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

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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Multi-Scale Invertible Neural Network for Wide-Range Variable-Rate Learned Image Compression

arXiv

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arXiv 2025年

作者： Tu, Hanyue Wu, Siqi Li, Li Zhou, Wengang Li, Houqiang National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application University of Science and Technology of China Hefei230027 China Department of Electrical Engineering University of Missouri ColumbiaMO65211 United States

Autoencoder-based structures have dominated recent learned image compression methods. However, the inherent information loss associated with autoencoders limits their rate-distortion performance at high bit rates and restricts their flexibility of rate adaptation. In this paper, we present a variable-rate image compression model based on invertible transform to overcome these limitations. Specifically, we design a lightweight multi-scale invertible neural network, which bijectively maps the input image into multi-scale latent representations. To improve the compression efficiency, a multi-scale spatial-channel context model with extended gain units is devised to estimate the entropy of the latent representation from high to low levels. Experimental results demonstrate that the proposed method achieves state-of-the-art performance compared to existing variable-rate methods, and remains competitive with recent multi-model approaches. Notably, our method is the first learned image compression solution that outperforms VVC across a very wide range of bit rates using a single model, especially at high bit rates. The source code is available at https://***/hytu99/MSINN-VRLIC. Copyright © 2025, The Authors. All rights reserved.

关键词： Image compression

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Semantic-Aware Late-Stage Supervised Contrastive Learning for Fine-Grained Action Recognition

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Pan, Yijun Zhao, Quan Zhang, Yueyi Wang, Zilei Sun, Xiaoyan Wu, Feng University of Science and Technology of China School of Artificial Intelligence and Data Science Hefei230026 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China University of Science and Technology of China National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application Hefei230026 China

Fine-grained action recognition typically faces challenges with lower inter-class variances and higher intra-class variances. Supervised contrastive learning is inherently suitable for this task, as it can decrease intra-class feature distances while increasing inter-class ones. However, directly applying it into fine-grained action recognition encounters two main problems. The first problem stems from the heavy training cost associated with supervised contrastive learning, which requires numerous training epochs, each involving double augmentation views per instance. To address this issue, we propose the late-stage supervised contrastive learning (late-SC) strategy, which effectively reduces the number of training epochs needed for the contrastive learning process. The second problem is that supervised contrastive loss does not explicitly consider the semantic distances between fine-grained actions when adjusting representation distances. This results in less reasonable and efficient adjustments to the representation space. To overcome this limitation, we introduce the semantic-aware temperature adaptation (STA) mechanism, enhancing the suitability of the supervised contrastive loss for fine-grained action recognition. We conduct experiments on several benchmark datasets for fine-grained action recognition, including Epic-Kitchens-55/100, SomethingSomething-V1, and Diving48-V2. The results demonstrate that our proposed method (referred to as LSC-STA) consistently enhances performance across various base feature extractors, without introducing additional inference overhead and incurring only a marginal increase in training expenses. © 1991-2012 IEEE.

关键词： Contrastive Learning

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Transmission dip based on coupling of bright mode and dark modes in a metasurface

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Physical Review B 2025年第4期111卷 045429-045429页

作者： Wei Huang Shi-Ting Cao Shi-Jun Liang Shan Yin Wentao Zhang Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin 541004 China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen 518055 China Institute of Brain-Inspired Intelligence National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China

In this paper, we propose a transmission dip in a terahertz (THz) metamaterial device by employing the coupling between bright mode and interference of dark modes. We provide the theoretical analysis, analytical calculations, and simulations to present the transmission spectrum of metamaterials. Furthermore, we observe the tendency of the transmission spectrum through experiments, which effectively verify our idea. Ideally, this transmission dip can produce a very high-Q peak in the middle of the transmission spectrum of electromagnetically induced transparency (EIT), which can be useful for highly sensitive metamaterial sensors, optical switches, and photomemory.

关键词： Light transmission

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Element-Arrangement Context Network for Facade Parsing

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Journal of Computer Science & technology 2022年第3期37卷 652-665页

作者： Yan Tao Yi-Teng Zhang Xue-Jin Chen National Engineering Laboratory for Brain-Inspired Intelligence Technology and Application University of Science and Technology of ChinaHefei 230026China

Facade parsing aims to decompose a building facade image into semantic regions of the facade *** each architectural element on a facade as a parameterized rectangle,we formulate the facade parsing task as object detection,allowing overlapping and nesting,which will support structural 3D modeling and editing for further *** contrast to general object detection,the spatial arrangement regularity and appearance similarity between the facade elements of the same category provide valuable context for accurate element *** this paper,we propose to exploit the spatial arrangement regularity and appearance similarity of facade elements in a detection *** element-arrangement context network(EACNet)consists of two unidirectional attention branches,one to capture the column-context and the other to capture row-context to aggregate element-specific features from multiple instances on the *** conduct extensive experiments on four public datasets(ECP,CMP,Graz50,and eTRIMS).The proposed EACNet achieves the highest mIoU(82.1%on ECP,77.35%on Graz50,and 82.3%on eTRIMS)compared with the state-of-the-art *** the quantitative and qualitative evaluation results demonstrate the effectiveness of our dual unidirectional attention branches to parse facade elements.

关键词： facade parsing element detection layout regularity spatial context

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Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Ma, Chenglong Li, Zilong Li, Yuanlin Han, Jing Zhang, Junping Zhang, Yi Liu, Jiannan Shan, Hongming Fudan University Institute of Science and Technology for Brain-inspired Intelligence Moe Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Shanghai200433 China Fudan University Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Shanghai200433 China Shanghai Ninth People's Hospital Department of Oral Maxillofacial Head and Neck Oncology China Shanghai Jiao Tong University School of Medicine College of Stomatology China Shanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology National Center for Stomatology National Clinical Research Center for Oral Diseases Shanghai200011 China Sichuan University School of Cyber Science and Engineering Chengdu Sichuan610065 China

Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel radiologist-in-the-loop self-training framework for MAR, termed RISE-MAR, which can integrate radiologists' feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo groundtruths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. © 2025 IEEE.

关键词： Computerized tomography

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Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction

arXiv

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arXiv 2025年

作者： Ma, Chenglong Li, Zilong Li, Yuanlin Han, Jing Zhang, Junping Zhang, Yi Liu, Jiannan Shan, Hongming Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Department of Oral Maxillofacial Head and Neck Oncology Shanghai Ninth People’ s Hospital Shanghai Jiao Tong University School of Medicine College of Stomatology Shanghai Jiao Tong University National Center for Stomatology National Clinical Research Center for Oral Diseases Shanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology Shanghai200011 China School of Cyber Science and Engineering Sichuan University Sichuan Chengdu610065 China

Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel radiologist-in-the-loop selftraining framework for MAR, termed RISE-MAR, which can integrate radiologists’ feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo ground-truths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. Code is available at https:// ***/Masaaki-75/rise-mar . © 2025, CC BY-NC-SA.

关键词： Computerized tomography

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NLRP3 inflammasome and gut microbiota–brain axis:A new perspective on white matter injury after intracerebral hemorrhage

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Neural Regeneration Research 2026年第1期21卷 62-80页

作者： Xiaoxi Cai Xinhong Cai Quanhua Xie Xueqi Xiao Tong Li Tian Zhou Haitao Sun Clinical Biobank Center Microbiome Medicine CenterDepartment of Laboratory MedicineThe Second School of Clinical MedicineZhujiang HospitalSouthern Medical UniversityGuangzhouGuangdong ProvinceChina Neurosurgery Center The National Key Clinical SpecialtyThe Engineering Technology Research Center of Education Ministry of China on Diagnosis and Treatment of Cerebrovascular DiseaseGuangdong Provincial Key Laboratory on Brain Function Repair and RegenerationThe Neurosurgery Institute of Guangdong ProvinceZhujiang HospitalSouthern Medical UniversityGuangzhouGuangdong ProvinceChina Key Laboratory of Mental Health of the Ministry of Education Guangdong–Hong Kong–Macao Greater Bay Area Center for Brain Science and Brain-Inspired IntelligenceSouthern Medical UniversityGuangzhouGuangdong ProvinceChina

Intracerebral hemorrhage is the most dangerous subtype of stroke,characterized by high mortality and morbidity rates,and frequently leads to significant secondary white matter *** recent decades,studies have revealed that gut microbiota can communicate bidirectionally with the brain through the gut microbiota–brain *** axis indicates that gut microbiota is closely related to the development and prognosis of intracerebral hemorrhage and its associated secondary white matter *** NACHT,LRR,and pyrin domain-containing protein 3(NLRP3)inflammasome plays a crucial role in this *** review summarizes the dysbiosis of gut microbiota following intracerebral hemorrhage and explores the mechanisms by which this imbalance may promote the activation of the NLRP3 *** mechanisms include metabolic pathways(involving short-chain fatty acids,lipopolysaccharides,lactic acid,bile acids,trimethylamine-N-oxide,and tryptophan),neural pathways(such as the vagus nerve and sympathetic nerve),and immune pathways(involving microglia and T cells).We then discuss the relationship between the activated NLRP3 inflammasome and secondary white matter injury after intracerebral *** activation of the NLRP3 inflammasome can exacerbate secondary white matter injury by disrupting the blood–brain barrier,inducing neuroinflammation,and interfering with nerve ***,we outline potential treatment strategies for intracerebral hemorrhage and its secondary white matter *** review highlights the critical role of the gut microbiota–brain axis and the NLRP3 inflammasome in white matter injury following intracerebral hemorrhage,paving the way for exploring potential therapeutic approaches.

关键词： gut microbiota gut microbiota–brain axis immune intracerebral hemorrhage neuroinflammation NLRP3 protein stroke therapeutics white matter injury

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Physical mechanisms and integration design of memristors

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Materials Today Nano 2025年 30卷

作者： Wang, Mengna Wang, Kun Sun, Bai Zhou, Guangdong Cao, Zelin Gao, Kaikai Ren, Fenggang Chen, Xiaoliang Li, Xiangming Shao, Jinyou Frontier Institute of Science and Technology and Interdisciplinary Research Center of Frontier Science and Technology Xi'an Jiaotong University Shaanxi Xi'an710049 China Micro-and Nano-technology Research Center State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China College of Artificial Intelligence Brain-inspired Computing & Intelligent Control of Chongqing Key Lab Southwest University Chongqing400715 China National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine The First Affiliated Hospital of Xi'an Jiaotong University Shaanxi Xi'an710049 China

Memristors with threshold transition behavior and rich dynamics are ideal candidates for simulating biological pulse neurons and constructing efficient neuromorphic systems. Based on changes in material structure, charge distribution, molecular polarization and other physical states, new integrated memory-computing devices have been constructed and applied to the sensor-memory-computing integrated systems. In this review, we first discuss the classification of memristive materials from the perspective of device structure design and focus on introducing the working mechanisms of valence variation, polarization, and carrier transfer covered internally. Further, the latest progress of memristors with multi-physical mechanisms is comprehensively summarized, including the formation/fracture of conductive filaments, carrier capture/release, polarization/depolarization. In particular, it is discussed in detail the principles and applications of memristors with different working mechanisms for constructing neuromorphic systems. Finally, the potential challenges and opportunities that may exist in the development of memristors in the coming years are discussed in depth. © 2025 Elsevier Ltd

关键词： Molecular structure

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Skin-inspired in-sensor encoding of strain vector using tunable quantum geometry

arXiv

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arXiv 2025年

作者： Liu, Zenglin Shi, Jingwen Cao, Jin Ma, Zecheng Yang, Zaizheng Cui, Yanwei Wang, Lizheng Dai, Yudi Chen, Moyu Wang, Pengfei Xie, Yongqin Chen, Fanqiang Shi, Youguo Xiao, Cong Yang, Shengyuan A. Cheng, Bin Liang, Shi-Jun Miao, Feng National Laboratory of Solid State Microstructures Institute of Brain-Inspired Intelligence School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Institute of Interdisciplinary Physical Sciences School of Science Nanjing University of Science and Technology Nanjing210094 China Institute of Applied Physics and Materials Engineering University of Macau 519000 China Institute of Physics Chinese Academy of Sciences Beijing100190 China

Human skin provides crucial tactile feedback, allowing us to skillfully perceive various objects by sensing and encoding complex deformations through multiple parameters in each tactile receptor. However, replicating this high-dimensional tactile perception with conventional materials' electronic properties remains a daunting challenge. Here, we present a skin-inspired method to encode strain vectors directly within a sensor. This is achieved by leveraging the strain-tunable quantum properties of electronic bands in the van der Waals topological semimetal Td -WTe2. We observe robust and independent responses from the second-order and third-order nonlinear Hall signals in Td -WTe2 when subjected to variations in both the magnitude and direction of strain. Through rigorous temperature-dependent measurements and scaling law analysis, we establish that these strain responses primarily stem from quantum geometry-related phenomena, including the Berry curvature and Berry-connection polarizability tensor. Furthermore, our study demonstrates that the strain-dependent nonlinear Hall signals can efficiently encode high-dimensional strain information using a single device. This capability enables accurate and comprehensive sensing of complex strain patterns in the embossed character "NJU". Our findings highlight the promising application of topological quantum materials in advancing next-generation, bio-inspired flexible electronics. © 2025, CC BY-NC-SA.

关键词： Van der Waals forces

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