Image 2_Immune-related RELT drives clear cell renal cell carcinoma progression through JAK/STAT signaling pathway activation.tiff by Yini Wang (410389)

“…Objective<p>The experiment aims to verify the function of Tumor Necrosis Factor Receptor Superfamily Member 19L (RELT) in clear cell renal cell carcinoma (ccRCC).…”

Data Sheet 1_Unsupervised method for representation transfer from one brain to another.docx by Daiki Nakamura (20349885)

“…<p>Although the anatomical arrangement of brain regions and the functional structures within them are similar across individuals, the representation of neural information, such as recorded brain activity, varies among individuals owing to various factors. …”

Table 3_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx by Jian Wang (5901)

“…Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

Image 2_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.jpeg by Yuquan Yuan (10570747)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

Image 1_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.jpeg by Yuquan Yuan (10570747)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

Image 3_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.jpeg by Yuquan Yuan (10570747)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

Table 1_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx by Jian Wang (5901)

“…Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

Table 1_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.docx by Yuquan Yuan (10570747)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

Image 4_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.jpeg by Yuquan Yuan (10570747)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

Image 1_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.tif by Jian Wang (5901)

“…Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

Table 2_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.docx by Yuquan Yuan (10570747)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

Table 2_Constructing a neutrophil extracellular trap model based on machine learning to predict clinical outcomes and immune therapy responses in oral squamous cell carcinoma.xlsx by Jian Wang (5901)

“…Six machine learning algorithms were employed for model training, with the best model selected based on 1-year, 3-year, and 5-year AUC values. …”

HVTN 705 data repo: Unbiased cell clustering analysis of vaccine-induced T cell responses in the Imbokodo HIV-1 vaccine trial by Valentin Voillet (19201102)

“…Traditional methods for analysing these responses might be biased towards specific functionalities or epitopes. This study presents an unsupervised and unbiased clustering analysis workflow, using the Leiden algorithm followed by selection of antigen-specific clusters using MIMOSA positivity calls, for high-dimensional flow cytometry data to identify distinct T cell populations associated with protection in the HVTN 705/HPX2008/Imbokodo HIV-1 vaccine efficacy trial.…”

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

“…For sketches obtained with a single hash function, our approach is supported by precise theoretical guarantees, including unbiasedness and optimality under a Bayesian framework within an intuitive class of linear estimators. …”

Experiment 2. by Lalit Pandey (13195488)

“…<p><b>(A)</b> We increased the number of hardcoded spatial operations by adding more layers to the CNN architecture. …”

A computational-based search of natural product derived multi-target ligands for the management of Alzheimer’s and Parkinson’s disease using structure-based pharmacophore modelling... by N. Chhabra (22645522)

“…Additionally, density functional theory (DFT) studies provided insights into the electronic characteristics and reactivity of top hits. …”

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

“…<p dir="ltr"><b>LC-MS/MS raw data</b></p><p dir="ltr">Spectrum matching and protein identification and validation were performed with MSFragger, and quantification of protein intensities with matching between runs was performed with IonQuant as components of the FragPipe analysis pipeline using the default settings of each module. …”

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). …”

Table 1_CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines.xlsx by Shubham Choudhury (9192026)

“…<p>The function of long non-coding RNA (lncRNA) is largely determined by its specific location within a cell. …”

Table 1_Identification and validation of immune and diagnostic biomarkers for interstitial cystitis/painful bladder syndrome by integrating bioinformatics and machine-learning.docx by Tao Zhou (117050)

“…Hub genes in IC/BPS patients were identified through the application of three distinct machine-learning algorithms. Additionally, the inflammatory status and immune landscape of IC/BPS patients were evaluated using the ssGSEA algorithm. …”