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Received — 15 September 2026 ⏭

FLOAT Drone: A Fully-actuated Coaxial Aerial Robot for Close-Proximity Operations

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junxiao Lin · Shuhang Ji · Yuze Wu · Tianyue Wu · Zhichao Han · Fei Gao
14 September 2026 at 12:00
arXiv:2503.00785v2 Announce Type: replace-cross Abstract: How to endow aerial robots with the ability to operate in close proximity remains an open problem. The core challenges lie in the propulsion system's dual-task requirement: generating manipulation forces while simultaneously counteracting gravity. These competing demands create dynamic coupling effects during physical interactions. Furthermore, rotor-induced airflow disturbances critically undermine operational reliability. Although fully-actuated unmanned aerial vehicles (UAVs) alleviate dynamic coupling effects via six-degree-of-freedom (6-DoF) force-torque decoupling, existing implementations fail to address the aerodynamic interference between drones and environments. They also suffer from oversized designs, which compromise maneuverability and limit their applications in various operational scenarios. To address these limitations, we present FLOAT Drone (FuLly-actuated cOaxial Aerial roboT), a novel fully-actuated UAV featuring two key structural innovations. By integrating control surfaces into fully-actuated systems for the first time, we significantly suppress lateral airflow disturbances during operations. Furthermore, a coaxial dual-rotor configuration enables a compact size while maintaining high hovering efficiency. Through dynamic modeling, we have developed hierarchical position and attitude controllers that support both fully-actuated and underactuated modes. Experimental validation through comprehensive real-world experiments confirms the system's functional capabilities in close-proximity operations.

MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuqi Zhan · Xinyue Wu · Tianyu Lin · Yutong Bao · Xiaoyu Wang · Weihao Cheng · Huangwei Chen · Feiwei Qin · Zhu Zhu
14 September 2026 at 12:00
arXiv:2603.01131v4 Announce Type: replace-cross Abstract: Clinical diagnosis is a gradual process of evidence integration, in which physicians move from symptoms and medical history to examinations, competing hypotheses, disease relations, and treatment decisions. Large language models have advanced medical text understanding and generation. Yet their clinical use remains limited by weak evidence grounding, opaque reasoning, and inconsistent links among differential diagnosis, final diagnosis, diagnostic basis, and treatment planning. We introduce MedCollab, a multi-agent framework for full-cycle clinical diagnosis and report generation. MedCollab coordinates specialist and examination agents according to patient records. It structures agent deliberation with an Issue-Based Information System (IBIS) protocol, so that each diagnostic position is supported by patient-specific evidence and medical knowledge. It also builds Hierarchical Disease Relation Chains (HDRC) to connect accepted hypotheses through progression, complication, and comorbidity relations. During multi-round deliberation, a verifier-guided consensus module evaluates evidence support, medical plausibility, and logical conflicts. It then adjusts agent contributions and filters unsupported reasoning. Experiments on ClinicalBench and MIMIC-IV show that MedCollab outperforms leading LLMs and medical multi-agent baselines in diagnostic accuracy, evidence consistency, and clinical reasoning quality. These results indicate that structured and auditable collaboration can produce more faithful and clinically coherent diagnostic reports.
Received — 10 September 2026 ⏭

Early stage nonsmall cell lung cancer: Toward a risk-adaptive paradigm in the era of biologic precision

✇Omics In Lung
By: Chaoqi Zhang · Xuanyu Gu · Pan Wang · Dongyu Li · Guochao Zhang · Ting Wang · Peng Wu · Wei Rao · Shan Muhammad · Binghua Tan · Yun Che · Mobai Zhu · Qilin Qiao · Fanbo Lai · Bohui Zhao · Kailun Fei · Yue Wu · Zhihui Zhang · Min Dai · Wanqing Chen · Yawei Zhang · Nan Bi · Jie Wang · Jie He
9 September 2026 at 18:00

CA Cancer J Clin. 2026 Sep-Oct;76(5):e70100. doi: 10.3322/caac.70100.

ABSTRACT

The clinical landscape of early stage nonsmall cell lung cancer is at transformative crossroads. Driven by the widespread adoption of low-dose computed tomography screening, the frequent detection of ground-glass opacities, and a rising incidence among never-smokers, the diagnostic center of gravity has shifted toward earlier, potentially curable disease. This shift has been accompanied by equally important therapeutic advances, including parenchyma-sparing surgical techniques, minimally invasive platforms enhanced by digital navigation, and the transformative integration of perioperative immunotherapy and targeted agents. Concurrently, noninvasive monitoring approaches, such as liquid biopsy, have emerged as powerful tools to guide precision management. Despite this progress, substantial barriers to achieving a universal cure persist. Clinicians continue to face uncertainty in the management of ground-glass opacities, the anatomy-based TNM staging system fails to capture the biologic heterogeneity of early tumors, and global disparities in access to innovation remain unresolved. To address these challenges, the authors propose a shift toward a risk-adaptive management paradigm that harnesses artificial intelligence-driven analytics and multi-omics profiling to tailor treatment intensity according to each patient's biologic risk. Such an approach would enable appropriate escalation for high-risk individuals while permitting safe de-escalation for those at low risk. This holistic, lifespan-oriented strategy must be embraced to deliver equitable and durable cures for patients with early stage nonsmall cell lung cancer.

PMID:42713910 | PMC:PMC13555834 | DOI:10.3322/caac.70100

Early stage nonsmall cell lung cancer: Toward a risk-adaptive paradigm in the era of biologic precision

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Chaoqi Zhang · Xuanyu Gu · Pan Wang · Dongyu Li · Guochao Zhang · Ting Wang · Peng Wu · Wei Rao · Shan Muhammad · Binghua Tan · Yun Che · Mobai Zhu · Qilin Qiao · Fanbo Lai · Bohui Zhao · Kailun Fei · Yue Wu · Zhihui Zhang · Min Dai · Wanqing Chen · Yawei Zhang · Nan Bi · Jie Wang · Jie He
9 September 2026 at 18:00

CA Cancer J Clin. 2026 Sep-Oct;76(5):e70100. doi: 10.3322/caac.70100.

ABSTRACT

The clinical landscape of early stage nonsmall cell lung cancer is at transformative crossroads. Driven by the widespread adoption of low-dose computed tomography screening, the frequent detection of ground-glass opacities, and a rising incidence among never-smokers, the diagnostic center of gravity has shifted toward earlier, potentially curable disease. This shift has been accompanied by equally important therapeutic advances, including parenchyma-sparing surgical techniques, minimally invasive platforms enhanced by digital navigation, and the transformative integration of perioperative immunotherapy and targeted agents. Concurrently, noninvasive monitoring approaches, such as liquid biopsy, have emerged as powerful tools to guide precision management. Despite this progress, substantial barriers to achieving a universal cure persist. Clinicians continue to face uncertainty in the management of ground-glass opacities, the anatomy-based TNM staging system fails to capture the biologic heterogeneity of early tumors, and global disparities in access to innovation remain unresolved. To address these challenges, the authors propose a shift toward a risk-adaptive management paradigm that harnesses artificial intelligence-driven analytics and multi-omics profiling to tailor treatment intensity according to each patient's biologic risk. Such an approach would enable appropriate escalation for high-risk individuals while permitting safe de-escalation for those at low risk. This holistic, lifespan-oriented strategy must be embraced to deliver equitable and durable cures for patients with early stage nonsmall cell lung cancer.

PMID:42713910 | DOI:10.3322/caac.70100

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 ⏭

Emotional intelligence in large language models is fragmented across perception, cognition, and interaction

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minghao Lv · Lu Chen · Enchang Zhang · Anji Zhou · Xiaoran Xue · Hanyi Zhang · Fenghua Tang · Zhuo Rachel Han · Mengyue Wu
26 May 2026 at 12:00
arXiv:2605.24686v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly integrated into emotionally sensitive domains, the structural integrity of their emotional intelligence (EI) becomes a critical frontier for safety and alignment. Current benchmarks often conflate superficial politeness with deep affective reasoning, failing to distinguish between perceptual accuracy and interactive efficacy. Here, we introduce FACET (Functional Affective Competence and Empathy Test), a psychometrically grounded framework comprising 480 expert-crafted items. Unlike previous metrics, FACET is theoretically anchored in the Mayer-Salovey-Caruso four-branch ability model, operationalizing EI through perception, facilitation, understanding, and management of emotions. Through an evaluation of nine frontier models (including GPT-5, Claude-Sonnet-4), we demonstrate that emotional intelligence is not a monolithic capability but is fragmented across cognitive and interactive dimensions. While frontier models demonstrate robust proficiency in objective emotion recognition and social reasoning, this does not consistently translate to interactive success. We categorize these discrepancies into three distinct performance profiles: cognitive-dominant, interactive-dominant, and context-dependent. These typologies indicate that emotional skills do not scale uniformly with general intelligence or model size; rather, they are shaped by specific alignment paradigms. Notably, we identify hidden emotion recognition as a universal performance bottleneck across all architectures. Our results suggest that current RLHF processes may optimize for "stochastic empathy", a statistical mimicry of emotional syntax, at the expense of integrated affective reasoning. These findings challenge the assumption of linear emotional scaling and provide a rigorous roadmap for developing socially aware agents capable of genuine clinical resonance.

A comparison of deep multiomics profiles across ethnicity, geography, and age

✇Cell
By: Nasim Barapour · John Z. Cao · Yue Wu · Shubham Gupta · Michael R. Hoopmann · Rui Qin · Mukul K. Midha · Myriam Mireault · Pablo Juanes-Velasco · Casey Hanson · Sara Ahadi · Emily Higgs · David H. Baxter · Christian Diener · Orit Dagan-Rosenfeld · Daniel Hornburg · Songjie Che · Fredrik Edfors · Stephanie J. Church · Mohan Babu · Durga Thota · Christopher Jin · Tristan Chou · Shannon Rego · Monika Avina · Lettie McGuire · Jessi W. Li · Thomas Karathanos · Daniel J. Panyard · Martín A. Acosta Parra · Aubrey K. Roberts · Amarnath K. Ranjit · Ekanath Rangan · Jose Juan Almagro Armenteros · Melanie Ashland · Kevin Erazo Castillo · Gavin Traber · Matthew Ellenberger · Ryan Kellogg · Wenyu Zhou · Hannes Rost · Martin Kjellberg · Tejaswini Mishra · Charu Kapil · Ulrike Kusebauch · Sushmita Patwardhan · Alicia Landeira-Viñuela · Angela-Patricia Hernandez · Mikkel Eggert Thomsen · Maliha Mashkoor · Thanadol Sutantiwanichkul · Tea Dodig-Crnkovic · Annika Bendes · Leo Dahl · Sean M. Gibbons · P. Venkat Rangan · Allan Stensballe · Jochen M. Schwenk · Richard D. Unwin · Manuel
14 May 2026 at 08:00
Multiomics profiling of healthy individuals reveals differences across molecular layers and key pathways related to immune, metabolic, and microbiome-linked processes across ethnicities, while geographic relocation reshapes these networks and influences aging trajectories.
Received — 25 February 2026 ⏭

From Medical Records to Diagnostic Dialogues: A Clinical-Grounded Approach and Dataset for Psychiatric Comorbidity

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianxi Wan · Jiaming Luo · Siyuan Chen · Kunyao Lan · Jianhua Chen · Haiyang Geng · Mengyue Wu
24 February 2026 at 13:00
arXiv:2510.25232v2 Announce Type: replace Abstract: Psychiatric comorbidity is clinically significant yet challenging due to the complexity of multiple co-occurring disorders. To address this, we develop a novel approach integrating synthetic patient electronic medical record (EMR) construction and multi-agent diagnostic dialogue generation. We create 502 synthetic EMRs for common comorbid conditions using a pipeline that ensures clinical relevance and diversity. Our multi-agent framework transfers the clinical interview protocol into a hierarchical state machine and context tree, supporting over 130 diagnostic states while maintaining clinical standards. Through this rigorous process, we construct PsyCoTalk, the first large-scale dialogue dataset supporting comorbidity, containing 3,000 multi-turn diagnostic dialogues validated by psychiatrists. This dataset enhances diagnostic accuracy and treatment planning, offering a valuable resource for psychiatric comorbidity research. Compared to real-world clinical transcripts, PsyCoTalk exhibits high structural and linguistic fidelity in terms of dialogue length, token distribution, and diagnostic reasoning strategies. Licensed psychiatrists confirm the realism and diagnostic validity of the dialogues. This dataset enables the development and evaluation of models capable of multi-disorder psychiatric screening in a single conversational pass.
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