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Toward understanding the role of genomic repeat elements in neurodegenerative diseases

Neural Regeneration Research 2025年第3期20卷 646-659页

作者： Zhengyu An Aidi Jiang Jingqi Chen Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghaiChina MOE Frontiers Center for Brain Science Fudan UniversityShanghaiChina MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan UniversityShanghaiChina Zhangjiang Fudan International Innovation Center ShanghaiChina

Neurodegenerative diseases cause great medical and economic burdens for both patients and society;however, the complex molecular mechanisms thereof are not yet well understood. With the development of high-coverage sequencing technology, researchers have started to notice that genomic repeat regions, previously neglected in search of disease culprits, are active contributors to multiple neurodegenerative diseases. In this review, we describe the association between repeat element variants and multiple degenerative diseases through genome-wide association studies and targeted sequencing. We discuss the identification of disease-relevant repeat element variants, further powered by the advancement of long-read sequencing technologies and their related tools, and summarize recent findings in the molecular mechanisms of repeat element variants in brain degeneration, such as those causing transcriptional silencing or RNA-mediated gain of toxic function. Furthermore, we describe how in silico predictions using innovative computational models, such as deep learning language models, could enhance and accelerate our understanding of the functional impact of repeat element variants. Finally, we discuss future directions to advance current findings for a better understanding of neurodegenerative diseases and the clinical applications of genomic repeat elements.

关键词： Alzheimer's disease ataxia deep learning long-read sequencing neurodegeneration neurodegenerative diseases Parkinson's disease repeat element structural variant

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ControlVideo: conditional control for one-shot text-driven video editing and beyond

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Science China(Information Sciences) 2025年第3期68卷 150-162页

作者： Min ZHAO Rongzhen WANG Fan BAO Chongxuan LI Jun ZHU Department of Computer Science and Technology Institute for AI Tsinghua-Bosch Joint ML CenterTsinghua Laboratory of Brain and Intelligence Lab Tsinghua University ShengShu Technology Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Pazhou Laboratory (Huangpu)

This paper presents ControlVideo for text-driven video editing — generating a video that aligns with a given text while preserving the structure of the source video. Building on a pre-trained text-to-image diffusion model, ControlVideo enhances the fidelity and temporal consistency by incorporating additional conditions(such as edge maps), and fine-tuning the key-frame and temporal attention on the source video-text pair via an in-depth exploration of the design space. Extensive experimental results demonstrate that ControlVideo outperforms various competitive baselines by delivering videos that exhibit high fidelity w.r.t. the source content, and temporal consistency, all while aligning with the text. By incorporating low-rank adaptation layers into the model before training, ControlVideo is further empowered to generate videos that align seamlessly with reference images. More importantly, ControlVideo can be readily extended to the more challenging task of long video editing(e.g., with hundreds of frames), where maintaining long-range temporal consistency is crucial. To achieve this, we propose to construct a fused ControlVideo by applying basic ControlVideo to overlapping short video segments and key frame videos and then merging them by pre-defined weight functions. Empirical results validate its capability to create videos across 140 frames, which is approximately 5.83 to 17.5 times more than what previous studies achieved. The code is available at https://***/thu-ml/controlvideo.

关键词： diffusion models controllable generation text-driven editing video editing long video editing

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BEVTemp: Enhancing Vision-Based Roadside 3D Object Detection with Temporal Information 21st

BEVTemp: Enhancing Vision-Based Roadside 3D Object Detection...

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21st Pacific Rim International Conference on Artificial intelligence, PRICAI 2024

作者： Miao, Gaoyuan Quan, Rong Pan, Cong Hu, Zhiheng Qin, Jie MoE Key Laboratory of Brain-Machine Intelligence Technology College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9789819601219

Autonomous driving systems relying solely on ego-vehicle sensors face critical safety challenges due to limited perceptual scope and restricted long-range sensing capabilities. Roadside cameras offer a complementary paradigm capable of mitigating blindspots and extending the perceptual horizon. However, existing roadside detection methods struggle to accurately detect small, distant objects, which are crucial for timely hazard anticipation. In this work, we introduce BEVTemp, a 3D object detector that leverages multi-frame information to construct temporal stereo pairs, enabling enhanced long-range perception. Our key insight is to exploit information about nearby objects from past frames to facilitate detecting small, distant targets in the present view. Furthermore, we introduce a dedicated small object enhancement module to encode multi-scale features and strengthen localization signals, explicitly enhancing the detection of smaller classes like bicycles and pedestrians. Through these innovations, our framework delivers robust and accurate 3D object detection across diverse scales and distances prevalent in roadside environments. On the DAIR-V2X-I and Rope3D datasets, BEVTemp achieves state-of-the-art performance, surpassing all previous methods by a significant 4% margin. Our work pioneers the use of multi-frame cues for roadside perception and presents a comprehensive solution for long-range and small object detection in this domain. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Roadsides

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TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification

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Machine intelligence Research 2025年第2期22卷 337-351页

作者： Yichao Yan Junjie Li Shengcai Liao Jie Qin MoE Key Lab of Artificial Intelligence AI InstituteShanghai Jiao Tong UniversityShanghai 200240China Inception Institute of Artificial Intelligence Abu Dhabi 999041UAE College of Artificial Intelligence Nanjing University of Aeronautics and Astronauticsthe Key Laboratory of Brain-Machine Intelligence TechnologyMinistry of EducationNanjing 211106China

Domain generalizable person re-identification(reid)is a challenging task in computer vision,which aims to apply a trained reid model to unseen *** works either combine the data in all the training domains to capture domain-invariant features,or adopt a mixture of experts to investigate domain-specific *** this work,we argue that both domain-specific and domain-invariant features are crucial for improving the generalization ability of reid *** this end,we design a novel framework,which we name two-stream adaptive learning(TAL),to simultaneously model these two kinds of ***,a domain-specific stream is proposed to capture the training domain statistics with batch normalization(BN)parameters,whereas an adaptive matching layer is designed to dynamically aggregate domain-level *** the meantime,we design an adaptive BN layer in the domain-invariant stream to approximate the statistic of unseen domains,such that our model is capable of handling various novel *** two streams work adaptively and collaboratively to learn generalizable reid *** validated by extensive experiments,our framework can be applied to both single-source and multi-source domain generalization tasks,where the results show that our framework notably outperforms the state-of-the-art methods.

关键词： Person re-identification domain generalization image retrieval representation learning computer vision.

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Attention Detection Using EEG Signals and Machine Learning: A Review

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Machine intelligence Research 2025年第2期22卷 219-238页

作者： Qianru Sun Yueying Zhou Peiliang Gong Daoqiang Zhang Key Laboratory of Brain-Machine Intelligence Technology Ministry of EducationNanjing University of Aeronautics and AstronauticsNanjing 211106China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China

Attention detection using electroencephalogram(EEG)signals has become a popular ***,there seems to be a notable gap in the literature regarding comprehensive and systematic reviews of machine learning methods for attention detection using EEG ***,this survey outlines recent advances in EEG-based attention detection within the past five years,with a primary focus on auditory attention detection(AAD)and attention level ***,we provide a brief overview of commonly used paradigms,preprocessing techniques,and artifact-handling methods,as well as listing accessible datasets used in these ***,we summarize the machine learning methods for classification in this field and divide them into two categories:traditional machine learning methods and deep learning *** also analyse the most frequently used methods and discuss the factors influencing each technique′s performance and ***,we discuss the existing challenges and future trends in this field.

关键词： Attention detection electroencephalogram(EEG) machine learning deep learning brain-computer interface.

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Role of the Thalamic Reticular Nucleus in Social Memory

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Neuroscience Bulletin 2025年第2期41卷 355-358页

作者： Xue Liu Jianing Zhu Junqiang Zheng Han Xu Department of Psychiatry of the Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou310058China Nanhu Brain-computer Interface Institute Hangzhou311100China College of Life Sciences Zhejiang UniversityHangzhou310058China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine IntegrationState Key Laboratory of Brain-machine IntelligenceZhejiang UniversityHangzhou311121China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang UniversityHangzhou310058China Lingang Laboratory Shanghai200031China

Social behaviors are crucial for gregarious animals,including *** order to exhibit appropriate behaviors in a complex social context,such as mating,aggression,avoidance,and cooperation,individuals need to remember their previous experiences with other members and accurately recognize them when they meet *** ability is called“social memory”[1].Many psychiatric disorders in humans,such as autism spectrum disorder,attention deficit hyperactivity disorder,and schizophrenia,are characterized by social memory *** with these disorders,along with corresponding animal models,often show defects associated with the thalamic reticular nucleus(TRN).The TRN,a thin layer of neurons surrounding the thalamus,mainly regulates and coordinates the transfer of information between the cortex and the thalamus,playing a role in higher brain functions such as consciousness,attention,and sensory ***,whether the TRN is involved in social memory remains unknown.

关键词： exhibit appropriate behaviors social behaviors mating social memory thalamic reticular nucleus gregarious animals autism spectrum disorderattention deficit hyperactivity disorderand psychiatric disorders

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Sex-Differential Neural Circuits and Behavioral Responses for Empathy

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Neuroscience Bulletin 2025年第1期41卷 192-194页

作者： Jingkai Fan Xinrong Wang Han Xu Department of Psychiatry of the Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou310058China Nanhu Brain-computer Interface Institute Hangzhou311100China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine IntegrationState Key Laboratory of Brain-machine IntelligenceZhejiang UniversityHangzhou311121China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang UniversityHangzhou310058China Lingang Laboratory Shanghai200031China

Empathy is one of the most important abilities for social *** a world that is largely socially constructed,when encountering altered affective states of others,particularly negative states,individuals often exhibit evolutionarily conserved empathic behaviors,such as mirroring,consolation,and helping[l,2].Appropriate empathic behaviors can foster the consolidation of social *** studies have suggested that both male and female rodents show some empathic behaviors when facing affective conspecifics[1].However,the role of sex in different empathic behaviors remains largely ***,there is a need to probe whether animals show sex-dependent empathic behavior and which neural circuits modulate these distinct behaviors.

关键词： mirror largely behavior

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Hedonic Coalition Formation Game and Contract-Based Federated Learning in AAV-Assisted Internet of Things

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IEEE Internet of Things Journal 2025年第9期12卷 11258-11272页

作者： Liu, Jia Li, Xiaoxu Xu, Yang Lyu, Chen Wang, Yichuan Liu, Xiaoying Xidian University School of Computer Science and Technology Xi’an710071 China Shanghai University of Finance and Economics School of Computing and Artificial Intelligence MoE Key Laboratory of Interdisciplinary Research of Computation and Economics Shanghai200433 China Xi’an University of Technology Shaanxi Key Laboratory of Network Computing and Security Technology Xi’an710071 China Zhejiang University of Technology School of Computer Science and Technology Hangzhou310023 China

Coupled with the rise of Deep Learning, the wealth of data and enhanced computation capabilities of Internet of Things (IoT) components enable effective artificial intelligence (AI)-based models to be built. Beyond ground data sources, autonomous aerial vehicles (AAVs)-based service providers for data collection and AI model training, i.e., Drones-as-a-Service (DaaS), have become increasingly popular in recent years. However, the stringent regulations governing data privacy potentially impede data sharing across independently owned AAVs. To this end, we propose in this article a federated learning (FL)-based architecture that enables privacy-preserving collaborative machine learning across a federation of independent DaaS providers for the development of IoT applications. Specifically, this work introduces a novel incentive mechanism based on the hedonic coalition formation game to enhance the sustainable efficiency and stability of the FL system. By establishing tailored operational rules and functions, the proposed mechanism enables IoT sensing nodes to autonomously form optimal coalitions with AAVs, thereby ensuring robust collaboration. To deal with incentive mismatches and information asymmetry, we leverage the contract theory and propose a self-disclosure mechanism that guarantees truthful reporting of AAV capabilities while optimizing the global model owner’s profits. The performance-based AAV type is also defined to offer a practical measure for heterogeneous AAVs and serve as the foundation for fair and effective contract design. Simulation results validate the superiority of the proposed approach, demonstrating significant improvements in utility optimization and system stability compared to existing benchmarks. © 2014 IEEE.

关键词： Federated learning

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IsoVISoR: Towards 3D Mesoscale brain Mapping of Large Mammals at Isotropic Sub-micron Resolution

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Neuroscience Bulletin 2025年第2期41卷 344-348页

作者： Chao-Yu Yang Yan Shen Xiaoyang Qi Lufeng Ding Yanyang Xiao Qingyuan Zhu Hao Wang Cheng Xu Pak-Ming Lau Pengcheng Zhou Fang Xu Guo-Qiang Bi Division of Life Sciences and Medicine CAS Key Laboratory of Brain Function and DiseaseUniversity of Science and Technology of ChinaHefei230026China Interdisciplinary Center for Brain Information Shenzhen-Hong Kong Institute of Brain ScienceShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China Center for Integrative Imaging Hefei National Research Center for Physical Sciences at the MicroscaleUniversity of Science and Technology of ChinaHefei230027China National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application MoE Key Laboratory of Brain-Inspired Intelligent Perception and CognitionSchool of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei230026China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial IntelligenceHefei Comprehensive National Science CenterHefei230088China Faculty of Life and Health Sciences Shenzhen University of Advanced TechnologyShenzhen518107China CAS Key Laboratory of Brain Connectome and Manipulation The Brain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen518055China

Dear Editor,The mammalian brain exhibits cross-scale complexity in neuronal morphology and connectivity,the study of which demands high-resolution morphological reconstruction of individual neurons across the entire brain[1-4].Current commonly used approaches for such mesoscale brain mapping include two main types of three-dimensional fluorescence microscopy:the block-face methods,and the lightsheet-based methods[5,6].In general,the high imaging speed and light efficiency of light-sheet microscopy make it a suitable tool for high-throughput volumetric imaging,especially when combined with tissue-clearing ***,large brain samples pose major challenges to this approach.

关键词： brain mapping three dimensional fluorescence microscopy mesoscale brain mapping lightsheet based methods block face methods high throughput volumetric imaging tissue clearing techniques

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Advancing crowd counting accuracy in diverse environments via comprehensive domain alignment strategies

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Multimedia Systems 2025年第3期31卷 1-15页

作者： Ye, Junjie Zhang, Wenxiao Yang, Xun School of Artificial Intelligence and Data Science University of Science and Technology of China JinZhai Road Baohe District Anhui Hefei230027 China MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China JinZhai Road Baohe District Anhui Hefei230027 China

We tackle the task of domain-adaptive crowd counting, which aims to improve the accuracy of crowd counting models when applied to different domains or environments. Existing methods often fail to adequately address the various differences between source and target domains, such as different crowd density, image style, and data imbalance during training. Typically, these methods only focus on one or two of these differences, resulting in less-than-optimal performance. Our approach introduces a comprehensive optimization strategy that fully considers the diverse differences between source and target domains. First, we introduce a sample weighting strategy to tackle the data imbalance between source and target domains, enabling the model to effectively utilize data from both domains for better alignment. Second, to account for the variation in crowd density, we employ a conditional domain adversarial network, which can better adapt the model to the different crowd density of the target domain. Additionally, we incorporate style contrastive learning to reduce the bias of the model to the source domain with changed image style. Together, these techniques ensure closer alignment with the target domain and enhance overall performance. Experimental evaluations on several public datasets show that our method significantly outperforms previous approaches. Code is available at https://***/yjj511/cdascc. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Contrastive Learning

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