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Normal view

Received — 27 May 2026 ⏭

A Non-Canonical Role of SMAD4 in Regulating 3D Genome Architecture to Inhibit Lung Squamous Cell Carcinoma Development

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qian Tang · Chen Lian · Xinyan Han · Xuhan Zhang · Zihan Wang · Boyu Wang · Taoyu Zhu · Xinrui Lin · Xiaolei Wang · Yaping Xu · Manyu Xiao · Zijin Wang · Junming Li · Silin Chen · Yunze Wang · Yufei Liu · Songsong Li · Zuolin Shen · Xi Lu · Xueqi Han · Yilu Zhou · Mingyang Xiao · Jiayi Ran · Xiaoran Cao · Xinyi Xu · Hugo Sámano-Sánchez · Alfredo Rodríguez · Lian Wang · Shuifang Chen · Zhanyu Xu · Shirong Zhang · Nuo Yang · Yong Tang · Jian Liu
26 May 2026 at 18:00

Adv Sci (Weinh). 2026 May 26:e75839. doi: 10.1002/advs.75839. Online ahead of print.

ABSTRACT

Lung squamous cell carcinoma (LUSC) lacks clearly defined key drivers and effective targeted therapies, reflecting an incomplete understanding of its molecular pathogenesis. Here, we identify SMAD4 as a critical regulator of three-dimensional (3D) genome organization in LUSC and uncover a mechanistic link between tumor suppressor loss and oncogenic transcriptional activation. By integrating clinical datasets, genetically engineered mouse models, human and murine LUSC cell lines, and multi-omics analyses, we demonstrate that SMAD4 deficiency promotes LUSC progression by unleashing EP300-mediated enhancer-promoter looping at the SOX2 locus. Mechanistically, SMAD4 does not directly bind SOX2 regulatory elements but instead constrains chromatin looping by sequestering EP300 away from loop anchor regions. Loss of SMAD4 leads to enhanced H3K27ac deposition, aberrant SOX2 activation, and increased LUSC tumor cell proliferation. Together, these findings reveal a non-canonical role for a transcription factor (e.g., SMAD4) in regulating dysregulated 3D genome architecture to inhibit tumor development.

PMID:42189071 | DOI:10.1002/advs.75839

A Non-Canonical Role of SMAD4 in Regulating 3D Genome Architecture to Inhibit Lung Squamous Cell Carcinoma Development

✇Omics In Lung
By: Qian Tang · Chen Lian · Xinyan Han · Xuhan Zhang · Zihan Wang · Boyu Wang · Taoyu Zhu · Xinrui Lin · Xiaolei Wang · Yaping Xu · Manyu Xiao · Zijin Wang · Junming Li · Silin Chen · Yunze Wang · Yufei Liu · Songsong Li · Zuolin Shen · Xi Lu · Xueqi Han · Yilu Zhou · Mingyang Xiao · Jiayi Ran · Xiaoran Cao · Xinyi Xu · Hugo Sámano-Sánchez · Alfredo Rodríguez · Lian Wang · Shuifang Chen · Zhanyu Xu · Shirong Zhang · Nuo Yang · Yong Tang · Jian Liu
26 May 2026 at 18:00

Adv Sci (Weinh). 2026 May 26:e75839. doi: 10.1002/advs.75839. Online ahead of print.

ABSTRACT

Lung squamous cell carcinoma (LUSC) lacks clearly defined key drivers and effective targeted therapies, reflecting an incomplete understanding of its molecular pathogenesis. Here, we identify SMAD4 as a critical regulator of three-dimensional (3D) genome organization in LUSC and uncover a mechanistic link between tumor suppressor loss and oncogenic transcriptional activation. By integrating clinical datasets, genetically engineered mouse models, human and murine LUSC cell lines, and multi-omics analyses, we demonstrate that SMAD4 deficiency promotes LUSC progression by unleashing EP300-mediated enhancer-promoter looping at the SOX2 locus. Mechanistically, SMAD4 does not directly bind SOX2 regulatory elements but instead constrains chromatin looping by sequestering EP300 away from loop anchor regions. Loss of SMAD4 leads to enhanced H3K27ac deposition, aberrant SOX2 activation, and increased LUSC tumor cell proliferation. Together, these findings reveal a non-canonical role for a transcription factor (e.g., SMAD4) in regulating dysregulated 3D genome architecture to inhibit tumor development.

PMID:42189071 | DOI:10.1002/advs.75839

Extracting Training Data from Diffusion Language Models via Infilling

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yihan Wang · N. Asokan
26 May 2026 at 12:00
arXiv:2605.24173v1 Announce Type: cross Abstract: Memorization in large language models has been studied almost exclusively through prefix-conditioned extraction, a natural choice for autoregressive models. However, diffusion language models (DLMs) can denoise masked tokens at arbitrary positions. Thus, prefix-only probing reveals only one facet of memorization in DLMs and significantly underestimates the risk of training-data extraction. In order to realistically model extractability of training data in DLMs, we introduce \emph{infilling extraction}, a data-extraction protocol parameterized by an arbitrary binary mask that subsumes prefix-only probing and accounts for the bidirectional inductive bias of DLMs. Instantiating it on LLaDA-8B and Dream-7B across five extraction modes, three training pipelines, and three corpora covering verbatim and partial leakage, we find that mask geometry governs extractability: edge-conditioned masks \emph{extract up to three times more} verbatim sequences than prefix-conditioned ones, and bidirectional access opens channels inaccessible in autoregressive models. In particular, we show that a realistic adversary with access to training data where personally identifiable information has been redacted, can even achieve higher recall on extracting redacted email addresses from DLMs than from scale-matched autoregressive models. Tunable parameters for decoding measurably affect extraction performance, while a follow-up supervised finetuning stage does not eliminate the prior memorization.

Explainable Retinal Imaging for Prediction of Multi-Organ Dysfunction in Type 2 Diabetes

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mini Han Wang · Liting Huang · Wei Hong · Boonthawan Wingwon
26 May 2026 at 12:00
arXiv:2605.24912v1 Announce Type: cross Abstract: Background: Type 2 diabetes mellitus (T2DM) is increasingly recognised as a systemic disease characterised by coordinated dysfunction across metabolic, renal, lipid, and inflammatory pathways. Existing clinical assessments often fail to capture this multi-dimensional burden. Methods: We conducted a retrospective study of 1,195 patients using routinely collected laboratory biomarkers. System-level abnormality indices were constructed to quantify organ-specific dysfunction, and multi-system involvement was defined as abnormalities in two or more systems. Supervised machine learning models, including logistic regression, random forest, and gradient boosting, were trained to predict multi-system dysregulation. Model interpretability was achieved using SHapley Additive exPlanations (SHAP). Results: The gradient boosting model demonstrated near-perfect discrimination (AUC = 1.000), significantly outperforming logistic regression (AUC = 0.925). Feature attribution analysis revealed that hyperglycaemia, renal impairment, dyslipidaemia, and inflammation were the dominant drivers of multi-system risk. Dose-response relationships observed in partial dependence analyses further supported the biological plausibility of model predictions. Conclusion: This study presents an interpretable, data-driven framework for quantifying systemic disease burden in T2DM. By linking routine biomarkers to multi-organ dysfunction, our approach provides both predictive accuracy and mechanistic insight, offering potential for improved risk stratification and precision medicine in diabetes care. The data and code used in this study are openly available on GitHub at: https://github.com/MiniHanWang/Type-2-Diabetes-1.git

Explainable Multi-Task Retinal Imaging Reveals Microvascular Signals for Systemic Risk Stratification in Type 2 Diabetes: A Pilot Study

✇cs.AI, q-bio.NC updates on arXiv.org
By: Mini Han Wang · Liting Huang · Wei Hong · Boonthawan Wingwon
26 May 2026 at 12:00
arXiv:2605.24913v1 Announce Type: cross Abstract: Retinal imaging provides a non-invasive window into systemic microvascular health and has emerged as a potential biomarker for systemic diseases. However, whether retinal features encode biologically meaningful systemic signals that can be reliably interpreted using explainable artificial intelligence (XAI) remains unclear. An explainable multi-task deep learning framework was developed to investigate associations between retinal microvascular features and systemic abnormalities in Type 2 Diabetes Mellitus. A total of 11,011 fundus images from 2,719 individuals were analysed using a shared neural network with task-specific heads for glycaemic status, kidney abnormality, and multi-system involvement. Model interpretability was evaluated using Gradient-weighted Class Activation Mapping (Grad-CAM), anatomical masking, and vessel alignment analysis. The framework demonstrated task-dependent predictive performance, with the best discrimination observed for kidney abnormality (AUC up to 0.63), whereas glycaemic status prediction showed limited performance (AUC = 0.49-0.61). Explainability analyses consistently localized model attention to retinal vessels and peripapillary regions. Masking experiments showed that occlusion of vascular regions caused the greatest performance decline, indicating that retinal vessels were the primary predictive source. Different architectures exhibited heterogeneous attention patterns, suggesting multiple representational pathways for systemic signal encoding. This pilot study demonstrates that retinal microvascular features contain measurable signals associated with systemic abnormalities, particularly microvascular damage. By integrating multi-task learning with quantitative XAI validation, this framework advances retinal imaging toward interpretable digital biomarkers for systemic risk stratification in diabetes.

Simulating Human Memory with Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qihan Wang · Nicholas Tomlin · Michael Hu · Brian Dillon · Tal Linzen
26 May 2026 at 12:00
arXiv:2605.25680v1 Announce Type: cross Abstract: Language models are increasingly being deployed as user simulators, but their memory is far more reliable than that of real users. To measure this gap, we run a series of classic memory experiments from psychology on both humans and language models. Across tasks, we find that out-of-the-box language models exhibit better memory than humans, even when prompted to imitate human behavior. We then show that better prompting strategies and the use of a compactor can cause language models to forget content in a more human-like way. Using these methods, we show preliminary evidence that language models with human-like memory constraints can function as more effective user simulators in a downstream education task. Finally, we release human reference data and benchmarks to support future work on simulating human memory with language models.

Agent Primitives: Reusable Latent Building Blocks for Multi-Agent Systems

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haibo Jin · Peng Kuang · Ye Yu · Xiaopeng Yuan · Haohan Wang
26 May 2026 at 12:00
arXiv:2602.03695v2 Announce Type: replace-cross Abstract: While existing multi-agent systems (MAS) can handle complex problems by enabling collaboration among multiple agents, they are often highly task-specific, relying on manually crafted agent roles and interaction prompts, which leads to increased architectural complexity and limited reusability across tasks. Moreover, most MAS communicate primarily through natural language, making them vulnerable to error accumulation and instability in long-context, multi-stage interactions within internal agent histories. In this work, we propose \textbf{Agent Primitives}, a set of reusable latent building blocks for LLM-based MAS. Inspired by neural network design, where complex models are built from reusable components, we observe that many existing MAS architectures can be decomposed into a small number of recurring internal computation patterns. Based on this observation, we instantiate three primitives: Review, Voting and Selection, and Planning and Execution. All primitives communicate internally via key-value (KV) cache, which improves both robustness and efficiency by mitigating information degradation across multi-stage interactions. To enable automatic system construction, an Organizer agent selects and composes primitives for each query, guided by a lightweight knowledge pool of previously successful configurations, forming a primitive-based MAS. Experiments show that primitives-based MAS improve average accuracy by 12.0-16.5\% over single-agent baselines, reduce token usage and inference latency by approximately 3$\times$-4$\times$ compared to text-based MAS, while incurring only 1.3$\times$-1.6$\times$ overhead relative to single-agent inference and providing more stable performance across model backbones.

Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaodong Lu · Xiaohan Wang · Jiajun Chai · Guojun Yin · Wei Lin · Zhijun Chen · Yu Luo · Fuzhen Zhuang · Yikun Ban · Deqing Wang
26 May 2026 at 12:00
arXiv:2602.08499v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is an effective paradigm for improving the reasoning capabilities of large language models. However, existing RLVR methods utilize rollouts in an indiscriminate and short-horizon manner: responses of heterogeneous quality within each prompt are treated uniformly, and historical rollouts are discarded after a single use. This leads to noisy supervision, poor sample efficiency, and suboptimal policy updates. We address these issues by formulating rollout scheduling in RLVR as a contextual bandit problem and proposing a unified neural scheduling framework that adaptively selects high-value rollouts throughout training. Each rollout is treated as an arm whose reward is defined by the induced performance gain between consecutive optimization steps. The resulting scheduler supports both noise-aware intra-group selection and adaptive global reuse of historical rollouts within a single principled framework. We provide theoretical justification by deriving sublinear regret bounds and showing that enlarging the rollout buffer improves the achievable performance upper bound. Experiments on six mathematical reasoning benchmarks demonstrate consistent gains in performance and training efficiency across multiple RLVR optimization methods.

Fetal monitoring for high-risk pregnancies using a wearable ultrasound patch

✇Nature Biotechnology - Issue - nature.com science feeds
By: Geonho Park · Yizhou Bian · Hao Huang · Sai Zhou · Siyu Qin · Muyang Lin · Xinyi Yang · Aaron Lee · Anand Ramkumar · Mariana Tome · Jayne Lander · Xiangjun Chen · Shenghan Wang · Pranavi Bheemreddy · Liam Stanton · Ren Sheng · Guihuan Guo · Mabel Shehada · Ruotao Wang · Alexa Roa · Chengchangfeng Lu · Wentong Yue · Ray S. Wu · Xiaoxiang Gao · Hongjie Hu · Amer Yaghi · Mark Liu · Lawrence Impey · Sally L. Collins · Aris T. Papageorghiou · Louise C. Laurent · Keith A. Wear · Antoniya Georgieva · Sheng Xu
26 May 2026 at 08:00

Nature Biotechnology, Published online: 26 May 2026; doi:10.1038/s41587-026-03140-1

A wearable ultrasound device is optimized for continuous monitoring of pregnancies.

Multi-omics Analysis Reveals the Protection of a Quadruple Probiotic Mixture in Experimental Autoimmune Hepatitis

✇(Multiomics OR Omics) AND (Pancreatic)
By: Yu Chen · Shuhui Wang · Anzhuo Chen · Zhuoying Lin · Han Wang · Wanneng Li · Jun Liu · Jia Yao · Dean Tian · Yu Lei · Mei Liu
23 May 2026 at 18:00

Probiotics Antimicrob Proteins. 2026 May 23. doi: 10.1007/s12602-026-11062-2. Online ahead of print.

ABSTRACT

Autoimmune hepatitis (AIH) is a chronic progressive inflammatory liver disease with a rising global incidence. The treatment of AIH remains challenging because first-line drugs show limited efficacy and systemic side effects. Gut microbiota plays a crucial role in the pathogenesis of AIH, leading to growing interest in developing probiotic-based therapies. In this study, we used multi-omics analysis to investigate the therapeutic effects of a quadruple probiotic mixture (Probiotic-quad) consisting of Bifidobacterium infantis, Lactobacillus acidophilus, Enterococcus faecalis, and Bacillus cereus in a well-established chronic AIH murine model. Our results showed that Probiotic-quad treatment significantly alleviated AIH progression, as evidenced by lower serum liver enzyme levels, ameliorated hepatic inflammatory infiltration and histopathological damage. Metagenomic sequencing results showed that gut dysbiosis in AIH mice was partially reversed after Probiotic-quad administration. Additionally, the integrity of the intestinal epithelial barrier was restored, accompanied by a reduction in serum lipopolysaccharide levels. Untargeted metabolomic and transcriptomic analysis revealed that Probiotic-quad treatment was linked to alterations in hepatic metabolism, including the citrate cycle and tryptophan metabolism, and was associated with reduced activation of the NF-κB and NOD-like receptor signaling pathways. These findings suggest that Probiotic-quad treatment ameliorates AIH severity and is potentially associated with changes in hepatic immune responses, metabolism, gut microbiota, and intestinal barrier function, highlighting its potential as an adjuvant therapy for AIH.

PMID:42176246 | DOI:10.1007/s12602-026-11062-2

Ferroptosis and macrophage polarization: mechanisms, interplay, and implications for medical applications

✇Cell Death Discovery nature.com science feeds
By: Yajuan Zhao · Jianbin Fu · Ping Zhao · Yuan Jiang · Yuhan Wang · Yaopeng Zhang · Zhaorui Li · Yongjun Zhao · Niannian Li · Bao Lang · Yanhua Luo
23 May 2026 at 08:00

Cell Death Discovery, Published online: 23 May 2026; doi:10.1038/s41420-026-03147-2

Ferroptosis and macrophage polarization: mechanisms, interplay, and implications for medical applications
Received — 8 April 2026 ⏭

Representation learning to advance multi-institutional studies with electronic health record data from US and France

✇cs.AI, q-bio.NC updates on arXiv.org
By: Doudou Zhou · Han Tong · Linshanshan Wang · Suqi Liu · Xin Xiong · Ziming Gan · Romain Griffier · Boris Hejblum · Yun-Chung Liu · Chuan Hong · Clara-Lea Bonzel · Tianrun Cai · Kevin Pan · Yuk-Lam Ho · Lauren Costa · Vidul A. Panickan · J. Michael Gaziano · Kenneth Mandl · Vianney Jouhet · Rodolphe Thiebaut · Zongqi Xia · Kelly Cho · Katherine Liao · Tianxi Cai
7 April 2026 at 12:00
arXiv:2502.08547v2 Announce Type: replace Abstract: The widespread adoption of electronic health records has created new opportunities for translational clinical research, yet this promise remains constrained by fragmented data across privacy-siloed institutions and substantial heterogeneity in local coding practices. While privacy-preserving collaborative learning allows institutions to work together without sharing patient-level data, it does not address inconsistencies in how clinical concepts are represented across sites. We introduce a graph-based framework that addresses this gap by treating data harmonization as a scalable representation learning problem. Rather than relying on fixed standards or manual mappings, the framework integrates institution-specific summary statistics from health records, curated biomedical knowledge graphs, and semantic information derived from large language models to learn a shared semantic space. This joint learning approach aligns diverse, site-specific vocabularies while preserving patient privacy. Evaluated across seven institutions and two languages, the framework provides a robust, data-centric foundation for training and deploying clinical models across heterogeneous healthcare systems.

SETDB2 induces abnormal SHP-1 splicing and promotes immunosuppression in hepatocellular carcinoma

✇Oncogene - Issue - nature.com science feeds
By: Qinlian Jiao · Yidan Ren · Xin Su · Zigan Li · Yuxuan Cai · Tangbin Hu · Yunshan Wang · Guangyong Zhang · Miaoqing Zhao · Maoxiao Feng
7 April 2026 at 08:00

Oncogene, Published online: 07 April 2026; doi:10.1038/s41388-026-03759-z

SETDB2 induces abnormal SHP-1 splicing and promotes immunosuppression in hepatocellular carcinoma
Received — 4 April 2026 ⏭

MonitorBench: A Comprehensive Benchmark for Chain-of-Thought Monitorability in Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Han Wang · Yifan Sun · Brian Ko · Mann Talati · Jiawen Gong · Zimeng Li · Naicheng Yu · Xucheng Yu · Wei Shen · Vedant Jolly · Huan Zhang
3 April 2026 at 12:00
arXiv:2603.28590v2 Announce Type: replace Abstract: Large language models (LLMs) can generate chains of thought (CoTs) that are not always causally responsible for their final outputs. When such a mismatch occurs, the CoT no longer faithfully reflects the actual reasons (i.e., decision-critical factors) driving the model's behavior, leading to the reduced CoT monitorability problem. However, a comprehensive and fully open-source benchmark for thoroughly evaluating CoT monitorability remains lacking. To address this gap, we propose MonitorBench, a systematic benchmark for evaluating CoT monitorability in LLMs. MonitorBench provides: (1) a diverse set of 1,514 test instances with carefully designed decision-critical factors across 19 tasks spanning 7 categories to characterize \textit{when} CoTs can be used to monitor the factors driving LLM behavior; and (2) two stress-test settings to quantify \textit{the extent to which} CoT monitorability can be degraded. Extensive experiments across multiple popular LLMs with varying capabilities show that CoT monitorability is higher when the decision-critical factors shape the intermediate reasoning process without merely influencing the final answer. More capable LLMs tend to exhibit lower monitorability. And all evaluated LLMs can intentionally reduce monitorability under stress-tests, with monitorability dropping by up to 30\% in some tasks that do not require structural reasoning over the decision-critical factors. Overall, MonitorBench provides a basis for further research on evaluating future LLMs, studying advanced stress-test monitorability techniques, and developing new monitoring approaches. The code is available at https://github.com/ASTRAL-Group/MonitorBench.

WFR-FM: Simulation-Free Dynamic Unbalanced Optimal Transport

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qiangwei Peng · Zihan Wang · Junda Ying · Yuhao Sun · Qing Nie · Lei Zhang · Tiejun Li · Peijie Zhou
3 April 2026 at 12:00
arXiv:2601.06810v2 Announce Type: replace-cross Abstract: The Wasserstein-Fisher-Rao (WFR) metric extends dynamic optimal transport (OT) by coupling displacement with change of mass, providing a principled geometry for modeling unbalanced snapshot dynamics. Existing WFR solvers, however, are often unstable, computationally expensive, and difficult to scale. Here we introduce WFR Flow Matching (WFR-FM), a simulation-free training algorithm that unifies flow matching with dynamic unbalanced OT. Unlike classical flow matching which regresses only a transport vector field, WFR-FM simultaneously regresses a vector field for displacement and a scalar growth rate function for birth-death dynamics, yielding continuous flows under the WFR geometry. Theoretically, we show that minimizing the WFR-FM loss exactly recovers WFR geodesics. Empirically, WFR-FM yields more accurate and robust trajectory inference in single-cell biology, reconstructing consistent dynamics with proliferation and apoptosis, estimating time-varying growth fields, and applying to generative dynamics under imbalanced data. It outperforms state-of-the-art baselines in efficiency, stability, and reconstruction accuracy. Overall, WFR-FM establishes a unified and efficient paradigm for learning dynamical systems from unbalanced snapshots, where not only states but also mass evolve over time. The Python code is available at https://github.com/QiangweiPeng/WFR-FM.
Received — 2 April 2026 ⏭

MindCube: Spatial Mental Modeling from Limited Views

✇cs.AI, q-bio.NC updates on arXiv.org
By: Qineng Wang · Baiqiao Yin · Pingyue Zhang · Jianshu Zhang · Kangrui Wang · Zihan Wang · Jieyu Zhang · Keshigeyan Chandrasegaran · Han Liu · Ranjay Krishna · Saining Xie · Jiajun Wu · Li Fei-Fei · Manling Li
1 April 2026 at 12:00
arXiv:2506.21458v2 Announce Type: replace Abstract: Can Vision-Language Models (VLMs) imagine the full scene from just a few views, like humans do? Humans form spatial mental models naturally, internal representations of unseen space, to reason about layout, perspective, and motion. Our MindCube benchmark with 21,154 questions across 3,268 images exposes this critical gap, where existing VLMs exhibit near-random performance. Using MindCube, we systematically evaluate how well VLMs build robust spatial mental models through representing positions (cognitive mapping), orientations (perspective-taking), and dynamics (mental simulation for "what-if" movements). We then explore three approaches to help approximate spatial mental models in VLMs, focusing on incorporating unseen intermediate views, natural language reasoning chains, and cognitive maps. The significant improvement comes from a synergistic approach, "map-then-reason", that jointly trains the model to first generate a cognitive map and then reason upon it. By training models to reason over these internal maps, we boosted accuracy from 37.8% to 57.8% (+20.0%). Adding reinforcement learning pushed performance even further to 61.3% (+23.5%). Our key insight is that such scaffolding of spatial mental models, actively constructing and utilizing internal structured spatial representations with flexible reasoning processes, significantly improves understanding of unobservable space.

A multicenter randomized clinical trial of portable transcranial alternating current stimulation for major depressive disorder

✇npj Digital Medicine
By: Yunhao Wu · Chuanbin Ning · Lei Shi · Jijun Wang · Kuanghao Ye · Ziyu Shen · Yuhan Wang · Duanwei Wang · Huihui Guo · Xian Qiu · Yixin Pan · Halimureti Paerhati · Debo Li · Yu Mo · Jing Wan · Huilin Hu · Yu Cao · Bomin Sun · Xin Lv
28 March 2026 at 08:00

npj Digital Medicine, Published online: 28 March 2026; doi:10.1038/s41746-026-02575-9

A multicenter randomized clinical trial of portable transcranial alternating current stimulation for major depressive disorder
Received — 26 March 2026 ⏭

A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chenhan Wang · Zhengyi Bao · Huipin Lin · Jiahao Nie · Chunxiang Zhu
25 March 2026 at 12:00
arXiv:2603.22323v1 Announce Type: cross Abstract: Accurately predicting the state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and efficient operation of electric vehicles while minimizing associated risks. However, current deep learning methods are limited in their ability to selectively extract features and model time dependencies for these two parameters. Moreover, most existing methods rely on traditional recurrent neural networks, which have inherent shortcomings in long-term time-series modeling. To address these issues, this paper proposes a multi-task targeted learning framework for SOH and RUL prediction, which integrates multiple neural networks, including a multi-scale feature extraction module, an improved extended LSTM, and a dual-stream attention module. First, a feature extraction module with multi-scale CNNs is designed to capture detailed local battery decline patterns. Secondly, an improved extended LSTM network is employed to enhance the model's ability to retain long-term temporal information, thus improving temporal relationship modeling. Building on this, the dual-stream attention module-comprising polarized attention and sparse attention to selectively focus on key information relevant to SOH and RUL, respectively, by assigning higher weights to important features. Finally, a many-to-two mapping is achieved through the dual-task layer. To optimize the model's performance and reduce the need for manual hyperparameter tuning, the Hyperopt optimization algorithm is used. Extensive comparative experiments on battery aging datasets demonstrate that the proposed method reduces the average RMSE for SOH and RUL predictions by 111.3\% and 33.0\%, respectively, compared to traditional and state-of-the-art methods.

Overcoming missing data in spatial metabolomics with machine learning imputation to accelerate downstream discovery

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Tingze Feng · Yuhan Wang · Shaojun Pei · Qiuping Wang · Yirong Li · Jing Lv · Tian Xia · Di Chen · Hai-Long Piao
23 March 2026 at 18:00

iScience. 2026 Mar 3;29(4):115203. doi: 10.1016/j.isci.2026.115203. eCollection 2026 Apr 17.

ABSTRACT

Mass spectrometry imaging (MSI)-based spatial metabolomics exhibits extensive missing values; yet, practical guidance on how imputation choices affect both imputation accuracy and downstream spatial analyses remains limited. In this study, we evaluated eight imputation methods, including both existing approaches and a graph convolutional network (GCN)-based method specifically designed for spatial metabolomics data, to identify suitable approaches for spatial metabolomics. To enable comprehensive assessment, we developed an evaluation framework focusing on two objective criteria: (a) imputation accuracy and (b) preservation of spatial cluster structure. We assembled six benchmark datasets spanning mouse brain and liver, human kidney and stomach, and plant seed sections, and conducted controlled dropout simulations of missing values. Across both evaluation dimensions, including imputation accuracy and preservation of spatial cluster structure, RF ranked first overall, and GCN ranked second in both dimensions. Overall, this systematic, dual-perspective benchmark study provides guidance for selecting imputation strategies in spatial metabolomics research.

PMID:41869568 | PMC:PMC12999350 | DOI:10.1016/j.isci.2026.115203

Low-dose intestinal irradiation enhances the efficacy and prognosis of PD-1 blockade in metastatic non-small cell lung cancer

✇Omics In Lung
By: Baiyang Huang · Jiarui Zhao · Jingyu Zhu · Xingpeng Wang · Min Li · Jing Xu · Kaiyue Wang · Xiaohan Wang · Weiqing Wang · Cong Bo · Jinquan Yao · Menglin Bai · Bo Cheng · Jinming Yu · Guoxin Cai · Xue Meng
18 March 2026 at 18:00

Clin Cancer Res. 2026 Mar 18. doi: 10.1158/1078-0432.CCR-25-4153. Online ahead of print.

ABSTRACT

PURPOSE: Intestinal low-dose irradiation (ILDR) may enhance immunotherapy efficacy by modulating the gut microbiota and metabolism; however, its role in metastatic non-small cell lung cancer (mNSCLC), particularly in the first-line setting, remains unclear.

EXPERIMENTAL DESIGN: This multicenter retrospective and prospective study included mNSCLC patients receiving first- and second-line programmed cell death protein 1 (PD-1) inhibitors along with abdominopelvic radiotherapy between 2018 and 2025. Patients were stratified by the mean intestinal radiation dose into <1 Gy, 1-3 Gy, and >3 Gy groups and treatment outcomes were compared. The blood and fecal samples were subjected to multi-omics profiling.

RESULTS: g>309 patients were included in the retrospective analysis. Optimal efficacy was observed with a small intestinal mean radiation dose (SIMRD) of 1-3 Gy, showing longer progression-free survival (PFS, 10.2 months) and overall survival (OS, 22.8 months) (P < 0.01), which was consistent across subgroups. Compared with 1-3 Gy, SIMRD >3 Gy (Hazard ratio [HR] = 4.87, P < 0.001) and <1 Gy (HR = 1.85, P < 0.001) independently predicted worse OS. Prospective results confirmed the best disease control rate (P = 0.041) and PFS (P = 0.046) with SIMRD of 1-3 Gy. Responders were enriched in Bacillota, Clostridia, and indole derivatives, particularly indole-3-carboxylic acid. Moreover, the 1-3 Gy group exhibited increased circulating macrophage inflammatory protein-3α and reduced circulating α4β7+ regulatory T cells.

CONCLUSIONS: ILDR influences the efficacy of PD-1 blockade in patients with mNSCLC, particularly when SIMRD is maintained within the 1-3 Gy range, likely through modulation of the gut microbiota-metabolite-immune axis.

PMID:41849236 | DOI:10.1158/1078-0432.CCR-25-4153

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