Table 1_Demethylase FTO mediates m6A modification of ENST00000619282 to promote apoptosis escape in rheumatoid arthritis and the intervention effect of Xinfeng Capsule.docx by Fanfan Wang (1669474)

“…</p>Methods<p>A retrospective analysis was conducted on 1603 RA patients using association rule mining and random walk algorithms to evaluate the efficacy of XFC. The proliferation and apoptosis of co-cultured RA-FLS were assessed using CCK-8, flow cytometry (FCM), and molecular biology techniques. …”

<b>NanoNeuroBot: Beyond Healing, Toward Human Connection</b> by ahmed hossam (21420446)

“…It uses a flexible electrode array, EMG signal sensors, and a smart AI app (built on TensorFlow and Flutter) to optimize stimulation patterns. …”

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). …”

Table 1_Machine learning integration with multi-omics data constructs a robust prognostic model and identifies PTGES3 as a therapeutic target for precision oncology in lung adenoca... by Lian-jie Ruan (22327876)

“…PTGES3 expression was evaluated via tissue microarray immunohistochemistry. Functional assays (CCK-8, colony formation, flow cytometry, Western blot) after lentiviral knockdown in lung cancer cells assessed its effects on proliferation, apoptosis, and cell cycle. …”

Data Sheet 1_Machine learning integration with multi-omics data constructs a robust prognostic model and identifies PTGES3 as a therapeutic target for precision oncology in lung ad... by Lian-jie Ruan (22327876)

“…PTGES3 expression was evaluated via tissue microarray immunohistochemistry. Functional assays (CCK-8, colony formation, flow cytometry, Western blot) after lentiviral knockdown in lung cancer cells assessed its effects on proliferation, apoptosis, and cell cycle. …”

Navigating complex care pathways–healthcare workers’ perspectives on health system barriers for children with tuberculous meningitis in Cape Town, South Africa by Dzunisani Patience Baloyi (19452687)

“…An integrated data system and alert functions could flag multiple healthcare visits and improve communication between different healthcare facilities during diagnosis and treatment. …”

Table 1_Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma.xlsx by Ke Ma (260231)

“…</p>Results<p>Our analysis revealed that the novel molecular subtypes exhibited differences in prognoses, biological functions, and immune infiltration profiles in LUAD. …”

Presentation 1_Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma.zip by Ke Ma (260231)

“…</p>Results<p>Our analysis revealed that the novel molecular subtypes exhibited differences in prognoses, biological functions, and immune infiltration profiles in LUAD. …”

Data Sheet 1_Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma.docx by Ke Ma (260231)

“…</p>Results<p>Our analysis revealed that the novel molecular subtypes exhibited differences in prognoses, biological functions, and immune infiltration profiles in LUAD. …”

Data Sheet 1_Resveratrol contributes to NK cell-mediated breast cancer cytotoxicity by upregulating ULBP2 through miR-17-5p downmodulation and activation of MINK1/JNK/c-Jun signali... by Bisha Ding (5803799)

“…</p>Methods<p>The effects of RES on ULBP2 expression were detected with qRT-PCR, western blot, flow cytometry analysis and immunohistochemistry. …”

AP-2α 相关研究 by Ya-Hong Wang (21080642)

“…The conidia suspension of AT13 strain (1×106 conidia/mL) was spread on PDA plates overlaid with sterile cellophane, and incubated at 25 °C under darkness. …”