Image 7_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg حسب Jian Zhang (1682)

"…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…"

Image 5_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg حسب Jian Zhang (1682)

"…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…"

Image 3_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg حسب Jian Zhang (1682)

"…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…"

Image 6_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg حسب Jian Zhang (1682)

"…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…"

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

"…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…"

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

"…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…"

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

"…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…"

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) حسب Erola Fenollosa (20977421)

"…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…"

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). …"

Safety assessment of cabozantinib in patients with renal cell carcinoma: retrospective pharmacovigilance study based on FAERS database حسب Zhipeng Wang (154599)

"…</p> <p>Reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS) algorithms were used to detect drug-related AEs signals from reporting data in FAERS database from 2016 to 2024.…"

Image 1_Integrative pharmacovigilance and AI-based framework uncovers potential drug triggers in juvenile idiopathic arthritis.pdf حسب Qiang Luo (387063)

"…Drug-signature activity was quantified with single-sample GSEA for the bulk data and AddModuleScore for the single-cell data.…"

Table 1_Integrative pharmacovigilance and AI-based framework uncovers potential drug triggers in juvenile idiopathic arthritis.xls حسب Qiang Luo (387063)

"…Drug-signature activity was quantified with single-sample GSEA for the bulk data and AddModuleScore for the single-cell data.…"

Supplementary Material for: Evaluation of safety of elacestrant in patients with breast cancer: Insights from FDA adverse event reporting system database analysis حسب figshare admin karger (2628495)

"…A total of 53 preferred terms (PTs) signal are detected across four algorithms, including known adverse reactions such as nausea, vomiting, fatigue, elevated blood cholesterol, and musculoskeletal pain. …"

Table 1_Multimodal diagnostic models and subtype analysis for neoadjuvant therapy in breast cancer.xlsx حسب Zheng Ye (15102)

"…Post data preprocessing, Lasso regression was utilized for feature extraction and selection. …"

Different Brands of Strong-Flavor Baijiu Identified Using Gas Chromatography-Mass Spectrometry and Near-Infrared Spectroscopy حسب Jian-Gang Yang (21012431)

"…We used multi-solvent liquid–liquid extraction combined with gas chromatography-mass spectrometry to detect trace components in strong-flavor baijiu. Near-infrared spectroscopy combined with three algorithms (extreme learning machine, partial least squares-discriminant analysis, and support vector machine) was used to classify the three styles of strong-flavor baijiu. …"

Table 1_Implications of artificial intelligence in periodontal treatment maintenance: a scoping review.docx حسب Raafat Musief Sarakbi (21356723)

"…Studies employing AI for diagnosis, prognosis, or periodontal maintenance using clinical or radiographic data were included. Deep learning algorithms such as convolutional neural networks (CNNs) and segmentation techniques were analyzed for their diagnostic accuracy. …"

Supplementary information_CEAM حسب Ehsan Pishva (20540561)

"…Existing computational tools for detecting cell type-specific DNAm changes are often limited by the accuracy of cell type deconvolution algorithms. …"

Table 2_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx حسب Rabie Adel El Arab (14113425)

"…</p>Ethical implications<p>Simultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.…"

Table 4_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx حسب Rabie Adel El Arab (14113425)

"…</p>Ethical implications<p>Simultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.…"

Table 3_Integrative review of artificial intelligence applications in nursing: education, clinical practice, workload management, and professional perceptions.docx حسب Rabie Adel El Arab (14113425)

"…</p>Ethical implications<p>Simultaneously, nurses voiced significant ethical concerns—chiefly around safeguarding patient data privacy, mitigating algorithmic bias, and preserving the compassionate, human-centered essence of nursing in an increasingly automated environment.…"