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EEG channels reduction using PCA to increase XGBoost’s accuracy for stroke detection

AIP Conference Proceedings 2017年第1期1862卷

作者： N. Fitriah S. K. Wijaya M. I. Fanany C. Badri M. Rezal 1Biomedical Engineering Study Program Universitas Indonesia Jl. Salemba Raya No. 4 Jakarta 10430 DKI Jakarta Indonesia 2Department of Physics Faculty of Mathematics and Natural Sciences (FMIPA) Universitas Indonesia Depok 16424 Indonesia 3Faculty of Computer Science Universitas Indonesia Depok 16424 Indonesia

In Indonesia, based on the result of Basic Health Research 2013, the number of stroke patients had increased from 8.3 ‰ (2007) to 12.1 ‰ (2013). These days, some researchers are using electroencephalography (EEG) result as another option to detect the stroke disease besides CT Scan image as the gold standard. A previous study on the data of stroke and healthy patients in National Brain Center Hospital (RS PON) used Brain Symmetry Index (BSI), Delta-Alpha Ratio (DAR), and Delta-Theta-Alpha-Beta Ratio (DTABR) as the features for classification by an Extreme Learning Machine (ELM). The study got 85% accuracy with sensitivity above 86 % for acute ischemic stroke detection. Using EEG data means dealing with many data dimensions, and it can reduce the accuracy of classifier (the curse of dimensionality). Principal Component Analysis (PCA) could reduce dimensionality and computation cost without decreasing classification accuracy. XGBoost, as the scalable tree boosting classifier, can solve real-world scale problems (Higgs Boson and Allstate dataset) with using a minimal amount of resources. This paper reuses the same data from RS PON and features from previous research, preprocessed with PCA and classified with XGBoost, to increase the accuracy with fewer electrodes. The specific fewer electrodes improved the accuracy of stroke detection. Our future work will examine the other algorithm besides PCA to get higher accuracy with less number of channels.

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The complexity calculation for group decision making using TOPSIS algorithm

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AIP Conference Proceedings 2016年第1期1755卷

作者： Hamdani Retantyo Wardoyo 1Computer Sciences Doctoral Program Departement of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia 2Departement of Computer Sciences Universitas Mulawarman Indonesia 3Departement of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

The complexity calculation is one of the things to be considered in choosing an algorithm to be applied to a decision support system (DSS). This study analyzes the complexity of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This algorithm is applied to the ranking process to resolve the problems associated with Multi-Criteria Decision Making (MCDM). MCDM is used for Group Decision Making (GDM) problems in making a decision. The end output of this study is making DM ranking resulted from decision-making. This problem also produces TOPSIS algorithm complexity used in group decision-making. This algorithm complexity is in the part of attribute normalization and weighting which results in O(n2). Whereas the complexity of a positive-negative ideal solution and V distance to A1, A2, A3, and A4 is O(n) and the algorithm complexity ranking results in O(1).

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Combining rating scale and FANP to determine the important weight of learning competency for students’ assessment

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AIP Conference Proceedings 2016年第1期1755卷

作者： Sri Andayani Sri Hartati Retantyo Wardoyo Djemari Mardapi 1Doctoral program of Computer Science Universitas Gadjah Mada Yogyakarta Indonesia 2Department of Computer Science and Electrical Universitas Gadjah Mada Yogyakarta Indonesia 3Study Program of Educational Research and Evaluation Graduate School Universitas Negeri Yogyakarta Indonesia

Determining the important weight of learning competency is an important problem for students’ assessment in credentialing and certification decision-making. Learning competencies should have importance weight that definitely affect the final decision, and can represent strengths and weaknesses of student achievement of certain competencies. The weight of learning competency is determined based on its difficulty level, which is identified by the depth, scope and complexity of material involved in it. The dependency among learning competencies must also be considered in determining the weight. Therefore, this study aims to develop a weighting method based on combining rating scale and fuzzy analytic network process (FANP) to determine the weight of learning competency. Using the proposed method, the weight of learning competency is obtained and then can be used in calculating the final score for students’ assessment.

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Light, the universe, and everything-12 Herculean tasks for quantum cowboys and black diamond skiers

arXiv

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arXiv 2018年

作者： Agarwal, Girish Allen, Roland E. Bezdìková, Iva Boyd, Robert W. Chen, Goong Hanson, Ronald Hawthorne, Dean L. Hemmer, Philip Kim, Moochan B. Kocharovskaya, Olga Lee, David M. Lidström, Sebastian K. Lidström, Suzy Losert, Harald Maier, Helmut Neuberger, John W. Padgett, Miles J. Raizen, Mark Rajendran, Surjeet Rasel, Ernst Schleich, Wolfgang P. Scully, Marlan O. Shchedrin, Gavriil Shvets, Gennady Sokolov, Alexei Svidzinsky, Anatoly Walsworth, Ronald L. Weiss, Rainer Wilczek, Frank Willner, Alan E. Yablonovich, Eli Zheludev, Nikolay Department of Physics and Astronomy Texas A&M University College StationTX77843 United States Department of Physics Faculty of Nuclear Sciences and Physical Engineering Czech Technical University in Prague Brehova 7 Praha 1-Staré mìsto11519 Czech Republic Department of Physics Technical University of Ostrava 17 Listopadu 15 Ostrava-Poruba70833 Czech Republic Department of Physics School of Electrical Engineering and Computer Science University of Ottawa OttawaONK1N6N5 Canada Institute of Optics Department of Physics and Astronomy University of Rochester RochesterNY14627 United States School of Physics and Astronomy University of Glasgow GlasgowG128QQ United Kingdom Department of Mathematics Texas A&M University College StationTX77843 United States Science Program Texas A&M University at Qatar Education City Doha Qatar Texas A&M University College StationTX77843 United States QuTech and Kavli Institute of Nanoscience Delft University of Technology P.O. Box 5046 Delft2600 GA Netherlands Cornell Laboratory of Ornithology Cornell University IthacaNY14850 United States Laboratory of Atomic and Solid State Physics Cornell University IthacaNY14853 United States Department of Electrical & Computer Engineering Texas A&M Energy Institute Texas A&M University TX77843 United States Department of Physics Strathclyde University GlasgowG11XJ United Kingdom Physica Scripta Royal Swedish Academy of Sciences StockholmSE-10405 Sweden Universität Ulm Albert-Einstein-Allee 11 Ulm89081 Germany Institut für Zahlentheorie und Wahrscheinlichkeitstheorie Universität Ulm Albert-Einstein-Allee 11 Ulm89081 Germany Department of Mathematics University of North Texas DentonTX76203-5017 United States College of Natural Sciences University of Texas AustinTX78712 United States Department of Physics University of California BerkeleyCA94720-7300 United States Institut fuer Quantenoptik QUEST-LFS Welfengarten 1 Hannover30167 Germany Hagler Insti

The Winter Colloquium on the Physics of Quantum Electronics (PQE) has been a seminal force in quantum optics and related areas since 1971. It is rather mindboggling to recognize how the concepts presented at these conferences have transformed scientific understanding and human society. In January, 2017, the participants of PQE were asked to consider the equally important prospects for the future, and to formulate a set of questions representing some of the greatest aspirations in this broad field. The result is this multi-authored paper, in which many of the world's leading experts address the following fundamental questions: (1) What is the future of gravitational wave astronomy? (2) Are there new quantum phases of matter away from equilibrium that can be found and exploited-such as the time crystal? (3) Quantum theory in uncharted territory: What can we learn? (4) What are the ultimate limits for laser photon energies? (5) What are the ultimate limits to temporal, spatial, and optical resolution? (6) What novel roles will atoms play in technology? (7) What applications lie ahead for nitrogen-vacancy centers in diamond? (8) What is the future of quantum coherence, squeezing, and entanglement for enhanced superresolution and sensing? (9) How can we solve (some of) humanity's biggest problems through new quantum technologies? (10) What new understanding of materials and biological molecules will result from their dynamical characterization with free electron lasers? (11) What new technologies and fundamental discoveries might quantum optics achieve by the end of this century? (12) What novel topological structures can be created and employed in quantum optics? Copyright © 2018, The Authors. All rights reserved.

关键词： Quantum optics

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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A second update on mapping the human genetic architecture of COVID-19

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Nature 2023年第7977期621卷 E7-E26页

作者： Masahiro Kanai Christine Stevens Benjamin M. Neale Mark Daly Andrea Ganna Amy Trankiem Mary K. Balaconis Samuli Ripatti Lindo Nkambul Mark J. Daly Sam Bryant Matthew Solomonson Shea J. Andrews Mattia Cordioli Juha Karjalainen Juha Mehtonen Matti Pirinen Andrea Corbetta Bradley Jermy Kati Donner Katja Kivinen Aarno Palotie Mari Kaunisto Kumar Veerapen Konrad J. Karczewski Alicia R. Martin Elizabeth G. Atkinson Kristin Tsuo Nikolas Baya Rahul Gupta Raymond K. Walters Duncan S. Palmer Gopal Sarma Nathan Cheng Wenhan Lu Claire Churchhouse Jacqueline I. Goldstein Daniel King Wei Zhou Cotton Seed Hilary Finucane F. Kyle Satterstrom Gita A. Pathak Akiko Iwasaki Karolina Chwialkowska Karolina Chwiałkowska Brooke N. Wolford Hajar Fauzan Ahmad Shea Andrews Kathrin Aprile von Hohenstaufen Puoti Palwende R. Boua Guillaume Butler-Laporte Celia M. T. Greenwood Bartha M. Knoppers Mark Lathrop Carmen L. Cadilla Francesca Colombo Nicole Dueker Chloe Kirk Atanu Kumar Dutta Yasser M. El-Sherbiny Jehan J. El-Jawhari Doaa Shahin Ahmed Elzeiny Tamer A. Mansour Madonna M. Eltoukhy Attia A. S. Mohamed Sahar Esmaeeli Annika Faucon Israel Fernández Cadenas Israel Fernandez-Cadenas Laia Llucià-Carol Natalia Cullell Elena Muiño Jara Cárcel-Márquez Margherita Francescatto Laurent Francioli Lindokuhle Nkambule Jatin Arora Soumya Raychaudhuri Caroline Cusick Lude Franke Macarena Fuentes Macarena Fuentes-Guajardo David Gómez Cabrero Emi N. Harry Philip Jansen József L. Szentpéteri Réka Várnai Elżbieta Kaja Athanasios Kousathanas Dorote Pasko Mark J. Caulfield Alex Stuckey Christopher A. Odhams Susan Walker Daniel Rhodes Richard H. Scott Loukas Moutsianas Afshan Siddiq Peter Goddard Sally Donovan Tala Zainy Fiona Maleady-Crowe Linda Todd Shahla Salehi Greg Elgar Georgia Chan Prabhu Arumugam Christine Patch Augusto Rendon Tom A. Fowler Jose E. Krieger Sanjay K. Patel Audrey Lemaçon Sophie Limou Pietro Lió Eirini Marouli Charles Hinds Minttu M. Marttila Carolina Medina-Gómez Isabelle Migeotte Soumyajit Mondal Andrés Moreno-Estrada Leire Moya Tomok Broad Institute of MIT and Harvard Cambridge MA USA Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Program in Bioinformatics and Integrative Genomics Harvard Medical School Boston MA USA Institute for Molecular Medicine Finland (FIMM) University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland University of Helsinki Helsinki Finland Institute for Molecular Medicine Finland (FIMM) HiLIFE University of Helsinki Helsinki Finland Public Health Faculty of Medicine University of Helsinki Helsinki Finland Analytic & Translational Genetics Unit Massachusetts General Hospital Boston MA USA Stanley Center for Psychiatric Research & Program in Medical and Population Genetics Cambridge UK Institute for Molecular Medicine Finland (FIMM) Univerisity of Helsinki Helsinki Finland University of California San Francisco San Francisco CA USA Department of Molecular and Human Genetics Baylor College of Medicine Houston TX USA Program in Biological and Biomedical Sciences Harvard Medical School Boston MA USA Department of Psychiatry School of Medicine Yale University New Haven CT USA Veteran Affairs Connecticut Healthcare System West Haven CT USA Yale University New Haven CT USA Centre for Bioinformatics and Data Analysis Medical University of Bialystok Bialystok Poland Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA Faculty of Industrial Sciences and Technology Universiti Malaysia Pahang Pahang Malaysia Icahn School of Medicine at Mount Sinai New York NY USA Genolier Innovation Network and Hub Swiss Medical Network Genolier Healthcare Campus Genolier Switzerland Immediate Independent Multidisciplinary Med & Biotech Research Association Milan Italy Clinical Research Unit of Nanor

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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Publisher Correction: Deep coverage whole genome sequences and plasma lipoprotein(a) in individuals of European and African ancestries

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Nature communications 2018年第1期9卷 3493页

作者： Seyedeh M Zekavat Sanni Ruotsalainen Robert E Handsaker Maris Alver Jonathan Bloom Timothy Poterba Cotton Seed Jason Ernst Mark Chaffin Jesse Engreitz Gina M Peloso Ani Manichaikul Chaojie Yang Kathleen A Ryan Mao Fu W Craig Johnson Michael Tsai Matthew Budoff Ramachandran S Vasan L Adrienne Cupples Jerome I Rotter Stephen S Rich Wendy Post Braxton D Mitchell Adolfo Correa Andres Metspalu James G Wilson Veikko Salomaa Manolis Kellis Mark J Daly Benjamin M Neale Steven McCarroll Ida Surakka Tonu Esko Andrea Ganna Samuli Ripatti Sekar Kathiresan Pradeep Natarajan Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Yale School of Medicine New Haven CT 06510 USA. Department of Computational Biology & Bioinformatics Yale University New Haven CT 06510 USA. Institute for Molecular Medicine University of Helsinki Helsinki Finland. Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Department of Genetics Harvard Medical School Boston MA 02115 USA. Department of Biotechnology Institute of Molecular and Cell Biology University of Tartu Tartu Estonia. Estonian Genome Center Tallinn Estonia. Analytic and Translational Genetics Unit Boston MA 02142 USA. Department of Biological Chemistry University of California Los Angeles Los Angeles CA 90095 USA. Department of Biostatistics Boston University School of Public Health Boston MA 02118 USA. Center for Public Health Genomics University of Virginia Charlottesville VA 22904 USA. Program in Personalized and Genomic Medicine Division of Endocrinology Diabetes & Nutrition Department of Medicine University of Maryland School of Medicine Baltimore MD 21201 USA. Department of Biostatistics School of Public Health and Community Medicine University of Washington Seattle WA 98195 USA. Department of Laboratory Medicine and Pathology University of Minnesota Minneapolis MN 55455 USA. Division of Cardiology Harbor-UCLA Medical Center Los Angeles Biomedical Research Institute Los Angeles CA 90509 USA. NHLBI Framingham Heart Study Framingham MA 20892 USA. Sections of Preventive medicine and Epidemiology and cardiovascular medicine Departments of Medicine and Epidemiology Boston university Schools of Medicine and Public health Boston MA 02118 USA. Departments of Pediatrics and Medicine The Institute for Translational Genomics and Population Sciences Los Angeles Biomedical Research Institute Harbor-UCLA Medical Center Torrance CA 90509 USA. Division of Cardiology

The original version of this article contained an error in the name of the author Ramachandran S. Vasan, which was incorrectly given as Vasan S. Ramachandran. This has now been corrected in both the PDF and HTML versi...

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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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Abstracts

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Environmental Management 1977年第1期1卷 67-96页

作者： Frankenfeld, John W. Schulz, Wolfgang McMurty, George J. Petersen, Gary W. May, G. A. Hering, F. S. Schwartz, J. I. Heywood, J. B. Chigier, N. A. Grohse, E. W. Walker, J. D. Colwell, R. R. Petrakis, L. Pergament, H. S. Thorpe, R. D. Schoepf, Richard W. Krzyczkowski, Roman Henneman, Suzanne S. Hudson, Charles L. Putnam, Evelyn S. Thiesen, Donna J. Parks, G. A. McCarty, Perry L. Leckie, J. O. Schrumpf, Barry J. Simonson, G. H. Paine, D. P. Lawrence, R. D. Pyott, W. T. Leh, M. Elders, W. Combs, J. Caplen, T. Harrison, F. L. Wong, K. M. Heft., R. E. Charnell, Robert L. Lehmann, Edward J. Mallon, Lawrence G. Hatfield, Cecile Adams, Gerald H. Johanning, James Talvitie, Antti Noll, Kenneth E. Miller, Terry Smiarowski, Joseph F. Willis, Cleve E. Foster, John H. Schlesinger, Benjamin Daetz, Douglas Lear, Donald U. Smith, Mona F. Hundemann, Audrey S. Crockett, Pernell W. Werner, Kirk G. Carroll, Thomas E. Maase, David L. Genco, Joseph E. Ifeadi, Christopher N. Lowman, F. G. Christensen, S. W. Van Winkle, W. Mattice, J. S. Harrison, Elizabeth A. Barker, James C. Chesness, Jerry L. Smith, Ralph E. Shaheeen, Donald G. Raney, R. Keith Borton, T. Wezernak, C. T. Raney, R. K. Sherwani, Jabbor K. Moreau, David H. Eisenberg, Norman A. Lynch, Cornelius J. Breeding, Roger J. Johnson, J. D. Foster, K. E. Mouat, D. A. Clark, R. Hyden, John William Owen, Wilfred Bayfield, Neil G. Barrow, Graham C. Stolz, Stephanie B. Wienckowski, Louis A. Brown, Betram S. Keyfitz, Nathan Wilson, W. L. Newman, Peter W. G. Bammi, Deepak Bammi, Dalip Goddard, James E. Chisholm, Tony Walsh, Cliff Brennan, Geoffrey Thompson, K. S. Richardson, R. Jensen, Clayton E. Brown, Dail W. Mirabito, John A. Cowing, Thomas G. Binghamton, Suny Siehl, George H. Albrecht, O. W. Alexander, Ariel Barde, Jean -Philippe Darby, William P. McMichael, Francis Clay Dunlap, Robert W. Muckleston, Keith W. Frankenhoff, Charles A. Giulini, Lorenzo T. Wyatt, T. Black, Peter E. Keating, William Thomas Leonard, M. E. Fisher, E. L. Brunelle, M. F. Dickinson, J. E. Pethig, Rudiger Clapham Exxon Research & Engineering Co. Linden Government Research Lab Coast Guard Washington D.C. Department of Transportation Washington D.C. Office For Remote Sensing of Earth Resources. NASA Earth Resources Survey Program Pennsylvania State University Washington D.C. Department of the Navy Washington D.C. Cambridge. Dept. of Mechanical Engineering Massachusetts Inst. of Technology USA Huntsville. School of Graduate Studies and Research Alabama Univ. USA Dept. of Microbiology. Office of Naval Research Maryland Univ. Arlington Research and Development Co. Pittsburgh AeroChem Research Labs. Inc. Princeton Dept. of the Interior Office of Library Services. Bibliographic Series (Final) Washington D.C. Interplan Corp. Santa Barbara Urban Mass Transportation Adm. Washington D.C. Naval Underwater Systems Center Newport Calif. Mercury Project. National Science Found Stanford Univ Washington D.C. Div. of Advanced Environmental Research & Tech. USA Corvallis. NASA Earth Resources Survey Program Oregon State Univ. Washington D.C. Riverside. Inst. of Geophysics and Planetary Physics National Science Foundation California Univ. Washington D.C. Livermore Lawrence Livermore Lab. California Univ. USA Atlantic Oceanographic and Meteorological Labs. National Oceanic and Atmospheric Administration Miami National Technical Information Service Springfield Miami Univ. Coral Gables School of Law National Oceanic and Atmospheric Administration Rockville National Academy of Sciences Office of Sea Grant Washington D. C. School of Law National Oceanic and Atmospheric Administration Office of Sea Grant. Rockville Norman. Dept. of Civil Engineering and Environmental Science. Urban Mass Transportation Administration Oklahoma Univ. Washington D. C. Knoxville.Dept. of Civil Engineering. Federal Highway Administration Tennessee Univ. Washington D. C. Amherst.Water Resources Research Center. Office of Water Research and Technology Massachusetts Univ. Washington D. C. Calif. Dept. of Industria

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