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Received — 27 May 2026 ⏭

LC-ERD: Mining Latent Logic for Self-Evolving Reasoning via Consistency-Regulated Reward Decomposition

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yanyu Chen · Jiyue Jiang · Dianzhi Yu · Zheng Wu · Jiahong Liu · Jiaming Han · Xiao Guo · Jinhu Qi · Yu Li · Yifei Zhang · Irwin King
26 May 2026 at 12:00
arXiv:2605.24005v1 Announce Type: new Abstract: The evolution of Large Language Model (LLM) reasoning is bottlenecked by the scarcity of high-quality process data. While self-alignment via endogenous rewards offers a solution, mining valid supervision faces three challenges: (1) Label Noise via Mimetic Bias, where rewards prioritize statistical likelihood over logical truth, creating a "correctness illusion" that masks compounding errors; (2) Coarse-Grained Supervision, where sparse global outcomes (e.g., in GRPO) fail to provide granular guidance, treating reasoning chains as monolithic; and (3) Distributional Collapse, where signals fail to generalize without amplifying pre-training biases. To address these, we introduce LC-ERD (Logic-Consistent Endogenous Reward Decomposition), a framework framing self-alignment as latent structure mining. We derive a Variational Logic Potential by aggregating consensus from the model's Latent Logic Expertise (LLE) to denoise the reasoning manifold, and introduce a Multi-Agent Value Decomposition protocol based on the IGM principle to quantify individual step utility. Experiments show LC-ERD delivers a robust self-evolution path, uncovering trade-offs between logic consistency and accuracy while identifying high-value reasoning patterns missed by standard rewards. Our code is available at https://github.com/Reinhardmannn/LC-ERD.

HyperGuide: Hyperbolic Guidance for Efficient Multi-Step Reasoning in Large Language Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yuyu Liu · Haotian Xu · Yanan He · Sarang Rajendra Patil · Mengjia Xu · Tengfei Ma
26 May 2026 at 12:00
arXiv:2605.24140v1 Announce Type: new Abstract: Multi-step reasoning remains a central challenge for large language models: single-pass generation is efficient but lacks accuracy; tree-search methods explore multiple paths but are computation-heavy. We address this gap by distilling reasoning progress into a hyperbolic geometric signal that guides step-by-step generation. Our approach is motivated by a structural observation: in combinatorial reasoning trees, solution-bearing states are few while dead ends are exponentially numerous. The hyperbolic space matches this asymmetry, with compact volume near the origin and exponentially expanding capacity toward the boundary, so that distance-to-origin naturally encodes solution proximity while angular separation distinguishes branches requiring different next operations. We train a lightweight head to project LLM hidden states into this space, then fine-tune a low-rank adapter interactively on its own reasoning attempts to act on the injected signal. Across multiple benchmarks, the geometric signal yields consistent gains, with larger improvements on deeper reasoning chains. Our code is publicly available at https://github.com/yuyuliu11037/HyperGuide.

StructBreak: Structural Cognitive Overload-Induced Safety Failures in MLLMs

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yang Luo · Xinran Liu · Tiantian Ji · Zhiyi Yin · Lingyun Peng · Shuyu Li
26 May 2026 at 12:00
arXiv:2605.25534v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at structural reasoning yet suffer from a sharp logical brittleness in structural consistency. We term this phenomenon Structural Cognitive Overload (SCO), a byproduct of the contention between deep reasoning and safety alignment. However, prior work has predominantly targeted typographic and pixel-level perturbations, leaving the study of SCO largely unexplored. To this end, we propose StructBreak, an automated end-to-end framework designed to quantify SCO. By leveraging StructBreak, we uncover a novel higher-order cognitive overload attack paradigm; notably, this attack operates under a practical black-box setting, requiring no internal model access. Consequently, we utilize this framework to establish a comprehensive benchmark spanning ten diverse threat scenarios. Empirical evaluations on six leading MLLMs reveal that SCO readily triggers toxic generation, yielding a 92% average ASR (up to 97% on Gemini 2.5). To elucidate the mechanism of SCO, we further conduct model-level interpretations spanning attention dynamics, latent space topology, and geometric analysis. Our findings reveal that StructBreak acts as a novel structural channel to circumvent safety filters. Furthermore, the limited efficacy of inherent safety mechanisms underscores that current alignment paradigms are insufficient for the era of complex multimodal reasoning.

PHGNet: Prototype-Guided Hypergraph Construction for Heterogeneous Spatiotemporal Forecasting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Ruiwen Gu · Yahao Liu · Zhenyu Liu · Qitai Tan · Xiao-Ping Zhang
26 May 2026 at 12:00
arXiv:2605.25554v1 Announce Type: new Abstract: As a core task in intelligent transportation systems, traffic forecasting plays a critical role in urban traffic management. Accurate traffic forecasting relies on modeling complex spatiotemporal dependencies, which is inherently challenging due to spatial heterogeneity in traffic systems.Despite significant progress, most existing methods are still limited to pairwise spatial dependency modeling, making it difficult to capture dynamic high-order interactions among nodes with similar traffic patterns. To address this issue, we propose PHGNet, a novel spatiotemporal forecasting framework based on prototype-guided hypergraph construction. At the core of PHGNet, a prototype learning mechanism is designed to adaptively assign pattern-similar nodes to hyperedges, thereby capturing high-order interactions with time-varying structures. To improve the reliability of dynamic hypergraph construction, we further develop a global-local node representation module to extract time-consistent features. For forecasting, iterative residual refinement and Temporal Query Attention are introduced to improve forecasting accuracy while supporting efficient parallel decoding. Extensive experiments on multiple real-world datasets demonstrate that PHGNet achieves superior predictive performance compared with state-of-the-art methods.

DBPnet: Damper Characteristics-Based Bayesian Physics-Informed Neural Network for Wheel Load Estimation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianyi Wang · Tianyi Zeng · Zimo Zeng · Feiyang Zhang · Yujin Wang · Xiangyu Li · Yiming Xu · Sikai Chen · Junfeng Jiao · Christian Claudel · Xinbo Chen
26 May 2026 at 12:00
arXiv:2605.24860v1 Announce Type: cross Abstract: Advanced driver assistance systems (ADAS) play an important role in modern automotive intelligence, significantly enhancing vehicle safety and stability. The performance of ADAS critically relies on accurate and reliable vehicle state estimation, particularly from vehicle dynamic sensors. Among these signals, wheel load is a key variable for chassis control and safety-critical functions, yet it remains difficult to estimate robustly due to complex suspension geometry, nonlinear dynamics, and measurement noise. To address this issue, we propose DBPnet, a Bayesian physics-informed neural network (PINN) with a physics-aware embedding module inspired by damper characteristics. First, this paper presents a suspension linkage-level modeling (SLLM) approach that constructs a nonlinear instantaneous dynamic model by explicitly considering the complex geometric structure of the suspension. Building upon SLLM, Bayesian inference is integrated into the PINN to effectively cope with noise and uncertainty in the vehicle chassis system, thereby improving the model's robustness. Then, a physics-informed loss function is employed to ensure consistency with fundamental physical principles, while the damper characteristics-inspired embedding module extracts temporal variation features of input signals and incorporates them into each layer of the PINN, ensuring that physical observations guide the neural network without being constrained by fixed physical models. Extensive evaluations on high-fidelity simulations and real-world experiments demonstrate that our DBPnet consistently achieves lower RMSE and MaxError than baseline methods. These results highlight the potential of our DBPnet to advance wheel load estimation and contribute to the development of more reliable ADAS actuator functions.

STREAM: A Data-Centric Framework for Mining High-Value Task-Oriented Dialogues from Streaming Media

✇cs.AI, q-bio.NC updates on arXiv.org
By: Liang Xue · Haoyu Liu · Cheng Wang · Pengyu Chen · Haozhuo Zheng · Yang Liu
26 May 2026 at 12:00
arXiv:2605.25162v1 Announce Type: cross Abstract: Large language models for vertical domains are bottlenecked by the scarcity of complex, domain-specific task-oriented dialogues. Existing data acquisition pipelines face a persistent trilemma: expert annotation is expensive, real-world service conversations are constrained by privacy and commercial restrictions, and static corpora quickly become temporally stale. We propose Stream, a data-centric framework that leverages publicly available streaming media (live streams and short videos) to synthesize high-value service dialogues at scale. Stream mines authentic interaction signals from noisy streams and synthesizes conversations by integrating role-grounded persona construction with Conversational Blueprint construction; it further adopts retrieval-augmented generation (RAG) to support knowledge-aware responses. Based on Stream, we release StreamDial, a large-scale multi-domain dataset covering Automotive, Restaurant, and Hotel. StreamDial contains 87,498 dialogue sessions and 1,497,320 turns in total, with an average of 17.11 turns per session and a comparable scale across domains. Each session is organized as a structured quadruplet $\langle P_u, P_a, B, H \rangle$ that pairs dialogue history with explicit user/agent personas and a Conversational Blueprint, capturing realistic service behaviors such as requirement mining, constraint conflicts, negotiation, and recovery. Evaluations with automatic judges and downstream tasks show that StreamDial improves intrinsic dialogue quality over strong baselines, and models trained with StreamDial improve Dialogue State Tracking across backbones; we further report a completed human-evaluation set and encouraging multilingual transfer on Qwen3-8B under a controlled training budget. The data is released in https://github.com/hitxueliang/DialogDataSetBySTREAM.

Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective

✇cs.AI, q-bio.NC updates on arXiv.org
By: Hengyu Liu · Tianyi Li · Zhihong Cui · Yushuai Li · Zhangkai Wu · Torben Bach Pedersen · Kristian Torp · Christian S. Jensen
26 May 2026 at 12:00
arXiv:2605.02010v2 Announce Type: replace Abstract: This position paper argues that reliable AI requires infrastructure for human validation of implicit knowledge. AI learns from both explicit knowledge (papers, documentation, structured databases) and implicit knowledge (reasoning patterns, debugging processes, intermediate steps). Implicit knowledge remains unexternalized because documentation cost exceeds perceived value -- yet AI learns from it indiscriminately, acquiring both beneficial patterns and harmful biases. Current reliability methods can only verify explicit knowledge against sources, creating a fundamental gap: the most valuable AI capabilities (reasoning, judgment, intuition) are precisely those we cannot verify. We propose Knowledge Objects (KOs) -- structured artifacts that externalize implicit knowledge into forms humans can inspect, verify, and endorse. KOs transform verification economics: what was previously too costly to verify becomes feasible, enabling accumulated human validation to improve reliability over time.

Coupled Variational Reinforcement Learning for Language Model General Reasoning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xueru Wen · Jie Lou · Yanjiang Liu · Hongyu Lin · Ben He · Xianpei Han · Le Sun · Yaojie Lu · Debing Zhang
26 May 2026 at 12:00
arXiv:2512.12576v3 Announce Type: replace-cross Abstract: While reinforcement learning has achieved impressive progress in language model reasoning, it is constrained by the requirement for verifiable rewards. Recent verifier-free RL methods address this limitation by utilizing the probabilities that LLMs generate reference answers as reward signals. However, these approaches typically sample reasoning traces conditioned only on the question. This design decouples reasoning-trace sampling from answer information, leading to inefficient exploration and incoherence between traces and final answers. In this paper, we propose \textit{\b{Co}upled \b{V}ariational \b{R}einforcement \b{L}earning} (CoVRL), which bridges variational inference and reinforcement learning by coupling prior and posterior distributions through a hybrid sampling strategy. By constructing and optimizing a composite distribution that integrates these two distributions, CoVRL enables efficient exploration while preserving strong thought-answer coherence. Extensive experiments on mathematical and general reasoning benchmarks show that CoVRL improves performance by 12.4\% over the base model and achieves an additional 2.3\% improvement over state-of-the-art verifier-free RL baselines, providing a principled framework for enhancing the general reasoning capabilities of language models.

Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses

✇cs.AI, q-bio.NC updates on arXiv.org
By: Xiao Li · Xiang Zheng · Yifeng Gao · Xinyu Xia · Yixu Wang · Xin Wang · Ye Sun · Yunhan Zhao · Ming Wen · Jiayu Li · Zixing Chen · Xun Gong · Yi Liu · Yige Li · Yutao Wu · Cong Wang · Jun Sun · Yixin Cao · Zhineng Chen · Jingjing Chen · Tao Gui · Qi Zhang · Zuxuan Wu · Xipeng Qiu · Xuanjing Huang · Tiehua Zhang · Zhipeng Wei · Kun Wang · Xinfeng Li · Hanxun Huang · Sarah Erfani · James Bailey · Jianping Wang · Chaowei Xiao · Ran He · Bo Li · Xingjun Ma · Yu-Gang Jiang
26 May 2026 at 12:00
arXiv:2605.02900v2 Announce Type: replace-cross Abstract: Embodied Artificial Intelligence (Embodied AI) integrates perception, cognition, planning, and interaction into agents that operate in open-world, safety-critical environments. As these systems gain autonomy and enter domains such as transportation, healthcare, and industrial or assistive robotics, ensuring their safety becomes both technically challenging and socially indispensable. Unlike digital AI systems, embodied agents must act under uncertain sensing, incomplete knowledge, and dynamic human-robot interactions, where failures can directly lead to physical harm. This survey provides a comprehensive and structured review of safety research in embodied AI, examining attacks and defenses across the full embodied pipeline, from perception and cognition to planning, action and interaction, and agentic system. We introduce a multi-level taxonomy that unifies fragmented lines of work and connects embodied-specific safety findings with broader advances in vision, language, and multimodal foundation models. Our review synthesizes insights from over 500 papers spanning adversarial, backdoor, jailbreak, and hardware-level attacks; attack detection, safe training and robust inference; and risk-aware human-agent interaction. This analysis reveals several overlooked challenges, including the fragility of multimodal perception fusion, the instability of planning under jailbreak attacks, and the trustworthiness of human-agent interaction in open-ended scenarios. By organizing the field into a coherent framework and identifying critical research gaps, this survey provides a roadmap for building embodied agents that are not only capable and autonomous but also safe, robust, and reliable in real-world deployment.

SURGE: Surrogate Gradient Adaptation in Binary Neural Networks

✇cs.AI, q-bio.NC updates on arXiv.org
By: Haoyu Huang · Boyu Liu · Linlin Yang · Yanjing Li · Yuguang Yang · Xuhui Liu · Canyu Chen · Zhongqian Fu · Baochang Zhang
26 May 2026 at 12:00
arXiv:2605.10989v3 Announce Type: replace-cross Abstract: The training of Binary Neural Networks (BNNs) is fundamentally based on gradient approximation for non-differentiable binarization operations (e.g., sign function). However, prevailing methods including the Straight-Through Estimator (STE) and its improved variants, rely on hand-crafted designs that suffer from gradient mismatch problem and information loss induced by fixed-range gradient clipping. To address this, we propose SURrogate GradiEnt Adaptation (SURGE), a novel learnable gradient compensation framework with theoretical grounding. SURGE mitigates gradient mismatch through auxiliary backpropagation. Specifically, we design a Dual-Path Gradient Compensator (DPGC) that constructs a parallel full-precision auxiliary branch for each binarized layer, decoupling gradient flow via output decomposition during backpropagation. DPGC enables bias-reduced gradient estimation by leveraging the full-precision branch to estimate components beyond STE's first-order approximation. To further enhance training stability, we introduce an Adaptive Gradient Scaler (AGS) based on an optimal scale factor to dynamically balance inter-branch gradient contributions via norm-based scaling. Experiments on image classification, object detection, and language understanding tasks demonstrate that SURGE performs best over state-of-the-art methods.

Simply Stabilizing the Loop via Fully Looped Transformer

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rao Fu · Zixuan Yang · Jiankun Zhang · Jing Ma · Hechang Chen · Yu Li · Yi Chang
26 May 2026 at 12:00
arXiv:2605.18797v2 Announce Type: replace-cross Abstract: Scaling model performance typically requires increasing model size. Looped Transformer offers a compelling alternative by iteratively reusing the same Transformer blocks, trading additional computation for improved performance without increasing parameter count or context length. Because the number of loop iterations can be adjusted at inference, it also provides a natural mechanism for balancing performance and test-time compute. However, Looped Transformer still suffers from training instability when the number of loop iterations increases. Our analysis reveals that this instability stems from two sources: gradient oscillation and residual explosion. To address these two problems, we propose the Fully Looped Transformer, which introduces two parameter-free modifications: (1) Fully Looped Architecture, which distributes inter-loop signals across all layers to mitigate residual explosion; (2) Attention Injection, which reuses the existing attention block to suppress gradient oscillation. These modifications stabilize training dynamics, enabling the Fully Looped Transformer to be trained stably up to 12 loop iterations, whereas other baseline looped models collapse in this regime. In milder settings where Looped Transformer does not collapse, Fully Looped Transformer still improves average downstream-task performance by up to 13.2\%. Overall, our experiments demonstrate that Fully Looped Transformer improves training stability, enhances downstream performance, and provides preliminary adaptability under different test-time compute budgets by varying loop iterations at inference.

Personalized neoadjuvant treatment regimen selection in locally advanced rectal cancer based on regimen-specific response modeling

✇npj Digital Medicine
By: Xiangyu Liu · Yuanling Tang · Song Zhang · Haiyang Bian · Hanlin Shu · Leen Liao · Xiaolin Pang · Qianting Lv · Jia Chen · Peirong Ding · Ping Liu · Yu Shen · Ziqiang Wang · Shouping Zhu · Jie Tian · Zhenyu Liu · Xin Wang
26 May 2026 at 08:00

npj Digital Medicine, Published online: 26 May 2026; doi:10.1038/s41746-026-02798-w

Personalized neoadjuvant treatment regimen selection in locally advanced rectal cancer based on regimen-specific response modeling

Human pancreatic progenitor organoids define genetic and epigenetic barriers to early PDAC transformation

✇(Multiomics OR Omics) AND (Pancreatic)
By: Xian Zhang · Wilfred Wong · Hyein S Cho · Dapeng Yang · Gary Dixon · Renhe Luo · Dingyu Liu · Denis Torre · Shigeaki Umeda · Federico González · Gokce Askan · Aslihan Yavas · Jeyaram Ravichandran Damodaran · Robert W Rickert · Rajya Kappagantula · Fong Cheng Pan · Victoria G Aveson · Adrien Grimont · William B Fall · Xianming Wang · Julian Pulecio · Samuel J Kaplan · Heng Pan · Xianlu Laura Peng · Jen Jen Yeh · Steven D Leach · Christine A Iacobuzio-Donahue · Rohit Chandwani · Christina S Leslie · Danwei Huangfu
20 May 2026 at 18:00

Dev Cell. 2026 May 19:S1534-5807(26)00159-0. doi: 10.1016/j.devcel.2026.04.012. Online ahead of print.

ABSTRACT

The lack of accurate human models that recapitulate pancreatic ductal adenocarcinoma (PDAC) initiation has hindered therapeutic development. Using pluripotent stem cell-derived pancreatic progenitor organoids, we established a human PDAC model that faithfully reproduces the genetic, epigenetic, and transcriptomic trajectories of tumor initiation and progression, validated against clinical datasets and tumor histopathology. We demonstrate that CDKN2A loss, which is nearly universal in patients but dispensable in mouse models, is essential for neoplastic transformation when combined with KRAS and TP53 mutations, whereas SMAD4 loss promotes tumor progression. Multi-omics profiling reveals epigenetic repression of the pancreatic lineage program during PDAC initiation, alongside AP-1-driven chromatin remodeling. We identify TET1 suppression as a mechanistic link between oncogenic ERK signaling and hypermethylation of essential pancreatic transcription factors. This model captures genetic and epigenetic determinants of human PDAC, reveals antagonism between oncogenic and lineage restriction programs, and supports TET-based lineage restoration as a potential early intervention strategy.

PMID:42161274 | PMC:PMC13196429 | DOI:10.1016/j.devcel.2026.04.012

Advancing solar and wind penetration in China through energy complementarity

✇Nature - Issue - nature.com science feeds
By: Yuan Hu · Hou Jiang · Chuan Zhang · Jianlong Yuan · Mengting Zhang · Ling Yao · Qiang Chen · Jichao Wu · Hualong Zhang · Subin Ma · Xiang Li · Weiyu Zhang · Quanhua Dong · Congcong Wen · Gege Yin · Fan Zhang · Chaohui Yu · Zhijun Jin · Yu Liu
20 May 2026 at 08:00

Nature, Published online: 20 May 2026; doi:10.1038/s41586-026-10570-z

Using high-resolution satellite imagery combined with a deep-learning-based framework to build a national energy inventory enables a data-driven assessment of solar–wind complementarity strategies to reduce power variability and enhance renewable energy penetration across China.

The neural mechanisms supporting the rise and fall of maternal aggression

✇Nature - Issue - nature.com science feeds
By: Takashi Yamaguchi · Rongzhen Yan · Mashrur Khan · Sota Kuno · Kanishk Tewatia · Takuya Osakada · Srinivas Parthasarathy · Michael E. Pacold · Nirao M. Shah · Dayu Lin
15 April 2026 at 08:00

Nature, Published online: 15 April 2026; doi:10.1038/s41586-026-10354-5

In mice, female aggression is governed by an amygdala–to–medial hypothalamus circuit that is strengthened during pregnancy and is dynamically amplified by oxytocin during lactation.

GPNMB Drives Brain Metastasis by Sculpting a Pathological Endothelial-Immune Interactome

✇Omics In Lung
By: Xuefei Liu · Jun Tan · Chun Wu · Guanyin Huang · Yixin Cheng · Jianyang Hu · Binyu Zhang · Mao Zhao · Boxi Zhao · Jingru Lian · Shuqian Zheng · Lin Zeng · Meng Xu · Yang Xu · Shan Zeng · Hao Yu · Hui Yang · Zhixiang Zuo · Chuanyu Liu · Weineng Feng · Weinan Guo · Chunying Li · Sai-Lan Liu · Qing Liu · Feiqiu Wen · Xin Hong
13 April 2026 at 18:00

Cancer Discov. 2026 Apr 15. doi: 10.1158/2159-8290.CD-25-1663. Online ahead of print.

ABSTRACT

Brain metastases (BM) remain a devastating disease with dismal prognosis. How circulating tumor cells (CTCs) penetrate the blood brain barrier (BBB) and reprogram the brain microenvironment remain unclear. Using spatially resolved multi-omic profiling of CTCs and brain metastases, integrated with experimental and clinical analyses, we identified Glycoprotein Non-Metastatic Melanoma Protein B (GPNMB) as a CTC-secreted driver of vascular disruption and brain colonization. CBX3 upregulation induced GPNMB expression, which bound endothelial EGFR, triggering CBL-mediated ubiquitination and degradation. Attenuated EGFR signaling suppressed FTO and disrupted endothelial junctions via YTHDF2-dependent TJP1 m6A methylation. Remarkably, GPNMB-induced BBB remodeling promoted immune infiltration via CXCL12-CXCR4 axis, and induced time course-dependent T cell exhaustion within the brain microenvironment. Clinically, elevated CBX3⁺GPNMB⁺ CTCs and plasma CXCL12 were significantly associated with BM progression in lung cancer and melanoma. Therapeutically, dual blockade of GPNMB and PD1 enhanced anti-BM efficacy in mice, unveiling GPNMB as a promising target for precision immunotherapy.

PMID:41973996 | DOI:10.1158/2159-8290.CD-25-1663

Pan-neurodegeneration proteomics reveals disease subtypes and molecular signatures

✇Cell
By: Him K. Shrestha · Huan Sun · Jay M. Yarbro · DongGeun Lee · Danting Liu · Erming Wang · Meghan McReynolds · Nan Zhang · Boer Xie · Shu Yang · Kaiwen Yu · Suresh Poudel · Yuxin Li · Zuo-Fei Yuan · Dehui Kong · Minghui Wang · Zhen Wang · Mingming Niu · Hong Wang · Masihuz Zaman · Ju Wang · David R. Vanderwall · Yu Sun · Zhiping Wu · Ping-Chung Chen · Bing Bai · Anthony A. High · Júlia Faura · Chunyu Liu · David A. Bennett · Erik C.B. Johnson · Nicholas T. Seyfried · Allan I. Levey · Vahram Haroutunian · Geidy E. Serrano · Thomas G. Beach · Michael DeTure · Takahisa Kanekiyo · Ronald C. Petersen · Guojun Bu · Pamela J. McLean · Dennis W. Dickson · Rosa Rademakers · Gang Yu · Xusheng Wang · Bin Zhang · Junmin Peng
23 March 2026 at 08:00
A pan-neurodegeneration atlas built from multilayer, deep proteomics of 2,279 brain samples across 6 major diseases integrates whole proteome, detergent-insoluble proteome, and posttranslational modifications to enable intra- and inter-disease comparisons to reveal disease-specific subtypes and dysregulated pathways, while identifying shared changes such as GPNMB upregulation and NPTX2 downregulation.
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