Data Sheet 1_Resveratrol contributes to NK cell-mediated breast cancer cytotoxicity by upregulating ULBP2 through miR-17-5p downmodulation and activation of MINK1/JNK/c-Jun signali... by Bisha Ding (5803799)

“…Natural killer group 2 member D (NKG2D) is a prominent activating receptor of NK cell. UL16-binding protein 2 (ULBP2), always expressed or elevated on cancer cells, functions as a key NKG2D ligand. …”

DataSheet1_Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models.docx by Yutong Fang (16621143)

“…We validated the protein and mRNA expressions of prognostic MRGs in tissues and cell lines through immunohistochemistry and qRT-PCR. …”

Table1_Mitochondrial-related genes as prognostic and metastatic markers in breast cancer: insights from comprehensive analysis and clinical models.xlsx by Yutong Fang (16621143)

“…We validated the protein and mRNA expressions of prognostic MRGs in tissues and cell lines through immunohistochemistry and qRT-PCR. …”

AP-2α 相关研究 by Ya-Hong Wang (21080642)

“…</p><p dir="ltr">(D) Phylogenetic analysis of the <i>VdAP-2α</i> protein in <i>V. dahliae</i> and its homologs in other fungi. …”

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows by Pierre-Alexis DELAROCHE (22092572)

“…Performance Profiling Algorithms Energy Measurement Methodology # Pseudo-algorithmic representation of measurement protocol def capture_energy_metrics(workflow_type: WorkflowEnum, asset_vector: List[PhotoAsset]) -> EnergyProfile: baseline_power = sample_idle_power_draw(duration=30) with PowerMonitoringContext() as pmc: start_timestamp = rdtsc() # Read time-stamp counter if workflow_type == WorkflowEnum.LOCAL: result = execute_local_pipeline(asset_vector) elif workflow_type == WorkflowEnum.CLOUD: result = execute_cloud_pipeline(asset_vector) end_timestamp = rdtsc() energy_profile = EnergyProfile( duration=cycles_to_seconds(end_timestamp - start_timestamp), peak_power=pmc.get_peak_consumption(), average_power=pmc.get_mean_consumption(), total_energy=integrate_power_curve(pmc.get_power_trace()) ) return energy_profile Statistical Analysis Framework Our analytical pipeline employs advanced statistical methodologies including: Variance Decomposition: ANOVA with nested factors for hardware configuration effects Regression Analysis: Generalized Linear Models (GLM) with log-link functions for energy modeling Temporal Analysis: Fourier transform-based frequency domain analysis of power consumption patterns Cluster Analysis: K-means clustering with Euclidean distance metrics for workflow classification Data Validation and Quality Assurance Measurement Uncertainty Quantification All energy measurements incorporate systematic and random error propagation analysis: Instrument Precision: ±0.1W for CPU power, ±0.5W for GPU power Temporal Resolution: 1ms sampling with Nyquist frequency considerations Calibration Protocol: NIST-traceable power standards with periodic recalibration Environmental Controls: Temperature-compensated measurements in climate-controlled facility Outlier Detection Algorithms Statistical outliers are identified using the Interquartile Range (IQR) method with Tukey's fence criteria (Q₁ - 1.5×IQR, Q₃ + 1.5×IQR). …”

Table 1_Shared genetic architecture between COVID-19 and irritable bowel syndrome: a large-scale genome-wide cross-trait analysis.xlsx by Xianqiang Liu (228089)

“…Independent risk loci were examined using genome-wide complex trait analysis (GCTA)-conditional and joint analysis (COJO), multi-marker analysis of genomic annotation (MAGMA), and functional mapping and annotation (FUMA), integrating various QTL information and methods to further identify risk genes and proteins. …”

Image 3_Integrating scRNA-seq and machine learning identifies MNAT1 as a therapeutic target in OSCC.tif by Han Gao (486886)

“…In vitro functional assays showed that depletion of MNAT1 impaired Cal27 cell proliferation and migration capacity.…”

Image 2_Integrating scRNA-seq and machine learning identifies MNAT1 as a therapeutic target in OSCC.tif by Han Gao (486886)

“…In vitro functional assays showed that depletion of MNAT1 impaired Cal27 cell proliferation and migration capacity.…”

Image 1_Integrating scRNA-seq and machine learning identifies MNAT1 as a therapeutic target in OSCC.tif by Han Gao (486886)

“…In vitro functional assays showed that depletion of MNAT1 impaired Cal27 cell proliferation and migration capacity.…”

Table 1_Integrating scRNA-seq and machine learning identifies MNAT1 as a therapeutic target in OSCC.docx by Han Gao (486886)

“…In vitro functional assays showed that depletion of MNAT1 impaired Cal27 cell proliferation and migration capacity.…”

Image 4_Integrating scRNA-seq and machine learning identifies MNAT1 as a therapeutic target in OSCC.tif by Han Gao (486886)

“…In vitro functional assays showed that depletion of MNAT1 impaired Cal27 cell proliferation and migration capacity.…”

Image 1_Integrated bioinformatics and molecular docking analysis reveal potential hub genes and targeted therapeutics in sepsis-associated acute lung injury.jpeg by Qiongyan Chen (22406809)

“…Hub genes were screened using PPI network construction and three machine learning algorithms, and validated by Western blot. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

Table 1_Integrated bioinformatics and molecular docking analysis reveal potential hub genes and targeted therapeutics in sepsis-associated acute lung injury.docx by Qiongyan Chen (22406809)

“…Hub genes were screened using PPI network construction and three machine learning algorithms, and validated by Western blot. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

Table 2_Integrated bioinformatics and molecular docking analysis reveal potential hub genes and targeted therapeutics in sepsis-associated acute lung injury.docx by Qiongyan Chen (22406809)

“…Hub genes were screened using PPI network construction and three machine learning algorithms, and validated by Western blot. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

Data Sheet 1_Inflammation-related biomarkers and berberine therapy in post-stroke depression: evidence from bioinformatics, machine learning, and experimental validation.docx by Wei Liu (20030)

“…Differentially expressed genes (DEGs) were identified using the limma package, followed by functional enrichment analysis with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). …”