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Received — 10 September 2026 ⏭

Reliable Near-Field Multi-User Positioning Informed by Two-Stage MUSIC

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiaying Li · Haifeng Wen · Changsheng You · Yuanwei Liu · Hong Xing
10 September 2026 at 12:00
arXiv:2609.09409v1 Announce Type: cross Abstract: Near-field localization is a promising technique for high-resolution multi-user positioning in future wireless systems, but its performance is often degraded by scattering-induced coherent propagation. Existing near-field localization methods, which require separate parameter estimation and path/source association, suffer from high computation overhead and accumulated errors, and usually do not provide any guarantee on reliability. In this paper, we propose \emph{MUSIC-Net}, an end-to-end near-field positioning deep learning (DL) framework informed by two-stage MUltiple SIgnal Classification (MUSIC) in mixed line-of-sight (LoS) and non-LoS (NLoS) multi-path scenarios, which embeds the two-stage MUSIC objects into training to isolate the LoS-related signal subspace and to identify a surrogate distance. The proposed framework directly recovers multi-user positions without the need for involved NLoS parameter estimation or path/source association. Furthermore, we introduce split conformal prediction (SCP) to move beyond point-estimation-based positioning towards statistically guaranteed (confidence) set estimation for all users. Numerical results show that the proposed MUSIC-Net achieves lower mean positioning error (MPER) than existing benchmarks and yields tighter SCP-calibrated prediction regions, demonstrating both accurate LoS localization and efficient uncertainty quantification (UQ) in coherent multi-path environments.

LogiScope-VQA: Benchmarking Vision-Language Models for Logistics Hazard Identification in Industrial Scenarios

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hanjing Zhou · Mingze Yin · Ying Lian · Jun Ma · Chang-Yu Hsieh · Yanbing Zhou
10 September 2026 at 12:00
arXiv:2609.09790v1 Announce Type: cross Abstract: Large Multimodal Models (LMMs) large-scale deployment in industrial warehouse settings specifically necessitates that models exhibit human-expert-level hazard-oriented perception, understanding, and reasoning capabilities. However, the scarcity of real industrial data, tightly coupled to commercial terms, significantly hampers further advancement. To bridge this gap, we curate LogiScope-VQA to investigate the practical applicability of mainstream LMMs in real-world logistics operations. LogiScope-VQA comprises 2,476 images and 2,918 videos primarily sourced from real-world logistics parks, along with 10,274 VQAs meticulously curated and validated by human annotators. Grounded in 18 core objects and 20 risk types, we devise 39 subtasks aligned with three principal themes: industrial element perception, warehouse knowledge understanding, and potential risk reasoning. Furthermore, we incorporate dynamic thinking-budget configurations and dual-dimensional risk bias analyses to elucidate the properties of LMMs. Extensive experiments unveil that even powerful proprietary models, including GPT-5.5, Gemini-3.1-Pro, and Claude-Opus-4.7, exhibit a significant gap relative to human performance. The unique challenge of jointly integrating perception, understanding, and reasoning for hazard identification poses substantial headroom for further improvement on LogiScope-VQA. We additionally reveal the pervasive security bias issue that impedes LLMs' practical deployment in real-world settings. The industrial dataset is publicly available under the CC BY-NC-SA 4.0 license.

BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junfeng Xia · Wenhao Ye · Junxiang Zhang · Jiayu Zuo · Mo Wang · Quanying Liu
10 September 2026 at 12:00
arXiv:2609.10518v1 Announce Type: cross Abstract: fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are commonly treated as a flat mixture and downstream tasks are adapted independently. We study whether measured learning relations can organize both stages without modifying the backbone. During pretraining, a lightweight Brain-DiT proxy estimates difficulty and directed facilitation across ten fMRI domains, yielding a priority-guided cumulative domain curriculum combined with high-to-low-noise timestep scheduling and joint consolidation. During adaptation, controlled first- and higher-order transfer across fifteen tasks constructs a directed taskonomy, from which budgeted integer programming (BIP) selects directly supervised source tasks and target-specific routes. The joint priority-domain and high-to-low-timestep curriculum reduces v-NMSE, PSD-NMSE, and FC-MSE by 6.5%, 16.3%, and 10.5%, respectively, relative to uniform sampling over both dimensions, and shows strong downstream performance across six in- and out-of-domain tasks. The taskonomy reveals asymmetric, target-dependent transfer, while exploratory sealed-test evaluation shows larger descriptive gains for BIP policies when higher-order route spaces are available than for matched random controls. Together, these findings support organizing fMRI pretraining and adaptation by measured learning relations rather than treating domains and tasks as independent flat sets.

ViSR-KGC: Visual Subgraph Reasoning with Vision-Language Models for Multimodal Knowledge Graph Completion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Jiafan Li · Mengxue Yang · Jiaqi Zhu · Liang Chang · Ying Li · Hongan Wang
10 September 2026 at 12:00
arXiv:2608.05833v3 Announce Type: replace Abstract: Knowledge graph completion (KGC) aims to infer missing entities or relations from incomplete graph structures, and has evolved into multimodal knowledge graph completion (MMKGC), where entities are associated with multiple modalities such as text and images. Traditional representation learning approaches follow the embedding-based paradigm and may struggle when relation-specific evidence is limited. Meanwhile, LLM-based reasoning methods typically linearize graph structures into textual prompts, which obscures structural topology and neglects vital visual information. While vision-language models (VLMs) excel at multimodal reasoning, they cannot natively interpret structured graph topology, particularly when it comes to knowledge graphs where nodes and edges carry complex semantics. To bridge this gap, we propose ViSR-KGC, a visual subgraph reasoning approach for KGC. It integrates three complementary capabilities to capture semantic correlations: identifying global topology dependencies via representation learning, analyzing local multimodal evidence using VLMs, and providing necessary commonsense knowledge inherent in pre-trained models. Based on learned multimodal embeddings, our framework first extracts a compact and query-aware subgraph from the MMKG. Then, this subgraph is transformed into a visually interpretable image using a layout strategy selected through empirical comparison. Finally, the visualized subgraph, entity images, textual descriptions, and candidate answers are combined into a unified prompt, enabling the VLM to infer the missing entity.

Lipotoxicity-induced ER-mitochondrial hypercoupling activates the mtDNA-cGAS-STING-NF-κB axis to drive follicular arrest in metabolically compromised PCOS

✇Cell Death Discovery nature.com science feeds
By: Mingrui Xue · Qi Zhu · Shuli Zhang · Guangzong Zhao · Xiaoqi Hong · Di Pang · Yue Xu · Xiaolu Li · Yuxin Liu · Ying Li · Qiucheng Jia · Sisi Xu · Hongshan Ge
8 September 2026 at 08:00

Cell Death Discovery, Published online: 08 September 2026; doi:10.1038/s41420-026-03339-w

Lipotoxicity-induced ER-mitochondrial hypercoupling activates the mtDNA-cGAS-STING-NF-κB axis to drive follicular arrest in metabolically compromised PCOS

Wuwei Huanglian Wan inhibits Helicobacter pylori and alleviates associated gastritis: host metabolic remodeling and altered IL-6/STAT3 signaling

✇Omics in Gastric
By: Wenxi Yang · Gengyuan Yu · Yanan Zhang · Dongsheng Ren · Yuying Li · Jin Yang · Meicun Yao
5 September 2026 at 18:00

J Ethnopharmacol. 2026 Sep 5;374(Pt 1):122370. doi: 10.1016/j.jep.2026.122370. Online ahead of print.

ABSTRACT

ETHNOPHARMACOLOGICAL RELEVANCE: Wuwei Huanglian Wan (WWHLW) is a traditional Tibetan medicine formula developed by Tibetan physician Takpe Pingcuo and officially documented in the Ministry of Health Drug Standards for Tibetan Medicines (Volume I, 1995). It has long been used for the treatment of gastrointestinal disorders, particularly conditions associated with gastrointestinal discomfort and inflammation. Given the overlap between its traditional indications and the clinical manifestations of H. pylori-associated gastritis (HAG), WWHLW represents a promising candidate for the management of H. pylori infection and related gastric inflammation. In addition, several constituent herbs of WWHLW have demonstrated anti-H. pylori and anti-inflammatory activities, providing a pharmacological basis for further investigating its therapeutic effects.

AIM OF THE STUDY: This study aimed to systematically evaluate the therapeutic effects of WWHLW against H. pylori infection and HAG, and to explore the biological processes associated with these effects through integrated multi-omics and experimental validation.

MATERIALS AND METHODS: The therapeutic effects of WWHLW were evaluated through in vitro antibacterial assays and an H. pylori-infected mouse model. UHPLC-HRMS/MS was employed for chemical profiling and identification of serum-absorbed constituents. Serum metabolomics, 16S rRNA gene sequencing, network pharmacology analysis, molecular docking analysis, and molecular biological analyses were integrated to investigate the metabolic, microbial, and signaling changes associated with its therapeutic activity.

RESULTS: WWHLW exhibited anti-H. pylori activity, with minimum inhibitory concentrations (MICs) of 0.2-0.5 mg/mL against both standard strains and multidrug-resistant clinical isolates. At MIC concentrations, WWHLW treatment altered the expression of multiple virulence-associated genes and reduced gastric H. pylori colonization by 93.8% in infected mice. UHPLC-HRMS/MS analysis putatively annotated 121 compounds in the WWHLW extracts, of which 10 prototype constituents were detected in serum after oral administration. Integrated metabolomics and network pharmacology analyses revealed alterations in lipid and amino acid-related metabolic pathways following WWHLW treatment. Gut microbiota analysis showed that WWHLW was associated with less pronounced alterations in microbial diversity and composition than antibiotic treatment. Correlation analysis further revealed statistical associations between microbial taxa and lipid and amino acid-related features. Experimental validation showed that WWHLW reduced inflammatory cytokine expression and suppressed STAT3 phosphorylation, consistent with altered IL-6/STAT3-related molecular changes.

CONCLUSIONS: WWHLW exhibits therapeutic potential against H. pylori infection and HAG through combined antibacterial, anti-inflammatory and metabolic regulatory effects. The protective activity of WWHLW was associated with reduced IL-6/STAT3 signaling, providing pharmacological evidence supporting its traditional use in gastrointestinal disorders.

PMID:42700849 | DOI:10.1016/j.jep.2026.122370

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