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Single-cell spatial transcriptomic atlas of the whole mouse brain

Neuron 2025年

作者： Han, Lei Liu, Zhen Jing, Zehua Liu, Yuxuan Peng, Yujie Chang, Huizhong Lei, Junjie Wang, Kexin Xu, Yuanfang Liu, Wei Wu, Zihan Li, Qian Shi, Xiaoxue Zheng, Mingyuan Wang, He Deng, Juan Zhong, Yanqing Pan, Hailin Lin, Junkai Zhang, Ruiyi Chen, Yu Wu, Jinhua Xu, Mingrui Ren, Biyu Cheng, Mengnan Yu, Qian Song, Xinxiang Lu, Yanbing Tang, Yuanchun Yuan, Nini Sun, Suhong An, Yingjie Ding, Wenqun Sun, Xing Wei, Yanrong Zhang, Shuzhen Dou, Yannong Zhao, Yun Han, Luyao Zhu, Qianhua Xu, Junfeng Wang, Shiwen Wang, Dan Bai, Yinqi Liang, Yikai Liu, Yuan Chen, Mengni Xie, Chun Bo, Binshi Li, Mei Zhang, Xinyan Ting, Wang Chen, Zhenhua Fang, Jiao Li, Shuting Jiang, Yujia Tan, Xing Zuo, Guolong Xie, Yue Li, Huanhuan Tao, Quyuan Li, Yan Liu, Jianfeng Liu, Yuyang Hao, Mingkun Wang, Jingjing Wen, Huiying Liu, Jiabing Yan, Yizhen Zhang, Hui Sheng, Yifan Yu, Shui Liao, Xiaoyan Jiang, Xuyin Wang, Guangling Liu, Huanlin Wang, Congcong Feng, Ning Liu, Xin Ma, Kailong Xu, Xiangjie Han, Tianyue Cao, Huateng Zheng, Huiwen Chen, Yadong Lu, Haorong Yu, Zixian Zhang, Jinsong Wang, Bo Wang, Zhifeng Xie, Qing Pan, Shanshan Liu, Chuanyu Xu, Chan Cui, Luman Li, Yuxiang Liu, Shiping Liao, Sha Chen, Ao Wu, Qing-Feng Wang, Jian Liu, Zhiyong Sun, Yidi Mulder, Jan Yang, Huanming Wang, Xiaofei Li, Chao Yao, Jianhua Xu, Xun Liu, Longqi Shen, Zhiming Wei, Wu Sun, Yan-Gang BGI Research Hangzhou 310030 China Lingang Laboratory Shanghai 200031 China CAS Key Laboratory of Computational Biology Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai 200031 China College of Life Sciences University of Chinese Academy of Sciences Beijing 100049 China Institute of Neuroscience State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology CAS Center for Excellence in Brain Science and Intelligence Technology University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai 200031 China Tencent AI Lab Shenzhen 518057 China BGI Research Shenzhen 518083 China Department of Anesthesiology Huashan Hospital State Key Laboratory of Medical Neurobiology Institute for Translational Brain Research MOE Frontiers Center for Brain Science Fudan University Shanghai 200032 China State Key Laboratory of Molecular Development Biology Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China BGI College & Henan Institute of Medical and Pharmaceutical Sciences Zhengzhou University Zhengzhou 450000 China School of Biology and Biological Engineering South China University of Technology Guangzhou 510006 China BGI Research Qingdao 266555 China China National GeneBank BGI Research Shenzhen 518120 China Shenzhen Proof-of-Concept Center of Digital Cytopathology BGI Research Shenzhen 518083 China Shanxi Medical University-BGI Collaborative Center for Future Medicine Shanxi Medical University Taiyuan 030001 China BGI Research Wuhan 430074 China BGI Research Chongqing 401329 China JFL-BGI STOmics Center Jinfeng Laboratory Chongqing 401329 China Department of Biology University of Copenhagen Copenhagen 2200 Denmark Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 201602 China Department of Protein Science Science for Life Laboratory KTH-Royal Institute of Technology Stockholm

A comprehensive atlas of genes, cell types, and their spatial distribution across a whole mammalian brain is fundamental for understanding the function of the brain. Here, using single-nucleus RNA sequencing (snRNA-seq) and Stereo-seq techniques, we generated a mouse brain atlas with spatial information for 308 cell clusters at single-cell resolution, involving over 4 million cells, as well as for 29,655 genes. We have identified cell clusters exhibiting preference for cortical subregions and explored their associations with brain-related diseases. Additionally, we pinpointed 155 genes with distinct regional expression patterns within the brainstem and unveiled 513 long non-coding RNAs showing region-enriched expression in the adult brain. Parcellation of brain regions based on spatial transcriptomic information revealed fine structure for several brain areas. Furthermore, we have uncovered 411 transcription factor regulons showing distinct spatiotemporal dynamics during neurodevelopment. Thus, we have constructed a single-cell-resolution spatial transcriptomic atlas of the mouse brain with genome-wide coverage. © 2025 Elsevier Inc.

关键词： development mouse brain non-coding gene parcellation Stereo-seq transcriptome

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Cross-Domain Animal Pose Estimation with Skeleton Anomaly-Aware Learning

引用

IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Han, Le Chen, Kaixuan Zhao, Lei Jiang, Yangbo Wang, Pengfei Zheng, Nenggan Zhejiang Hangzhou310007 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310007 China Zhejiang University State Key Laboratory of Blockchain and Data Security Zhejiang Hangzhou310007 China Institute of Blockchain and Data Security. China Zhejiang University School of Software Technology Ningbo China Zhejiang University State Key Lab of Brain-Machine Intelligence Hangzhou310007 China Zhejiang Provincial Government ZJU Collaborative Innovation Center for Artificial Intelligence by MOE Hangzhou310007 China Bengbu University School of Computer and Information Engineering Bengbu233030 China

Animal pose estimation is often constrained by the scarcity of annotations and the diversity of scenarios and species. The pseudo-label generation based unsupervised domain adaptation paradigm, which discriminates the predicted keypoints of unlabeled data based on the skeleton position consistency, has demonstrated effectiveness for such problems. However, existing methods generate pseudo-labels with massive false positives, because they cannot effectively distinguish sample pairs with the same errors. In this study, we propose a cross-domain animal pose estimation model from a novel perspective of skeleton anomaly learning. We construct a graph contrastive learning mechanism to acquire the skeleton anomaly-aware knowledge, which enables the generation of accurate pseudo-labels for target domain and imposes graph constraint on unlabeled data. And a skeleton anomaly-feedback based domain adaptation framework is designed to facilitate implicit alignment of object-specific features and joint training of cross-domain. Besides, we propose a novel rat pose dataset named UDARP-9.4K to address the gap of small-sized animal pose datasets encompassing diverse experimental scenarios. The related datasets are reviewed and evaluated in detail. Extensive experiments are conducted on UDARP-9.4K and two public datasets to demonstrate the superiority of the proposed model in cross-scenarios and cross-species animal pose estimation tasks. Further analysis reveals the effectiveness of the proposed model for skeleton structure feature learning. The UDARP-9.4K dataset is available here. © 1991-2012 IEEE.

关键词： Invertebrates

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Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network

arXiv

引用

arXiv 2022年

作者： Liu, Haofeng Li, Heng Fu, Huazhu Xiao, Ruoxiu Gao, Yunshu Hu, Yan Liu, Jiang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China IHPC A*STAR Singapore The School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Eye Research Institute Singapore National Eye Centre Singapore

As an economical and efficient fundus imaging modality, retinal fundus images have been widely adopted in clinical fundus examination. Unfortunately, fundus images often suffer from quality degradation caused by imaging interferences, leading to misdiagnosis. Despite impressive enhancement performances that state-of-the-art methods have achieved, challenges remain in clinical scenarios. For boosting the clinical deployment of fundus image enhancement, this paper proposes the pyramid constraint to develop a degradation-invariant enhancement network (PCE-Net), which mitigates the demand for clinical data and stably enhances unknown data. Firstly, high-quality images are randomly degraded to form sequences of low-quality ones sharing the same content (SeqLCs). Then individual low-quality images are decomposed to Laplacian pyramid features (LPF) as the multi-level input for the enhancement. Subsequently, a feature pyramid constraint (FPC) for the sequence is introduced to enforce the PCE-Net to learn a degradation-invariant model. Extensive experiments have been conducted under the evaluation metrics of enhancement and segmentation. The effectiveness of the PCE-Net was demonstrated in comparison with state-of-the-art methods and the ablation study. The source code of this study is publicly available at https://***/HeverLaw/PCENet-Image-Enhancement. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer

arXiv

引用

arXiv 2023年

作者： Yang, Peng Zhang, Laoming Liu, Haifeng Li, Guiying Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen518055 China Guangdong OPPO Mobile Telecommunications Corp. Ltd Shenzhen518052 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness is reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Weight Light, Hear Right: Heart Sound Classification with a Low-Complexity Model

Weight Light, Hear Right: Heart Sound Classification with a ...

引用

European Signal Processing Conference (EUSIPCO)

作者： Jiahao Ji Lixian Zhu Haojie Zhang Kun Qian Kele Xu Zikai Song Bin Hu Björn W. Schuller Yoshiharu Yamamoto Key Laboratory of Brain Health Intelligent Evaluation and Intervention (Beijing Institute of Technology) Ministry of Education Beijing China School of Medical Technology Beijing Institute of Technology Beijing China School of Computer Science National University of Defense Technology P. R. China GLAM - the Group on Language Audio & Music Imperial College London UK CHI - Chair of Health Informatics. MRI Technical University of Munich Germany Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Cardiovascular diseases (CVDs) remain a leading cause of mortality, necessitating early self-diagnosis for effective management. computer-aided CVD diagnosis via computerised auscultation has gained traction, advancing intelligent diagnostics. However, integrating complex neural networks into medical edge devices remains challenging. This paper introduces a novel lightweight model for heart sound classification based on broadcast residual learning, which can be seamlessly deployed in portable health monitoring devices for real-time cardiac auscultation. Comparative experiments validate the model's efficacy, achieving 89.1% accuracy and 89.7% Fl score with just 8.05 K parameters and 10.6 M MACC, showcasing superior performance within constrained complexity.

关键词： Heart Performance evaluation Microcontrollers Computational modeling Neural networks Europe Signal processing Real-time systems Monitoring Medical diagnostic imaging

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Dadm: Enhancing Eeg Artifact Removal by Fusing Artifact Representation into the Time-Frequency Domain

SSRN

引用

SSRN 2024年

作者： Li, Haoran Feng, Fan Kang, Jiarong Zhang, Jin Gong, Xiaoli Lu, Tingjuan Sun, Zhe Solé-Casals, Jordi College of Computer Science Nankai University Tianjin China Department of Biomedical Engineering School of Medicine Tsinghua University Beijing China Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China State Key Laboratory of High-end Server and Storage Technology China 903rd Hospital of PLA China Faculty of Medicine Faculty of Health Data Science Juntendo University Japan Data and Signal Processing Group University of Vic Central University of Catalonia Catalonia Vic08500 Spain

The electroencephalogram (EEG) provides essential data for analyzing brain activities. However, artifacts such as electrooculography (EOG) and electromyography (EMG) often interleave with the EEG signals, significantly affecting the quality of EEG signal analysis. The heterogeneous distribution of these artifacts in the time-frequency domain makes it challenging to remove multiple artifacts using a unified model. In this paper, we propose an Artifact-Aware EEG Denoising Model, referred to as DADM, to effectively remove various types of artifacts in a unified manner. We first obtain an artifact representation that indicates the type of artifact from a pre-trained artifact classification model. This artifact representation is then used as prior knowledge, which is fused into the denoising model in the frequency-time domain. This enables the model to become aware of the artifact type and precisely remove artifacts based on their type. Due to the heterogeneous distributions of artifacts in the frequency domain, we introduce a frequency enhancement module that can identify specific types of artifacts based on their representation and remove them using a hard attention mechanism. Additionally, we design a time-domain compensation module to enhance the denoising capability of DADM by compensating for potential losses of global information. Comprehensive experiments demonstrate that DADM significantly outperforms the Novel CNN in denoising EEG signals, showing a notable 7.77% improvement in correlation coefficient (CC) metrics. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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Niching diversity estimation for multi-modal multi-objective optimization

arXiv

引用

arXiv 2021年

作者： Peng, Yiming Ishibuchi, Hisao Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Niching is an important and widely used technique in evolutionary multi-objective optimization. Its applications mainly focus on maintaining diversity and avoiding early convergence to local optimum. Recently, a special class of multi-objective optimization problems, namely, multi-modal multi-objective optimization problems (MMOPs), started to receive increasing attention. In MMOPs, a solution in the objective space may have multiple inverse images in the decision space, which are termed as equivalent solutions. Since equivalent solutions are overlapping (i.e., occupying the same position) in the objective space, standard diversity estimators such as crowding distance are likely to select one of them and discard the others, which may cause diversity loss in the decision space. In this study, a general niching mechanism is proposed to make standard diversity estimators more efficient when handling MMOPs. In our experiments, we integrate our proposed niching diversity estimation method into SPEA2 and NSGA-II and evaluate their performance on several MMOPs. Experimental results show that the proposed niching mechanism notably enhances the performance of SPEA2 and NSGA-II on various MMOPs. Copyright © 2021, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Hypervolume-optimal μ-distributions on line/plane-based pareto fronts in three dimensions

arXiv

引用

arXiv 2021年

作者： Shang, Ke Ishibuchi, Hisao Chen, Weiyu Nan, Yang Liao, Weiduo Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Hypervolume is widely used in the evolutionary multi-objective optimization (EMO) field to evaluate the quality of a solution set. For a solution set with μ solutions on a Pareto front, a larger hypervolume means a better solution set. Investigating the distribution of the solution set with the largest hypervolume is an important topic in EMO, which is the so-called hypervolume optimal μ-distribution. Theoretical results have shown that the μ solutions are uniformly distributed on a linear Pareto front in two dimensions. However, the μ solutions are not always uniformly distributed on a single-line Pareto front in three dimensions. They are only uniform when the single-line Pareto front has one constant objective. In this paper, we further investigate the hypervolume optimal μ-distribution in three dimensions. We consider the line- and plane-based Pareto fronts. For the line-based Pareto fronts, we extend the single-line Pareto front to two-line and three-line Pareto fronts, where each line has one constant objective. For the plane-based Pareto fronts, the linear triangular and inverted triangular Pareto fronts are considered. First, we show that the μ solutions are not always uniformly distributed on the line-based Pareto fronts. The uniformity depends on how the lines are combined. Then, we show that a uniform solution set on the plane-based Pareto front is not always optimal for hypervolume maximization. It is locally optimal with respect to a (μ + 1) selection scheme. Our results can help researchers in the community to better understand and utilize the hypervolume indicator. © 2021, CC BY.

关键词： Multiobjective optimization

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Clustering-based subset selection in evolutionary multiobjective optimization

arXiv

引用

arXiv 2021年

作者： Chen, Weiyu Ishibuchi, Hisao Shang, Ke Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Subset selection is an important component in evolutionary multiobjective optimization (EMO) algorithms. Clustering, as a classic method to group similar data points together, has been used for subset selection in some EMO algorithms. However, clustering-based methods have not been evaluated in the context of subset selection from solution sets obtained by EMO algorithms. In this paper, we first review some classic clustering algorithms. We also point out that another popular subset selection method, i.e., IGD-based subset selection, can be viewed as clustering. Then, we perform a comprehensive experimental study to evaluate the performance of various clustering algorithms in different scenarios. Experimental results are analyzed in detail, and some suggestions about the use of clustering algorithms for subset selection are derived. Additionally, we demonstrate that decision maker's preference can be introduced to clustering-based subset selection. Copyright © 2021, The Authors. All rights reserved.

关键词： Clustering algorithms

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SNP2Vec: Scalable Self-Supervised Pre-Training for Genome-Wide Association Study

arXiv

引用

arXiv 2022年

作者： Cahyawijaya, Samuel Yu, Tiezheng Liu, Zihan Mak, Tiffany T.W. Zhou, Xiaopu Ip, Nancy Y. Fung, Pascale Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Division of Life Science State Key Laboratory of Molecular Neuroscience Molecular Neuroscience Center The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Hong Kong Center for Neurodegenerative Diseases Hong Kong Science Park Hong Kong Guangdong Provincial Key Laboratory of Brain Science Disease and Drug Development Shenzhen–Hong Kong Institute of Brain Science HKUST Shenzhen Research Institute Shenzhen China

Self-supervised pre-training methods have brought remarkable breakthroughs in the understanding of text, image, and speech. Recent developments in genomics has also adopted these pre-training methods for genome understanding. However, they focus only on understanding haploid sequences, which hinders their applicability towards understanding genetic variations, also known as single nucleotide polymorphisms (SNPs), which is crucial for genome-wide association study. In this paper, we introduce SNP2Vec, a scalable self-supervised pre-training approach for understanding SNP. We apply SNP2Vec to perform long-sequence genomics modeling, and we evaluate the effectiveness of our approach on predicting Alzheimer’s disease risk in a Chinese cohort. Our approach significantly outperforms existing polygenic risk score methods and all other baselines, including the model that is trained entirely with haploid sequences. We release our code and dataset on https://***/HLTCHKUST/ snp2vec. © 2022, CC BY-SA.

关键词： Genes

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