Table 1_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Image 12_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patie... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Image 3_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Image 6_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Image 7_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

Image 1_Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patien... by Ran Du (552530)

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technology</p>Results<p>Thirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”

ISAURO Cognitive Framework v0.1.1 – Public Research Summary (Under 4-Year Embargo) by Megan DeHerrera (21361439)

“…<p>{</p><p dir="ltr"> "title": "ISAURO Cognitive Framework v0.1.1 – Public Research Summary (Under 4-Year Embargo)",</p><p dir="ltr"> "authors": [</p><p> {</p><p dir="ltr"> "name": "Megan Irene DeHerrera",</p><p dir="ltr"> "affiliation": "Revelación Cognitive Research",</p><p dir="ltr"> "orcid_id": "https://orcid.org/0009-0000-2408-9132"</p><p> }</p><p> ],</p><p dir="ltr"> "description": "The ISAURO Cognitive Framework, developed by Megan Irene DeHerrera under Revelación Cognitive Research, is a modular, culturally-aware AI architecture inspired by the adaptive functions of the human brain. It integrates recursive cognition, logic-based memory routing, and trust-calibrated reasoning across a neuromodular system.…”

Data Sheet 1_Integrative multi-omics identifies MEIS3 as a diagnostic biomarker and immune modulator in hypertrophic cardiomyopathy.docx by Jinchen He (18929662)

“…Machine learning algorithms (LASSO and Random Forest) were used to identify key diagnostic genes. …”

Table 1_Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.docx by Fanhai Bu (22315168)

“…Introduction<p>Acute ischemic stroke (AIS) patients often experience poor functional outcomes post-intravenous thrombolysis (IVT). …”

Data Sheet 1_Diagnostic lncRNA biomarkers and immune-related ceRNA networks for osteonecrosis of the femoral head in metabolic syndrome identified by plasma RNA sequencing and mach... by Haoyan Sun (22172911)

“…The MetS dataset from the Gene Expression Omnibus (GEO) was integrated, and weighted gene co-expression network analysis (WGCNA), functional enrichment, protein-protein interaction (PPI) network analysis, MCODE, CytoHubba-MCC, and random forest (RF) algorithms were employed to identify hub mRNAs and their associated lncRNAs. …”

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

“…Sciatic nerves from scLRP1+/+ and scLRP1-/- mice were rinsed in PBS to remove the blood and frozen with liquid nitrogen in cryogenic storage tubes (#5016-0001, Thermo Fisher Scientific). …”

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). …”