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Received — 18 September 2026 ⏭

Advancing cancer detection and treatment using longitudinal routine clinical data

✇Cell
By: Fei Liu · Kai Wang · Hui Xu · Cheng Tang · Xian Shen · Meihao Wang · Lei Yang · Li Yang · Li Liu · Changxi Hu · Gen Li · Wei Wu · Zixing Zou · Bingzhou Li · Sian Liu · Jin Kang · Jungho Kong · Ting Li · Io Nam Wong · Xiaoying Huang · Gang Chen · Wenyang Lu · Ian Ziyar · Charlotte L. Zhang · Yiwen Sun · Weihong Lin · Caiwen Ou · Manson Fok · Taiwa Hou · Winston Wang · Kanmin Xue · Yun Yin · Hao Zhu · Jonathan Gootenberg · Omar O. Abudayyeh · Michael Karin · Alexandre Loupy · John E.J. Rasko · Trey Ideker · Huiyan Luo · Eric Oermann · Kang Zhang · International Consortium of Digital Twins in Healthcare and Medicine
27 July 2026 at 08:00
Liu et al. develop Oncoformer, a multimodal transformer that reads routine laboratory tests and chest X-rays already collected in everyday care. Across more than 3.6 million individuals, it detects cancer, infers tumor stage, and stratifies treatment response and recurrence risk, pointing toward risk-adapted cancer care built on data already in hand.
Received — 13 September 2026 ⏭

NBR1-Mediated Autophagic Degradation of YTHDF1 Curtails <em>FDX1</em> Translation to Drive Concurrent Multikinase Inhibitor Resistance and Cuproptosis Tolerance

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Weikai Wang · Xin Jiang · Xiaona Chen · Kaiyu Liang · Miaoqin Chen · Kaizhong Lu · Qi Wei · Yuchen Wu · Liyuan Zhu · Yuan Chen · Tianlun Hou · Jingfeng Luo · Chentao Li · Xian Wang · Hongchuan Jin · Lifeng Feng
12 September 2026 at 18:00

Cancer Commun (Lond). 2026 Sep 11;46:0048. doi: 10.34133/cancomm.0048. eCollection 2026.

ABSTRACT

Background: Cancer cells frequently acquire adaptive resistance to targeted therapies; however, strategies capable of concurrently overcoming treatment tolerance and reactivating cell death pathways are currently lacking. Here, we investigated the dual role of ferredoxin 1 (FDX1) in modulating both multikinase inhibitor (MKI) sensitivity and cuproptosis susceptibility in hepatocellular carcinoma (HCC), and sought to develop a therapeutic approach for reversing resistance. Methods: HCC models, both in vitro and in vivo, were employed to investigate the role of FDX1 in MKI resistance and cuproptosis evasion. Polysome profiling, SunTag translation reporters, CRISPR-Cas9 mutagenesis, and mass spectrometry were employed to delineate the underlying mechanisms. A codelivery nanoliposome system was engineered and tested in orthotopic HCC models. Results: Prolonged exposure to MKIs led to the down-regulation of FDX1 protein levels, resulting in MKI resistance and cuproptosis tolerance in HCC both in vitro and in vivo. Mechanistically, we found that MKIs inactivated protein kinase B (PKB, also known as AKT)-mechanistic target of rapamycin (mTOR) signaling, thereby suppressing the SET and MYND domain-containing protein 2 (SMYD2)-mediated methylation of YTH domain family protein 1 (YTHDF1) at lysine 515 (K515). Hypomethylated YTHDF1 was degraded via next to BRCA1 gene 1 protein (NBR1)-dependent autophagy, leading to the repression of N6-methyladenosine modification-dependent translation of FDX1 mRNA. FDX1 deficiency drove MKI resistance by reactivating AKT survival signaling while impairing cuproptosis through reduced divalent copper ions (Cu2+) to monovalent copper ions (Cu+) conversion and the loss of protein lipoylation. Additionally, restoring FDX1 expression through NBR1 knockdown or YTHDF1 overexpression overcame MKI resistance and resensitized HCC cells to cuproptosis. Finally, a nanoliposomal system, super cuproptosis detonator liposome, designed for the codelivery of NBR1 small interfering RNA, a copper ionophore, and sorafenib restored FDX1-dependent cuproptosis and exhibited marked anti-HCC efficacy, suppressing HCC growth in vivo. Conclusions: MKIs suppressed SMYD2-mediated YTHDF1 methylation at K515 via the inactivation of AKT-mTOR signaling. This led to the inhibition of FDX1 translation, resulting in AKT signaling reactivation and protein lipoylation impairment, effects that contributed to both MKI resistance and cuproptosis tolerance in HCC. Overcoming MKI resistance and resensitizing cells to cuproptosis by targeting NBR1-mediated YTHDF1 degradation using a nanoliposomal codelivery system represents a promising strategy for HCC treatment.

PMID:42729649 | PMC:PMC13562797 | DOI:10.34133/cancomm.0048

A global digital navigator of human health for precision medicine

✇Nature Medicine
By: Kang Zhang · Jiahong Dong · Hang Wong · Sian Liu · Kai Wang · Wei Wu · Kaixin Zhou · Kanmin Xue · Edward Zhang · Jonathan Gootenberg · Omar O. Abudayyeh · Michelle Williams · Xian Shen · Alexandre Loupy · Trey Ideker · Patrick Tan · Joseph Wu · Akrit Sodhi · Eric Oermann · Tao Xu · Guang Ning
11 September 2026 at 08:00

Nature Medicine, Published online: 11 September 2026; doi:10.1038/s41591-026-04621-1

The International Consortium of Digital Twins in Healthcare and Medicine was established to advance medical digital twin technology as a new infrastructure for precision health.
Received — 10 September 2026 ⏭

Intranasal delivery of a vasoactive intestinal peptide-based circRNA vaccine induces systemic and mucosal immunity against RSV in mice

✇Molecular Therapy
By: Junqi Xiang · Jie Xu · Jing Zhu · Bo Yang · Mingling Wan · Jiangyu Cai · Shuying Chen · Yuncheng Wang · Shanrong Yang · Yaokai Wang · Li Lin · Qiqi Zhang · Na Zang · Enmei Liu · Yushun Wan
19 August 2026 at 08:00
A vasoactive intestinal peptide (VIP)-based protein carrier self-assembles with respiratory syncytial virus circular RNA vaccines for intranasal delivery, inducing systemic antibodies, mucosal IgA, and Th1-biased protection in mice. This platform offers a protein-guided strategy for respiratory mucosal RNA vaccination and broadens the application of VIP in vaccine delivery.

Skin-innervating glutamatergic neurons modulate aging

✇Cell
By: Zhikai Wang · Xinping Jin · Yue Wu · Wenxiang Ding · Fengjiao Zhang · Yang Sun · Fangzhou Lou · Jing Bai · Xiuli Yang · Hong Zhou · Jihuan Liang · Xiaojie Cai · Yan Li · Xichen Zheng · Jiajia Han · Fang Tong · Qing Yang · Zhixiao Fang · Liuqi Zhao · Qingqing Shen · Zhenyao Xu · Siyu Deng · Li Fan · Jiajia Tong · Linhui Tong · Junjie Wang · Min Gao · Zilong Fang · Qun Li · Rui Chen · Lina Huang · Zhen Li · Yuqin Yang · Jing Zou · Ying Wu · Xiwen Zhang · Honglin Wang
17 July 2026 at 08:00
Within the skin, glutamatergic neurons expressing neurofilament heavy chain (Nefh) play a role in aging. Loss of Nefh during aging drives skin fibroblast senescence and collagen loss, whereas glutamate supplementation improves skin aging phenotypes.
Received — 27 May 2026 ⏭

Bilevel Optimization of Synthetic Trajectories for Multi-Turn LLM Fine-Tuning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Shresth Verma · Mauricio Tec · Cheol Woo Kim · Kai Wang · Milind Tambe
26 May 2026 at 12:00
arXiv:2605.24743v1 Announce Type: cross Abstract: While LLMs excel at single-turn generation, they struggle with long-horizon, multi-turn interactions. Offline reinforcement learning (RL) offers a scalable approach, yet its performance hinges on the availability and quality of multi-turn trajectory data. A common remedy is to augment training with synthetic trajectories generated by LLMs or simulators, but synthetic data is highly heterogeneous in quality, and naively treating all trajectories as equally informative can degrade performance. We propose BOOST, a bilevel optimization framework where the inner level trains the LLM on reweighted data and the outer level trains a lightweight reweighting head on held-out real validation tasks, assigning continuous trajectory-level weights without requiring an external judge. To ground this approach, we derive a PAC-Bayesian bound revealing a three-way trade-off: synthetic data increases diversity but risks task-shift, while concentrating weight on high-quality trajectories improves empirical performance at the cost of effective sample size. Empirically, our method consistently outperforms multiple baselines. Analysis reveals it upweights synthetic trajectories that align with the real data distribution and exhibit higher qualitative merit.

From Muscle Bursts to Motor Intent: Self-Supervised Token Modeling for Heterogeneous EMG

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zhenghao Huang · Huilin Yao · Kaikai Wang
26 May 2026 at 12:00
arXiv:2605.03462v3 Announce Type: replace-cross Abstract: Surface electromyography provides a practical way to infer human movement intention from wearable muscle recordings, but models trained under a single acquisition setting often lose reliability when the user, session, electrode layout, or gesture protocol changes. This paper proposes AEMG, a self-supervised learning approach designed to extract reusable neuromuscular representations from diverse EMG sources. Eight public gesture datasets are first transformed into a shared signal format to reduce discrepancies in channel configuration, sensor topology, and recording protocol. Instead of relying on fixed-length sliding windows, AEMG identifies contraction events from energy variations and represents them as compact neuromuscular tokens, while ordered token groups describe the coordinated activity of multiple muscles during motion. A spatially and temporally conditioned Transformer is then used to encode these token sequences, preserving information about electrode position, activation timing, and sequential structure. For pre-training, the model constructs a discrete library of contraction prototypes through vector-quantized reconstruction and further learns contextual dependencies by recovering masked neuromuscular tokens from surrounding observations. Experiments under leave-one-subject-out and low-label adaptation settings show that the learned representation improves robustness to unseen users and reduces the amount of calibration data required for gesture recognition. These findings suggest that event-level token modeling offers a scalable route toward adaptable and data-efficient EMG-based motor-intent understanding.
Received — 8 April 2026 ⏭

3D-IDE: 3D Implicit Depth Emergent

✇cs.AI, q-bio.NC updates on arXiv.org
By: Chushan Zhang · Ruihan Lu · Jinguang Tong · Yikai Wang · Hongdong Li
7 April 2026 at 12:00
arXiv:2604.03296v1 Announce Type: cross Abstract: Leveraging 3D information within Multimodal Large Language Models (MLLMs) has recently shown significant advantages for indoor scene understanding. However, existing methods, including those using explicit ground-truth 3D positional encoding and those grafting external 3D foundation models for implicit geometry, struggle with the trade-off in 2D-3D representation fusion, leading to suboptimal deployment. To this end, we propose 3D-Implicit Depth Emergence, a method that reframes 3D perception as an emergent property derived from geometric self-supervision rather than explicit encoding. Our core insight is the Implicit Geometric Emergence Principle: by strategically leveraging privileged geometric supervision through mechanisms like a fine-grained geometry validator and global representation constraints, we construct an information bottleneck. This bottleneck forces the model to maximize the mutual information between visual features and 3D structures, allowing 3D awareness to emerge naturally within a unified visual representation. Unlike existing approaches, our method enables 3D perception to emerge implicitly, disentangling features in dense regions and, crucially, eliminating depth and pose dependencies during inference with zero latency overhead. This paradigm shift from external grafting to implicit emergence represents a fundamental rethinking of 3D knowledge integration in visual-language models. Extensive experiments demonstrate that our method surpasses SOTA on multiple 3D scene understanding benchmarks. Our approach achieves a 55% reduction in inference latency while maintaining strong performance across diverse downstream tasks, underscoring the effectiveness of meticulously designed auxiliary objectives for dependency-free 3D understanding. Source code can be found at github.com/ChushanZhang/3D-IDE.
Received — 2 April 2026 ⏭

A monocyte-centered framework for predicting immunochemotherapy efficacy in lung squamous cell carcinoma patients

✇Omics In Lung
By: Jiangnan Zhao · Zijin Wang · Manyu Xiao · Yeyuan Zhang · Boxin Liu · Silin Chen · Yiping Tian · Dongqing Lv · Pingli Wang · Hai Song · Yuefeng Wu · Jian Liu · Kai Wang
30 March 2026 at 18:00

EMBO Mol Med. 2026 Mar 30. doi: 10.1038/s44321-026-00410-y. Online ahead of print.

ABSTRACT

Lung cancer is the leading cause of cancer-related mortality worldwide, with lung squamous cell carcinoma (LUSC) comprising 20-30% of cases. Immunochemotherapy (IC) is the standard first-line treatment for advanced LUSC, yet reliable predictors of therapeutic response remain unavailable. Using single-cell multi-omics profiling of paired pre- and post-treatment tumor and blood samples, we observed that patients responding to IC exhibited significantly higher baseline levels of peripheral blood monocytes, tumor-infiltrating classical monocytes, and APOBEC3A+ monocytes across both compartments compared with non-responders. These associations were independently validated in additional cohorts using routine complete blood count testing and multiplex immunofluorescence analysis of native tumor tissues. Our findings reveal monocyte-related parameters as clinically accessible indicators that link systemic immunity with the tumor microenvironment and hold promise for predicting IC responsiveness in patients with LUSC.

PMID:41912871 | DOI:10.1038/s44321-026-00410-y

A monocyte-centered framework for predicting immunochemotherapy efficacy in lung squamous cell carcinoma patients

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Jiangnan Zhao · Zijin Wang · Manyu Xiao · Yeyuan Zhang · Boxin Liu · Silin Chen · Yiping Tian · Dongqing Lv · Pingli Wang · Hai Song · Yuefeng Wu · Jian Liu · Kai Wang
30 March 2026 at 18:00

EMBO Mol Med. 2026 Mar 30. doi: 10.1038/s44321-026-00410-y. Online ahead of print.

ABSTRACT

Lung cancer is the leading cause of cancer-related mortality worldwide, with lung squamous cell carcinoma (LUSC) comprising 20-30% of cases. Immunochemotherapy (IC) is the standard first-line treatment for advanced LUSC, yet reliable predictors of therapeutic response remain unavailable. Using single-cell multi-omics profiling of paired pre- and post-treatment tumor and blood samples, we observed that patients responding to IC exhibited significantly higher baseline levels of peripheral blood monocytes, tumor-infiltrating classical monocytes, and APOBEC3A+ monocytes across both compartments compared with non-responders. These associations were independently validated in additional cohorts using routine complete blood count testing and multiplex immunofluorescence analysis of native tumor tissues. Our findings reveal monocyte-related parameters as clinically accessible indicators that link systemic immunity with the tumor microenvironment and hold promise for predicting IC responsiveness in patients with LUSC.

PMID:41912871 | DOI:10.1038/s44321-026-00410-y

Received — 11 March 2026 ⏭

PaLMR: Towards Faithful Visual Reasoning via Multimodal Process Alignment

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yantao Li · Qiang Hui · Chenyang Yan · Kanzhi Cheng · Fang Zhao · Chao Tan · Huanling Gao · Jianbing Zhang · Kai Wang · Xinyu Dai · Shiguo Lian
10 March 2026 at 12:00
arXiv:2603.06652v1 Announce Type: cross Abstract: Reinforcement learning has recently improved the reasoning ability of Large Language Models and Multimodal LLMs, yet prevailing reward designs emphasise final-answer correctness and consequently tolerate process hallucinations--cases where models reach the right answer while misperceiving visual evidence. We address this process-level misalignment with PaLMR, a framework that aligns not only outcomes but also the reasoning process itself. PaLMR comprises two complementary components: a perception-aligned data layer that constructs process-aware reasoning data with structured pseudo-ground-truths and verifiable visual facts, and a process-aligned optimisation layer that constructs a hierarchical reward fusion scheme with a process-aware scoring function to encourage visually faithful chains-of-thought and improve training stability. Experiments on Qwen2.5-VL-7B show that our approach substantially reduces reasoning hallucinations and improves visual reasoning fidelity, achieving state-of-the-art results on HallusionBench while maintaining strong performance on MMMU, MathVista, and MathVerse. These findings indicate that PaLMR offers a principled and practical route to process-aligned multimodal reasoning, advancing the reliability and interpretability of MLLMs.

Yo'City: Personalized and Boundless 3D Realistic City Scene Generation via Self-Critic Expansion

✇cs.AI, q-bio.NC updates on arXiv.org
By: Keyang Lu · Sifan Zhou · Hongbin Xu · Gang Xu · Zhifei Yang · Yikai Wang · Zhen Xiao · Jieyi Long · Ming Li
10 March 2026 at 12:00
arXiv:2511.18734v3 Announce Type: replace-cross Abstract: Realistic 3D city generation is fundamental to a wide range of applications, including virtual reality and digital twins. However, most existing methods rely on training a single diffusion model, which limits their ability to generate personalized and boundless city-scale scenes. In this paper, we present Yo'City, a novel agentic framework that enables user-customized and infinitely expandable 3D city generation by leveraging the reasoning and compositional capabilities of off-the-shelf large models. Specifically, Yo'City first conceptualizes the city through a top-down planning strategy that defines a hierarchical "City-District-Grid" structure. The Global Planner determines the overall layout and potential functional districts, while the Local Designer further refines each district with detailed grid-level descriptions. Subsequently, the grid-level 3D generation is achieved through a "produce-refine-evaluate" isometric image synthesis loop, followed by image-to-3D generation. To simulate continuous city evolution, Yo'City further introduces a user-interactive, relationship-guided expansion mechanism, which performs scene graph-based distance- and semantics-aware layout optimization, ensuring spatially coherent city growth. To comprehensively evaluate our method, we construct a diverse benchmark dataset and design six multi-dimensional metrics that assess generation quality from the perspectives of semantics, geometry, texture, and layout. Extensive experiments demonstrate that Yo'City consistently outperforms existing state-of-the-art methods across all evaluation aspects.

MeanCache: From Instantaneous to Average Velocity for Accelerating Flow Matching Inference

✇cs.AI, q-bio.NC updates on arXiv.org
By: Huanlin Gao · Ping Chen · Fuyuan Shi · Ruijia Wu · Li YanTao · Qiang Hui · Yuren You · Ting Lu · Chao Tan · Shaoan Zhao · Zhaoxiang Liu · Fang Zhao · Kai Wang · Shiguo Lian
10 March 2026 at 12:00
arXiv:2601.19961v3 Announce Type: replace-cross Abstract: We present MeanCache, a training-free caching framework for efficient Flow Matching inference. Existing caching methods reduce redundant computation but typically rely on instantaneous velocity information (e.g., feature caching), which often leads to severe trajectory deviations and error accumulation under high acceleration ratios. MeanCache introduces an average-velocity perspective: by leveraging cached Jacobian--vector products (JVP) to construct interval average velocities from instantaneous velocities, it effectively mitigates local error accumulation. To further improve cache timing and JVP reuse stability, we develop a trajectory-stability scheduling strategy as a practical tool, employing a Peak-Suppressed Shortest Path under budget constraints to determine the schedule. Experiments on FLUX.1, Qwen-Image, and HunyuanVideo demonstrate that MeanCache achieves 4.12X and 4.56X and 3.59X acceleration, respectively, while consistently outperforming state-of-the-art caching baselines in generation quality. We believe this simple yet effective approach provides a new perspective for Flow Matching inference and will inspire further exploration of stability-driven acceleration in commercial-scale generative models.
Received — 5 March 2026 ⏭

HALyPO: Heterogeneous-Agent Lyapunov Policy Optimization for Human-Robot Collaboration

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hao Zhang · Yaru Niu · Yikai Wang · Ding Zhao · H. Eric Tseng
5 March 2026 at 13:00
arXiv:2603.03741v1 Announce Type: cross Abstract: To improve generalization and resilience in human-robot collaboration (HRC), robots must handle the combinatorial diversity of human behaviors and contexts, motivating multi-agent reinforcement learning (MARL). However, inherent heterogeneity between robots and humans creates a rationality gap (RG) in the learning process-a variational mismatch between decentralized best-response dynamics and centralized cooperative ascent. The resulting learning problem is a general-sum differentiable game, so independent policy-gradient updates can oscillate or diverge without added structure. We propose heterogeneous-agent Lyapunov policy optimization (HALyPO), which establishes formal stability directly in the policy-parameter space by enforcing a per-step Lyapunov decrease condition on a parameter-space disagreement metric. Unlike Lyapunov-based safe RL, which targets state/trajectory constraints in constrained Markov decision processes, HALyPO uses Lyapunov certification to stabilize decentralized policy learning. HALyPO rectifies decentralized gradients via optimal quadratic projections, ensuring monotonic contraction of RG and enabling effective exploration of open-ended interaction spaces. Extensive simulations and real-world humanoid-robot experiments show that this certified stability improves generalization and robustness in collaborative corner cases.
Received — 25 February 2026 ⏭

Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiaojiang Peng · Jingyi Chen · Zebang Cheng · Bao Peng · Fengyi Wu · Yifei Dong · Shuyuan Tu · Qiyu Hu · Huiting Huang · Yuxiang Lin · Jun-Yan He · Kai Wang · Zheng Lian · Zhi-Qi Cheng
24 February 2026 at 13:00
arXiv:2601.16449v2 Announce Type: replace-cross Abstract: Understanding human emotions from multimodal signals poses a significant challenge in affective computing and human-robot interaction. While multimodal large language models (MLLMs) have excelled in general vision-language tasks, their capabilities in emotional reasoning remain limited. The field currently suffers from a scarcity of large-scale datasets with high-quality, descriptive emotion annotations and lacks standardized benchmarks for evaluation. Our preliminary framework, Emotion-LLaMA, pioneered instruction-tuned multimodal learning for emotion reasoning but was restricted by explicit face detectors, implicit fusion strategies, and low-quality training data with limited scale. To address these limitations, we present Emotion-LLaMAv2 and the MMEVerse benchmark, establishing an end-to-end pipeline together with a standardized evaluation setting for emotion recognition and reasoning. Emotion-LLaMAv2 introduces three key advances. First, an end-to-end multiview encoder eliminates external face detection and captures nuanced emotional cues via richer spatial and temporal multiview tokens. Second, a Conv Attention pre-fusion module is designed to enable simultaneous local and global multimodal feature interactions external to the LLM backbone. Third, a perception-to-cognition curriculum instruction tuning scheme within the LLaMA2 backbone unifies emotion recognition and free-form emotion reasoning. To support large-scale training and reproducible evaluation, MMEVerse aggregates twelve publicly available emotion datasets, including IEMOCAP, MELD, DFEW, and MAFW, into a unified multimodal instruction format. The data are re-annotated via a multi-agent pipeline involving Qwen2 Audio, Qwen2.5 VL, and GPT 4o, producing 130k training clips and 36k testing clips across 18 evaluation benchmarks.
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