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Nitrogen dioxide exposure promotes CD8(+)T cell infiltration and contributes to increased susceptibility to ulcerative colitis: An integrative multi-omics, artificial intelligence, and mouse model study

J Hazard Mater. 2026 Sep 15;516:143449. doi: 10.1016/j.jhazmat.2026.143449. Epub 2026 Aug 30.

ABSTRACT

The global incidence of ulcerative colitis (UC) has significantly increased in rapidly industrializing nations, with numerous studies highlighting environmental exposures, particularly nitrogen dioxide (NO2), as potential contributors to disease susceptibility. However, the clinical implications and molecular mechanisms linking NO2 exposure to UC susceptibility remain poorly understood. This study investigated the associations between NO2 and UC by integrating multi-omics data. We identified a CD8+ T cell subpopulation with a distinct phenotype characterized by perforin production, which potentially exacerbated colonic inflammation related to NO2 exposure. To validate this hypothesis, we established mouse models exposed to NO2, confirming increased CD8+ T cell infiltration and elevated perforin secretion through immunofluorescent (IF) staining. Employing artificial intelligence techniques, we identified Cell Division Cycle 25B (CDC25B) as a gene of interest correlated with putative NO2-related UC signatures. Finally, through molecular docking (MD) and molecular dynamics simulations (MDS), we identified ozanimod as one of several computationally nominated compounds associated with the CDC25B‑related network; however, none of these computational predictions were experimentally validated in the present study. Collectively, these findings suggest a correlative link between perforin or CD8+ T cell-associated colonic inflammation and NO2-associated UC susceptibility, and nominate CDC25B as a candidate gene for further investigation.

PMID:42679583 | DOI:10.1016/j.jhazmat.2026.143449

C^2ROPE: Causal Continuous Rotary Positional Encoding for 3D Large Multimodal-Models Reasoning

arXiv:2602.10551v2 Announce Type: replace-cross Abstract: Recent advances in 3D Large Multimodal Models (LMMs) built on Large Language Models (LLMs) have established the alignment of 3D visual features with LLM representations as the dominant paradigm. However, the inherited Rotary Position Embedding (RoPE) introduces limitations for multimodal processing. Specifically, applying 1D temporal positional indices disrupts the continuity of visual features along the column dimension, resulting in spatial locality loss. Moreover, RoPE follows the prior that temporally closer image tokens are more causally related, leading to long-term decay in attention allocation and causing the model to progressively neglect earlier visual tokens as the sequence length increases. To address these issues, we propose C^2RoPE, an improved RoPE that explicitly models local spatial Continuity and spatial Causal relationships for visual processing. C^2RoPE introduces a spatio-temporal continuous positional embedding mechanism for visual tokens. It first integrates 1D temporal positions with Cartesian-based spatial coordinates to construct a triplet hybrid positional index, and then employs a frequency allocation strategy to encode spatio-temporal positional information across the three index components. Additionally, we introduce Chebyshev Causal Masking, which determines causal dependencies by computing the Chebyshev distance of image tokens in 2D space. Evaluation results across various benchmarks, including 3D scene reasoning and 3D visual question answering, demonstrate C^2RoPE's effectiveness. The code is be available at https://github.com/ErikZ719/C2RoPE.
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