

Wetland classification scheme for the contemporary period. by Nic Gedney (19762301)

Wetlands over the lowland Amazon basin from the Hess product [19,20]. by Nic Gedney (19762301)

Decadal seasonality of the hydrological cycle over the wetland regions. by Nic Gedney (19762301)

Wetland classification using the ensemble simulations. by Nic Gedney (19762301)

SHAP waterfall plot. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”

SHAP decision plot. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”

SHAP dependence plots. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”

SHAP dependence plots with interaction coloring. by Wentao Yang (205781)

“…Across 10 models, CatBoost performed best on the test set (AUC = 0.970, accuracy = 0.920, F1 = 0.918), with robust calibration and decision-curve net benefit. SHAP interpretation ranked eGDR among the most influential predictors: SHAP summary and dependence plots indicated that higher eGDR decreased the model’s predicted probability of frailty. …”

Tissue, days post-infection (dpi) and the top 10 most significant genes with increased and decreased expression with valid gene symbols for the response contrasts. by Gillian P. McHugo (8965919)

Median SHAP values (calculated for CSCHF at age 5000 days) for binary features in the competing risks RSF model for different disease categories. The features are sorted in decreas... by Eric Wang (30587)