DataSheet1_Comprehensive Analysis of N6-Methyladenosine Regulators in the Subcluster Classification and Drug Candidates Prediction of Severe Obstructive Sleep Apnea.PDF by Niannian Li (123776)

“…Consensus clustering algorithm was used to identify m6A patterns. And then, we explored the character of immune microenvironment, molecular functionals, protein-protein interaction networks and miRNA-TF coregulatory networks for each subcluster. …”

DataSheet_1_A Three-Gene Classifier Associated With MicroRNA-Mediated Regulation Predicts Prostate Cancer Recurrence After Radical Prostatectomy.csv by Bo Cheng (1371330)

“…</p>Methods<p>Candidate classifiers were constructed using two machine-learning algorithms (a least absolute shrinkage and selector operation [LASSO]-based classifier and a decision tree-based classifier) based on a discovery cohort (n = 156) from The Cancer Genome Atlas (TCGA) database. …”

DataSheet_1_Integrated Analysis of Gene Co-Expression Network and Prediction Model Indicates Immune-Related Roles of the Identified Biomarkers in Sepsis and Sepsis-Induced Acute Re... by Tingqian Ming (12980714)

“…In addition, various machine learning algorithms were also applied to develop extra models based on the four genes. …”

Data_Sheet_1_Mortality Prediction in Sepsis With an Immune-Related Transcriptomics Signature: A Multi-Cohort Analysis.pdf by Louis Kreitmann (475618)

“…</p>Methods<p>Publicly available microarray data of sepsis patients with widely variable demographics, clinical characteristics and ethnical background were co-normalized, and the performance of the IPP gene set to predict 30-day mortality was assessed using a combination of machine learning algorithms.</p>Results<p>We collected data from 1,801 arrays sampled on sepsis patients and 598 sampled on controls in 17 studies. …”

Replication package for "The Economics of Orbit Use: Open Access, External Costs, and Runaway Debris Growth" by Akhil Rao (14117295)

“…</p><p dir="ltr">- **simFnsEqns_1.0.tar.gz**: An R package with Rcpp model functions for enhanced computation.</p><p dir="ltr">## Installation and Setup</p><p dir="ltr">Analysis was conducted using R version 3.6 under Ubuntu 22.04 on a machine with dual Intel Xeon 6230 CPUs and 256 GB RAM. …”

Video_1_Advanced Design and Manufacture of Mechanoactive Materials Inspired by Skin, Bones, and Skin-on-Bones.MOV by Melissa Louise Knothe Tate (9293813)

“…In so doing, it demonstrates the principle to emulate natural smart properties using recursive logic, the basis of many computer algorithms, and to design and manufacture mechanoactive materials and products using advanced manufacturing methods that also incorporate principles of recursive logic. …”

Construction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer by Yanliang Chen (15235486)

“…</p> <p>Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (<i>APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2</i> and <i>TSPAN2</i>) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. …”

DataSheet_1_CXCL17 Is a Specific Diagnostic Biomarker for Severe Pandemic Influenza A(H1N1) That Predicts Poor Clinical Outcome.docx by Jose Alberto Choreño-Parra (10203338)

“…CXCL17 not only differentiated pandemic influenza A(H1N1) from other respiratory infections but showed prognostic value for influenza-associated mortality and renal failure in machine-learning algorithms and regression analyses. …”

DataSheet_1_CXCL17 Is a Specific Diagnostic Biomarker for Severe Pandemic Influenza A(H1N1) That Predicts Poor Clinical Outcome.docx by Jose Alberto Choreño-Parra (10203338)

“…CXCL17 not only differentiated pandemic influenza A(H1N1) from other respiratory infections but showed prognostic value for influenza-associated mortality and renal failure in machine-learning algorithms and regression analyses. …”

DataSheet2_A genome-wide association study of plasma concentrations of warfarin enantiomers and metabolites in sub-Saharan black-African patients.XLSX by Innocent G. Asiimwe (12340573)

“…In exploratory work, we conducted the first warfarin pharmacokinetics-related GWAS in sub-Saharan Africans and identified novel SNPs that will require external replication and functional characterization before they can be considered for inclusion in warfarin dosing algorithms.…”

DataSheet1_A genome-wide association study of plasma concentrations of warfarin enantiomers and metabolites in sub-Saharan black-African patients.zip by Innocent G. Asiimwe (12340573)

“…In exploratory work, we conducted the first warfarin pharmacokinetics-related GWAS in sub-Saharan Africans and identified novel SNPs that will require external replication and functional characterization before they can be considered for inclusion in warfarin dosing algorithms.…”

Data for Reinforcement Learning-based Congestion Control: A Systematic Evaluation of Fairness, Efficiency and Responsiveness by Luca Giacomoni (4466608)

“…We show that, as fairness is not embedded directly into reward functions, existing approaches exhibit unfairness in almost all tested network setups. …”

EMG and data glove dataset for dexterous myoelectric control by Agamemnon Krasoulis (6582983)

“…Scientific Data, 2014" (http://www.nature.com/articles/sdata201453)</div><div><br></div><div>The experiment comprised nine movements including single-finger as well as functional movements. The subjects had to repeat the instructed movements following visual cues (i.e. movies) shown on the screen of a computer monitor.…”

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). …”

Nanomachine-Assisted Ion Transport Across Membranes: From Mechanism to Rational Design and Applications by Hong Zhang (25820)

“…Assisting ion transport across membranes by means of sophisticated molecular machines has promising applications in the treatment of diseases induced by dysregulated ion transport. …”

Nanomachine-Assisted Ion Transport Across Membranes: From Mechanism to Rational Design and Applications by Hong Zhang (25820)

“…Assisting ion transport across membranes by means of sophisticated molecular machines has promising applications in the treatment of diseases induced by dysregulated ion transport. …”