Image 5_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 10_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 8_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. …”

Table 4_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 11_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 2_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 9_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. …”

Table 2_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. …”

Table 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. …”

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.…”

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). …”

Data_Sheet_1_Back-Propagation Learning in Deep Spike-By-Spike Networks.pdf by David Rotermund (7213793)

“…What is missing, however, are algorithms for finding weight sets that would optimize the output performances of deep SbS networks with many layers. …”

Data_Sheet_2_Back-Propagation Learning in Deep Spike-By-Spike Networks.ZIP by David Rotermund (7213793)

“…What is missing, however, are algorithms for finding weight sets that would optimize the output performances of deep SbS networks with many layers. …”