Table 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Image 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Image 4_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Table 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Image 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…The PRGS, established by StepCox[both]+Ridge modeling, demonstrated robust prognostic stratification and predictive power across independent datasets. …”

Table2_A Novel Necroptosis-Related Gene Signature in Skin Cutaneous Melanoma Prognosis and Tumor Microenvironment.XLSX by Binyu Song (13025763)

“…A predictive nomogram had been built for providing a more beneficial prognostic indicator for the clinic. Functional enrichment analysis showed significant enrichment of immune-related signaling pathways, a higher degree of immune cell infiltration in the low-risk group than in the high-risk group, a negative correlation between risk scores and most immune checkpoint inhibitors (ICIs), anticancer immunity steps, and a more sensitive response to immunotherapy in the low-risk group.…”

DataSheet1_A Novel Necroptosis-Related Gene Signature in Skin Cutaneous Melanoma Prognosis and Tumor Microenvironment.docx by Binyu Song (13025763)

“…A predictive nomogram had been built for providing a more beneficial prognostic indicator for the clinic. Functional enrichment analysis showed significant enrichment of immune-related signaling pathways, a higher degree of immune cell infiltration in the low-risk group than in the high-risk group, a negative correlation between risk scores and most immune checkpoint inhibitors (ICIs), anticancer immunity steps, and a more sensitive response to immunotherapy in the low-risk group.…”

Table3_A Novel Necroptosis-Related Gene Signature in Skin Cutaneous Melanoma Prognosis and Tumor Microenvironment.XLSX by Binyu Song (13025763)

“…A predictive nomogram had been built for providing a more beneficial prognostic indicator for the clinic. Functional enrichment analysis showed significant enrichment of immune-related signaling pathways, a higher degree of immune cell infiltration in the low-risk group than in the high-risk group, a negative correlation between risk scores and most immune checkpoint inhibitors (ICIs), anticancer immunity steps, and a more sensitive response to immunotherapy in the low-risk group.…”

Table1_A Novel Necroptosis-Related Gene Signature in Skin Cutaneous Melanoma Prognosis and Tumor Microenvironment.XLSX by Binyu Song (13025763)

“…A predictive nomogram had been built for providing a more beneficial prognostic indicator for the clinic. Functional enrichment analysis showed significant enrichment of immune-related signaling pathways, a higher degree of immune cell infiltration in the low-risk group than in the high-risk group, a negative correlation between risk scores and most immune checkpoint inhibitors (ICIs), anticancer immunity steps, and a more sensitive response to immunotherapy in the low-risk group.…”

Video_1_Intelligent Spacecraft Visual GNC Architecture With the State-Of-the-Art AI Components for On-Orbit Manipulation.MP4 by Zhou Hao (10745067)

“…The novel intelligent GNC system can be a stepping stone toward future fully autonomous orbital robot systems.…”

Uncertainty and Novelty in Machine Learning by Derek Scott Prijatelj (20364288)

“…This demonstrates identifying information in finite steps to asymptotic statistics and PAC-learning, where we recover identification within finite observations at the cost of uncertainty and error.…”

Polyanion sodium cathode materials dataset by Martin Hoffmann Petersen (13626778)

“…</p><p dir="ltr">The complete dataset, including all structures with atomic charge as well as the structures without atomic charge obtained during structural optimization (298,144 structures), is available as <i>polyanion_cathode_dataset_with_optimization_steps/Combined.xyz</i>.</p><p dir="ltr"><br>To extract structural compositions and physical properties, the ase.io.read function from ASE version 3.23.0 is used. …”

DataSheet1_Day-Ahead Optimal Dispatch for Active Distribution Network Considering Probability Model of Controllable Distributed Generation.docx by Jianxi Wang (463030)

“…The chance constrained programming is solved by the heuristic method, and the deterministic optimization steps are solved by the second-order cone programming method, respectively. …”

Table_1_Multiscale ecological niche modeling exhibits varying climate change impacts on habitat suitability of Madrean Pine-Oak trees.DOCX by Joshua D. Carrell (14781922)

“…<p>Anthropogenic climate change and increasing greenhouse gas emissions are expected to globally impact the biological function, community structure, and spatial distribution of biodiversity. …”

Expression vs genomics for predicting dependencies by Broad DepMap (5514062)

“…If you are interested in trying machine learning, the files Features.hdf5 and Target.hdf5 contain the data munged in a convenient form for standard supervised machine learning algorithms.</p><p dir="ltr"><br></p><p dir="ltr">Some large files are in the binary format hdf5 for efficiency in space and read-in. …”

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). …”

DataSheet1_Wearable System to Guide Crosswalk Navigation for People With Visual Impairment.PDF by Hojun Son (12187253)

“…In this paper, we demonstrate a system that performs all the functions necessary to safely guide BVI at a signalized crosswalk. …”

Data_Sheet_1_NOSA, an Analytical Toolbox for Multicellular Optical Electrophysiology.PDF by Sebastian Oltmanns (9118286)

“…All features combined, NOSA will facilitate the first steps into using GEVIs and help to realize their full potential for revealing cell-type specific connectivity and functional interactions.…”