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Received β€” 2 April 2026 ⏭

Mechanisms Underlying Drought Adaptability in Duolang Sheep Based on Metabolomic and Transcriptomic Analyses

βœ‡Omics In Lung
By: Bingjie Jiang Β· Bin Chen Β· Yaling Yang Β· Yong Chen Β· Wujun Liu
27 March 2026 at 18:00

Biology (Basel). 2026 Mar 12;15(6):461. doi: 10.3390/biology15060461.

ABSTRACT

This study investigates the mechanisms underlying drought adaptability in Duolang sheep, a local breed from two distinct habitats in Xinjiang-an arid southern region and a grassland northern region-aiming to identify key factors driving differential environmental adaptation. Integrated multi-omics analyses were performed, including serum biochemical assays, untargeted metabolomics of perirenal and tail fat tissues, and transcriptomic profiling of lung, liver, and kidney samples. Our results revealed notable differences: (1) serum levels of GSH-Px, IL-2, and IgG were significantly higher in the southern group (p < 0.01); (2) metabolomic analysis identified key differential metabolites, including EPA (involved in unsaturated fatty acid biosynthesis), choline (glycerophospholipid metabolism), L-serine and glutathione (cofactor biosynthesis), and taurine (sulfur metabolism); and (3) transcriptomic analysis revealed significant differential expression of genes such as FGF21 (thermogenesis), CD14 and DUSP2 (MAPK signaling pathway), GOT1 (arginine biosynthesis), and AVPR2 (vasopressin-regulated water reabsorption). Integrative correlation analysis further indicated that glutathione, EPA, GOT1, and CD14 are involved in energy and lipid metabolism, while taurine, AVPR2, and DUSP2 contribute to oxidative stress resistance and immune regulation. These molecular and metabolic adjustments collectively enhance drought adaptability in southern Xinjiang Duolang sheep. In conclusion, adaptation to arid environments requires enhanced antioxidant capacity and immune function, with metabolites such as EPA supporting lipid metabolism and genes such as FGF21 regulating fatty acid oxidation to limit triglyceride accumulation.

PMID:41892221 | PMC:PMC13023672 | DOI:10.3390/biology15060461

Received β€” 11 March 2026 ⏭

Neural Dynamics-Informed Pre-trained Framework for Personalized Brain Functional Network Construction

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Hongjie Jiang Β· Yifei Tang Β· Shuqiang Wang
10 March 2026 at 12:00
arXiv:2603.07524v1 Announce Type: cross Abstract: Brain activity is intrinsically a neural dynamic process constrained by anatomical space. This leads to significant variations in spatial distribution patterns and correlation patterns of neural activity across variable and heterogeneous scenarios. However, dominant brain functional network construction methods, which relies on pre-defined brain atlases and linear assumptions, fails to precisely capture varying neural activity patterns in heterogeneous scenarios. This limits the consistency and generalizability of the brain functional networks constructed by dominant methods. Here, a neural dynamics-informed pre-trained framework is proposed for personalized brain functional network construction. The proposed framework extracts personalized representations of neural activity patterns in heterogeneous scenarios. Personalized brain functional networks are obtained by utilizing these representations to guide brain parcellation and neural activity correlation estimation. Systematic evaluations were employed on 18 datasets across tasks, such as virtual neural modulation and abnormal neural circuit identification. Experimental results demonstrate that the proposed framework attains superior performance in heterogeneous scenarios. Overall, the proposed framework challenges the dominant brain functional network construction method.

DSH-Bench: A Difficulty- and Scenario-Aware Benchmark with Hierarchical Subject Taxonomy for Subject-Driven Text-to-Image Generation

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Zhenyu Hu Β· Qing Wang Β· Te Cao Β· Luo Liao Β· Longfei Lu Β· Liqun Liu Β· Shuang Li Β· Hang Chen Β· Mengge Xue Β· Yuan Chen Β· Chao Deng Β· Peng Shu Β· Huan Yu Β· Jie Jiang
10 March 2026 at 12:00
arXiv:2603.08090v1 Announce Type: cross Abstract: Significant progress has been achieved in subject-driven text-to-image (T2I) generation, which aims to synthesize new images depicting target subjects according to user instructions. However, evaluating these models remains a significant challenge. Existing benchmarks exhibit critical limitations: 1) insufficient diversity and comprehensiveness in subject images, 2) inadequate granularity in assessing model performance across different subject difficulty levels and prompt scenarios, and 3) a profound lack of actionable insights and diagnostic guidance for subsequent model refinement. To address these limitations, we propose DSH-Bench, a comprehensive benchmark that enables systematic multi-perspective analysis of subject-driven T2I models through four principal innovations: 1) a hierarchical taxonomy sampling mechanism ensuring comprehensive subject representation across 58 fine-grained categories, 2) an innovative classification scheme categorizing both subject difficulty level and prompt scenario for granular capability assessment, 3) a novel Subject Identity Consistency Score (SICS) metric demonstrating a 9.4\% higher correlation with human evaluation compared to existing measures in quantifying subject preservation, and 4) a comprehensive set of diagnostic insights derived from the benchmark, offering critical guidance for optimizing future model training paradigms and data construction strategies. Through an extensive empirical evaluation of 19 leading models, DSH-Bench uncovers previously obscured limitations in current approaches, establishing concrete directions for future research and development.
Received β€” 17 February 2026 ⏭

AD-Bench: A Real-World, Trajectory-Aware Advertising Analytics Benchmark for LLM Agents

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Lingxiang Hu Β· Yiding Sun Β· Tianle Xia Β· Wenwei Li Β· Ming Xu Β· Liqun Liu Β· Peng Shu Β· Huan Yu Β· Jie Jiang
17 February 2026 at 13:00
arXiv:2602.14257v1 Announce Type: cross Abstract: While Large Language Model (LLM) agents have achieved remarkable progress in complex reasoning tasks, evaluating their performance in real-world environments has become a critical problem. Current benchmarks, however, are largely restricted to idealized simulations, failing to address the practical demands of specialized domains like advertising and marketing analytics. In these fields, tasks are inherently more complex, often requiring multi-round interaction with professional marketing tools. To address this gap, we propose AD-Bench, a benchmark designed based on real-world business requirements of advertising and marketing platforms. AD-Bench is constructed from real user marketing analysis requests, with domain experts providing verifiable reference answers and corresponding reference tool-call trajectories. The benchmark categorizes requests into three difficulty levels (L1-L3) to evaluate agents' capabilities under multi-round, multi-tool collaboration. Experiments show that on AD-Bench, Gemini-3-Pro achieves Pass@1 = 68.0% and Pass@3 = 83.0%, but performance drops significantly on L3 to Pass@1 = 49.4% and Pass@3 = 62.1%, with a trajectory coverage of 70.1%, indicating that even state-of-the-art models still exhibit substantial capability gaps in complex advertising and marketing analysis scenarios. AD-Bench provides a realistic benchmark for evaluating and improving advertising marketing agents, the leaderboard and code can be found at https://github.com/Emanual20/adbench-leaderboard.

Orcheo: A Modular Full-Stack Platform for Conversational Search

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Shaojie Jiang Β· Svitlana Vakulenko Β· Maarten de Rijke
17 February 2026 at 13:00
arXiv:2602.14710v1 Announce Type: cross Abstract: Conversational search (CS) requires a complex software engineering pipeline that integrates query reformulation, ranking, and response generation. CS researchers currently face two barriers: the lack of a unified framework for efficiently sharing contributions with the community, and the difficulty of deploying end-to-end prototypes needed for user evaluation. We introduce Orcheo, an open-source platform designed to bridge this gap. Orcheo offers three key advantages: (i) A modular architecture promotes component reuse through single-file node modules, facilitating sharing and reproducibility in CS research; (ii) Production-ready infrastructure bridges the prototype-to-system gap via dual execution modes, secure credential management, and execution telemetry, with built-in AI coding support that lowers the learning curve; (iii) Starter-kit assets include 50+ off-the-shelf components for query understanding, ranking, and response generation, enabling the rapid bootstrapping of complete CS pipelines. We describe the framework architecture and validate Orcheo's utility through case studies that highlight modularity and ease of use. Orcheo is released as open source under the MIT License at https://github.com/ShaojieJiang/orcheo.
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