Inflammation-Associated Stromal Reprogramming Can Initiate Metaplasia and Facilitate Dysplastic Progression via ECM حسب Deng Pan (47215)

"…_csr.csr_matrix<br>X.dtype == 'float32'</p><p><br></p><p dir="ltr">AnnData.obs<br>===========</p><p dir="ltr">index - cell barcodes + sample_diagnosis<br>samplename - coded sample ID<br>n_genes - number of measured genes in the cell<br>n_molecules - number of molecules sequenced<br>doublet_score - whether the droplet contained two cells (scrublet)<br>percent_mito - percent of genes measured that are mitochondrial<br>leiden - cluster labels from leiden algorithm<br>louvain - cluster labels from the louvain algorithm<br>nobatch_leiden - non-batch corrected leiden cluster labels<br>nobatch_louvain - non-batch corrected louvain cluster labels<br>diagnosis - tissue diagnosis, N normal, M metaplasia, D dysplasia, T tumor<br>phase - cell cycle phase<br>sample_diagnosis - sample ID + tissue diagnosis<br>patient - patient ID<br>treatment - whether the patient recieved any treatment<br>procedure - how the sample was aquired<br>hcl_refined - human cell landscape refined cell type name<br>hcl_celltype - human cell landscape cell type best match<br>hcl_score - human cell landscape matching score<br>CLid - cell ontology ID<br>CL_name - cell ontology cell type name</p><p><br></p><p dir="ltr"><br></p><p dir="ltr">AnnData.var<br>===========</p><p dir="ltr">index - gene symbols<br>gene_ids - ensembl gene IDs<br>feature_types - type of the feature<br>genome - genome build<br>is_mito - whether the gene is mitochondrial<br>is_ribo - whether the gene is ribosomal</p><p dir="ltr"><br></p><p dir="ltr">AnnData_embeddings:<br>========================</p><p dir="ltr">PCA (obsm.X_pca)<br>UMAP (obsm.X_umap)<br>PCA_nobatch (obsm.X_pca_original)<br>UMAP_nobatch (obsm.X_umap_nobatch)<br>neighbors (AnnData.uns)</p><p dir="ltr"><br></p><p><br></p><p dir="ltr">Marker Genes:<br>=============<br>AnnData.uns['rank_genes_groups_filtered'].keys()</p><p dir="ltr">names - one list per leiden cluster<br>logfoldchanges - one cluster vs all others</p><p dir="ltr">scores - wilcoxon statistic<br>pvals - wilcoxon p-value</p><p dir="ltr">pvals_adj - BH adjusted p-values</p><p dir="ltr">params = {'corr_method': 'benjamini-hochberg', <br>'groupby': 'leiden',<br>'method': 'wilcoxon',<br>'reference': 'rest',<br>'use_raw': True}</p><p dir="ltr"><br></p>…"

An Automated Platform for Analysis of Phosphoproteomic Datasets:  Application to Kidney Collecting Duct Phosphoproteins حسب Jason D. Hoffert (502031)

"…In addition, the output files generated by this program are compatible with downstream phosphorylation site assignment using the Ascore algorithm, as well as phosphopeptide quantification via QUOIL. …"

An Automated Platform for Analysis of Phosphoproteomic Datasets:  Application to Kidney Collecting Duct Phosphoproteins حسب Jason D. Hoffert (502031)

"…In addition, the output files generated by this program are compatible with downstream phosphorylation site assignment using the Ascore algorithm, as well as phosphopeptide quantification via QUOIL. …"

An Automated Platform for Analysis of Phosphoproteomic Datasets:  Application to Kidney Collecting Duct Phosphoproteins حسب Jason D. Hoffert (502031)

"…In addition, the output files generated by this program are compatible with downstream phosphorylation site assignment using the Ascore algorithm, as well as phosphopeptide quantification via QUOIL. …"

An Automated Platform for Analysis of Phosphoproteomic Datasets:  Application to Kidney Collecting Duct Phosphoproteins حسب Jason D. Hoffert (502031)

"…In addition, the output files generated by this program are compatible with downstream phosphorylation site assignment using the Ascore algorithm, as well as phosphopeptide quantification via QUOIL. …"

Interaction of Rare-Earth Metals and Some Perfluorinated β‑Diketones حسب Maxim A. Lutoshkin (6651425)

"…Systems have been investigated spectrophotometrically using a multiwave nonlinear least-squares regression algorithm for data processing. Conditional stability constants were obtained for a wide pH region (2.0–5.4) at constant ionic strength (<i>I</i> = 0.5 M, NaCl). …"

An Automated Platform for Analysis of Phosphoproteomic Datasets:  Application to Kidney Collecting Duct Phosphoproteins حسب Jason D. Hoffert (502031)

"…In addition, the output files generated by this program are compatible with downstream phosphorylation site assignment using the Ascore algorithm, as well as phosphopeptide quantification via QUOIL. …"

Supplementary Material - Genome-Wide Association Study Reveals Genomic Regions and Candidate Genes Influencing Berry and Cluster Traits in Grapes (<i>Vitis</i> spp.) حسب Geovani Luciano de Oliveira (19347139)

"…Using two different algorithms, the GWAS identified 56 significant SNPs distributed across 17 chromosomes (Chr), validating previously identified QTLs and uncovering novel associations. …"

Raw LC-MS/MS and RNA-Seq Mitochondria data حسب Stefano Martellucci (16284377)

"…Differentially altered pathways were evaluated by using the enrich plot package in R for visualization of functional enrichment (i.e., dot plot).</p>…"

AP-2α 相关研究 حسب 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.…"

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). …"