Image 4_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.jpeg حسب Tianyue Sun (15831659)

"…Tumor-infiltrating immune cells in this group demonstrated enhanced activation and anti-tumor functions. Furthermore, CD8⁺ T cells showed increased Granzyme B levels when co-cultured with CRC cells in which Psat1 or Shmt2 was knocked down. …"

Image 5_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.jpeg حسب Tianyue Sun (15831659)

"…Tumor-infiltrating immune cells in this group demonstrated enhanced activation and anti-tumor functions. Furthermore, CD8⁺ T cells showed increased Granzyme B levels when co-cultured with CRC cells in which Psat1 or Shmt2 was knocked down. …"

Image3_A young child formula supplemented with a synbiotic mixture of scGOS/lcFOS and Bifidobacterium breve M-16V improves the gut microbiota and iron status in healthy toddlers.pd... حسب Charmaine Chew (9739238)

"…PICRUSt, a predictive functionality algorithm based on 16S ribosomal gene sequencing, was used to correlate potential microbial functions with iron status measurements. …"

Image 2_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.jpeg حسب Tianyue Sun (15831659)

"…Tumor-infiltrating immune cells in this group demonstrated enhanced activation and anti-tumor functions. Furthermore, CD8⁺ T cells showed increased Granzyme B levels when co-cultured with CRC cells in which Psat1 or Shmt2 was knocked down. …"

Image 6_Single-cell and bulk transcriptome analyses reveal elevated amino acid metabolism promoting tumor-directed immune evasion in colorectal cancer.jpeg حسب Tianyue Sun (15831659)

"…Tumor-infiltrating immune cells in this group demonstrated enhanced activation and anti-tumor functions. Furthermore, CD8⁺ T cells showed increased Granzyme B levels when co-cultured with CRC cells in which Psat1 or Shmt2 was knocked down. …"

DataSheet2_Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms.PDF حسب D. S. Bug (19791612)

DataSheet2_Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms.PDF حسب D. S. Bug (19791612)

DataSheet1_Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms.PDF حسب D. S. Bug (19791612)

DataSheet1_Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms.PDF حسب D. S. Bug (19791612)

Data Sheet 1_Multi-transcriptomics analysis of ferroptosis related genes reveals CAFs exosomal COX4I2 as a novel therapeutic target in osteosarcoma.docx حسب Xiaoying Niu (8747229)

"…We identified hub genes via machine learning algorithms (SVM, RF, XGBoost, BORUTA) and single-cell RNA sequencing. …"

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows حسب 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). …"

Data Sheet 1_Recognition of pivotal immune genes NR1H4 and IL4R as diagnostic biomarkers in distinguishing ovarian clear cell cancer from high-grade serous cancer.zip حسب Yumin Ke (14194880)

"…To identify differentially immune-related genes (DIRGs) linked to OCCC. We assessed DIRGs functional enrichment and built a protein-protein interaction (PPI) to explore DIRGs interactions. …"

Supplementary file 1_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.pdf حسب Yan Wu (70890)

"…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …"

Table 11_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.xlsx حسب Yan Wu (70890)

"…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …"

Table 10_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.xlsx حسب Yan Wu (70890)

"…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …"

Data Sheet 6_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.csv حسب Ge Jin (347352)

"…However, the biological function and clinical value of ubiquitination-related genes (UbLGs) in CC remain unclear. …"

Table 12_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.xlsx حسب Yan Wu (70890)

"…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …"

Data Sheet 8_Identification and validation of ubiquitination-related genes for predicting cervical cancer outcome.xlsx حسب Ge Jin (347352)

"…However, the biological function and clinical value of ubiquitination-related genes (UbLGs) in CC remain unclear. …"

Table 14_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.xlsx حسب Yan Wu (70890)

"…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …"

Table 9_Identification of the microglia-associated signature in experimental autoimmune encephalomyelitis.xlsx حسب Yan Wu (70890)

"…A machine learning approach incorporating five distinct algorithms was applied to select a robust multigene signature. …"