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Alzheimer’s disease early diagnostic and staging biomarkers revealed by large-scale cerebrospinal fluid and serum proteomic profiling

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The Innovation 2024年第1期5卷 118-126页

作者： Qing-Qing Tao Xue Cai Yan-Yan Xue Weigang Ge Liang Yue Xiao-Yan Li Rong-Rong Lin Guo-Ping Peng Wenhao Jiang Sainan Li Kun-Mu Zheng Bin Jiang Jian-Ping Jia Tiannan Guo Zhi-Ying Wu Department of Neurology and Research Center of Neurology in the Second Affiliated Hospital and Key Laboratory of Medical Neurobiology of Zhejiang Province Zhejiang University School of MedicineHangzhou 310009China Liangzhu Laboratory Zhejiang UniversityHangzhou 311100China Westlake Laboratory of Life Sciences and Biomedicine Key Laboratory of Structural Biology of Zhejiang ProvinceSchool of Life SciencesWestlake UniversityHangzhou 310024China Institute of Basic Medical Sciences Westlake Institute for Advanced StudyHangzho 310024China Department of Neurology First Affiliated HospitalZhejiang University School of MedicineHangzhou 310009China Department of Neurology First Affiliated HospitalSchool of MedicineXiamen UniversityXiamen 361009China Innovation Center for Neurological Disorders and Department of Neurology Xuanwu HospitalCapital Medical UniversityNational Clinical Research Center for Geriatric DiseasesBeijing 100053China MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain MedicineZhejiang UniversityHangzhou 310058China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai 200031China

Amyloid-b,tau pathology,and biomarkers of neurodegeneration make up the core diagnostic biomarkers of Alzheimer disease(AD).However,these proteins represent only a fraction of the complex biological processes underlying AD,and individuals with other brain diseases in which AD pathology is a comorbidity also test positive for these diagnostic *** ADspecific early diagnostic and disease staging biomarkers are *** this study,we performed tandem mass tag proteomic analysis of paired cerebrospinal fluid(CSF)and serum samples in a discovery cohort comprising 98 *** biomarkers were validated by parallel reaction monitoring–based targeted proteomic assays in an independent multicenter cohort comprising 288 *** quantified 3,238 CSF and 1,702 serum proteins in the discovery cohort,identifying 171 and 860 CSF proteins and 37 and 323 serum proteins as potential early diagnostic and staging biomarkers,*** the validation cohort,58 and 21 CSF proteins,as well as 12 and 18 serum proteins,were verified as early diagnostic and staging biomarkers,*** 19-protein CSF and an 8-protein serum biomarker panels were built by machine learning to accurately classify mild cognitive impairment(MCI)due to AD from normal cognition with areas under the curve of 0.984 and 0.881,*** 19-protein CSF biomarker panel also effectively discriminated patients with MCI due to AD from patients with other neurodegenerative ***,we identified 21 CSF and 18 serum stage-associated proteins re-flecting AD *** findings provide a foundation for developing bloodbased tests for AD screening and staging in clinical practice.

关键词： cerebrospinal Alzheimer fluid

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High-Density Ultra-Flexible Neural Probe for Monitoring Electrophysiological Signals of Free-Moving Mice With Epilepsy 22

High-Density Ultra-Flexible Neural Probe for Monitoring Elec...

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22nd International Conference on Solid-State Sensors, Actuators and Microsystems, Transducers 2023

作者： Wang, Han Cheng, Qian Zhou, Cunkai Tian, Ye Xu, Chengjian Wei, Xiaoling Zhou, Zhitao Tao, Tiger H. Sun, Liuyan Shanghai Institute Of Microsystem And Information Technology Chinese Academy Of Sciences China University Of Chinese Academy Of Sciences China Shanghai University Of Electric Power China ShanghaiTech University China Center For Excellence In Brain Science And Intelligence Technology Chinese Academy Of Sciences China China Guangdong Institute Of Intelligence Science And Technology China Tianqiao And Chrissy Chen Institute For Translational Research China University Of Science And Technology Of China China

ISBN: (纸本)9784886864352

Our study presents a successful application of a high-density ultra-flexible neural probe for monitoring electrophysiological signals throughout all stages of epilepsy, including resting, pro-ictal, and seizure states, in free-moving mice. This novel probe provides not only wide brain region coverage but also high spatial resolution neural recordings, enabling us to observe the propagation of local fast ripples across multiple brain regions. Our findings demonstrate a powerful platform for studying epilepsy and have the potential to inform future clinical diagnosis. © 2023 IEEJ.

关键词： epilepsy multi-brain area monitoring Ultra-flexible neural probe

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Environmental Sensing and Motion Control for Underwater Robots

Environmental Sensing and Motion Control for Underwater Robo...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Feng Ding Rui Wang Yu Wang Shuo Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Science School of Automation Central South University University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Underwater robot is an underwater equipment with autonomous sensing ability, which can replace human in the high-risk underwater environment to complete tasks such as resource exploration, information collection, underwater fishing, etc. In this paper, we first review current research status of underwater sensing technologies from three aspects: tactile sensing, visual sensing and acoustic sensing. Then we provide an overview of motion control techniques for underwater robots in terms of both traditional motion control methods and reinforcement learning-based motion control methods. Finally, the problems in current researches are summarized and the future development directions are proposed.

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A machine learning-assisted multifunctional tactile sensor for smart prosthetics

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InfoMat 2023年第9期5卷 78-89页

作者： Yue Li Lin Yang Shihao Deng Hong Huang Yingyi Wang Zuoping Xiong Simin Feng Shuqi Wang Tie Li Ting Zhang i-Lab Nano-X Vacuum Interconnected WorkstationKey Laboratory of Multifunction Nanomaterials and Smart SystemsSuzhou Institute of Nano-Tech and Nano-Bionics(SINANO)Chinese Academy of Sciences(CAS)Suzhouthe People's Republic of China School of Materials Science and Engineering Nanjing University of Science and TechnologyNanjingthe People's Republic of China Department of Nano Science and Technology Institute University of Science and Technology of ChinaSuzhouthe People's Republic of China Department of Civil Engineering Xi'an Jiaotong-Liverpool UniversitySuzhouthe People's Republic of China Department of Health and Environmental Sciences Xi'an Jiaotong-Liverpool UniversitySuzhouthe People's Republic of China Jiangxi Institute of Nanotechnology Nanchangthe People's Republic of China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of ScienceShanghaithe People's Republic of China

The absence of tactile perception limits the dexterity of a prosthetic hand and its acceptance by *** the sensing properties of the skin using a flexible tactile sensor could have profound implications for prosthetics,whereas existing tactile sensors often have limited functionality with *** this study,we propose a machine-learning-assisted multifunctional tactile sensor for smart prosthetics,providing a human-like tactile sensing approach for *** flexible sensor is based on a poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS)-melamine sponge,which enables the detection of force and temperature with low cross-coupling owing to two separate sensing mechanisms:the open-circuit voltage of the sensor as a force-insensitive intrinsic variable to measure the absolute temperature and the resistance as a temperature-insensitive extrinsic variable to measure ***,by analyzing the unsteady heat conduction and characterizing it using real-time thermal imaging,we demonstrated that the process of open-circuit voltage variation resulting from the unsteady heat conduction is closely correlated with the heatconducting capabilities of materials,which can be utilized to discriminate between *** by the decision tree algorithm,the device is endowed with thermal conductivity sensing ability,which allows it to identify 10 types of substances with an accuracy of 94.7%.Furthermore,an individual wearing an advanced myoelectric prosthesis equipped with the above sensor can sense pressure,temperature,and recognize different *** demonstrated that our multifunctional tactile sensor provides a new strategy to help amputees feel force,temperature and identify the material of objects without the aid of vision.

关键词： machine learning material classification multifunctional tactile sensor smart prosthetics unsteady heat conduction

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Improving Stability and Performance of Spiking Neural Networks through Enhancing Temporal Consistency

arXiv

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arXiv 2023年

作者： Zhao, Dongcheng Shen, Guobin Dong, Yiting Li, Yang Zeng, Yi Brain-inspired Cognitive Intelligence Lab Institute of Automation Chinese Academy of Sciences China Center for Excellence in Brain Science and Intelligence Technology CAS China School of Future Technology University of Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China

Spiking neural networks have gained significant attention due to their brain-like information processing capabilities. The use of surrogate gradients has made it possible to train spiking neural networks with backpropagation, leading to impressive performance in various tasks. However, spiking neural networks trained with backpropagation typically approximate actual labels using the average output, often necessitating a larger simulation timestep to enhance the network’s performance. This delay constraint poses a challenge to the further advancement of SNNs. Current training algorithms tend to overlook the differences in output distribution at various timesteps. Particularly for neuromorphic datasets, inputs at different timesteps can cause inconsistencies in output distribution, leading to a significant deviation from the optimal direction when combining optimization directions from different moments. To tackle this issue, we have designed a method to enhance the temporal consistency of outputs at different timesteps. We have conducted experiments on static datasets such as CIFAR10, CIFAR100, and ImageNet. The results demonstrate that our algorithm can achieve comparable performance to other optimal SNN algorithms. Notably, our algorithm has achieved state-of-the-art performance on neuromorphic datasets DVS-CIFAR10 and N-Caltech101, and can achieve superior performance in the test phase with timestep T=1. Copyright © 2023, The Authors. All rights reserved.

关键词： Backpropagation

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MouseVenue3D:A Markerless Three-Dimension Behavioral Tracking System for Matching Two-Photon brain Imaging in Free-Moving Mice

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Neuroscience Bulletin 2022年第3期38卷 303-317页

作者： Yaning Han Kang Huang Ke Chen Hongli Pan Furong Ju Yueyue Long Gao Gao Runlong Wu Aimin Wang Liping Wang Pengfei Wei Shenzhen Key Laboratory of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science Guangdong Provincial Key Laboratory of Brain Connectome and BehaviorCAS Center for Excellence in Brain Science and Intelligence TechnologyBrain Cognition and Brain Disease InstituteShenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen-Hong Kong Institute of Brain ScienceShenzhen Fundamental Research InstitutionsShenzhen518055China University of Chinese Academy of Sciences Beijing100049China University of Rochester RochesterNY14627USA Honam University Gwangju62399South Korea State Key Laboratory of Membrane Biology Institute of Molecular MedicinePeking UniversityBeijing100101China Department of Electronics Peking UniversityBeijing100871China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking UniversityBeijing100101China

Understanding the connection between brain and behavior in animals requires precise monitoring of their behaviors in three-dimensional(3-D)***,there is no available three-dimensional behavior capture system that focuses on ***,we present MouseVenue3D,an automated and low-cost system for the efficient capture of 3-D skeleton trajectories in markerless *** improved the most time-consuming step in 3-D behavior capturing by developing an automatic calibration ***,we validated this process in behavior recognition tasks,and showed that 3-D behavioral data achieved higher accuracy than 2-D ***,MouseVenue3D was combined with fast high-resolution miniature two-photon microscopy for synchronous neural recording and behavioral tracking in the freely-moving ***,we successfully decoded spontaneous neuronal activity from the 3-D behavior of *** findings reveal that subtle,spontaneous behavior modules are strongly correlated with spontaneous neuronal activity patterns.

关键词： Computational neuroethology Behavioral and neural recording 3-D pose estimation Multi-view cameras Automatic calibration

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Mitophagy in neurodegenerative disease pathogenesis

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Neural Regeneration Research 2024年第5期19卷 998-1005页

作者： Kan Yang Yuqing Yan Anni Yu Ru Zhang Yuefang Zhang Zilong Qiu Zhengyi Li Qianlong Zhang Shihao Wu Fei Li Department of Developmental and Behavioural Pediatric&Child Primary Care Brain and Behavioural Research Unit of Shanghai Institute for Pediatric Research and MOE-Shanghai Key Laboratory for Children’s Environmental HealthXinhua HospitalShanghai Jiao Tong University School of MedicineShanghaiChina Center for Excellence in Brain Science and Intelligence Technology Institute of NeuroscienceState Key Laboratory of NeuroscienceCAS Key Laboratory of Primate NeurobiologyChinese Academy of SciencesShanghaiChina College of Materials and Chemical Engineering Hunan Institute of EngineeringXiangtanHunan ProvinceChina School of Medicine Yunnan UniversityKunmingYunnan ProvinceChina Songjiang Research Institute Shanghai Jiao Tong University School of MedicineShanghaiChina Neurosurgery Department Kunming Yenan HospitalKunmingYunnan ProvinceChina

Mitochondria are critical cellular energy resources and are central to the life of the *** selectively clears damaged or dysfunctional mitochondria through autophagic machinery to maintain mitochondrial quality control and *** neurons are postmitotic and consume substantial energy,thus require highly efficient mitophagy pathways to turn over damaged or dysfunctional *** evidence indicates that mitophagy is pivotal to the pathogenesis of neurological ***,more work is needed to study mitophagy pathway components as potential therapeutic *** this review,we briefly discuss the characteristics of nonselective autophagy and selective autophagy,including ERphagy,aggrephagy,and *** then introduce the mechanisms of Parkin-dependent and Parkin-independent mitophagy pathways under physiological ***,we summarize the diverse repertoire of mitochondrial membrane receptors and phospholipids that mediate ***,we review the critical role of mitophagy in the pathogenesis of neurodegenerative diseases including Alzheimer’s disease,Parkinson’s disease,and amyotrophic lateral ***,we discuss recent studies considering mitophagy as a potential therapeutic target for treating neurodegenerative ***,our review may provide novel views to better understand the roles of mitophagy in neurodegenerative disease pathogenesis.

关键词： Alzheimer’s disease amyotrophic lateral sclerosis autophagy mitochondria mitophagy mitophagy receptor Parkin Parkinson’s disease PINK1

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A Novel Two-Stream Model for Human Motor Characteristics Learning

A Novel Two-Stream Model for Human Motor Characteristics Lea...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xiang, Tian-Yu Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Yang, Hong-Jun Feng, Zhen-Qiu Hao, Jian-Long Hou, Zeng-Guang Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Institute of Systems Engineering Macau University of Science and Technology The Joint Laboratory of Intelligence Science and Technology Taipa China

ISBN: (纸本)9781665481090

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios. © 2022 IEEE.

关键词： Convolution

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META-IMITATION LEARNING BY WATCHING VIDEO DEMONSTRATIONS 10

META-IMITATION LEARNING BY WATCHING VIDEO DEMONSTRATIONS

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10th International Conference on Learning Representations, ICLR 2022

作者： Li, Jiayi Lu, Tao Cao, Xiaoge Cai, Yinghao Wang, Shuo School of Artificial Intelligence University of Chinese Academy of Sciences China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences China

Meta-Imitation Learning is a promising technique for the robot to learn a new task from observing one or a few human demonstrations. However, it usually requires a significant number of demonstrations both from humans and robots during the meta-training phase, which is a laborious and hard work for data collection, especially in recording the actions and specifying the correspondence between human and robot. In this work, we present an approach of meta-imitation learning by watching video demonstrations from humans. In comparison to prior works, our approach is able to translate human videos into practical robot demonstrations and train the meta-policy with adaptive loss based on the quality of the translated data. Our approach relies only on human videos and does not require robot demonstration, which facilitates data collection and is more in line with human imitation behavior. Experiments reveal that our method achieves the comparable performance to the baseline on fast learning a set of vision-based tasks through watching a single video demonstration. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Robots

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Density-Guided Semi-Supervised 3D Semantic Segmentation with Dual-Space Hardness Sampling

Density-Guided Semi-Supervised 3D Semantic Segmentation with...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Jianan Li Qiulei Dong School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Densely annotating the large-scale point clouds is laborious. To alleviate the annotation burden, contrastive learning has attracted increasing attention for tackling semi-supervised 3D semantic segmentation. However, existing point-to-point contrastive learning techniques in literature are generally sensitive to outliers, resulting in insufficient modeling of the point-wise representations. To address this problem, we propose a method named DDSemi for semi-supervised 3D semantic segmentation, where a density-guided contrastive learning technique is explored. This technique calculates the contrastive loss in a point-to-anchor manner by estimating an anchor for each class from the memory bank based on the finding that the cluster centers tend to be located in dense regions. In this technique, an inter-contrast loss is derived from the perturbed unlabeled point cloud pairs, while an intra-contrast loss is derived from a single unlabeled point cloud. The derived losses could enhance the discriminability of the features and implicitly constrain the semantic consistency between the perturbed unlabeled point cloud pairs. In addition, we propose a dual-space hardness sampling strategy to pay more attention to the hard samples located in sparse regions of both the geometric space and feature space by reweighting the point-wise intra-contrast loss. Experimental results on both indoor-scene and outdoor-scene datasets demonstrate that the proposed method outperforms the comparative state-of-the-art semi-supervised methods.

关键词： Point cloud compression Computer vision Three-dimensional displays Annotations Semantic segmentation Semantics Contrastive learning

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