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Normal view

Received — 13 April 2026 ⏭

Genetic mutation and dysfunction of AT2 cells drive B(a)P/LPS-induced inflammation-related lung tumorigenesis: evidence and mechanism of autophagy

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Zhihao Tang · Xianchao Zhou · Pingping Shang · Quanping Yan · Ping Lu · Yanting Lei · Chenfeng Hua · Xiaoshan Zhou · Qiao Zhang · Wei Wang · Huaiyong Chen · Jian He · Feifei Feng
9 April 2026 at 18:00

Acta Biochim Biophys Sin (Shanghai). 2026 Mar 25. doi: 10.3724/abbs.2025238. Online ahead of print.

ABSTRACT

The environmental pollutant benzo(a)pyrene (B(a)P), a representative polycyclic aromatic hydrocarbon (PAH), is a recognized carcinogen, and chronic pulmonary inflammation is closely associated with lung carcinogenesis. Although alveolar type 2 (AT2) cells are the origin of lung adenocarcinoma, the genetic and functional changes in AT2 cells and the mechanisms involved in inflammation-related lung tumorigenesis have not been elucidated. Here, C57BL/6J mice are exposed to B(a)P and the inflammatory irritant lipopolysaccharide (LPS) to establish a model of inflammation-related lung tumorigenesis. Single-cell RNA sequencing is performed on lung tissues. DNA mutations in AT2 cells are analyzed via whole-exome sequencing. The protein expression of AT2 cells in lung cancer tissue is determined by immunofluorescence staining. The results reveal that LPS promotes B(a)P-induced lung tumorigenesis; in the whole lungs of B(a)P/LPS, a decreased proportion, altered differentiation trajectory, and increased gene mutation number in AT2 cells are observed. Additionally, in B(a)P/LPS-treated lung cancer tissue, the levels of γ-H2AX DNA damage and the proliferation marker Ki67 in AT2 cells are increased, whereas the levels of differentiation markers are decreased. Single-cell RNA transcriptomics reveals that the autophagy-related genes Foxo3 and Ppp2r5, which are enriched in the PI3K-Akt pathway, and the autophagy-related genes in AT2 cells in lung cancer are decreased in the B(a)P/LPS group. Thus, chronic inflammation promotes DNA damage, gene mutation and dysfunction in AT2 cells, and decreased autophagy in AT2 cells may be an important mechanism for inflammation-related lung tumorigenesis.

PMID:41952558 | DOI:10.3724/abbs.2025238

Received — 8 April 2026 ⏭

Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics

✇cs.AI, q-bio.NC updates on arXiv.org
By: Lianhao Zhou · Hongyi Ling · Cong Fu · Yepeng Huang · Michael Sun · Wendi Yu · Xiaoxuan Wang · Xiner Li · Xingyu Su · Junkai Zhang · Xiusi Chen · Chenxing Liang · Xiaofeng Qian · Heng Ji · Wei Wang · Marinka Zitnik · Shuiwang Ji
7 April 2026 at 12:00
arXiv:2510.09901v2 Announce Type: replace Abstract: Computing has long served as a cornerstone of scientific discovery. Recently, a paradigm shift has emerged with the rise of large language models (LLMs), introducing autonomous systems, referred to as agents, that accelerate discovery across varying levels of autonomy. These language agents provide a flexible and versatile framework that orchestrates interactions with human scientists, natural language, computer language and code, and physics. This paper presents our view and vision of LLM-based scientific agents and their growing role in transforming the scientific discovery lifecycle, from hypothesis discovery, experimental design and execution, to result analysis and refinement. We critically examine current methodologies, emphasizing key innovations, practical achievements, and outstanding limitations. Additionally, we identify open research challenges and outline promising directions for building more robust, generalizable, and adaptive scientific agents. Our analysis highlights the transformative potential of autonomous agents to accelerate scientific discovery across diverse domains.
Received — 17 March 2026 ⏭

Neural-Quantum-States Impurity Solver for Quantum Embedding Problems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yinzhanghao Zhou · Tsung-Han Lee · Ao Chen · Nicola Lanat\`a · Hong Guo
16 March 2026 at 12:00
arXiv:2509.12431v2 Announce Type: replace-cross Abstract: Neural quantum states (NQS) have emerged as a promising approach to solve second-quantized Hamiltonians, because of their scalability and flexibility. In this work, we design and benchmark an NQS impurity solver for the quantum embedding (QE) methods, focusing on the ghost Gutzwiller Approximation (gGA) framework. We introduce a graph transformer-based NQS framework able to represent arbitrarily connected impurity orbitals of the embedding Hamiltonian (EH) and develop an error control mechanism to stabilize iterative updates throughout the QE loops. We validate the accuracy of our approach with benchmark gGA calculations of the Anderson Lattice Model, yielding results in excellent agreement with the exact diagonalisation impurity solver. Finally, our analysis of the computational budget reveals the method's principal bottleneck to be the high-accuracy sampling of physical observables required by the embedding loop, rather than the NQS variational optimization, directly highlighting the critical need for more efficient inference techniques.
Received — 11 March 2026 ⏭

Bridging Domains through Subspace-Aware Model Merging

✇cs.AI, q-bio.NC updates on arXiv.org
By: Levy Chaves · Chao Zhou · Rebekka Burkholz · Eduardo Valle · Sandra Avila
10 March 2026 at 12:00
arXiv:2603.05768v2 Announce Type: replace-cross Abstract: Model merging integrates multiple task-specific models into a single consolidated one. Recent research has made progress in improving merging performance for in-distribution or multi-task scenarios, but domain generalization in model merging remains underexplored. We investigate how merging models fine-tuned on distinct domains affects generalization to unseen domains. Through an analysis of parameter competition in the task matrix using singular value decomposition, we show that merging models trained under different distribution shifts induces stronger conflicts between their subspaces compared to traditional multi-task settings. To mitigate this issue, we propose SCORE (Subspace COnflict-Resolving mErging), a method designed to alleviate such singular subspace conflicts. SCORE finds a shared orthogonal basis by computing the principal components of the concatenated leading singular vectors of all models. It then projects each task matrix into the shared basis, pruning off-diagonal components to remove conflicting singular directions. SCORE consistently outperforms, on average, existing model merging approaches in domain generalization settings across a variety of architectures and model scales, demonstrating its effectiveness and scalability.

Arginine methylation-dependent stabilization of SUV39H1 promotes breast cancer growth

✇Oncogene - Issue - nature.com science feeds
By: Wei Zhao · Ge Wang · Peng Wang · Bo Ma · Bo Liu · Yenan Fu · Youzhi Tang · Xinwei Duan · Kunhao Zhou · Jing Zhang · Wei-Guo Zhu · Hongquan Zhang · Yu Yu
7 March 2026 at 08:00

Oncogene, Published online: 07 March 2026; doi:10.1038/s41388-026-03712-0

Arginine methylation-dependent stabilization of SUV39H1 promotes breast cancer growth
Received — 25 February 2026 ⏭

Large Language Model-Assisted UAV Operations and Communications: A Multifaceted Survey and Tutorial

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yousef Emami · Hao Zhou · Radha Reddy · Atefeh Hajijamali Arani · Biliang Wang · Kai Li · Luis Almeida · Zhu Han
24 February 2026 at 13:00
arXiv:2602.19534v1 Announce Type: cross Abstract: Uncrewed Aerial Vehicles (UAVs) are widely deployed across diverse applications due to their mobility and agility. Recent advances in Large Language Models (LLMs) offer a transformative opportunity to enhance UAV intelligence beyond conventional optimization-based and learning-based approaches. By integrating LLMs into UAV systems, advanced environmental understanding, swarm coordination, mobility optimization, and high-level task reasoning can be achieved, thereby allowing more adaptive and context-aware aerial operations. This survey systematically explores the intersection of LLMs and UAV technologies and proposes a unified framework that consolidates existing architectures, methodologies, and applications for UAVs. We first present a structured taxonomy of LLM adaptation techniques for UAVs, including pretraining, fine-tuning, Retrieval-Augmented Generation (RAG), and prompt engineering, along with key reasoning capabilities such as Chain-of-Thought (CoT) and In-Context Learning (ICL). We then examine LLM-assisted UAV communications and operations, covering navigation, mission planning, swarm control, safety, autonomy, and network management. After that, the survey further discusses Multimodal LLMs (MLLMs) for human-swarm interaction, perception-driven navigation, and collaborative control. Finally, we address ethical considerations, including bias, transparency, accountability, and Human-in-the-Loop (HITL) strategies, and outline future research directions. Overall, this work positions LLM-assisted UAVs as a foundation for intelligent and adaptive aerial systems.
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