Length distribution of FP SVs. by De-Min Duan (20685513)

Housekeeping and unexpressed genes. by Zhiwei Guo (335819)

“…Using four machine learning models and two feature selection algorithms, we developed classifiers for predicting preterm birth. …”

Data Sheet 1_Accurate and rapid single nucleotide variation detection in PCSK9 gene using nanopore sequencing.pdf by Ilaria Massaiu (3487946)

“…</p>Conclusion<p>The proposed nanopore-based SNV identification workflows may support the development of long-read, targeted gene panels, offering a promising tool for both diagnostic and discovery applications, particularly in multi-gene settings such as oncology and cardiology.…”

Data Sheet 2_Accurate and rapid single nucleotide variation detection in PCSK9 gene using nanopore sequencing.xlsx by Ilaria Massaiu (3487946)

“…</p>Conclusion<p>The proposed nanopore-based SNV identification workflows may support the development of long-read, targeted gene panels, offering a promising tool for both diagnostic and discovery applications, particularly in multi-gene settings such as oncology and cardiology.…”

S1 Graphical abstract - by José M. Rivera-Arbeláez (12418512)

“…The software uses a shape-detection algorithm to single out and track the movement of pillars’ tips for the most common shapes of EHT platforms. …”

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

“…The target for average reads per sample was approximately 25 million. The QC pipeline included: 1) quality check of the raw sequencing data using FastQC (v 0.11.9) and MultiQC (v 1.9); 2) mapping the sequencing reads to the human genome (build 102) using HISAT2 (v 2.2.1), followed by SAMtools (v 1.12) to convert BAM (Binary Alignment Map) into SAM (Sequence Alignment Map) files; 3) assembly of RNA-seq reads into transcripts using StringTie (v 2.1.4); and 4) calculation of expression levels from read counts, producing a gene count matrix. …”

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). …”