Image 2_The relationship between immune cell infiltration and necroptosis gene expression in sepsis: an analysis using single-cell transcriptomic data.jpeg by Shouyi Wang (4449574)

“…GO analysis indicated significant enrichment in biological processes such as the regulation of apoptotic signaling pathways and IκB kinase/NF-κB signaling. KEGG pathway analysis revealed involvement in necroptosis, apoptosis, and NOD-like receptor signaling pathways. …”

Table 1_The relationship between immune cell infiltration and necroptosis gene expression in sepsis: an analysis using single-cell transcriptomic data.docx by Shouyi Wang (4449574)

“…GO analysis indicated significant enrichment in biological processes such as the regulation of apoptotic signaling pathways and IκB kinase/NF-κB signaling. KEGG pathway analysis revealed involvement in necroptosis, apoptosis, and NOD-like receptor signaling pathways. …”

Sample ESTIMATE score dataset (AR vs CTRL). by MaoMeng Wang (22177417)

“…A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. …”

STRING PPI network edges dataset. by MaoMeng Wang (22177417)

“…A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. …”

Structural changes of the nasal mucosa. by MaoMeng Wang (22177417)

“…A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. …”

General symptom scores of the mice. by MaoMeng Wang (22177417)

“…A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. …”

Correlated primer sequence table. by MaoMeng Wang (22177417)

“…A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. …”

DEG-WGCNA overlapping genes dataset. by MaoMeng Wang (22177417)

“…A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. …”

Risk-stratified KEGG pathway enrichment dataset. by MaoMeng Wang (22177417)

“…A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. …”

Module-trait correlation heatmap. by MaoMeng Wang (22177417)

“…A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. …”

Raw expression profile dataset. by MaoMeng Wang (22177417)

“…A gene co-expression network was constructed via the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to identify disease-related modules. Differentially expressed genes (DEGs) were identified using the linear models for microarray data (limma) R package (version 3.34.7), followed by functional enrichment analysis using DAVID. …”

a. How various statistical models account for modulation classification performance across the entire dataset. by Chris Scholes (3309477)

“…</b> As <b>b</b> for Vector Strength; <b>d.</b> Examples of the shuffled autocorrelation functions for the sustained chopper neuron in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003213#pbio.3003213.g002" target="_blank">Fig 2</a> (modulation frequency = 125 Hz) and primary-like neuron in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.3003213#pbio.3003213.g003" target="_blank">Fig 3</a> (modulation frequency = 150 Hz). …”

Additional data for the polyanion sodium cathode materials dataset by Martin Hoffmann Petersen (13626778)

“…All simulations are executed within the canonical (NVT) ensemble and a sample frequency was set to 1fs.…”

Data Sheet 1_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.xlsx by Wentong Li (492392)

“…Biomarker validation was performed through cross-validation using LASSO, SVM-RFE, and Random Forest algorithms. Immune microenvironment analysis was conducted using CIBERSORT, while single-cell transcriptomics was analyzed within the Seurat framework.…”

Data Sheet 5_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.csv by Wentong Li (492392)

“…Biomarker validation was performed through cross-validation using LASSO, SVM-RFE, and Random Forest algorithms. Immune microenvironment analysis was conducted using CIBERSORT, while single-cell transcriptomics was analyzed within the Seurat framework.…”

Supplementary file 1_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.docx by Wentong Li (492392)

“…Biomarker validation was performed through cross-validation using LASSO, SVM-RFE, and Random Forest algorithms. Immune microenvironment analysis was conducted using CIBERSORT, while single-cell transcriptomics was analyzed within the Seurat framework.…”

Data Sheet 2_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.xlsx by Wentong Li (492392)

“…Biomarker validation was performed through cross-validation using LASSO, SVM-RFE, and Random Forest algorithms. Immune microenvironment analysis was conducted using CIBERSORT, while single-cell transcriptomics was analyzed within the Seurat framework.…”

Data Sheet 3_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.xlsx by Wentong Li (492392)

“…Biomarker validation was performed through cross-validation using LASSO, SVM-RFE, and Random Forest algorithms. Immune microenvironment analysis was conducted using CIBERSORT, while single-cell transcriptomics was analyzed within the Seurat framework.…”

Data Sheet 4_Multi-omics exploration of chaperone-mediated immune-proteostasis crosstalk in vascular dementia and identification of diagnostic biomarkers.xlsx by Wentong Li (492392)

“…Biomarker validation was performed through cross-validation using LASSO, SVM-RFE, and Random Forest algorithms. Immune microenvironment analysis was conducted using CIBERSORT, while single-cell transcriptomics was analyzed within the Seurat framework.…”

Visual Poster | Machiavellian Marketing: The Necessary Evolution of Persuasion, Power, and Digital Propaganda by Hadrian Stone (22244185)

“…It synthesizes research from marketing psychology, propaganda theory, attention economics, and platform-specific behavioral design to demonstrate how modern markets function as systems of narrative competition.</p><p><br></p><p dir="ltr">The poster distills the major argument of the original paper: traditional marketing models have been rendered obsolete by the algorithmic attention economy. …”