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. …”

PBMC T cell subsets vary between mock- and sequentially-infected mice. by Yuhao Li (1559569)

Structure-based alignment and homology modelling of MeTIR domains. by Bulelani L. Sizani (21485746)

<i>Gpdh1; Ldh</i> double mutants display increased <i>upd3</i> expression. by Madhulika Rai (4352803)

LPS induces changes in the expression of Ebi2, Ch25h, Cyp7b1 and Hsd3b7 enzymes in the mouse brain. by Fionä Caratis (12759506)

Multilevel logistic regression analysis of individual and community level factors. by Mulugeta Wassie (10803155)

Subjects:

Socio- demographic characteristics of study participants. by Mulugeta Wassie (10803155)

Subjects:

Differentially-expressed NLR genes in susceptible T200 and tolerant TME3 genotypes infected with SACMV. by Bulelani L. Sizani (21485746)

Hypothetical model of the molecular mechanism underlying the ability of SACMV to suppress the host RNA silencing mechanism (PTGS) and systemic acquired resistance (SAR). by Bulelani L. Sizani (21485746)

IQGAP1 is a protein that plays a critical role in regulating the level of apoptosis in endothelial cells. by Shaojun Huang (12489901)

“…<p>(A) The Annexin V–FITC/propidium iodide (PI) assay results indicate that Si-IQGAP1 can slightly decrease the apoptosis rate of normal cells, whereas knocking down IQGAP1 in PA-induced cells (PA + Si-IQGAP1) can significantly reduce the apoptosis rate. …”

Total count of HiFi reads indicating gene variation in 24 cassava genotypes. by Bulelani L. Sizani (21485746)

Changes in global brain blood volume mediate CSF in- and outflux. by Juliana Zimmermann (10507091)

Variations of (a) non-dimensional diameter and (b) the frequency of droplet generation in terms of the tilting angle for different water flow rates. by Amirhossein Favakeh (21417884)

PCA of bacterial communities. by Jun Sun (48981)

“…A significant positive correlation was observed between AK and both <i>Firmicutes</i> and <i>Kapabacteria</i> individually; furthermore, AP exhibited a highly significant positive correlation with <i>Kapabacteria</i> but a significant negative correlation with <i>unidentified Archaea</i>. …”

OUT Venn diagram. by Jun Sun (48981)

“…A significant positive correlation was observed between AK and both <i>Firmicutes</i> and <i>Kapabacteria</i> individually; furthermore, AP exhibited a highly significant positive correlation with <i>Kapabacteria</i> but a significant negative correlation with <i>unidentified Archaea</i>. …”

Upper quartile of district towns evaluated by the SD-TOPSIS technique ‐ combination of A, B1, B2 and C1. by Roman Vavrek (13200141)

Lower quartile of district towns evaluated by the SD-TOPSIS technique ‐ combination of A, B1, B1 and C1. by Roman Vavrek (13200141)

Sequentially infected mice have altered circulating immune cell compositions. by Yuhao Li (1559569)