University of Arizona authors' scholarly works published and cited works year 2020 from OpenAlex by Yan Han (11106597)

“…</li><li><b>Data Retrieval:</b> The process involves using the oa_fetch function from the openalexR package with the entity="works" parameter and specifying the institutions.ror.…”

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). …”

Image 3_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.tif by Yunpeng Zou (3723007)

“…PLEK was further validated by qRT-PCR and Western blot in OS samples, and its function assessed via siRNA knockdown in macrophages within TME co-cultured with OS cells. …”

Image 2_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.tif by Yunpeng Zou (3723007)

“…PLEK was further validated by qRT-PCR and Western blot in OS samples, and its function assessed via siRNA knockdown in macrophages within TME co-cultured with OS cells. …”

Image 4_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.tif by Yunpeng Zou (3723007)

“…PLEK was further validated by qRT-PCR and Western blot in OS samples, and its function assessed via siRNA knockdown in macrophages within TME co-cultured with OS cells. …”

Image 5_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.tif by Yunpeng Zou (3723007)

“…PLEK was further validated by qRT-PCR and Western blot in OS samples, and its function assessed via siRNA knockdown in macrophages within TME co-cultured with OS cells. …”

Image 1_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.tif by Yunpeng Zou (3723007)

“…PLEK was further validated by qRT-PCR and Western blot in OS samples, and its function assessed via siRNA knockdown in macrophages within TME co-cultured with OS cells. …”

Data Sheet 1_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.zip by Yunpeng Zou (3723007)

“…PLEK was further validated by qRT-PCR and Western blot in OS samples, and its function assessed via siRNA knockdown in macrophages within TME co-cultured with OS cells. …”

Data Sheet 2_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.pdf by Yunpeng Zou (3723007)

“…PLEK was further validated by qRT-PCR and Western blot in OS samples, and its function assessed via siRNA knockdown in macrophages within TME co-cultured with OS cells. …”

<b>Road intersections Data with branch information extracted from OSM</b> & <b>C</b><b>odes to implement the extraction </b>&<b> I</b><b>nstructions on how to </b><b>reproduce each... by Zihao Tang (19794537)

“…</li><li><b>utils_g.py</b>: Provides utility functions that assist in geometric operations and template matching processes.…”

DataSheet1_Application of a risk score model based on glycosylation-related genes in the prognosis and treatment of patients with low-grade glioma.docx by Binbin Zou (11358315)

“…Their potential roles within the LGG microenvironment are also not well understood.…”

Data Sheet 1_PLK2 as a key regulator of glycolysis and immune dysregulation in polycystic ovary syndrome.docx by Hua Ma (2443177)

“…Immune infiltration was assessed using CIBERSORT, ESTIMATE, and ssGSEA algorithms. Functional enrichment analysis (GO, KEGG, and Hallmark) was performed to annotate PLK2-related pathways. …”

Table 12_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 9_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 8_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 1_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 2_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 3_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 5_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.doc by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”

Table 13_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.doc by Ting Wu (106368)

“…</p>Results<p>Through machine learning algorithms, RPL14, SMARCD3, and TCN1 were identified as candidate biomarkers. …”