Image 4_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg حسب Jian Zhang (1682)

"…These findings highlight the need for additional research to explore the possibility of minimizing BPA contamination as a potential DR risk mitigation strategy, rooted in hypothesis-generating insights.…"

Image 1_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg حسب Jian Zhang (1682)

"…These findings highlight the need for additional research to explore the possibility of minimizing BPA contamination as a potential DR risk mitigation strategy, rooted in hypothesis-generating insights.…"

Image 2_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg حسب Jian Zhang (1682)

"…These findings highlight the need for additional research to explore the possibility of minimizing BPA contamination as a potential DR risk mitigation strategy, rooted in hypothesis-generating insights.…"

Image 4_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg حسب Minhao Huang (4952764)

"…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …"

Table 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx حسب Minhao Huang (4952764)

"…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …"

Table 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx حسب Minhao Huang (4952764)

"…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …"

Image 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg حسب Minhao Huang (4952764)

"…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …"

Image 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg حسب Minhao Huang (4952764)

"…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …"

Table 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx حسب Minhao Huang (4952764)

"…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …"

Image 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg حسب Minhao Huang (4952764)

"…A pan-death prognostic signature (Cell-Death Score, CDS), constructed via multi-algorithm machine learning and optimized using CoxBoost to incorporate 25 key genes, demonstrated robust performance in training (1-/3-year AUC = 0.894/0.943) and validation cohort (C-index = 0.717), effectively stratifying high-risk patients (HR = 3.21, p < 0.0001). …"

<b>dGenhancer v2</b>: A software tool for designing oligonucleotides that can trigger gene-specific Enhancement of Protein Translation. حسب Adam Master (20316450)

"…<br> An excel-based calculator - dGenhancer can be used to search for putative 5’UTR cis-acting elements, which functional activity could be determined by Gibbs energy-dependent secondary structure formation. …"

An Ecological Benchmark of Photo Editing Software: A Comparative Analysis of Local vs. Cloud Workflows حسب 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_Viroscope™: a universal solution for plant virus and viroid diagnostics using HTS and cloud-based analysis.docx حسب Verónica Morgante (13991745)

"…Notably, 25% of plants harbored emergent viruses with functional evidence, highlighting potential risks undetected by traditional procedures. …"

Table 3_Viroscope™: a universal solution for plant virus and viroid diagnostics using HTS and cloud-based analysis.docx حسب Verónica Morgante (13991745)

"…Notably, 25% of plants harbored emergent viruses with functional evidence, highlighting potential risks undetected by traditional procedures. …"

Table 2_Viroscope™: a universal solution for plant virus and viroid diagnostics using HTS and cloud-based analysis.xlsx حسب Verónica Morgante (13991745)

"…Notably, 25% of plants harbored emergent viruses with functional evidence, highlighting potential risks undetected by traditional procedures. …"

DataSheet1_Terrain-Perception-Free Quadrupedal Spinning Locomotion on Versatile Terrains: Modeling, Analysis, and Experimental Validation.PDF حسب Hongwu Zhu (183424)

"…Motivated by this problem, this study presents an algorithmic method to enable accurate spinning motions over uneven terrain and constrain the spinning radius of the center of mass (CoM) to be bounded within a small range to minimize the drift risks. …"

Video1_Terrain-Perception-Free Quadrupedal Spinning Locomotion on Versatile Terrains: Modeling, Analysis, and Experimental Validation.MP4 حسب Hongwu Zhu (183424)

"…Motivated by this problem, this study presents an algorithmic method to enable accurate spinning motions over uneven terrain and constrain the spinning radius of the center of mass (CoM) to be bounded within a small range to minimize the drift risks. …"

Video2_Terrain-Perception-Free Quadrupedal Spinning Locomotion on Versatile Terrains: Modeling, Analysis, and Experimental Validation.MP4 حسب Hongwu Zhu (183424)

"…Motivated by this problem, this study presents an algorithmic method to enable accurate spinning motions over uneven terrain and constrain the spinning radius of the center of mass (CoM) to be bounded within a small range to minimize the drift risks. …"