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Coevolution of Myoelectric Hand Control under the Tactile Interaction among Fingers and Objects

Cyborg and Bionic Systems 2022年第1期2022卷 285-302页

作者： Yuki Kuroda Yusuke Yamanoi Shunta Togo Yinlai Jiang Hiroshi Yokoi Joint Doctoral Program for Sustainability Research Graduate School of Informatics and Engineering The University of Electro-CommunicationsTokyoJapan Department of Mechan ical and Intelligent System Engineering Graduate School of Informatics and Engineering The University of Electro Communications TokyoJapan Center for Neuroscience and Biomedical Engineering The University of Electro Communications TokyoJapan Bejing Innovation Center for Inelligent Robots and Systems BeijingChina

The usability of a prosthetic hand difers significantly from that of a real ***,the complexity of manipulation increases as the number of degrees of freedom to be controlled increases,making manipulation with biological signals extremely *** overcome this problem,users need to select a grasping posture that is adaptive to the object and a stable grasping method that prevents the object from *** previous studies,these have been left to the operating skills of the user,which is extremely difficult to *** this study,we demonstrate how stable and adaptive grasping can be achieved according to the object regardless of the user's operation *** required grasping technique is achieved by determining the correlation bet ween the motor output and each sensor through the interaction between the prosthetic hand and the surrounding stimuli,such as myoelectricity,sense of touch,and grasping *** agents of the 16 DOF robot hand were trained with the myoelectric signals of six participants,including one child with a congenital forearm ***,each agent could open and close the hand in response to the myoeletric stimuli and could accomplish the object pickup *** the tasks,the agents successfully identified grasping patterns suitable for practical and stable positioning of the *** addition,the agents were able to pick up the object in a similar posture regardless of the partiipant,suggesting that the hand was optimized by evolutionary computation to a posture that prevents the object from being dropped.

关键词： robot Objects overcome

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Enhanced Performance and Data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Enhancing wound healing and adhesion through dopamine-assisted gelatin-silica hybrid dressings

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International Journal of Biological Macromolecules 2024年第Part2期258卷 128845页

作者： Lin, Yu-Chien Wang, Huey-Yuan Tang, Yao-Chun Lin, Wan-Rong Tseng, Ching-Li Hu, Chih-Chien Chung, Ren-Jei Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei10608 Taiwan School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore Department of Stomatology MacKay Memorial Hospital Taipei104217 Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph. D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Research Center of Biomedical Device College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan International Ph. D. Program in Cell Therapy and Regenerative Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Bone and Joint Research Center Chang Gung Memorial Hospital Linkou33305 Taiwan Taipei10608 Taiwan

Gelatin, widely employed in hydrogel dressings, faces limitations when used in high fluid environments, hindering effective material adhesion to wound sites and subsequently reducing treatment efficacy. The rapid degradation of conventional hydrogels often results in breakdown before complete wound healing. Thus, there is a pressing need for the development of durable adhesive wound dressings. In this study, 3-glycidoxypropyltrimethoxysilane (GPTMS) was utilized as a coupling agent to create gelatin-silica hybrid (G-H) dressings through the sol-gel method. The coupling reaction established covalent bonds between gelatin and silica networks, enhancing structural stability. Dopamine (DP) was introduced to this hybrid (G-H-D) dressing to further boost adhesiveness. The efficacy of the dressings for wound management was assessed through in-vitro and in-vivo tests, along with ex-vivo bioadhesion testing on pig skin. Tensile bioadhesion tests demonstrated that the G-H-D material exhibited approximately 2.5 times greater adhesion to soft tissue in wet conditions compared to pure gelatin. Moreover, in-vitro and in-vivo wound healing experiments revealed a significant increase in wound healing rates. Consequently, this material shows promise as a viable option for use as a moist wound dressing. © 2023

关键词： Adhesion

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Noggin expression in human nucleus pulposus cells: A comparative study of degenerative and traumatic intervertebral discs

Advanced Orthopaedics

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Advanced Orthopaedics 2025年 1卷 55-62页

作者： Shuimu Chen Sebastian F. Bigdon Sonja Häckel Zhen Li Christoph E. Albers Benjamin Gantenbein Department of Orthopaedic Surgery & Traumatology Inselspital Bern University Hospital Faculty of Medicine University of Bern Bern CH-3010 Switzerland Tissue Engineering for Orthopaedics & Mechanobiology (TOM) Bone & Joint Program Department for BioMedical Research (DBMR) Faculty of Medicine University of Bern Bern CH-3008 Switzerland Graduate School for Cellular and Biomedical Sciences (GCB) University of Bern Bern CH-3012 Switzerland Graduate School for Health Sciences (GHS) University of Bern CH-3012 Bern Switzerland AO Research Institute Davos Davos CH-7270 Switzerland

Low back pain (LBP) is a health issue that leads to considerable disability and socioeconomic burdens. The degeneration of the human intervertebral disc (IVD) is a key factor in the pathogenesis of LBP. During disc degeneration, the nucleus pulposus (NP), a critical component of IVD, undergoes significant alterations in structural composition and function. Recent studies have highlighted the importance of various genes in IVD health. Noggin has been described as an essential gene in nucleus pulposus cells (NPCs) and a central role in inhibiting osteogenesis. In this study, we conducted a comparative analysis of Noggin expression in human NPCs from degenerative and healthy IVD. Twenty-six IVD samples (nineteen from degeneration and seven from trauma) were collected to separate NPCs for RNA extraction. Noggin expression was quantified at the transcript level by qPCR to analyze the difference between degenerative and traumatic IVD. Results indicated that Noggin expression was lower in NPCs from degenerated IVD compared to trauma cases. The severity of IVD degeneration correlated with decreased Noggin levels. Traumatic males exhibited higher Noggin expression than traumatic females and those with IVD degeneration. Noggin expression peaked in young adults (18–45 years), decreased in middle age (46–69 years), and rose again in the elderly (over 69 years), but remained lower than in the youth. Furthermore, upper lumbar discs (T12/L1-L2/3) showed higher Noggin levels than lower lumbar discs (L3/4/-L5/S1). This research elucidates valuable insights into the variability of Noggin concerning sex, age, degree of disc degeneration, and disc segment. It provides implications for IVD health and LBP treatment.

关键词： Low back pain Human intervertebral disc Nucleus pulposus cells Noggin

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ORMIR_XCT: A Python package for high resolution peripheral quantitative computed tomography image processing

arXiv

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arXiv 2023年

作者： Kuczynski, Michael T. Neeteson, Nathan J. Stok, Kathryn S. Burghardt, Andrew J. Espinosa Hernandez, Michelle A. Vicory, Jared Tse, Justin J. Durongbhan, Pholpat Bonaretti, Serena Wong, Andy Kin On Boyd, Steven K. Manske, Sarah L. McCaig Institute for Bone and Joint Health University of Calgary Calgary Canada Biomedical Engineering Graduate Program University of Calgary Calgary Canada Department of Biomedical Engineering The University of Melbourne Parkville Australia Department of Radiology and Biomedical Imaging University of California San Francisco United States Rehabilitation Sciences Institute The University of Toronto Toronto Canada Joint Department of Medical Imaging University Health Network Toronto Canada Kitware Inc. CarrboroNC United States Department of Radiology Cumming School of Medicine University of Calgary Calgary Canada Swiss Center for Musculoskeletal Imaging Balgrist Campus Zurich Switzerland Department of Epidemiology Dalla Lana School of Public Health University of Toronto Toronto Canada

High resolution peripheral quantitative computed tomography (HR-pQCT) is animaging technique capable of imaging trabecular bone in-vivo. HR-pQCT has awide range of applications, primarily focused on bone to improve ourunderstanding of musculoskeletal diseases, assess epidemiological associations,and evaluate the effects of pharmaceutical interventions. Processing HR-pQCTimages has largely been supported using the scanner manufacturer scriptinglanguage (Image Processing Language, IPL, Scanco Medical). However, byexpanding image processing workflows outside of the scanner manufacturersoftware environment, users have the flexibility to apply more advancedmathematical techniques and leverage modern software packages to improve imageprocessing. The ORMIR_XCT Python package was developed to reimplement someexisting IPL workflows and provide an open and reproducible package allowingfor the development of advanced HR-pQCT data processing workflows. © 2023, CC BY-SA.

关键词： High level languages

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Self-driven directional polypyrrole-polyethyleneimine nanopigment-infused medical textiles with potential asymmetrical ionic convection and biological effects for follicle restoration

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Journal of Polymer Research 2023年第11期30卷 403-403页

作者： Chuang, Sih-Chi Yu, Shih-An Hung, Pei-Chia Chiang, Chih-Wei Su, Wen-Ta Jheng, Pei-Ru Huang, Wei-Yung Hao, Ping-Chien Liang, Jia Wei Lin, Yung-Wei Fang, Hsu-Wei Chuang, Er-Yuan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei10608 Taiwan Hsuan Chen Technology Co. Ltd. New Taipei22055 Taiwan Bone and Joint Research Center Department of Orthopedics Taipei Medical University Hospital School of Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering Taipei Medical University Taipei11031 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei11031 Taiwan Taipei Medical University Taipei11031 Taiwan Department of Urology Wan Fang Hospital Taipei Medical University Taipei11696 Taiwan Cell Physiology and Molecular Image Research Center Taipei Medical University-Wan Fang Hospital Taipei11696 Taiwan International Ph.D. Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan

Hair loss is one of the most common and distressing conditions that can be traumatic for an individual. Many non-pharmacological and pharmacological treatments for hair loss have been developed. However, serious side effects or limitations in terms of the hair regeneration potential still exist. One of the non-pharmacological treatments, biophysical stimulation, has shown promise in hair regeneration. Nevertheless, the associated discomfort and the need for external components such as an electrical power supply pose challenges to its widespread use. In this study, we present an innovative cationic fabric that utilizes repeatable asymmetrical ionic flow of a physiological fluid to internally stimulate hair follicles and skin. Characterization, ultrasonic, biochemical, and histological experiments were conducted. The use of this developed treatment significantly increased expressions of heat shock proteins and potentially improved immunoregulation in the skin, as well as increased the number of hair follicles in skin defect animals, with no adverse risks. © 2023, The Polymer Society, Taipei.

关键词： Proteins

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Technology Roadmap for Flexible Sensors

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ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

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Differences in creep response of GBM cells migrating in confinement

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International Biomechanics 2020年第1期7卷 44-57页

作者： Khan, Ishan Bui, Loan Bachoo, Robert Kim, Young-Tae Chuong, Cheng-Jen University of Texas at Arlington and University of Texas Southwestern Medical Center at Dallas Joint Graduate Program in Biomedical Engineering United States Department of Neurology & Neurotherapeutics University of Texas Southwestern Medical Center at Dallas Dallas TX United States University of Texas at Arlington Department of Bioengineering TX United States

Using a microfluidic platform to apply negative aspiration pressure (–20, –25, –30, –35 and –40 cm H2O), we compared the differences in creep responses of Glioblastoma Multiforme (GBM) cells while migrating in co... 详细信息

Using a microfluidic platform to apply negative aspiration pressure (–20, –25, –30, –35 and –40 cm H₂O), we compared the differences in creep responses of Glioblastoma Multiforme (GBM) cells while migrating in confinement and at a stationary state on a 2D substrate. Cells were either migrating in a channel of 5 x 5 μm cross-section or stationary at the entrance to the channel. In response to aspiration pressure, we found actively migrating GBM cells exhibited a higher stiffness than stationary cells. Additionally, migrating cells absorbed more energy elastically with a relatively small dissipative energy loss. At elevated negative pressure loads up to–30 cm H₂O, we observed a linear increase in elastic deformation and a higher distribution in elastic storage than energy loss, and the response plateaued at further increasing negative pressure loads. To explore the underlying cause, we carried out immuno-cytochemical studies of these cells and found a polarized actin and myosin distribution at the front and posterior ends of the migrating cells, whereas the distribution of the stationary group demonstrated no specific regional differences. These differences in creep response and cytoskeletal protein distribution demonstrate the importance of a migrating cell’s kinematic state to the mechanism of cell migration. © 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： actomyosin contraction Cancer cell creep glioblastoma viscoelastic properties

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Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time

arXiv

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arXiv 2021年

作者： Zhu, Feng Sedler, Andrew R. Grier, Harrison A. Ahad, Nauman Davenport, Mark A. Kaufman, Matthew T. Giovannucci, Andrea Pandarinath, Chethan Neuroscience Graduate Program Emory University Center for Machine Learning Georgia Tech Computational Neuroscience Graduate Program The University of Chicago School of Electrical and Computer Engineering Georgia Tech Dept. of Organismal Biology and Anatomy The University of Chicago Neuroscience Institute The University of Chicago Joint Dept. of Biomedical Engineering UNC Chapel Hill NC State University UNC Chapel Hill Neuroscience Center Coulter Dept. of Biomedical Engineering Emory University Georgia Tech Dept. of Neurosurgery Emory University

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of latent dynamics from data in which the set of observed variables changes at each time step. The resulting models are able to infer activity for missing samples by combining observations with learned latent dynamics. We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data. In electrophysiology, SBTT enables accurate inference of neuronal population dynamics with lower interface bandwidths, providing an avenue to significant power savings for implanted neuroelectronic interfaces. In applications to two-photon calcium imaging, SBTT accurately uncovers high-frequency temporal structure underlying neural population activity, substantially outperforming the current state-of-the-art. Finally, we demonstrate that performance could be further improved by using limited, high-bandwidth sampling to pretrain dynamics models, and then using SBTT to adapt these models for sparsely-sampled data. © 2021, CC BY-NC-SA.

关键词： Neurons

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Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time 21

Deep inference of latent dynamics with spatio-temporal super...

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Feng Zhu Andrew R. Sedler Harrison A. Grier Nauman Ahad Mark A. Davenport Matthew T. Kaufman Andrea Giovannucci Chethan Pandarinath Neuroscience Graduate Program Emory University Center for Machine Learning Georgia Tech Computational Neuroscience Graduate Program The University of Chicago School of Electrical and Computer Engineering Georgia Tech Dept. of Organismal Biology and Anatomy The University of Chicago and Neuroscience Institute The University of Chicago Joint Dept. of Biomedical Engineering UNC Chapel Hill and NC State University and UNC Chapel Hill Neuroscience Center and NC State/UNC Closed-Loop Engineering for Advanced Rehabilitation (CLEAR) Coulter Dept. of Biomedical Engineering Emory University and Georgia Tech and Dept. of Neurosurgery Emory University

ISBN: (纸本)9781713845393

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