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. …”

Oryza australiensis species-specific gene and protein candidates by Sabrina Morrison (19846869)

“…</p><p dir="ltr"><i>O. australiensis</i> genes that were not mapped with any <i>O. sativa</i> Illimuna reads were designated as ‘unmapped’ genes and were functionally annotated with OmicsBox 3.1.11. …”

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). …”

Figure 3 from Membrane-bound Heat Shock Protein mHsp70 Is Required for Migration and Invasion of Brain Tumors by Maxim Shevtsov (15023874)

“…<p>Mass spectrometry analysis of the tumor-derived lipid rafts. <b>A,</b> Designation of the brain tumor regions on postcontrast T1-weighted image for obtaining tumor samples from necrotic, tumor, and peritumoral regions. …”

Matlab source codes by Kyung-Chan Kim (7469375)

“…The package consists of six MATLAB source files, each with the following functions:</p><p dir="ltr"><b>1.</b><b> </b><b>tracer_main.m</b><br>The main program. …”

A Smoothed-Bayesian Approach to Frequency Recovery from Sketched Data by Mario Beraha (11669142)

“…For sketches with multiple hash functions, we introduce an approach based on <i>multi-view</i> learning to construct computationally efficient frequency estimators. …”

O-RAN-Based Cyberinfrastructure Training for FutureG Wireless Comm. and Sensing by Yao Zheng (21752159)

“…This poster presents the design and implementation of a cloud-based testbed that integrates Reconfigurable Intelligent Surfaces cloud-based testbed that integrates Reconfigurable Intelligent Surfaces (RIS) into an O-RAN-compliant environment. …”

Revisits the Selectivity toward C₂₊ Products for CO₂ Electroreduction over Subnano-Copper Clusters Based on Structural Descriptors by Xuning Wang (3603323)

“…To shed light on the feasibility and potential of Cu subnanoclusters as catalysts for CO₂ER toward C₂₊ products, we employ global optimization by Revised Particle Swarm Optimization algorithm, density functional theory calculations, and microkinetic modeling on a range of Cu subnanoclusters with varying sizes to investigate CO₂ER reactivity. …”

Image 1_Single-cell and multi-omics analysis reveals the role of stem cells in prognosis and immunotherapy of lung adenocarcinoma patients.tif by Jianan Zheng (9134099)

“…Multiple machine learning algorithms were systematically compared in order to develop an optimal prognostic model. …”

<b>ODS1 - Dataset of sputum smear microscopy images for Tuberculosis</b> by Marly G F Costa (19812192)

“…The 6th image in the stack is the autofocus image. It was designed to evaluate algorithms for selecting the best autofocusing measurement, and for evaluating image fusion algorithms, for example. …”

Image 2_Single-cell and multi-omics analysis reveals the role of stem cells in prognosis and immunotherapy of lung adenocarcinoma patients.tif by Jianan Zheng (9134099)

“…Multiple machine learning algorithms were systematically compared in order to develop an optimal prognostic model. …”

A paired dataset of multi-modal MRI at 3 Tesla and 7 Tesla with manual hippocampal subfield segmentations on 7T T2-weighted images by Shuyu Li (18859198)

“…<p dir="ltr">The hippocampus, a region of critical interest within clinical neuroscience, is recognized as a complex structure comprising distinct subfields with unique functional attributes, connectivity patterns, and susceptibilities to disease. …”