Down-regulation of VANGL2 in IBD patients triggers overt activation of NLRP3 inflammasome. by Huaji Jiang (16907514)

Lys823 in NLRP3 is essential for its K27-linked ubiquitination and autophagic degradation. by Huaji Jiang (16907514)

VANGL2 specifically inhibits the activation of NLRP3 inflammasome. by Huaji Jiang (16907514)

VANGL2 recruits NLRP3 to OPTN for selective autophagic degradation. by Huaji Jiang (16907514)

VANGL2 recruits MARCH8 to promote the K27-linked ubiquitination of NLRP3. by Huaji Jiang (16907514)

Podocyte numbers in control and iPLA₂γ KO mice (WT1-positive cells). by Andrey V. Cybulsky (4007012)

Trends of the temperature, precipitation, and TVDI in the Santun River Irrigation Area, Xinjiang, during 2005–2023. by Yuxin Wei (11510974)

Major hyperparameters of RF-SVR. by Jintao Li (448681)

“…<div><p>Accurate medium- to long-term runoff forecasting is of great significance for flood control, drought mitigation, comprehensive water resource management, and ecological restoration. …”

Pseudo code for coupling model execution process. by Jintao Li (448681)

“…<div><p>Accurate medium- to long-term runoff forecasting is of great significance for flood control, drought mitigation, comprehensive water resource management, and ecological restoration. …”

Major hyperparameters of RF-MLPR. by Jintao Li (448681)

“…<div><p>Accurate medium- to long-term runoff forecasting is of great significance for flood control, drought mitigation, comprehensive water resource management, and ecological restoration. …”

Results of RF algorithm screening factors. by Jintao Li (448681)

“…<div><p>Accurate medium- to long-term runoff forecasting is of great significance for flood control, drought mitigation, comprehensive water resource management, and ecological restoration. …”

Schematic diagram of the basic principles of SVR. by Jintao Li (448681)

“…<div><p>Accurate medium- to long-term runoff forecasting is of great significance for flood control, drought mitigation, comprehensive water resource management, and ecological restoration. …”

IRE1 depletion increases chromosome instability. by Iratxe Zuazo-Gaztelu (21071939)

GSRS was positively associated with T cell exhaustion (TEX) and predicted a reduced likelihood of response to immunotherapy. by Yu Zhang (12946)

Structure diagram of ensemble model. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting formula parameter table. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Test plan. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Fitting surface parameters. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Model generalisation validation error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”

Empirical model prediction error analysis. by Hongqi Wang (2208238)

“…Comparative analysis highlights the significant enhancement in prediction accuracy achieved by the proposed ensemble model over single machine learning models, with root mean square error (RMSE) values below 0.05 and mean absolute percentage error (MAPE) values remaining under 2.5% in both frozen and unfrozen states. …”