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RIBOi: a database for ribosome-interacting proteins

生物化学与生物物理学报（英文版） 2019年第4期51卷 441-443页

作者： Jinguo Huang Likun Huang Kebo Cai Zhaowei Xu Sheng-Ce Tao Jing-Fang Wang Key Laboratory of Systems Biomedicine(Ministry of Education) Shanghai Center for Systems BiomedicineShanghai Jiao Tong University Shanghai 200240 China Key Laboratory of Genetics Breeding and Multiple Utilization of Crops Ministry of Education College of Life Sciences Fujian Agriculture and Forestry UniversityFuzhou 350002 China Fujian Key Laboratory of Crop Breeding by Design Fujian Agriculture and Forestry University Fuzhou 350002 China Department of Ophthalmology Xin Hua Hospital affiliated to Shanghai Jiao Tong University School of Medicine Shanghai 200092 China Key Laboratory of Systems Biomedicine(Ministry of Education) Shanghai Center for Systems BiomedicineShanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Oncogenes and Related Genes Shanghai 200240 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai 200240 China

Antibiotics have been widely applied as effective agents for curing infectious diseases caused by pathogenic ***,the emergence of antibiotic resistance shows a growing threat to the effectiveness of clinical applications of *** overcome this challenge,identifying new promising targets for drug development is now urgently *** inhibition of translation process is one of the most important mechanisms for many of the existing antibiotics.

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Dynamic Balance and Trajectory Tracking Control of Quadruped Robots Based on Virtual Model Control

Dynamic Balance and Trajectory Tracking Control of Quadruped...

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第三十九届中国控制会议

作者： Guangrong Chen Bowen Hou Sheng Guo Junzheng Wang Robotics Research Center Beijing Jiaotong University Beijing Engineering and Technology Research Center of Rail Transit Line Safety and Disaster Prevention State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology

As for a motion control framework of robots, virtual model control（VMC） can use virtual components to create virtual forces/torques. Actually, the virtual forces/torques are generated by joint actuators when the virtual components interact between robots and environments. In this paper, a virtual model control is proposed to do the dynamic balance control of quadruped robots in trot gait. In each leg, virtual model control includes swing phase control of robots and stance phase counterparts. In whole body, based on the forces/torques distribution method between two stance legs, virtual model control is mainly about the attitude control containing roll, pitch and yaw. Then, an intuitive approach of velocity control is employed for the locomotion of quadruped robots. Based on the velocity planning and control, a trajectory tracking control approach is investigated by considering four factors: terrain complexity index, curvature radius of given trajectory, distance to terminal, and maximum velocity of quadruped robots. Finally, the effectiveness of proposed controllers is validated by co-simulations.

关键词： Virtual model control Dynamic balance control Trajectory tracking control Quadruped robots

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A Guide for Human Walking Model and Control——Insights from Mechanical Property Analysis of Human Walking

A Guide for Human Walking Model and Control——Insights from...

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第32届中国控制与决策会议

This paper aims to establish the simplest human walking model and provide a guide for controlling biped ***,a human motion capture system was utilized to sample the motion data of human walking and a representative subject was ***,one of the simplest human walking model was set *** mechanical property analysis of human walking was done based on the established walking model via ***,the feasibility and effectiveness of proposed walking model were validated by numerical ***,the intrinsical relationships among human walking,biped walking model and control were discussed to provide a guide and insight for controlling biped robots.

关键词： Human Walking Model Biped Robots Leg Stiffness Legged Locomotion Model and Control

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Optimal reaction control for the flexible base redundant manipulator system

Optimal reaction control for the flexible base redundant man...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Zhang, Yaowen Liu, Yechao Xie, Zongwu Moallem, Mehrdad Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin Heilongjiang Province150006 China School of Mechatronic Systems Engineering Simon Fraser University VancouverBCV3T 0A3 Canada

ISBN: (纸本)9781728163215

The null space of the Jacobian for the redundant manipulators can be utilized to reach dual control goals and optimal effects. For the flexible base redundant manipulator system, the reaction force from the motion of the manipulator should be carefully handled while the end effector is tracking a desired trajectory. Different optimal goals are discussed, including the minimization of base deflection force/torque and base energy dissipation path. Considering the base reaction dynamic, the reaction force/torque is treated as the input of the base and the deflection energy is treated as the output, an integrated optimal goal is built and solved based on the LQR control to get an optimal feedback for the closed-loop dynamics. We discuss the different optimal goals and implement trough the redefined acceleration. We test the different control laws on the three- link manipulators which mounted on a three-DoF flexible base. Simulation results demonstrate that optimal goal in LQR form can be advantageous in terms of flexible base manipulator. © 2019 IEEE.

关键词： Redundant manipulators

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Author Correction: Super-resolution multicolor fluorescence microscopy enabled by an apochromatic super-oscillatory lens with extended depth-of-focus

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Nature communications 2024年第1期15卷 1510页

作者： Wenli Li Pei He Dangyuan Lei Yulong Fan Yangtao Du Bo Gao Zhiqin Chu Longqiu Li Kaipeng Liu Chengxu An Weizheng Yuan Yiting Yu Ningbo Institute of Northwestern Polytechnical University College of Mechanical Engineering Northwestern Polytechnical University Xi'an 710072 China. Key Laboratory of Micro/Nano Systems for Aerospace (Ministry of Education) Northwestern Polytechnical University Xi'an 710072 China. Shaanxi Province Key Laboratory of Micro and Nano Electro-Mechanical Systems Northwestern Polytechnical University Xi'an 710072 China. Department of Materials Science and Engineering City University of Hong Kong Hong Kong 999077 China. dangylei@cityu.edu.hk. Department of Materials Science and Engineering City University of Hong Kong Hong Kong 999077 China. The Institute of AI and Robotics Fudan University Shanghai 200433 China. Key Laboratory of Spectral Imaging Technology of Chinese Academy of Sciences Xi'an Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an 710119 China. Department of Electrical and Electronic Engineering Joint Appointment with School of Biomedical Sciences The University of Hong Kong Hong Kong 999077 China. State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin Heilongjiang 150001 China. Ningbo Institute of Northwestern Polytechnical University College of Mechanical Engineering Northwestern Polytechnical University Xi'an 710072 China. yyt@***. Key Laboratory of Micro/Nano Systems for Aerospace (Ministry of Education) Northwestern Polytechnical University Xi'an 710072 China. yyt@***. Shaanxi Province Key Laboratory of Micro and Nano Electro-Mechanical Systems Northwestern Polytechnical University Xi'an 710072 China. yyt@***.

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Programmable cell aggregation by a synthetic biosilicification approach

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iScience 2025年第6期28卷

作者： Wang, Qing Sun, Jing Zang, Lei Yao, Huaxiong Wang, Lin Zhu, Runtao Li, Jie Zeng, Simin Tang, Hongting Wang, Teng Liu, Ji Wang, Bo Li, Bo Liu, Zhiyuan Dai, Zhuojun State Key Laboratory of Quantitative Synthetic Biology Shenzhen Institute of Synthetic Biology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Chemistry and Chemical Engineering Harbin Institute of Technology Harbin 150001 China Center of Neural Engineering CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen 518055 China Department of Civil Engineering University of Nottingham Ningbo China 199 Taikang East Road Ningbo 315100 China

Programmable cell aggregation offers valuable insights into the natural development of synthetic multicellular systems and enables precise control over spatial organization and material structuring. Previous efforts have focused on modifying cells with designed organic-based adhesive modules and assembling cells into defined patterns. Here, we present a different approach to guide cell assembly by tuning the organic-inorganic interactions. Our method involves engineering cells to express a silicifying peptide on their surfaces, which promotes silica deposition on the cell walls. This peptide simultaneously binds to the silica synthesized on adjacent cells, triggering cell clustering. The engineered cells exhibit rapid aggregation, with approximately 95% of cells assembling within 15 min. We further show that this capability can facilitate materials assembly and chemical production. Our biosilicification-based approach offers novel insights into natural multicellularity mechanisms and holds potential for applications in biomanufacturing and materials engineering. © 2025 The Author(s)

关键词： Bioengineering Biomaterials Synthetic biology

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BTI Aging Monitoring based on SRAM Start-up Behavior

BTI Aging Monitoring based on SRAM Start-up Behavior

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Asian Test Symposium (ATS)

作者： Shengyu Duan Peng Wang Gaole Sai School of Computer Engineering and Science Shanghai University Shanghai China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

Bias Temperature Instability (BTI) is one of the dominant CMOS aging mechanisms. It causes time-dependent variation, threatening circuit lifetime reliability. BTI-induced circuit errors are not detectable at the fabrication stage. On-line monitoring schemes are therefore necessary to capture the degradations during the operational time. Traditional aging monitoring techniques exhibit high implementation complexity and low stability. In this paper, we propose a BTI monitoring approach by simply tracking the start-up behavior of SRAM cells. SRAM is a widely used on-chip device in many applications. We study the impact of BTI for SRAM start-up values and age some cells in a manipulated manner. The BTI degradation is evaluated based on the number of SRAM cells starting with a certain value. This technique can be used to estimate the degradation for on-chip logic circuits without introducing additional circuitry, and thus has very low implementation complexity. We use an SRAM array with 1024 cells to estimate the degradations for multiple logic circuits, and show the average mean absolute percentage error as 8.48%. In addition, this technique is robust considering process, voltage and temperature variations.

关键词： Degradation Monitoring Logic circuits Transistors SRAM cells Aging System-on-chip

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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Effect of visual feedback on behavioral control and functional activity during bilateral hand movement

Research Square

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Research Square 2020年

作者： Guo, Jing Li, Long Zheng, Yang Quratul, Ain Liu, Tian Wang, Jue The Key Laboratory of Biomedical Information Engineering of Ministry of Education Institute of Health and Rehabilitation Science School of Life Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China National Engineering Research Center for Healthcare Devices Guangdong Guangzhou510500 China The Key Laboratory of Neuro-informatics & Rehabilitation Engineering of Ministry of Civil Affairs Shaanxi Xi’an710049 China State Key Laboratory for Manufacturing Systems Engineering School of Mechanical Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China

Background: Previous researches state vision as vital source of information for movement control and more precisely for accurate hand movement. Further, fine bimanual motor activity may be associated to various oscillatory activities within distinct brain areas and inter-hemispheric interactions. However, neural coordination among the distinct brain areas responsible to enhance motor accuracy is still not adequate. Methods: In the current study we investigated task-dependent modulation by simultaneously measuring high time resolution electroencephalogram (EEG), electromyogram (EMG) and force along with bi-maual and uni-manual motor tasks. The errors were controlled using visual feedback. In order to complete the uni-manual tasks, subject was asked to grip the strain gauge using index finger and thumb of right hand thereby exerting force on connected visual feedback system. Whereas the bi-manual task involved finger abduction of left index finger in two contractions along with visual feedback system and at the same time the right hand gripped using definite force on two conditions that whether visual feedback existed or not for right hand. Results: Primarily, the existence of visual feedback for right hand significantly decreased brain network global and local efficiency in theta and alpha band when compared with the elimination of visual feedback. Conclusions: Brain network activity in theta and alpha band coordinate to facilitate fine hand movement. The findings may provide a new neurological insight on virtual reality auxiliary equipment and subjects with neurological disorders that cause movement errors requiring accuracy motor training. © 2020, CC BY.

关键词： Efficiency

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Medical Additive Manufacturing: From a Frontier Technology to the Research and Development of Products

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engineering 2020年第11期6卷 1217-1221页

作者： Guixing Qiu Wenjiang Ding Wei Tian Ling Qin Yu Zhao Lianmeng Zhang Jian Lu Daijie Chen Guangyi Yuan Chengtie Wu Bingheng Lu Ruxu Du Jimin Chen Mo Elbestawi Zhongwei Gu Dichen Li Wei Sun Yuanjin Zhao Jie He Dadi Jin Bin Liu Kai Zhang Jianmo Li Kam WLeong Dewei Zhao Dingjun Hao Yingfang Ao Xuliang Deng Huilin Yang ShaoKeh Hsu Yingqi Chen Long Li Jianping Fan Guohui Nie Yun Chen Hui Zeng Wei Chen Yuxiao Lai Department of Orthopedics Peking Union Medical College HospitalChinese Academy of Medical Science 8 Peking Union Medical CollegeBejjing 100730.China National Engineering Research Center of Light Alloys Net Forming&State Key Laboratony of Metal Matrix Composite Shanghai Jiao Tong UniversityShanghai 200240China Notional Engineering Research Center of Mg Materials and Applicarions and School of Materials Science 8r Engineering.Shanghai Jlao Tong University Shanghai 200240China Department of Spine Surgery Beijing Jishuitan HospitalBeijing 100035China Center for Troanslational Medicine Research and Development Institute of Biomedical and Health EngineeringShenzhen Insitute of Advanced Technology.Chinese Academy of SciencesShenzhen 518055China Guangdong Engineering Laboratony of Biomaterials Additive Manufacturing Guangdong 518055China Musculoskeletal Research Laboratory of the Department of Orthopedics 8 Traumatology and Innovative Orthopedic Biomaterial and Drug Translational Research Laboratory Li Ka Shing Institute of Health SciencesThe Chinese University of Hong KongHong Kong 999077.China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Wuhan University of TechnologyWuhan 430070China Center for Advanced Structural Materials City University of Hong Kong Shenzhen Research InstiuteShenzhen 518057.China Hong Kong Branch of National Precious Metals Material Engineering Research Center Department of Material Science and EngineeringCity Untiversity of Hong KongHong Kong 999077.China Department of Mechanical Engineering ity Untiversity of Hong Kong.Hong Kong 9977 China School of Pharmacy.Shanghai Jiao Tong University Shanghai 200240China China State Instute of Pharmaceutical Industry Shanghai Institute of Pharmaceutical IndustryShonghai 201203China Shanghai Innovation Instinute for Materials Shanghai 200444.China State Key Laboratory of High-Performance Ceramies and Superfine Microstructure Shanghai Institute of CeramicsChinese Acadermy 0f SciencesShanghai 200050Ch

*** and development(R&D)and the challenges of raw materials for medical additive manufacturing Raw materials for medical additive manufacturing have a wide range of commonalities that are also seen in many other fields,making them an important basis in the field of three-dimensional(3D)*** and challenges related to material types,powder properties,formability,viscoelasticity,and so forth also share common *** example,many metal materials are used in the field of aviation,while metals,polymers,and inorganic materials are used in the field of *** most widely used materials in biomedicine are *** homogeneous and non-homogeneous composites are also available for 3D printing,and impose an additional challenge in additive manufacturing;the use of heterogeneous composites in 3D printing is particularly challenging.

关键词： composites printing additive

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