Sequential viral infection is associated with reduced antibody response to intramuscular and intranasal ChAd-SARS-CoV-2 vaccination. by Yuhao Li (1559569)

Data Sheet 1_The gastric microbiome altered by A4GNT deficiency in mice.zip by Dawei Gong (3070773)

“…Background<p>Selective antimicrobial effects have been found for α1,4-linked N-acetylglucosamine residues at the terminus of O-glycans attached to a core protein of gastric gland mucin. A4gnt encodes α1,4-N-acetylglucosaminyl transferase, which is responsible for the biosynthesis of α1,4-linked N-acetylglucosamine. …”

This figure shows the forest plot for the multivariable logistic regression found in Table 1. by Scott J. Halperin (17835402)

The CYP7B1 inhibitor, clotrimazole, and the EBI2 receptor antagonist, NIBR189, attenuate LPS-induced astrocyte migration <i>in vitro.</i> by Fionä Caratis (12759506)

Effect of <i>T. oliverianum</i> extracts on liver enzymes and total bilirubin. by Rania F. Ahmed (10032039)

Effects of <i>T. oliverianum</i> extracts on hepatic content of GSH and MDA. by Rania F. Ahmed (10032039)

Effect of <i>T. olivarium</i> extracts on hepatic content of TNF-<i>α.</i> by Rania F. Ahmed (10032039)

Flow chart of data screening and analysis. by Yan She (8639796)

“…In contrast, the BMD, BV/TV, and Tb.N in the 12-week model group exhibited a modest but non-significant decrease relative to the 9-week model group. …”

Tight junction and adhesion proteins in the brain are downregulated during systemic inflammation. by Fionä Caratis (12759506)

“…<p>A. The mRNA levels of <i>Occludin</i> were significantly reduced after 12 hours post LPS injection (12 h: 57% + /- 12% vs. vehicle) and started to return to the vehicle levels after 24 hours. …”

Sequential viral exposure modulates antibody levels and B cell lineages in tissues. by Yuhao Li (1559569)

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

Sequential viral infection modulates the intestinal bacterial microbiome. by Yuhao Li (1559569)

Presents the bibliometric search results. by Yan She (8639796)

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. …”