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iSALE configuration files, collected data, and plotting means for the mesoscale models

Our real-world data from 96 patients confirms that abrocitinib induces rapid clinical improvement and broadly downregulates genes in cytokine and JAK-STAT signaling pathways. We also identified that patients achieving EASI-90 response had a distinct baseline profile characterized by lower Th2 cytokines and a specific transcriptomic signature of high WNT8B and low TTLL13p and KRT2 expression. This signature was validated as a predictive biomarker in an independent cohort, suggesting it can be used to stratify patients for a higher likelihood of achieving minimal disease activity with abrocitinib.

Recent breakthroughs in spatial transcriptomics technologies have enhanced our understanding of diverse cellular identities, spatial organizations, and functions. Yet existing spatial transcriptomics tools are still limited in either transcriptomic coverage or spatial resolution, hindering unbiased, hypothesis-free transcriptomic analyses at high spatial resolution. Here we develop Reverse-padlock Amplicon Encoding FISH (RAEFISH), an image-based spatial transcriptomics method with whole-genome coverage and single-molecule resolution in intact tissues. We demonstrate spatial profiling of 23,000 human or 22,000 mouse transcripts in single cells and tissue sections. Our analyses reveal transcript-specific subcellular localization, cell-type-specific and cell-type-invariant zonation-dependent transcriptomes, and gene programs underlying preferential cell-cell interactions. Finally, we further develop our technology for direct spatial readout of gRNAs in an image-based high-content CRISPR screen. Overall, these developments provide the research community with a broadly applicable technology that enables high-coverage, high-resolution spatial profiling of both long and short, native and engineered RNA species in many biomedical contexts.

In this repository, there are the benchmark instances to test the CAFVRPILA problem.

In this repository, there are benchmark instances to test the DVRPFPTW.

The central role in the pathogenesis of rheumatoid arthritis (RA) is played by fibroblast-like synoviocytes (FLS), which drive disease progression through aberrant proliferation, inflammatory cell recruitment, and subsequent cartilage and bone degradation. CUL4A is an important member of the Cullin family, which is a scaffolding protein for the E3 ubiquitin ligase complex. In this study, we investigated the functional significance and molecular mechanisms of CUL4A in the pathogenesis of RA. We identified elevated CUL4A expression in synovial tissues, TNFα-stimulated FLS, and peripheral blood mononuclear cells from RA patients. Knockdown of CUL4A led to a marked suppression of both cytokine production and glycolytic activity in FLS in vitro. RNA-seq analysis and validation revealed the PI3K/AKT pathway as a key mediator in this process, with a significant functional interaction between CUL4A and fibroblast growth factor 2 (FGF2). Furthermore, knocking down FGF2 significantly suppresses synovial inflammation in RA FLS. In vivo experiments, knocking down CUL4A significantly reduced synovial inflammation in K/BxN serum transfer arthritis mice and inhibited glycolytic activity. Additionally, the expression level of CUL4A mRNA shows a positive correlation with both clinical disease activity and inflammatory markers in RA. In conclusion, our findings reveal that CUL4A, via FGF2 binding, initiates PI3K/AKT pathway signaling, fostering the glycolysis process and synovial inflammation. Consequently, CUL4A holds significant promise as a target for early intervention against this pathogenic cascade.

Instances to test the Agricultural Fleet Vehicle routing problem with Capacitated Implements and fair profits and times windows.

This dataset contains anonymized YouTube comment data associated with the 2019 online controversy known as Dramageddon, involving beauty influencers James Charles, Tati Westbrook, and Jeffree Star. The dataset was created for research on online hostility, cancel culture, and competitive communication dynamics among influencers. The dataset includes public user comments collected from 14 YouTube videos posted during May–June 2019, including primary source videos from the influencers involved and reaction videos from commentary channels. A total of ~15,000 comments were collected using the YouTube Data API v3. All comments are anonymized and contain no personally identifiable information. Each comment record is enriched with metadata and derived variables, including: - Sentiment score (range −1 to +1) - Toxicity score (probability 0–1) - Cancel behavior classification (cold, cool, hot) - Moral language category - Engagement metrics (likes, reply depth) - Time of posting - Video-level metadata (creator, phase of controversy) This dataset supports research in computational social science, communication studies, digital sociology, and platform governance. It has been used in studies on cancel culture, moral contagion, algorithmic amplification, and influencer reputation dynamics. This dataset contains only publicly available YouTube comments retrieved in accordance with the YouTube Terms of Service. All usernames, channel IDs, and profile references were hashed or removed during preprocessing to ensure anonymization. No attempts were made to identify or contact any YouTube users. The dataset is provided strictly for research purposes. Users must agree to comply with ethical guidelines for internet research (AoIR 2019) and cite the dataset appropriately.

ngetes doang

Supplemental tables summarizing the test name, purpose, initial validation, and key studies for six gene expression profile (GEP) tests: 2-GEP, 23-GEP, 31-GEP, CP-GEP, 11-GEP, and 40-GEP.