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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Zhang, Yi-Kang Zhang, Heng Liu, Yong-Ge Yang, Qing Liu, Cheng-Lin National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences 95 Zhongguancun East Road Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Center for Excellence of Brain Science and Intelligence Technology Beijing China School of Computer & Information Engineering Anyang Normal University Henan China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Henan China

ISBN: (纸本)9781728128610

Oracle character is one kind of the earliest hieroglyphics, which can be dated back to Shang Dynasty in China. Oracle character recognition is important for modern archaeology, ancient text understanding, and historical chronology, etc. To overcome the limitation and class imbalance of training data in oracle character recognition, we propose a classification method based on deep metric learning. We use a convolutional neural network (CNN) to map the character images to an Euclidean space where the distance between different samples can measure their similarities such that classification can be performed by the Nearest Neighbor (NN) rule. Because new categories are still being discovered in reality, our model enables the rejection of unseen categories and the configuration of new categories. To accelerate NN classification, we also propose a prototype pruning method with little loss of accuracy. The proposed method exceeds the state of the art on the public dataset Oracle-20K and outperforms CNN with softmax layer on a new dataset Oracle-AYNU. © 2019 IEEE.

关键词： Character recognition

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Kinetic and Kinematic Sensors-free Approach for Estimation of Continuous Force and Gesture in sEMG Prosthetic Hands

arXiv

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arXiv 2024年

作者： Liu, Gang Wang, Zhenxiang Xi, Chuanmei He, Ziyang Guo, Shanshan Zhang, Rui Yao, Dezhong School of Electrical and information Engineering Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology Zhengzhou University Zhengzhou450001 China Engineering Department of International College Zhengzhou University 450001 China School of Cyber Science and Engineering Zhengzhou University Zhengzhou450000 China Shanghai Key Laboratory of Brain-Machine Intelligence for Information Behavior Shanghai International Studies University China Clinical Hospital of Chengdu Brain Science Institute MOE Key Laboratory for NeuroInformation University of Electronic Science and Technology of China Chengdu China Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu China

Regression-based sEMG prosthetic hands are widely used for their ability to provide continuous kinetic and kinematic parameters. However, establishing these models requires complex sensors systems to collect corresponding kinetic and kinematic data in synchronization with sEMG, which is cumbersome and user-unfriendly. This paper proposes a kinetic and kinematic sensors-free approach for controlling sEMG prosthetic hands, enabling continuous decoding and execution of three hand movements: individual finger flexion/extension, multiple finger flexion/extension, and fist opening/closing. This approach utilizes only two data points (-1 and 1), representing maximal finger flexion force label and extension force label respectively, and their corresponding sEMG data to establish a near-linear model based on sEMG data and labels. The model's output labels values are used to control the direction and magnitude of fingers forces, enabling the estimation of continuous gestures. To validate this approach, we conducted offline and online experiments using four models: Dendritic Net (DD), Linear Net (LN), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN). The offline analysis assessed each model's ability to classify finger force direction and interpolate intermediate force values, while online experiments evaluated real-time control performance in controlling gestures and accurately adjusting forces. Our results demonstrate that the DD and LN models provide excellent real-time control of finger forces and gestures, highlighting the practical potential of this sensors-free approach for prosthetic applications. This study significantly reduces the complexity of collecting kinetic and kinematic parameters in sEMG-based regression prosthetics, thus enhancing the usability and convenience of prosthetic hands. © 2024, CC BY-NC-SA.

关键词： Multilayer neural networks

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Controllability analysis of transcriptional regulatory networks for Saccharomyces cerevisiae 44

Controllability analysis of transcriptional regulatory netwo...

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44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018

作者： Liu, Suling Wang, Pei Xu, Qiong Chen, Aimin Lü, Jinhu School of Mathematics and Statistics Henan University Kaifeng China School of Mathematics and Statistics Institute of Applied Mathematics Laboratory of Data Analysis Technology Henan University Kaifeng China School of Mathematics and Statistics Institute of Applied Mathematics Henan University Kaifeng China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data Brain Machine Intelligence Beihang University Beijing China

ISBN: (纸本)9781509066841

Structural controllability of complex networks has been a research focus in recent years. However, few works considered the structural controllability of biological networks, especially for dynamic biological networks. In this paper, structural controllability of one static and five dynamic transcriptional regulatory networks (TRNs) for Saccharomyces cerevisiae (S. cerevisiae) have been investigated. The five dynamic networks included two endogenous and three exogenous ones. We clarified the controllability properties of these networks, as well as explored the differences among different types of networks. Our results revealed that the structural properties of different types of networks are significantly different. We found that the TRNs are rather difficult to be fully controlled. However, the dynamic TRNs were relatively easier to be controlled than the static one, and one needs relatively more external inputs to control the exogenous TRNs than the endogenous ones. Based on the structural controllability analysis of subnetworks with the same sizes, as well as randomly perturbed networks by preserving degree distributions, we further illustrated that the structural controllability of the six networks may be mainly dominated by their degree distributions. Our investigations clarify the control properties of TRNs in S. cerevisiae, it provides some insights on real-world control of biological networks. © 2018 IEEE.

关键词： Complex networks

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sEMG-Based Gesture Recognition in Multiple Scenarios Using a Phase Locked Value-Based Deep Learning Method

sEMG-Based Gesture Recognition in Multiple Scenarios Using a...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xueze Zhang Minjie Wang Zihua Chen Lu Wang Hongbo Wang Xiaoyang Kang MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Institude of Meta-Medical Academy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics Fudan University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Surface electromyography (sEMG) signals-based gesture recognition method is widely employed in human-computer interaction task. In this paper, we proposed a phase locked value (PLV)-based feature extraction method for sEMG-based gesture recognition. We conducted validation experiments on public datasets by integrating the previous proposed gesture recognition algorithms, STCN-GR and ConSSL. PLV-STCN-GR and PLV-ConSSL were utilized to test the performance of proposed feature extraction method in intra-session, inter-session and inter-subject scenarios. In general, the proposed PLV-based sEMG decoding framework has achieved good recognition results on public datasets and demonstrated the possibility of its application in practical scenarios.

关键词： Human computer interaction Deep learning Gesture recognition Feature extraction Electromyography Decoding Bioinformatics

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Co-localization of two-color rAAV2-retro confirms the dispersion characteristics of efferent projections of mitral cells in mouse accessory olfactory bulb

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Zoological Research 2020年第2期41卷 148-156页

作者： Ning Zheng Zhi-Zhong Wang Song-Wei Wang Fang-Jia Yang Xu-Tao Zhu Chen Lu Anne Manyande Xiao-Ping Rao Fu-Qiang Xu Center of Brain Science State Key Laboratory of Magnetic Resonance and Atomic and Molecular PhysicsNational Center for Magnetic Resonance in WuhanKey Laboratory of Magnetic Resonance in Biological SystemsWuhan Institute of Physics and MathematicsInnovation Academy for Precision Measurement Science and TechnologyChinese Academy of SciencesWuhanHubei 430071China University of the Chinese Academy of Sciences Beijing 100049China Department of Automation School of Electrical EngineeringZhengzhou UniversityZhengzhouHenan 450001China School of Life Science Wuhan UniversityWuhanHubei 430072China School of Human and Social Sciences University of West LondonMiddlesex TW89GAUK Divisions of Biomedical Photonics Wuhan National Laboratory for OptoelectronicsWuhanHubei 430074China

The accessory olfactory bulb(AOB), located at the posterior dorsal aspect of the main olfactory bulb(MOB), is the first brain relay of the accessory olfactory system(AOS), which can parallelly detect and process volatile and nonvolatile social chemosignals and mediate different sexual and social behaviors with the main olfactory system(MOS). However, due to its anatomical location and absence of specific markers, there is a lack of research on the internal and external neural circuits of the AOB. This issue was addressed by singlecolor labeling and fluorescent double labeling using retrograde rAAVs injected into the bed nucleus of the stria terminalis(BST), anterior cortical amygdalar area(ACo), medial amygdaloid nucleus(MeA), and posteromedial cortical amygdaloid area(PMCo) in mice. We demonstrated the effectiveness of this AOB projection neuron labeling method and showed that the mitral cells of the AOB exhibited efferent projection dispersion characteristics similar to those of the MOB. Moreover, there were significant differences in the number of neurons projected to different brain regions, which indicated that each mitral cell in the AOB could project to a different number of neurons in different cortices. These results provide a circuitry basis to help understand the mechanism by which pheromone information is encoded and decoded in the AOS.

关键词： Accessory olfactory bulb Efferent projections Retrograde rAAVs Projection neuron labeling Dispersion characteristics Circuitry basis

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Control of a nursing bed based on a hybrid brain-computer interface

Control of a nursing bed based on a hybrid brain-computer in...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Nengneng Peng Rui Zhang Haihua Zeng Fei Wang Kai Li Yuanqing Li Xiaobin Zhuang School of Automation Science and Engineering South China University of Technology Guangzhou Key Laboratory of Brain Computer Interaction and Applications Guangzhou China

ISBN: (纸本)9781457702198

In this paper, we propose an intelligent nursing bed system which is controlled by a hybrid brain-computer interface (BCI) involving steady-state visual evoked potential (SSVEP) and P300. Specifically, the hybrid BCI includes an asynchronous brain switch based on SSVEP and P300, and a P300-based BCI. The brain switch is used to turn on/off the control system of the electric nursing bed through idle/control state detection, whereas the P300-based BCI is for operating the nursing bed. At the beginning, the user may focus on one group of flashing buttons in the graphic user interface (GUI) of the brain switch, which can simultaneously evoke SSVEP and P300, to switch on the control system. Here, the combination of SSVEP and P300 is used for improving the performance of the brain switch. Next, the user can control the nursing bed using the P300-based BCI. The GUI of the P300-based BCI includes 10 flashing buttons, which correspond to 10 functional operations, namely, left-side up, left-side down, back up, back down, bedpan open, bedpan close, legs up, legs down, right-side up, and right-side down. For instance, he/she can focus on the flashing button “back up” in the GUI of the P300-based BCI to activate the corresponding control such that the nursing bed is adjusted up. Eight healthy subjects participated in our experiment, and obtained an average accuracy of 93.75% and an average false positive rate (FPR) of 0.15 event/min. The effectiveness of our system was thus demonstrated.

关键词： Medical services Switches Graphical user interfaces Electroencephalography Support vector machines Legged locomotion

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Establishing dorsal-ventral patterning in human neural tube organoids with synthetic organizers

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Cell Stem Cell 2025年

作者： Tao Luo Cong Liu Tao Cheng Guo-Qin Zhao Ying Huang Jing-Yun Luan Junyu Guo Xiang Liu Yi-Fan Wang Yang Dong Yu Xiao Enhui He Rui-Zhen Sun Xiuyu Chen Jiekai Chen Jun Ma Sean Megason Junfeng Ji Peng-Fei Xu Center of Stem Cell and Regenerative Medicine and Bone Marrow Transplantation Center of the First Affiliated Hospital Zhejiang University School of Medicine Hangzhou Zhejiang 310058 China Nanhu Brain-Computer Interface Institute Hangzhou Zhejiang 311100 China Center for Genetic Medicine the Fourth Affiliated Hospital of School of Medicine and International School of Medicine Zhejiang University Yiwu 322000 China Institute of Genetics Zhejiang University School of Medicine Hangzhou Zhejiang 310058 China Women's Hospital Zhejiang University School of Medicine Hangzhou Zhejiang 310006 China Precision Medicine Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore 117599 Singapore College of Computer Science and Technology Zhejiang University Hangzhou China The State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou China Department of Histology and Embryology Harbin Medical University Harbin Heilongjiang 150081 China Center for Biomedical Digital Science Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine Guangzhou Institutes of Biomedicine and Health Chinese Academy of Sciences Guangzhou 510530 China Department of Systems Biology Harvard Medical School 200 Longwood Avenue Boston MA 02115 USA

Precise dorsal-ventral (D-V) patterning of the neural tube (NT) is essential for the development and function of the central nervous system. However, existing models for studying NT D-V patterning and related human diseases remain inadequate. Here, we present organizers derived from pluripotent stem cell aggregate fusion (“ORDER”), a method that establishes opposing BMP and SHH gradients within neural ectodermal cell aggregates. Using this approach, we generated NT organoids with ordered D-V patterning from both zebrafish and human pluripotent stem cells (hPSCs). Single-cell transcriptomic analysis revealed that the synthetic human NT organoids (hNTOs) closely resemble the human embryonic spinal cord at Carnegie stage 12 (CS12) and exhibit greater similarity to human NT than to mouse models. Furthermore, using the hNTO model, we demonstrated the critical role of WNT signaling in regulating intermediate progenitors, modeled TCTN2-related D-V patterning defects, and identified a rescue strategy.

关键词： BMP SHH neural tube organoid pluripotent stem cells

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Non-Invasive brain-computer interfaces: a New Perspective on the Assessment and Classification of Individuals with Methamphetamine Addiction

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SN Comprehensive Clinical Medicine 2023年第1期5卷 1-12页

作者： Zhan, Gege Su, Haolong Wang, Pengchao Wang, Junkongshuai Jiang, Haifeng Zhang, Lihua Kang, Xiaoyang Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Academy for Engineering & Technology Fudan University Shanghai China Shanghai Mental Center Shanghai Jiao Tong University School of Medicine Shanghai China CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) Chinese Academy of Sciences Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Yiwu Research Institute of Fudan University Yiwu City China

Methamphetamine addiction is a brain disease that causes abnormalities in the structure and function of the brain. EEG, a common signal acquired based on the noninvasive brain-computer interface, can reflect the altered brain activity associated with methamphetamine addiction. EEG-based analysis methods provide a perspective to explore the neural mechanisms of methamphetamine addiction and the effects on brain activity. This paper reports the results of a review of EEG-based assessment and classification of methamphetamine addiction. Current methods commonly used in EEG-based methamphetamine addiction research include traditional resting-state EEG analysis, brain network analysis, and analysis of event-related potentials. A small number of studies have classified methamphetamine addiction and healthy individuals based on resting state EEG features or event-related potentials. EEG is one of the common tools used to examine the effects of methamphetamine on brain function. In follow-up studies, new methods for analyzing resting-state EEG and event-related potentials may help to investigate the neural mechanisms of methamphetamine addiction.

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A new tau dephosphorylation-targeting chimera for the treatment of tauopathies

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中国药理学报（英文版） 2024年第11期45卷 2267-2276页

作者： Jing-fen Su Yue Xiao Lin-yu Wei Hui-yang Lei Fei Sun Wei-xia Wang Shi-hong Li Xiao-chuan Wang Jie Zheng Jian-zhi Wang Department of Pathophysiology School of Basic MedicineKey Laboratory of Education Ministry of China/Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Department of Pathophysiology School of Basic MedicineKey Laboratory of Education Ministry of China/Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430030China Department of Pathophysiology School of Basic MedicineKey Laboratory of Education Ministry of China/Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Sino-UK Joint Laboratory of Brain Function and Injury of Henan Province Department of Physiology and PathophysiologySchool of Basic Medical SciencesXinxiang Medical UniversityXinxiang 453003China Department of Anesthesiology the First Affiliated Hospital of Gannan Medical UniversityGanzhou 341000China Neuroscience Research Institute and Department of Neurobiology School of Basic Medical SciencesPeking University Key Laboratory for Neuroscience Ministry of Education/National Health CommissionPeking UniversityBeijing 100083China Beijing Life Science Academy Beijing 102209China Department of Pathophysiology School of Basic MedicineKey Laboratory of Education Ministry of China/Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Co-innovation Center of Neuroregeneration Nantong UniversityNantong 226000China

Abnormal accumulation of hyperphosphorylated tau protein plays a pivotal role in a collection of neurodegenerative diseases named tauopathies,including Alzheimer's disease(AD).We have recently conceptualized the design of hetero-bifunctional chimeras for selectively promoting the proximity between tau and phosphatase,thus specifically facilitating tau dephosphorylation and ***,we sought to optimize the construction of tau dephosphorylating-targeting chimera(DEPTAC)and obtained a new chimera D14,which had high efficiency in reducing tau phosphorylation both in cell and tauopathy mouse models,while showing limited ***,D14 ameliorated neurodegeneration in primary cultured hippocampal neurons treated with toxic tau-K1 8 fragments,and improved cognitive functions of tauopathy *** results suggested D14 as a cost-effective drug candidate for the treatment of tauopathies.

关键词： Tau dephosphorylation targeting chimera(DEPTAC) tauopathy Alzheimer's disease drug development

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Metal coordination-driven assembly of stimulator of interferon genes-activating nanoparticles for tumor chemo-immunotherapy

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BMEMat(BioMedical Engineering Materials) 2024年第2期2卷 118-133页

作者： Guiqiang Zhang Ning Wang Yuan Ma Shumei Zhai To Ngai Shilei Ni Xinyi Jiang Jianwei Jiao Jiwei Cui Key Laboratory of Colloid and Interface Chemistry of the Ministry of Education School of Chemistry and Chemical EngineeringShandong UniversityJinanChina Medical Science and Technology Innovation Center Shandong First Medical University&Shandong Academy of Medical SciencesJinanChina Department of Neurosurgery Qilu Hospital and Institute of Brain and Brain-Inspired ScienceCheeloo College of MedicineShandong UniversityJinanChina Department of Chemistry The Chinese University of Hong KongHong KongChina Department of Pharmaceutics School of Pharmaceutical SciencesCheeloo College of MedicineShandong UniversityJinanChina

Activating the stimulator of interferon genes(STING)signaling pathway is critical for enhancing antitumor immunity and remodeling the immunosuppressive tumor microenvironment(TME).Herein,we report the preparation of STING-activating nanoparticles via metal coordination-driven assembly of a synthetic STING agonist(i.e.,SR717)and a chemotherapeutic drug(i.e.,curcumin).After intravenous administration,the assembled nanoparticles could efficiently accumulate in tumors to improve the bioavailability of SR717 and trigger potent STING pathway activation for effective immune ***,the released curcumin evokes immunogenic cell death in tumors and regulates amino acid metabolism by inhibiting the activation of indoleamine 2,3-dioxygenase 1,leading to the reversal of the immunosuppressive *** antitumor immunity induced by nanoparticles significantly inhibits the growth of primary,recurrent,and metastatic *** assembled nanoparticles are promising for the co-delivery of STING agonists and drugs in improved tumor chemo-immunotherapy.

关键词： amino acid metabolism immunotherapy nanoparticles self-assembly STING

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