Table 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.xlsx by Peng Liu (120506)

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. Further, based upon the upstream regulatory network, TP53, RARB, RXRB, and CTCF potentially exerted regulatory functions. …”

Image 1_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. Further, based upon the upstream regulatory network, TP53, RARB, RXRB, and CTCF potentially exerted regulatory functions. …”

Image 4_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. Further, based upon the upstream regulatory network, TP53, RARB, RXRB, and CTCF potentially exerted regulatory functions. …”

Image 5_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. Further, based upon the upstream regulatory network, TP53, RARB, RXRB, and CTCF potentially exerted regulatory functions. …”

Image 3_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. Further, based upon the upstream regulatory network, TP53, RARB, RXRB, and CTCF potentially exerted regulatory functions. …”

Image 2_Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms.tiff by Peng Liu (120506)

“…Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. Further, based upon the upstream regulatory network, TP53, RARB, RXRB, and CTCF potentially exerted regulatory functions. …”

Interaction of Rare-Earth Metals and Some Perfluorinated β‑Diketones by Maxim A. Lutoshkin (6651425)

“…In general, the received set of data can be described from purely electrostatic grounds within the framework of the periodic law. Spectral, keto–enol, acid–base, and complexing properties were reproduced using density functional theory modeling and explain some of the regularities discovered.…”

Raw LC-MS/MS and RNA-Seq Mitochondria data by Stefano Martellucci (16284377)

“…The sample error rate was < 2% and the distribution of reads per sample in a lane was within a reasonable tolerance. Data generated during sequencing runs were transferred to the high-performance computing (HPC) cluster, with individual base calls transferred for downstream analysis. …”

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). …”