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arXiv 2021年

作者： Gigan, Sylvain Katz, Ori de Aguiar, Hilton B. Andresen, Esben Ravn Aubry, Alexandre Bertolotti, Jacopo Bossy, Emmanuel Bouchet, Dorian Brake, Joshua Brasselet, Sophie Bromberg, Yaron Cao, Hui Chaigne, Thomas Cheng, Zhongtao Choi, Wonshik Čižmár, Tomáš Cui, Meng Curtis, Vincent R. Defienne, Hugo Hofer, Matthias Horisaki, Ryoichi Horstmeyer, Roarke Ji, Na LaViolette, Aaron K. Mertz, Jerome Moser, Christophe Mosk, Allard P. Pégard, Nicolas C. Piestun, Rafael Popoff, Sebastien Phillips, David B. Psaltis, Demetri Rahmani, Babak Rigneault, Hervé Rotter, Stefan Tian, Lei Vellekoop, Ivo M. Waller, Laura Wang, Lihong Weber, Timothy Xiao, Sheng Xu, Chris Yamilov, Alexey Yang, Changhuei Yılmaz, Hasan Cornell University United States University of California Berkeley United States Department of Biomedical Engineering Boston University United States Laboratoire Kastler Brossel ENS-Université PSL CNRS Sorbonne Université Collège de France 24 rue Lhomond Paris75005 France Univ. Grenoble Alpes CNRS LIPhy Grenoble38000 France Aix Marseille Univ. CNRS Centrale Marseille Institut Fresnel Marseille France Institute for Basic Science Department of Physics Korea University Korea Republic of Institut Langevin ESPCI PSL University CNRS Paris France Aix Marseille Univ CNRS Centrale Marseille Institut Fresnel MarseilleF-13013 France School of Physics and Astronomy University of Exeter ExeterEX4 4QL United Kingdom Leibniz Institute of Photonic Technology Albert-Einstein-Straße 9 Jena07745 Germany Laboratory of Applied Photonics Devices School of Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Laboratory of Optics School of Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Université de Lille CNRS UMR 8523 PhLAM Laboratoire de Physique des Lasers Atomes et Molécules LilleF-59000 France Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Applied Physical Sciences UNC Chapel Hill United States Department of Electrical Engineering and Computer Sciences UC Berkeley United States Department of Biomedical Engineering UNC Chapel Hill United States UNC Neuroscience Centre UNC Chapel Hill United States Department of Applied Physics Hebrew University of Jerusalem Jerusalem Israel Department of Science and Technology University of Twente Enschede Netherlands University of Tokyo Japan Institute of Scientific Instruments of the Czech Academy of Sciences Královopolská 147 Brno612 64 Czech Republic Dept. of Applied Physics Yale University United States Dept. of Physics Missouri University of Science and Technology United States Bi

The last decade has seen the development of a wide set of tools, such as wavefront shaping, computational or fundamental methods, that allow to understand and control light propagation in a complex medium, such as biological tissues or multimode fibers. A vibrant and diverse community is now working on this field, that has revolutionized the prospect of diffraction-limited imaging at dept. in tissues. This roadmap highlights several key aspects of this fast developing field, and some of the challenges and opportunities ahead. Copyright © 2021, The Authors. All rights reserved.

关键词： Multimode fibers

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The Form of High-Performance Computing: A Survey

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IOP Conference Series: Materials science and Engineering 2019年第5期662卷

作者： Priati Assiroj H L H S Warnars R Kosala B Ranti S Supangat A I Kistijantoro E Abdurrachman Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Information System Dept. Buana Perjuangan University Karawang Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Information System Department Bina Nusantara University Jakarta Information System Technology Department Institut Teknologi Bandung School of Electronics Engineering and Informatics Institut Teknologi Bandung

Computational technology is an important thing that needed in the whole of human life. Along with the development of computer technology, there are a significant data created and wait to be processed; we will get more advantages if it well-processed, if not we will see the data explosion, the data is just a useless stack. Research mitigates the other research paper and makes a conclusion and a report by PRISMA method. The development of computer technology will support the development of an organization. Considering any factors in choosing the right and suitable high-performance computer resource due to our organization is a must. The existing high-performance computing forms are Supercomputer, Grid Computing, Cluster Computing, and Cloud Computing. Based on this work, the last form of high-performance computing, Cloud Computing is one of the most used forms thus we mitigate its advantages and disadvantages. Recently, to process this vast data, we must have a high-performance computer and to provide it, this is not a cheap resource.

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Viroinformatics-based investigation of SARS-CoV-2 core proteins for potential therapeutic targets

arXiv

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arXiv 2020年

作者： Agrawal, Lokesh Poullikkas, Thanasis Eisenhower, Scott Monsanto, Carlo Bakku, Ranjith Kumar Universidad Integral del Caribe y América Latina Kaminda Cas Grandi #79 Curaçao United States Graduate School of Comprehensive Human Sciences University of Tsukuba 1-1-1 Tennodai Tsukuba305-8577 Japan Molecular Neuroscience Unit Okinawa Institute of Science and Technology Graduate University Kunigami-gun Okinawa904-0412 Japan Human Biology School of Integrative and Global Majors University of Tsukuba 1-1-1 Tennodai Tsukuba305-0006 Japan Department of Experimental Pathology Faculty of Medicine University of Tsukuba 2-1-1 Tennodai Tsukuba305-8576 Japan Department of Infection Biology Faculty of Medicine University of Tsukuba 1-1-1 Tennodai Tsukuba305-8575 Japan Research Workgroup Ronin Institute 127 Haddon Place MontclairNJ07043-2314 United States Faculty of Engineering Information and Systems Dept. of Computer Science University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8572 Japan University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki305-8572 Japan

Due to SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) being a novel virus, there are currently no known effective antiviral drugs capable of slowing its progress. To accelerate the discovery of potential drug candidates, bioinformatics based in silico drug discovery can be applied as a very robust tool. In the present study, more than 60 antiviral drugs already available on the market, were chosen after literature survey. These can be used in clinical trials for the treatment of COVID-19. In this study, these candidate drugs were ranked based on their potential to interact with the Spike protein and RdRp (RNA-dependent RNA polymerase) of SARS-CoV-2. Additionally, the mechanism of their action as well as how the virus infection can utilize Hemoglobin to decrease the oxygen level in blood is explained. Moreover, multiple sequence alignments of the Spike protein with 75 sequences of different viruses from the Orthocoronavirinae subfamily were performed. This gives insight into the evolutionarily conserved domains that can be targeted using drug or antibody treatment. This multidimensional study opens a new layer of understanding about the most effective drug-targetable sites on the Spike protein of SARS-CoV-2. Copyright © 2020, The Authors. All rights reserved.

关键词： Viruses

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An interactive tool for natural language processing on clinical text

arXiv

引用

arXiv 2017年

作者： Trivedi, Gaurav Pham, Phuong Chapman, Wendy Hwa, Rebecca Wiebe, Janyce Hochheiser, Harry Intelligent Systems Program University of Pittsburgh Dept. of Computer Science University of Pittsburgh Dept. of Biomed. Informatics University of Utah Dept. of Computer Science and Intelligent Systems Program University of Pittsburgh

Natural Language Processing (NLP) systems often make use of machine learning techniques that are unfamiliar to endusers who are interested in analyzing clinical records. Although NLP has been widely used in extracting information from clinical text, current systems generally do not support model revision based on feedback from domain experts. We present a prototype tool that allows end users to visualize and review the outputs of an NLP system that extracts binary variables from clinical text. Our tool combines multiple visualizations to help the users understand these results and make any necessary corrections, thus forming a feedback loop and helping improve the accuracy of the NLP models. We have tested our prototype in a formative think-aloud user study with clinicians and researchers involved in colonoscopy research. Results from semi-structured interviews and a System Usability Scale (SUS) analysis show that the users are able to quickly start refining NLP models, despite having very little or no experience with machine learning. Observations from these sessions suggest revisions to the interface to better support review workflow and interpretation of results. Copyright © 2017, The Authors. All rights reserved.

关键词： Visualization

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OS02.6.A Lacosamide in monotherapy in brain tumour-related epilepsy (BTRE): results from an Italian multicentre retrospective study

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Neuro-Oncology 2022年第SUPPLEMENT_2期24卷 ii11-ii11页

作者： Bruno, F Mo, F Meletti, S Belcastro, V Quadri, S Napolitano, M Bello, L Dainese, F Scarpelli, M Florindo, I Mascia, A Pauletto, G Pellerino, A Giovannini, G Polosa, M Sessa, M Conti Nibali, M Di Gennaro, G Gigli, G Cavallieri, F Pisanello, A Rudà, R University and City of Health and Science Turin Italy Turin Italy Division of Neurology Modena University Hospital Modena Italy Modena Italy Center for Neuroscience and Neurotechnology Department of Biomedical Metabolic and Neural Sciences University of Modena and Reggio Emilia Italy Modena and Reggio Emilia Italy Division of Neurology Maggiore Hospital Lodi Italy Lodi Italy Division of Neurology ASST Papa Giovanni XXIII of Bergamo Italy Bergamo Italy Division of Neurology and Stroke Unit Hospital A. Cardarelli Napoli Italy Naples Italy Division of Neurosurgical Oncology Department of Oncology and Hemato-oncology Università degli Studi di Milano Italy Milan Italy Epilepsy Centre Division of Neurology Hospital of Venezia Italy Venice Italy Division of Neurology Department of Neuroscience AOUI Verona Verona Italy Verona Italy Division of Neurology Hospital of Parma Italy Parma Italy IRCCS Neuromed Pozzilli Italy Pozzilli Italy Neurology Unit Department of Neuroscience “S. Maria della Misericordia” University Hospital Udine Italy Udine Italy Clinical and Experimental Medicine PhD Program University of Modena and Reggio Emilia Modena Italy Modena and Reggio Emilia Italy Division of Neurosurgery ASST Lariana Como Italy Como Italy Department of Medicine (DAME) University of Udine Udine Italy Udine Italy Clinical Neurology Unit “S. Maria della Misericordia” University Hospital Udine Italy Udine Italy Neurology Unit Neuromotor & Rehabilitation Department Azienda USL-IRCCS di Reggio Emilia Reggio Emilia Italy Reggio Emilia Italy Dept. of Neurology Castelfranco and Treviso Hospitals Italy Treviso Italy

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Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination

arXiv

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arXiv 2017年

作者： Seung, Sebastian Zung, Jonathan Neuroscience Institute and Computer Science Dept. Princeton University PrincetonNJ08544 United States

Much has been learned about plasticity of biological synapses from empirical studies. Hebbian plasticity is driven by correlated activity of presynaptic and postsynaptic neurons. Synapses that converge onto the same neuron often behave as if they compete for a fixed resource;some survive the competition while others are eliminated. To provide computational interpretations of these aspects of synaptic plasticity, we formulate unsupervised learning as a zero-sum game between Hebbian excitation and anti-Hebbian inhibition in a neural network model. The game formalizes the intuition that Hebbian excitation tries to maximize correlations of neurons with their inputs, while anti-Hebbian inhibition tries to decorrelate neurons from each other. We further include a model of synaptic competition, which enables a neuron to eliminate all connections except those from its most strongly correlated inputs. Through empirical studies, we show that this facilitates the learning of sensory features that resemble parts of objects. Copyright © 2017, The Authors. All rights reserved.

关键词： Neurons

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Deep learning improves template matching by normalized cross correlation

arXiv

引用

arXiv 2017年

作者： Buniatyan, Davit Macrina, Thomas Ih, Dodam Zung, Jonathan Seung, Sebastian Neuroscience Institute and Computer Science Dept. Princeton University PrincetonNJ08544 United States

Template matching by normalized cross correlation (NCC) is widely used for finding image correspondences. We improve the robustness of this algorithm by preprocessing images with "siamese" convolutional networks trained to maximize the contrast between NCC values of true and false matches. The improvement is quantified using patches of brain images from serial section electron microscopy. Relative to a parameter-tuned bandpass filter, siamese convolutional networks significantly reduce false matches. Furthermore, all false matches can be eliminated by removing a tiny fraction of all matches based on NCC values. The improved accuracy of our method could be essential for connectomics, because emerging petascale datasets may require billions of template matches to assemble 2D images of serial sections into a 3D image stack. Our method is also expected to generalize to many other computer vision applications that use NCC template matching to find image correspondences. Copyright © 2017, The Authors. All rights reserved.

关键词： Template matching

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Learning binary latent variable models: A tensor eigenpair approach

arXiv

引用

arXiv 2018年

作者： Jaffe, Ariel Weiss, Roi Carmi, Shai Kluger, Yuval Nadler, Boaz Dept. of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot7610001 Israel Braun School of Public Health and Community Medicine Hebrew University of Jerusalem Jerusalem9112102 Israel Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Dept. of Pathology Yale Cancer Center Yale University School of Medicine New HavenCT06520 United States Program of Applied Mathematics Yale University New HavenCT06511 United States

Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Front Cover, Volume 43, Issue 10

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Human Mutation 2022年第10期43卷

作者： Sheng-Jia Lin Barbara Vona Hillary M. Porter Mahmoud Izadi Kevin Huang Yves Lacassie Jill A. Rosenfeld Saadullah Khan Cassidy Petree Tayyiba A. Ali Nazif Muhammad Sher A. Khan Noor Muhammad Pengfei Liu Marie-Louise Haymon Franz Rüschendorf Il-Keun Kong Linda Schnapp Natasha Shur Lynn Chorich Lawrence Layman Thomas Haaf Ehsan Pourkarimi Hyung-Goo Kim Gaurav K. Varshney Genes & Human Disease Research Program Oklahoma Medical Research Foundation Oklahoma City Oklahoma USA Institute of Human Genetics Julius Maximilians University Würzburg Würzburg Germany Institute for Auditory Neuroscience and InnerEarLab University Medical Center Göttingen Göttingen Germany Institute of Human Genetics University Medical Center Göttingen Göttingen Germany Gaurav K. Varshney Genes & Human Disease Research Program Oklahoma Medical Research Foundation Oklahoma City OK 73104 USA. Children's National Hospital Rare Disease Institute Washington District of Columbia USA Division of Genomics and Translational Medicine College of Health and Life Sciences Hamad Bin Khalifa University Doha Qatar Department of Pediatrics Louisiana State University Health Sciences Center Head Division of Clinical Genetics and Dept. of Genetics Children's Hospital 1986-2016 New Orleans Los Angeles USA Department of Molecular and Human Genetics Baylor College of Medicine Houston Texas USA Baylor Genetics Laboratories Houston Texas USA Department of Biotechnology and Genetic Engineering Kohat University of Science & Technology Kohat Khyber Pakhtunkhwa Pakistan Children Hospital New Orleans Louisiana Pediatric Radiology Tulane Associate Professor of Radiology New Orleans Los Angeles USA Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany Department of Animal Sciences Division of Applied Life Science (BK21 Four) Gyeongsang National University Jinju South Korea Section of Reproductive Endocrinology Infertility & Genetics Department of Obstetrics and Gynecology Department of Neuroscience and Regenerative Medicine Medical College of Georgia Augusta University Augusta USA Neurological Disorders Research Center Qatar Biomedical Research Institute Hamad Bin Khalifa University Doha Qatar

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