Table 1_The knowledge paradox: an inverted U-shaped association between HIV knowledge and stigma among older men in Sichuan Province, Southwest China.docx by Zhihua Ye (3133311)

“…Background<p>Older men (≥50 years) in China face elevated HIV infection risks, yet HIV stigma remains a significant barrier to prevention. Although HIV knowledge is frequently assumed to reduce stigma, the evidence is inconsistent. …”

A Locally Linear Dynamic Strategy for Manifold Learning. by Weifan Wang (4669081)

“…The three density plots reveal a consistent inverse relationship: learning primarily modifies modes that were initially insensitive (low sensitivity, high change, top-left cluster), while leaving highly sensitive modes largely unchanged (high sensitivity, low change, bottom-right cluster).…”

Molecular signatures underlying multisensory gamma stimulation-associated cognitive benefits in the hippocampus. by Md Rezaul Islam (19649494)

“…<p><b>A)</b> Experimental scheme for the single nuclei RNA-seq experiment in Ts65Dn mice <b>B)</b> Unbiased clustering of hippocampal snRNA-seq data for 15884 nuclei represented on a UMAP. …”

Image 3_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.tif by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Table 3_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Table 8_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Table 2_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Table 1_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Image 2_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.tif by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Table 6_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Table 5_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Image 5_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.tif by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Image 1_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.tiff by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Table 7_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Table 4_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.xlsx by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Image 6_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.tif by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Image 4_Cellular interactions and Ion channel signatures in atrial fibrillation remodeling: insights from single-cell analysis and machine learning.tif by Bin He (75597)

“…Differential gene expression and clustering were performed, followed by principal component analysis (PCA) to identify significant cell types. …”

Table 1_Integrated gut microbiota and metabolomic profiling reveals key associations between amino acid levels and gut microbial composition in patients with obesity.docx by Musen Qi (22489075)

“…Metabolomic analysis revealed that in patients with obesity, the abundance of carnosine (log2FC = 1.16, FDR = 0.0016, VIP = 0.707), creatinine (log2FC = 0.21, FDR = 0.0009, VIP = 2.02), and cystine (log2FC = 0.55, FDR = 0.009, VIP = 1.47) was significantly increased compared with that in the controls; in contrast, that of ornithine (log2FC = −0.59, FDR = 0.0009, VIP = 1.19), citrulline (log2FC = −0.59, FDR = 0.0003, VIP = 0.707), glycine (log2FC = −0.54, FDR = 0.0003, VIP = 1.41), and serine (log2FC = −0.38, FDR = 0.0019, VIP = 1.62) was significantly decreased. …”

<b>不同辣椒</b><b>chili</b><b>straw on growth performance, rumen fungal flora structure, function and economic benefits of sheep</b> by Jinlong Li (20481890)

“…Key findings include:(1) Dry matter intake (DMI) increased significantly in the 10% CS group (P<0.01), whereas DMI and final body weight decreased in the 20% CS group (P<0.01 or P<0.05); Final body weight and average daily gain (ADG) showed an upward trend in the 10% CS group (P > 0.05), while ADG displayed a downward trend in the 20% CS group (P>0.05); (2) The ACE and Chao1 indices were significantly elevated in the 20% CS group (P<0.05); (3) Principal coordinates analysis (PCoA) and non-metric multidimensional scaling (NMDS) revealed distinct clustering of the 20% CS group compared to the control (CON) group; (4) Relative abundances of Ascomycota and Cladosporium increased, whereas those of Basidiomycota and Kazachstania decreased in CS-supplemented groups (P>0.05); (5) FUNGuild functional prediction indicated that rumen fungi were predominantly symbiotrophic, with increased relative abundances of symbiotrophic and pathotrophic fungi and decreased saprotrophic fungi in CS groups (P>0.05). (6) The gross profit and net profit of CS10 % group increased. …”

Image 1_Construction of a novel cuproptosis-related gene signature for predicting microenvironment, prognosis and therapeutic response in cervical cancer.tif by Yaqi Cui (10798865)

“…Kaplan-Meier survival analysis revealed that the prognosis of patients with cervical cancer in Cluster B was significantly better than in Cluster A (p=0.027). …”