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Optofluidic Systems: Miniaturized, Battery-Free Optofluidic Systems with Potential for Wireless Pharmacology and Optogenetics (Small 4/2018)

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Small 2018年第4期14卷

作者： Kyung Nim Noh Sung Il Park Raza Qazi Zhanan Zou Aaron D. Mickle Jose G. Grajales-Reyes Kyung-In Jang Robert W. Gereau IV Jianliang Xiao John A. Rogers Jae-Woong Jeong Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL 61801 USA Department of Electrical and Computer Engineering Texas A&M University College Station TX 77843 USA Center for Remote Health Science Technologies Texas A&M University College Station TX 77843 USA Department of Electrical Computer and Energy Engineering University of Colorado Boulder Boulder CO 80309 USA Department of Mechanical Engineering University of Colorado Boulder Boulder CO 80309 USA Department of Anesthesiology Washington University Pain Center Washington University School of Medicine St. Louis MO 63130 USA Department of Robotics Engineering Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu 42988 Republic of Korea Department of Neuroscience Washington University Pain Center Washington University School of Medicine St. Louis MO 63130 USA Department of Materials Science and Engineering Biomedical Engineering Neurological Surgery Chemistry Mechanical Engineering Electrical and Computer Science Northwestern University Evanston IL 60208 USA Center for Bio-Integrated Electronics Simpson Querrey Institute for BioNanotechnology Northwestern University Evanston IL 60208 USA School of Electrical Engineering Korea Advanced Institute of Science and Technology (KAIST) Daejeon 34141 Republic of Korea

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Nonlinear metric learning for visual tracking

Nonlinear metric learning for visual tracking

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jiwen Lu Junlin Hu Yap-Peng Tan Beijing Advanced Innovation Center for Imaging Technology Beijing China Department of Automation Tsinghua University Beijing China School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (纸本)9781467372596

We propose a nonlinear metric learning (NML) method for visual tracking. Instead of utilizing the hand-crafted similarity measures, the NML tracker can automatically learn distance metrics from training data itself to categorize object and backgrounds in visual tracking. To exploit the nonlinear structures of samples, the NML tracker seeks several hierarchical nonlinear transformations by adopting the neural network architectures to map candidates and template into a latent subspace where the distance of each positive pair is smaller than that of each negative pair. In this learned metric space, the candidate that maintains the minimum distance to the template is treated as the final tracking result. Evaluation on 20 challenging videos shows the efficacy of the NML tracker.

关键词： Visualization Target tracking Videos Training Training data

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A MICROFLUIDIC SYSTEM FOR ASSESSING FRYING OIL QUALITY

A MICROFLUIDIC SYSTEM FOR ASSESSING FRYING OIL QUALITY

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Mei Liu Shaorong Xie Ji Ge Zhensong Xu Zhizheng Wu Changhai Ru Yu Sun Shanghai Key Laboratory of Intelligent Manufacturing and Robotics School of Mechatronic Engineering and Automation Shanghai University 200072 China Department of Mechanical and Industrial Engineering University of Toronto Toronto ON M5S 3G8 Canada Jiangsu Provincial Key Laboratory of Advanced Robotics and Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University Suzhou 215123 China

ISBN: (纸本)9781509006090

This paper reports a microfluidic system for assessing frying oil degradation. The hydrodynamic focusing microfluidic device developed for this purpose generates water-in-oil droplets. By correlating droplet size and the widely accepted oil quality metric, the total polar compound (TPM), it was proven that the device is capable of evaluating frying oil degradation. Compared to existing bulky instruments and methods, this device and measurement technique are fast, simple, and cost-effective.

关键词： SYSTEM ASSESSING FRYING oils Frying oils oil degradation

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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An algorithm for 3D reconstruction based on point-cloud image sequence

An algorithm for 3D reconstruction based on point-cloud imag...

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2015 IEEE advanced Information technology,Electronic and automation Control Conference(IAEAC 2015)

作者： Xusheng Shi Bin Zhao Advanced Manufacturing Technology and Systems Research Center in Department of Industrial and Manufacturing Systems Engineering of Beihang University School of Automation Science and Electrical Engineering of Beihang University

ISBN: (纸本)9781479919819

A method named space carving was proposed which is for the calculation of object's surface point-cloud based on its *** process of the algorithm based on the space carving theory was developed by C++ and the point-cloud was got for reconstructing object's 3D model in the ***,the method of this paper was proved,it have not any limit with the size of the object,and only one digital camera and rotating stage is needed in the whole process,so compare with the traditional modeling system,it greatly reduces the cost of the system.

关键词： 3D reconstruction space carving object's pointcloud model

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Surgical Robot Control Based on Torque Control Method

Surgical Robot Control Based on Torque Control Method

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2015 5th International Conference on Information Engineering for Mechanics and Materials(ICIMM 2015)

作者： Bin Zhao Xusheng Shi School of Automation Science and Electrical Engineering Beihang University Advanced Manufacturing Technology and Systems Research Center Department of Industrial and Manufacturing Systems Engineering Beihang University

Surgical robots are usually controlled by the method of the independent PD or the PD model based on gravity compensation. However, the system is still a open loop control with both of the control models. The two models can not consider the dynamics effectively either. The position error can not be guaranteed. To solve the problem, a torque control model based on dynamic model and adaptive model is proposed. And the position tolerance of PD control based on gravity compensation and that of control based on Torque Control stability theory are compared by Matlab software. The maximal tolerance of the new method is red robot end-effector is 0.15 mm.

关键词： Surgical Robot Torque control Adaptive control

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The 5th International Conference on Biomedical Engineering and Biotechnology (ICBEB 2016) Abstracts

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BMC MEDICAL IMAGING 2016年第SUPPL 1期16卷 65-65页

作者： [Anonymous] Department of MRI Shandong Medical Imaging Research Institute Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China Department of Interventional Radiology Shandong Provincial Hospital Affiliated to Shandong University Jinan Shandong 250021 People’s Republic of China College of Information Science and Technology Engr. Research Center of Digitized Textile & Fashion Tech. for Ministry of Education Donghua University Shanghai 201620 China Intelligent multimedia information processing Lab College of Software Northeastern University Shenyang Liaoning Province 110004 China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China School of Computer Science and Technology Nanjing Normal University Nanjing China Department of Electrical Engineering The City College of New York CUNY New York USA Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing Nanjing China School of Electronic Science and Engineering Nanjing University Nanjing Jiangsu 210046 China College of Engineering Nanyang Technological University Singapore 639798 Singapore School of Electronic Information Shanghai Dianji University Shanghai China School of Natural Sciences and Mathematics Shepherd University Shepherdstown WV 25443 USA Davis College of Agriculture Natural Resources and Design West Virginia University Morgantown WV 26505 USA State Key Laboratory of Millimeter Waves Southeast University Nanjing 210096 China Center of Medical Physics and Technology Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei China College of Agricultural and Life Sciences University of Florida Gainesville FL 32611 USA Courant Institute of Mathematical Sciences New York University New York NY 10012 USA Translational Imaging Division & MRI Unit Columbia University and New York State Psychiatric Institute New York NY 10032 USA Guangxi Key Laboratory of Manufacturing System & Adv

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Robotic Software System for the Disaster Circumstances: System of Team KAIST in the DARPA robotics Challenge Finals

Robotic Software System for the Disaster Circumstances: Syst...

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IEEE-RAS International Conference on Humanoid Robots

作者： Jeongsoo Lim Inwook Shim Okkee Sim Hyunmin Joe Inhyeok Kim Jungho Lee Jun-Ho Oh Humanoid Research Center School of Mechanical Aerospace & Systems Engineering Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Robotics and Computer Vision Laboratory Department of Electrical Engineering College of Information Science and Technology Korea Advanced Institute of Science and Technology Rainbow Co.

ISBN: (纸本)9781479968862

We developed a software system for operating the humanoid robot DRC-HUBO+ in disaster circumstances that the US Defense advanced Research Projects Agency suggested. This system was developed under the consideration of the stability of whole system, the systemic software environment for multiple developers, and the recovery routine when the system encounters a seriously abnormal situation. With these design goals, we devised our strategy for three domains: system, vision, and communication. Following the strategy we made two software frameworks (PODO and VPC) and one logical flow of data for remote process management. With this software system, we conducted all Tasks in the DARPA robotics Challenge Finals, and we won the competition.

关键词： Software systems DARPA design target robotics Disasters Religious Missions flow datas

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