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Received — 10 September 2026 ⏭

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

CSF clearance through arachnoid fenestrations to olfactory meningeal lymphatics

✇Cell
By: Seon Pyo Hong · Cheolhwa Jin · Myung Jin Yang · Hokyung Jin · Jin-Hui Yoon · Du Ri Choi · Junho Jung · Chae Min Yuk · Salli Antila · Su Jin Seo · Leul A. Admasu · Jincheol Seo · Kyung Seob Lim · Won-Suk Chung · Kari Alitalo · Donald M. McDonald · Gou Young Koh
21 July 2026 at 08:00
Cerebrospinal fluid (CSF) crosses arachnoid fenestrations near the olfactory bulbs to reach dural lymphatics that traverse the cribriform plate, connect to nasal lymphatics, and drain to cervical lymph nodes. Aging impairs this pathway, but intranasal VEGF-C restores the lymphatics and the CSF outflow.
Received — 27 May 2026 ⏭

From Accounting to Coordination: A Virtual Water-Aware Electricity-Computation-Water Nexus Framework for Data Center Dispatch

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haiyang You · Chengwei Lou · Jin Zhao · Yue Zhou · Lu Zhang · Jin Yang
26 May 2026 at 12:00
arXiv:2605.25854v1 Announce Type: new Abstract: The expansion of data centers (DCs) drives a sustained increase in electricity demand and associated water withdrawals at generation sites. These withdrawals occur at generation sites and are virtually allocated to demand based on network power flows. Consequently, the actual water footprint of a specific load varies dynamically with generation dispatch and network conditions. Existing approaches typically rely on static statistical accounting to quantify these water footprints. However, such static methods fail to capture how dispatch optimization and workload relocation dynamically affect water withdrawals. As a result, static statistical accounting approaches remain decoupled from the optimization process, rendering them incapable of guiding workload relocation or power dispatch to mitigate water stress. To address this limitation, this paper develops an operational electricity-computation-water (ECW) nexus framework that internalizes virtual water impacts directly into power system dispatch. The framework represents dispatch optimization as a differentiable optimization layer embedded within a deep learning architecture, enabling efficient end-to-end learning of coordination policies while preserving operational feasibility. Combined with fixed-point coordination, the framework enforces consistency between virtual water attribution and physical generation-side withdrawals. Case studies on the IEEE 30-bus and 118-bus test systems demonstrate reliable convergence, exact power-water consistency, and reductions of approximately 3-5% in generation-related freshwater withdrawals under water-constrained conditions.
Received — 11 March 2026 ⏭

HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenxuan Huang · Mingyu Tsoi · Yanhao Huang · Xinjie Mao · Xue Xia · Hao Wu · Jiaqi Wei · Yuejin Yang · Lang Yu · Cheng Tan · Xiang Zhang · Zhangyang Gao · Siqi Sun
10 March 2026 at 12:00
arXiv:2603.01396v2 Announce Type: replace Abstract: Single-cell perturbation studies face dual heterogeneity bottlenecks: (i) semantic heterogeneity--identical biological concepts encoded under incompatible metadata schemas across datasets; and (ii) statistical heterogeneity--distribution shifts from biological variation demanding dataset-specific inductive biases. We propose HarmonyCell, an end-to-end agent framework resolving each challenge through a dedicated mechanism: an LLM-driven Semantic Unifier autonomously maps disparate metadata into a canonical interface without manual intervention; and an adaptive Monte Carlo Tree Search engine operates over a hierarchical action space to synthesize architectures with optimal statistical inductive biases for distribution shifts. Evaluated across diverse perturbation tasks under both semantic and distribution shifts, HarmonyCell achieves a 95% valid execution rate on heterogeneous input datasets (versus 0% for general agents) while matching or even exceeding expert-designed baselines in rigorous out-of-distribution evaluations. This dual-track orchestration enables scalable automatic virtual cell modeling without dataset-specific engineering.
Received — 5 March 2026 ⏭

Bridging Pedagogy and Play: Introducing a Language Mapping Interface for Human-AI Co-Creation in Educational Game Design

✇cs.AI, q-bio.NC updates on arXiv.org
By: Daijin Yang · Erica Kleinman · Casper Harteveld
5 March 2026 at 13:00
arXiv:2603.03644v1 Announce Type: cross Abstract: Educational games can foster critical thinking, problem-solving, and motivation, yet instructors often find it difficult to design games that reliably achieve specific learning outcomes. Existing authoring environments reduce the need for programming expertise, but they do not eliminate the underlying challenges of educational game design, and they can leave non-expert designers reliant on opaque suggestions from AI systems. We designed a controlled natural language framework-based web tool that positions language as the primary interface for LLM-assisted educational game design. In the tool, users and an LLM assistant collaboratively develop a structured language that maps pedagogy to gameplay through four linked components. We argue that, by making pedagogical intent explicit and editable in the interface, the tool has the potential to lower design barriers for non-expert designers, preserves human agency in critical decisions, and enables alignment and reflections between pedagogy and gameplay during and after co-creation.
Received — 25 February 2026 ⏭

TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shirui Chen · Cole Harrison · Ying-Chun Lee · Angela Jin Yang · Zhongzheng Ren · Lillian J. Ratliff · Jiafei Duan · Dieter Fox · Ranjay Krishna
24 February 2026 at 13:00
arXiv:2602.19313v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have seen rapid progress in pretraining, their advancement in Reinforcement Learning (RL) remains hampered by low sample efficiency and sparse rewards in real-world settings. Developing generalizable process reward models is essential for providing the fine-grained feedback necessary to bridge this gap, yet existing temporal value functions often fail to generalize beyond their training domains. We introduce TOPReward, a novel, probabilistically grounded temporal value function that leverages the latent world knowledge of pretrained video Vision-Language Models (VLMs) to estimate robotic task progress. Unlike prior methods that prompt VLMs to directly output progress values, which are prone to numerical misrepresentation, TOPReward extracts task progress directly from the VLM's internal token logits. In zero-shot evaluations across 130+ distinct real-world tasks and multiple robot platforms (e.g., Franka, YAM, SO-100/101), TOPReward achieves 0.947 mean Value-Order Correlation (VOC) on Qwen3-VL, dramatically outperforming the state-of-the-art GVL baseline which achieves near-zero correlation on the same open-source model. We further demonstrate that TOPReward serves as a versatile tool for downstream applications, including success detection and reward-aligned behavior cloning.

CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures

✇cs.AI, q-bio.NC updates on arXiv.org
By: Punya Syon Pandey · Yongjin Yang · Jiarui Liu · Zhijing Jin
24 February 2026 at 13:00
arXiv:2508.11915v2 Announce Type: replace-cross Abstract: Game-theoretic interactions between agents with Large Language Models (LLMs) have revealed many emergent capabilities, yet the linguistic diversity of these interactions has not been sufficiently quantified. In this paper, we present the Conversational Robustness Evaluation Score: CORE, a metric to quantify the effectiveness of language use within multi-agent systems across different game-theoretic interactions. CORE integrates measures of cluster entropy, lexical repetition, and semantic similarity, providing a direct lens of dialog quality. We apply CORE to pairwise LLM dialogs across competitive, cooperative, and neutral settings, further grounding our analysis in Zipf's and Heaps' Laws to characterize word frequency distributions and vocabulary growth. Our findings show that cooperative settings exhibit both steeper Zipf distributions and higher Heap exponents, indicating more repetition alongside greater vocabulary expansion. In contrast, competitive interactions display lower Zipf and Heaps exponents, reflecting less repetition and more constrained vocabularies. These results provide new insights into how social incentives influence language adaptation, and highlight CORE as a robust diagnostic for measuring linguistic robustness in multi-agent LLM systems. Our code is available at https://github.com/psyonp/core.
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