Image3_A young child formula supplemented with a synbiotic mixture of scGOS/lcFOS and Bifidobacterium breve M-16V improves the gut microbiota and iron status in healthy toddlers.pd... by Charmaine Chew (9739238)

“…PICRUSt, a predictive functionality algorithm based on 16S ribosomal gene sequencing, was used to correlate potential microbial functions with iron status measurements. …”

MCCN Case Study 2 - Spatial projection via modelled data by Donald Hobern (21435904)

“…This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the <a href="https://pypi.org/project/PyKrige/" rel="nofollow" target="_blank">pykrige</a> and <a href="https://pypi.org/project/rioxarray/" rel="nofollow" target="_blank">rioxarray</a> packages.…”

Data Sheet 1_Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.pdf by Guangzong Li (16696443)

“…Eleven ML algorithms were trained and validated using Python, and external validation and performance evaluations were conducted. …”

Table 2_Harnessing serum VOCs and machine learning for the early detection of MAFLD.xlsx by Xin Li (51274)

“…Clinical and biochemical parameters such as age, body mass index, liver function, and lipid profiles were also compared between groups.…”

Image 2_Harnessing serum VOCs and machine learning for the early detection of MAFLD.jpeg by Xin Li (51274)

“…Clinical and biochemical parameters such as age, body mass index, liver function, and lipid profiles were also compared between groups.…”

Image 1_Harnessing serum VOCs and machine learning for the early detection of MAFLD.jpeg by Xin Li (51274)

“…Clinical and biochemical parameters such as age, body mass index, liver function, and lipid profiles were also compared between groups.…”

Table 1_Harnessing serum VOCs and machine learning for the early detection of MAFLD.xlsx by Xin Li (51274)

“…Clinical and biochemical parameters such as age, body mass index, liver function, and lipid profiles were also compared between groups.…”

Table 1_Trajectories of health conditions predict cardiovascular disease risk among middle-aged and older adults: a national cohort study.docx by Wenlong Li (571749)

“…Trajectories of multimorbidity status, activities of daily living (ADLs) limitations, body roundness index (BRI), pain, sleep duration, depressive symptoms, and cognitive function were identified using latent class growth models (LCGMs). …”

Assessing the risk of acute kidney injury associated with a four-drug regimen for heart failure: a ten-year real-world pharmacovigilance analysis based on FAERS events by Sen Lin (182597)

“…Disproportionality analysis and subgroup analysis were performed using four algorithms. Time-to-onset (TTO) analysis was used to assess the temporal risk patterns of ADE occurrence. …”

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). …”

AP-2α 相关研究 by Ya-Hong Wang (21080642)

“…</p><p dir="ltr"><b>Figure 4 | Comparative transcriptomic analysis of the functional regulation of </b><b><i>VdAP-2α</i></b><b> in </b><b><i>Verticillium dahliae.…”