Table 2_Magnesium application partially reversed the negative effects of mulching on rhizosphere nitrogen cycling in a Phyllostachys praecox forest.docx by Hong Zhao (325)

“…The rhizosphere nitrogen-cycling network reorganized as the relative contributions of gdhA, AOB-amoA, napA, nirK, and nosZ increased. Surprisingly, nitrite accumulated under mulching due to a decrease in the nxrA/AOB ratio. …”

Table 1_Magnesium application partially reversed the negative effects of mulching on rhizosphere nitrogen cycling in a Phyllostachys praecox forest.xlsx by Hong Zhao (325)

“…The rhizosphere nitrogen-cycling network reorganized as the relative contributions of gdhA, AOB-amoA, napA, nirK, and nosZ increased. Surprisingly, nitrite accumulated under mulching due to a decrease in the nxrA/AOB ratio. …”

The oxysterol 7α, 25OHC synthesising and degrading enzymes are expressed in the mouse brain microvessels. by Fionä Caratis (12759506)

Sequential viral exposure creates an inflammatory host environment. by Yuhao Li (1559569)

Sequential viral exposure shapes immune cell profiles in tissues. by Yuhao Li (1559569)

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. …”

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)