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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Zhang, Boyue Cui, Shaowei Zhang, Chaofan Hu, Jingyi Wang, Shuo School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665481090

In robot grasp and dexterous manipulation tasks, tactile sensing is important for the control adjustment of the manipulator. In this paper, we present a novel low-cost parallel gripper with high-resolution tactile sensing, named the GelStereo Gripper. Furthermore, an adaptive grasp strategy is proposed to endow the gripper with tactile-feedback grasp stability-maintaining ability. We install the gripper on our robot platform and conduct various grasp experiments by utilizing proposed control methods. Experimental results verify the reliability of the GelStereo gripper and also prove the effectiveness of the proposed strategy for experimental objects with different features. © 2022 IEEE.

关键词： Grippers

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Generalized Anthropomorphic Functional Grasping with Minimal Demonstrations

arXiv

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arXiv 2023年

作者： Wei, Wei Wang, Peng Wang, Sizhe Institute of Automation the School of Artificial Intelligence Chinese Academy of Sciences Beijing100190 China the CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science and Innovation Chinese Academy of Sciences 999077 Hong Kong

—This article investigates the challenge of achieving functional tool-use grasping with high-DoF anthropomorphic hands, with the aim of enabling anthropomorphic hands to perform tasks that require human-like manipulation and tool-use. However, accomplishing human-like grasping in real robots present many challenges, including obtaining diverse functional grasps for a wide variety of objects, handling generalization ability for kinematically diverse robot hands and precisely completing object shapes from a single-view perception. To tackle these challenges, we propose a six-step grasp synthesis algorithm based on fine-grained contact modeling that generates physically plausible and human-like functional grasps for category-level objects with minimal human demonstrations. With the contact-based optimization and learned dense shape correspondence, the proposed algorithm is adaptable to various objects in same category and a board range of robot hand models. To further demonstrate the robustness of the framework, over 10K functional grasps are synthesized to train our neural network, named DexFG-Net, which generates diverse sets of human-like functional grasps based on the reconstructed object model produced by a shape completion module. The proposed framework is extensively validated in simulation and on a real robot platform. Simulation experiments demonstrate that our method outperforms baseline methods by a large margin in terms of grasp functionality and success rate. Real robot experiments show that our method achieved an overall success rate of 79% and 68% for tool-use grasp on 3-D printed and real test objects, respectively, using a 5-Finger Schunk Hand. The experimental results indicate a step towards human-like grasping with anthropomorphic hands. Copyright © 2023, The Authors. All rights reserved.

关键词： Demonstrations

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Motor symptom machine rating system for complete MDS-UPDRS III in Parkinson’s disease:A proof-of-concept pilot study

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Chinese Medical Journal 2024年第13期137卷 1632-1634页

作者： Xue Zhu Zhonglue Chen Yun Ling Ningdi Luo Qianyi Yin Yichi Zhang Aonan Zhao Guanyu Ye Haiyan Zhou Jing Pan Liche Zhou Linghao Cao Pei Huang Pingchen Zhang Cheng Chen Weikun Shi Shinuan Lin Haimei Zhuang Jin Zhao Kang Ren Yuyan Tan Jun Liu Department of Neurology and Institute of Neurology Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghai 200025China GYENNO Science Co. Ltd.Nanshan DistrictShenzhenGuangdong 518100China HUST-GYENNO CNS Intelligent Digital Medicine Technology Center WuhanHubei 430074China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhanHubei 430074China CAS Center for Excellence in Brain Science and Intelligence Technology Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghai 200025China

To the Editor:Parkinson’s disease(PD)is one of the most common neurodegenerative movement disorders.[1]The severity of PD-related motor symptoms is usually semiquantitatively(“normal”,“slight”,“mild”,“moderate”,and“severe”)evaluated by expert physicians according to the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale Part III(MDS-UPDRS III).[2]However,the MDS-UPDRS III is semiquantitative and subjective,which might mask mild treatment effects or even provide false-positive results.

关键词： MDS proof severity

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A single-cell transcriptome atlas of the aging human and macaque retina

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National science Review 2021年第4期8卷 48-65页

作者： Wenyang Yi Yufeng Lu Suijuan Zhong Mei Zhang Le Sun Hao Dong Mengdi Wang Min Wei Haohuan Xie Hongqiang Qu Rongmei Peng Jing Hong Ziqin Yao Yunyun Tong Wei Wang Qiang Ma Zeyuan Liu Yuqian Ma Shouzhen Li Chonghai Yin Jianwei Liu Chao Ma Xiaoqun Wang Qian Wu Tian Xue Eye Center at The First Affiliated Hospital of USTC Hefei National Laboratory for Physical Sciences at the MicroscaleSchool of Life Sciences Division of Life Sciences and Medicine University of Science and Technology of China State Key Laboratory of Brain and Cognitive Science Institute of Brain-Intelligence TechnologyBioland Laboratory Institute of Biophysics Chinese Academy of Sciences University of Chinese Academy of Sciences State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University IDG/McGovern Institute for Brain Research Beijing Normal University Neurodegenerative Disorder Research Center CAS Key Laboratory of Brain Function and DiseaseUniversity of Science and Technology of China Department of Ophthalmology Beijing Key Laboratory of Restoration of Damaged Ocular Nerve Peking University Third Hospital Chinese Academy of Medical Sciences & Peking Union Medical College State Key Laboratory of Brain and Cognitive Science Institute of Brain-Intelligence Technology (Shanghai)Bioland Laboratory (Guangzhou) Institute of Biophysics Chinese Academy of Sciences Institute for Stem Cell and Regeneration Chinese Academy of Sciences Advanced Innovation Center for Human Brain Protection Beijing Institute for Brain DisordersCapital Medical University Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences National Resource Center for Non-Human Primates(Kunming) Primate Research Center at IBP(Beijing) Chinese Academy of Sciences

The human retina is a complex neural tissue that detects light and sends visual information to the ***, the molecular and cellular processes that underlie aging primate retina remain unclear. Here, we provide a comprehensive transcriptomic atlas based on 119 520 single cells of the foveal and peripheral retina of humans and macaques covering different ages. The molecular features of retinal cells differed between the two species, suggesting distinct regional and species specializations of the human and macaque retinae. In addition, human retinal aging occurred in a region-and cell-type-specific manner. Aging of human retina exhibited a foveal to peripheral gradient. MYO9A-rods and a horizontal cell subtype were greatly reduced in aging retina, indicating their vulnerability to aging. Moreover, we generated a dataset showing the cell-type-and region-specific gene expression associated with 55 types of human retinal disease, which provides a foundation to understanding of the molecular and cellular mechanisms underlying human retinal diseases. Such datasets are valuable to understanding of the molecular characteristics of primate retina, as well as molecular regulation of aging progression and related diseases.

关键词： single-cell RNA sequencing primate retina aging cell heterogeneity age-related disease cell-cell communication

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GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks

arXiv

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arXiv 2022年

作者： Yao, Xingting Li, Fanrong Mo, Zitao Cheng, Jian Institute of Automation Chinese Academy of Sciences China School of Future Technology University of Chinese Academy of Sciences China CAS Center for Excellence in Brain Science and Intelligence Technology China

Spiking Neural Networks (SNNs) have been studied over decades to incorporate their biological plausibility and leverage their promising energy efficiency. Throughout existing SNNs, the leaky integrate-and-fire (LIF) model is commonly adopted to formulate the spiking neuron and evolves into numerous variants with different biological features. However, most LIF-based neurons support only single biological feature in different neuronal behaviors, limiting their expressiveness and neuronal dynamic diversity. In this paper, we propose GLIF, a unified spiking neuron, to fuse different bio-features in different neuronal behaviors, enlarging the representation space of spiking neurons. In GLIF, gating factors, which are exploited to determine the proportion of the fused bio-features, are learnable during training. Combining all learnable membrane-related parameters, our method can make spiking neurons different and constantly changing, thus increasing the heterogeneity and adaptivity of spiking neurons. Extensive experiments on a variety of datasets demonstrate that our method obtains superior performance compared with other SNNs by simply changing their neuronal formulations to GLIF. In particular, we train a spiking ResNet-19 with GLIF and achieve 77.35% top-1 accuracy with six time steps on CIFAR-100, which has advanced the state-of-the-art. Codes are available at https://***/Ikarosy/Gated-LIF. Copyright © 2022, The Authors. All rights reserved.

关键词： Neural networks

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Synaptic degeneration in the prefrontal cortex of a rat AD model revealed by volume electron microscopy

引用

Journal of Molecular Cell Biology 2022年第3期14卷 61-64页

作者： Yi Jiang Linlin Li Keliang Pang Jiazheng Liu Bohao Chen Jingbin Yuan Lĳun Shen Xi Chen Bai Lu Hua Han National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeĳing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeĳing 101408China School of Pharmaceutical Sciences IDG/McGovern Institute for Brain ResearchTsinghua UniversityBeĳing 100084China Beĳing Advanced Innovation Center for Structural Biology School of Life SciencesTsinghua UniversityBeĳing 100084China School of Future Technology University of Chinese Academy of SciencesBeĳing 101408China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Alzheimer’s disease(AD),a neurodegenerative disease,is identified as the most common cause of dementia(Goedert and Spillantini,2006).Typical symptoms of AD in neuropathology are closely associated with changes in synapses and neurons(Serrano-Pozo et al.,2011).The prefrontal cortex(PFC)plays a crucial role in executive function,controlling the highest level of cognitive and emotional processes,and is also vulnerable to neurodegeneration in AD(Salat et al.,2001).While synaptic degeneration is believed to underlie the progressive decline of cognition in AD,specific changes in synaptic structures relevant to AD remain elusive due to a shortage of quantitative *** dysfunction,while key to AD pathophysiology,is difficult to monitor and study in human AD patients.

关键词： degeneration cortex Alzheimer

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An Intronic Variant of CHD7 Identified in Autism Patients Interferes with Neuronal Differentiation and Development

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Neuroscience Bulletin 2021年第8期37卷 1091-1106页

作者： Ran Zhang Hui He Bo Yuan Ziyan Wu Xiuzhen Wang Yasong Du Yuejun Chen Zilong Qiu Institute of Neuroscience State Key Laboratory of NeuroscienceKey Laboratory of Primate NeurobiologyCenter for Excellence in Brain Science and Intelligence TechnologyChinese Academy of SciencesShanghai200031China Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology Shanghai201210China University of the Chinese Academy of Sciences Beijing100049China Shanghai Mental Health Center School of MedicineShanghai Jiao Tong UniversityShanghai200030China

Genetic composition plays critical roles in the pathogenesis of autism spectrum disorder(ASD).Especially,inherited and de novo intronic variants are often seen in patients with ***,the biological significance of intronic variants is difficult to ***,among a Chinese ASD cohort,we identified a recurrent inherited intronic variant in the CHD7 gene,which is specifically enriched in East Asian ***7 has been implicated in numerous developmental disorders including CHARGE syndrome and *** investigate whether the ASD-associated CHD7 intronic variant affects neural development,we established human embryonic stem cells carrying this variant using CRISPR/cas9 methods and found that the level of CHD7 mRNA significantly decreased compared to *** differentiation towards the forebrain neuronal lineage,we found that neural cells carrying the CHD7 intronic variant exhibited developmental delay and maturity ***,we found that TBR1,a gene also implicated in ASD,was significantly increased in neurons carrying the CHD7 intronic variant,suggesting the intrinsic relevance among ASD ***,the morphological defects found in neurons carrying CHD7 intronic mutations were rescued by knocking down TBR1,indicating that TBR1 may be responsible for the defects in CHD7-related ***,the CHD7 intronic variant generated three abnormal forms of transcripts through alternative splicing,which all exhibited loss-of-function in functional *** study provides crucial evidence supporting the notion that the intronic variant of CHD7 is potentially an autism susceptibility site,shedding new light on identifying the functions of intronic variants in genetic studies of autism.

关键词： Autism CHD7 Intronic variant Inherited variant TBR1

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Few-Shot Knowledge Graph Completion based on Data Enhancement

Few-Shot Knowledge Graph Completion based on Data Enhancemen...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Zepeng Geng, Peilun Cao, Shuo Hu, Bin Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou730000 China Beijing Institute of Technology School of Medical Technology Beijing100081 China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chines Academy of Sciences Shanghai200000 China

ISBN: (纸本)9781665468190

Knowledge graphs (KGs) are widely used in various natural language processing applications. In order to expand the coverage of a KG, KG completion has attracted extensive attention. The commonly used embedding methods based on a large amount of training data can play an important role in this work. However, with few of triples, the performance of these methods will be greatly reduced. The completion of this kind of few-shot task is more challenging. In this work, we propose a method of data enhancement to increase the data quantity and solve the problem of sample shortage. Specifically, we first observe that the representation vectors of the relation in a KG are approximately subordinate to Gaussian distribution. Then we construct a Gaussian distribution for the relation of each triple in few-shot task according to the distributions of its similar relations in background graph. Further, we sample from the Gaussian distribution of each triple to expand the training data. Finally, we use an adaptive attentional network model FAAN proposed by Sheng et al. as the baseline model. Experimental results on two public datasets NELL-One and Wiki-One show that the proposed method achieves better performance. © 2022 IEEE.

关键词： Knowledge graph

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Geometrically Reconfigurable Silk-Based Electronic Implants 38

Geometrically Reconfigurable Silk-Based Electronic Implants

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38th IEEE International Conference on Micro Electro Mechanical Systems, MEMS 2025

作者： Ni, Siyuan Zhu, Ziyi Yan, Zhiwen Liang, Zhengyu Jiang, Jianbo Zou, Dujuan Yang, Huiran Zhou, Zhitao Sun, Liuyang Tao, Tiger H. Qian, Yun Wei, Xiaoling Liu, Keyin State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China School of Graduate Study University of Chinese Academy of Sciences Beijing China Shanghai Sixth People's Hospital Shanghai Jiao Tong University School of Medicine China 2020 X-Lab Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China ShanghaiTech University China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Jiangxi Nanchang China Guangdong Institute of Intelligence Science and Technology Zhuhai Guangdong Hengqin China Tianqiao and Chrissy Chen Institute for Translational Research Shanghai China

ISBN: (纸本)9798331508890

We report a geometrically reconfigurable silk-based electronic implant designed for peripheral nerve monitoring. Metallic conductive structures were fabricated on the surface of a geometrically reconfigurable membrane using Micro-Electro-Mechanical Systems (MEMS) technology. By controlling the cutting angles of the membrane, we achieved water-responsive self-wrapping spiral electrodes, which exhibit excellent geometric adaptability to nerve tissues. In vitro tests demonstrate that our electrodes possess good mechanical, biocompatibility, and electrical properties. In vivo applications confirmed that our electrodes can effectively record compound nerve action potentials (CNAP). Our approach offers a new method for developing implantable electronic devices that require mechanical and geometrical adaptability. © 2025 IEEE.

关键词： Silk

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Using Label-text Correlation and Deviation Punishment for Fine-grained Suicide Risk Detection in Social Media

Using Label-text Correlation and Deviation Punishment for Fi...

引用

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Li, Zepeng An, Zhengyi Cheng, Wenchuan Zhou, Jiawei Hu, Bin Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou730000 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Shanghai Institutes for Biological Sciences Chines Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai200000 China

ISBN: (纸本)9781665468190

Suicide causes serious harm to individuals, families and society, and becomes a social problem of widespread concern. Therefore, it is necessary to find and intervene individuals at risk of suicide as soon as possible. In recent years, social media data has successfully been leveraged for suicide risk detection. However, for fine-grained suicide risk detection, the existing models ignore the deviation between the predicted results and the real results when making wrong predictions, and do not pay attention to the semantic information contained in the labels. This paper proposes a deep learning model based on Label-Text Correlation and Deviation Punishment (LTC-DP). While learning the semantic relation adequately between the text and the corresponding label, the model can give different punishment adaptively according to the deviation degrees between the predicted results and the real result. The experimental results show that compared with the baseline model, the proposed model has better performance in fine-grained suicide risk detection. In addition, we release a fine-grained suicide risk detection data set based on Weibo, the data set is available at https://***/cxyazy/FGCSD-main. © 2022 IEEE.

关键词： Deep neural networks

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