National Institute for Physiological Sciences
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Key Measures and Capital Investment in the Fiscal Year Ending March 31, 2021
March 26, 2020 Central Japan Railway Company (JR Central) Key Measures and Capital Investment in the Fiscal Year Ending March 31, 2021 In the fiscal year ending March 31, 2021 (FY2020), we will continue to place the highest priority on ensuring safe and reliable transportation. At the same time, we will steadily move forward with our efforts in promoting earthquake countermeasures, including derailment and deviation countermeasures for the Tokaido Shinkansen, and carrying out large-scale renovation work on civil engineering structures. We will also strive to enhance our transportation services. For example, we will set more flexible train services in accordance with demand by applying the "12 Nozomi Timetable" and commence to launch the next-generation Shinkansen N700S. In addition, looking ahead to the Olympic and Paralympic games, we will make efforts to serve many customers in Japan and overseas so that they can use our services comfortably. We will proceed steadily with the Chuo Shinkansen Project introducing the Superconducting Maglev System, while giving serious consideration to the safety of construction work, environmental preservation, and coordination with local communities. We will also make steady steps toward deploying the high-speed railway system overseas. In the segment of affiliated businesses, we will operate JR Central Towers and JR Gate Tower integrally and demonstrate synergic effects to boost earnings. In order to steadily move forward with these initiatives continuously, JR Central will continue to work on strengthening its earning power and improving its technological capabilities. We will also strive to enhance efficiency and reduce costs by exercising wisdom over business execution overall, including making capital investments, with a view to enhancing management capabilities. -
Independent Evidence for an Association Between General Cognitive Ability and a Genetic Locus for Educational Attainment J
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Hofstra Northwell Academic Works (Hofstra Northwell School of Medicine) Donald and Barbara Zucker School of Medicine Journal Articles Academic Works 2015 Independent evidence for an association between general cognitive ability and a genetic locus for educational attainment J. W. Trampush Hofstra Northwell School of Medicine T. Lencz Hofstra Northwell School of Medicine S. Guha Northwell Health S. Mukherjee Northwell Health P. DeRosse Northwell Health See next page for additional authors Follow this and additional works at: https://academicworks.medicine.hofstra.edu/articles Part of the Psychiatry Commons Recommended Citation Trampush JW, Lencz T, Guha S, Mukherjee S, DeRosse P, John M, Andreassen O, Deary I, Glahn D, Malhotra AK, . Independent evidence for an association between general cognitive ability and a genetic locus for educational attainment. 2015 Jan 01; 168(5):Article 927 [ p.]. Available from: https://academicworks.medicine.hofstra.edu/articles/927. Free full text article. This Article is brought to you for free and open access by Donald and Barbara Zucker School of Medicine Academic Works. It has been accepted for inclusion in Journal Articles by an authorized administrator of Donald and Barbara Zucker School of Medicine Academic Works. Authors J. W. Trampush, T. Lencz, S. Guha, S. Mukherjee, P. DeRosse, M. John, O. A. Andreassen, I. J. Deary, D. C. Glahn, A. K. Malhotra, and +41 additional authors This article is available at Donald and Barbara Zucker School of Medicine Academic Works: https://academicworks.medicine.hofstra.edu/articles/927 HHS Public Access Author manuscript Author Manuscript Author ManuscriptAm J Med Author Manuscript Genet B Neuropsychiatr Author Manuscript Genet. -
Genetic Influence on Cognitive Development Between Childhood
Molecular Psychiatry https://doi.org/10.1038/s41380-018-0277-0 ARTICLE Genetic influence on cognitive development between childhood and adulthood 1 1 1 2 3 Josephine Mollon ● Emma E. M. Knowles ● Samuel R. Mathias ● Ruben Gur ● Juan Manuel Peralta ● 4,5,6 4,5,6 2 3 7 1,8 Daniel J. Weiner ● Elise B. Robinson ● Raquel E. Gur ● John Blangero ● Laura Almasy ● David C. Glahn Received: 17 April 2018 / Revised: 15 August 2018 / Accepted: 11 September 2018 © Springer Nature Limited 2018 Abstract Successful cognitive development between childhood and adulthood has important consequences for future mental and physical wellbeing, as well as occupational and financial success. Therefore, delineating the genetic influences underlying changes in cognitive abilities during this developmental period will provide important insights into the biological mechanisms that govern both typical and atypical maturation. Using data from the Philadelphia Neurodevelopmental Cohort (PNC), a large population-based sample of individuals aged 8 to 21 years old (n = 6634), we used an empirical relatedness matrix to establish the heritability of general and specific cognitive functions and determine if genetic factors influence 1234567890();,: 1234567890();,: cognitive maturation (i.e., Gene × Age interactions) between childhood and early adulthood. We found that neurocognitive measures across childhood and early adulthood were significantly heritable. Moreover, genetic variance on general cognitive ability, or g, increased significantly between childhood and early adulthood. Finally, we did not find evidence for decay in genetic correlation on neurocognition throughout childhood and adulthood, suggesting that the same genetic factors underlie cognition at different ages throughout this developmental period. Establishing significant Gene × Age interactions in neurocognitive functions across childhood and early adulthood is a necessary first step in identifying genes that influence cognitive development, rather than genes that influence cognition per se. -
Whole‐Exome Sequencing in 20,197 Persons for Rare Variants In
RESEARCH PAPER Whole-exome sequencing in 20,197 persons for rare variants in Alzheimer’s disease Neha S. Raghavan1,2, Adam M. Brickman1,2,3, Howard Andrews1,2,4, Jennifer J. Manly1,2,3, Nicole Schupf1,2,3,7, Rafael Lantigua1,6, Charles J. Wolock8, Sitharthan Kamalakaran8, Slave Petrovski8,9, Giuseppe Tosto1,2,3, Badri N. Vardarajan1,2,3,5, David B. Goldstein3,6,8, Richard Mayeux1,2,3,4,7 & The Alzheimer’s Disease Sequencing Projecta 1The Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 2The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 3Department of Neurology, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 4Department of Psychiatry, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 5Department of Systems Biology, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 6Department of Medicine, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 7The Department of Epidemiology, Mailman School of Public Health, College of Physicians and Surgeons, Columbia University, The New York Presbyterian Hospital, New York, New York 8Institute of Genomic Medicine, Columbia University, The New York Presbyterian Hospital, New York, New York 9AstraZeneca Centre for Genomics Research, Precision Medicine and Genomics, IMED Biotech Unit, AstraZeneca, Cambridge, CB2 0AA, United Kingdom Correspondence Abstract Richard Mayeux, Department of Neurology, Objective 710 West 168th Street, Columbia University, : The genetic bases of Alzheimer’s disease remain uncertain. -
Cognitive Characterization of Schizophrenia Risk Variants Involved in Synaptic Transmission: Evidence of CACNA1C 'S Role in Working Memory
Neuropsychopharmacology (2017) 42, 2612–2622 © 2017 American College of Neuropsychopharmacology. All rights reserved 0893-133X/17 www.neuropsychopharmacology.org Cognitive Characterization of Schizophrenia Risk Variants Involved in Synaptic Transmission: Evidence of CACNA1C 's Role in Working Memory 1 1 2 3 4,5 3 Donna Cosgrove , Omar Mothersill , Kimberley Kendall , Bettina Konte , Denise Harold , Ina Giegling , 3 6 6 7 Annette Hartmann , Alex Richards , Kiran Mantripragada , The Wellcome Trust Case Control Consortium , Michael J Owen6, Michael C O’Donovan6, Michael Gill4, Dan Rujescu3, James Walters2, Aiden Corvin4, Derek W Morris1 and Gary Donohoe*,1 1 The Cognitive Genetics & Cognitive Therapy Group, The School of Psychology and Discipline of Biochemistry, The Centre for Neuroimaging & Cognitive Genomics, National University of Ireland Galway, Galway, Ireland; 2Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK; 3Department of Psychiatry, Psychotherapy and Psychosomatics, Martin-Luther-University Halle-Wittenberg, Halle, Germany; 4 Neuropsychiatric Genetics Research Group, Department of Psychiatry, Institute of Molecular Medicine, Trinity College Dublin, Dublin, Ireland; 5 6 School of Biotechnology, Dublin City University, Dublin, Ireland; MRC Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK With 4100 common variants associated with schizophrenia risk, establishing their biological significance is a priority. We sought to establish cognitive effects of -
Mikawa Toyota Station
JR Aichi Kanjo Line Total travel time Route from Nagoya Station to Toyota Kaikan. 80 min. Travel Your travel plan Departure/Arrival time Fare Details Remarks Nagoya Station 名古屋 D 8:45 STEP New Rapid It is five stations from Nagoya Station to 35 min. 620 yen To Toyohashi Okazaki Station. Okazaki Station ()豊橋行 1 JR Tokaido Main Line 岡崎 A 9:20 Okazaki Station 岡崎 D 9:36 STEP 26 min. 440 yen Local It is nine stations from Okazaki Station to Mikawa-Toyota Station. Mikawa-Toyota Station A 10:02 2 Aichi Kanjo Line 三河豊田 Mikawa-Toyota Station D 10:02 This is in the best situation. 1 15 min. Please plan to take 25-30 minutes to be safe. Walking Toyota Kaikan Museum A 10:17 Hoei Taxi Meitetsu Taxi Mikawa-Toyota Station D 10:02 approx. 0565-28-0228 0565-32-1541 2 5 min. 900 yen Taxi Toyota Kaikan Museum A 10:07 Please Note: If taxi is not at station, STEP ( you may have to wait up 20-30 minutes. ) 3 Mikawa-Toyota Station D 10:20 It is two stops from Mikawa Toyota Eki-Mae Bus 3 4 min. 170 yen Stop to Toyota Honsha-Mae Bus Stop. Meitetsu Bus Toyota Honsha-Mae A 10:24 Mikawa-Toyota Station D 10:06 5 min. It is three stops from Mikawa Toyota Eki-Mae Bus Stop to Toyota Kaikan Bus Stop. 4 + + 100 yen Toyota There is an underground tunnel to help you Toyota Kaikan Museum A 10:16 5 min. Oiden Bus Walking cross the road upon arrival. -
National Institute for Physiological Sciences
Natio na l Institutes of Nat National Institutes of Natural Sciences ur al Scie National Institute for nc es Na Physiological Sciences ti onal onal In st i tu te for Physiological Sciences Physiological for te 2019 2019 National Institutes of Natural Sciences National Institute for Physiological Sciences Myodaiji,Okazaki 444-8585,Japan Phone:+81-564-55-7700 Fax:+81-564-52-7913 https://www.nips.ac.jp C O N T E N T S INTRODUCTION 1 Center for Genetic Analysis of Behavior 36 Outlines of NIPS Section of Viral Vector Development 37 Outlines of the Institute 2 Section of Mammalian Transgenesis 38 A Short History of the Institute 3-6 Section of Metabolic Physiology 39 Organization of the Institute 7 Center for Communication Networks 40 Advisory Committee for Research and Management 8 Section of Research Archives 41 Director General / Vice Director General / Chief Section of Physiology & Medicine Education 42 Researchers 8 Section of Network Management 43 Emeritus Professors/Emeritus Technical Staff/ Section of Health & Safety Management 44 Deceased Emeritus Professors 8 Research Departments Research Enhancement Strategy Office 45 Department of Molecular & Cellular Physiology Okazaki Research Facilities(NIPS) Division of Biophysics & Neurobiology 9 Center for Animal Resources and Collaborative Study 46 Division of Membrane Physiology 10 Division of Coordinator for Animal Experimentation 46 Division of Neural Development & Regeneration 11 Exploratory Research Center on Life and Living Systems 47 Department of Homeostatic Regulation NIPS Research -
Cognitive Genomics: Linking Genes to Behavior in the Human Brain
PERSPECTIVE Cognitive genomics: Linking genes to behavior in the human brain Genevieve Konopka Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX 75390-9111, USA ABSTRACT Correlations of genetic variation in DNA with functional brain activity have already provided a starting point for delving into human cognitive mechanisms. However, these analyses do not provide the specific genes driving the associations, which are complicated by intergenic Downloaded from http://direct.mit.edu/netn/article-pdf/1/1/3/1091843/netn_a_00003.pdf by guest on 30 September 2021 localization as well as tissue-specific epigenetics and expression. The use of brain-derived an open access journal expression datasets could build upon the foundation of these initial genetic insights and yield genes and molecular pathways for testing new hypotheses regarding the molecular bases of human brain development, cognition, and disease. Thus, coupling these human brain gene expression data with measurements of brain activity may provide genes with critical roles in brain function. However, these brain gene expression datasets have their own set of caveats, most notably a reliance on postmortem tissue. In this perspective, I summarize and examine the progress that has been made in this realm to date, and discuss the various frontiers remaining, such as the inclusion of cell-type-specific information, additional physiological measurements, and genomic data from patient cohorts. Progress in understanding the inner workings of the brain has come a long way from the preneu- roscience era of phrenology, when we were limited to conjectures about human behavior Citation: Konopka G. (2017). Cognitive genomics: Linking genes to behavior based on the shape of the skull. -
1 Functional Network Dynamics in a Neurodevelopmental Disorder Of
bioRxiv preprint doi: https://doi.org/10.1101/463323; this version posted November 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Dynamic functional connectivity networks and gene expression Functional network dynamics in a neurodevelopmental disorder of known genetic origin Erin Hawkins*1, Danyal Akarca*1, Mengya Zhang1, Diandra Brkic1, Mark Woolrich2, Kate Baker1,3, & Duncan Astle1 *these authors took joint responsibility for first authorship 1 MRC Cognition and Brain Sciences Unit, University of Cambridge 15 Chaucer Road, Cambridge, CB2 7EF, UK 2 Oxford Centre for Human Brain Activity, University of Oxford University Department of Psychiatry, Warneford Hospital, Oxford, OX3 7JX, UK 3 Department of Medical Genetics, University of Cambridge Cambridge Institute for Medical Research, Cambridge, CB2 0XY, UK. Corresponding author: Erin Hawkins Email: [email protected] Telephone: +44 (0)1223 273 686 1 bioRxiv preprint doi: https://doi.org/10.1101/463323; this version posted November 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Dynamic functional connectivity networks and gene expression Abstract Cognitive processing depends on the temporal co-ordination of functional brain networks. This fundamental aspect of neurophysiology potentially bridges the genetic regulation of neuronal activity and developmental cognitive impairments. -
Gene Discovery and Polygenic Prediction from a 1.1-Million-Person GWAS of Educational Attainment
Gene discovery and polygenic prediction from a 1.1-million-person GWAS of educational attainment AUTHORS: James J. Lee1,†, Robbee Wedow2,3,4,†, Aysu Okbay5,6,*†, Edward Kong7, Omeed Maghzian7, Meghan Zacher8, Tuan Anh Nguyen-Viet9, Peter Bowers7, Julia Sidorenko10,11, Richard Karlsson Linnér5,6, Mark Alan Fontana12, Tushar Kundu9, Chanwook Lee7, Hui Li7, Ruoxi Li9, Rebecca Royer9, Pascal N. Timshel13,14, Raymond K. Walters15,16, Emily A. Willoughby1, Loïc Yengo10, 23andMe Research Team17, COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Maris Alver11, Yanchun Bao18, David W. Clark19, Felix R. Day20, Nicholas A. Furlotte17, Peter K. Joshi19, Kathryn E. Kemper10, Aaron Kleinman17, Claudia Langenberg20, Reedik Mägi11, Joey W. Trampush21, Shefali Setia Verma22, Yang Wu10, Max Lam Zhan Yang23, Jing Hua Zhao20, Zhili Zheng10,24, Jason D. Boardman2,3,4, Harry Campbell19, Jeremy Freese25, Kathleen Mullan Harris26,27, Caroline Hayward28, Pamela Herd29, Meena Kumari18, Todd Lencz30,31,32, Jian'an Luan20, Anil K. Malhotra30,31,32, Andres Metspalu11, Lili Milani11, Ken K. Ong20, John R. B. Perry20, David J. Porteous33, Marylyn D. Ritchie22, Melissa C. Smart18, Blair H. Smith34, Joyce Y. Tung17, Nicholas J. Wareham20, James F. Wilson19,28, Jonathan P. Beauchamp35, Dalton C. Conley36, Tõnu Esko11, Steven F. Lehrer37,38,39, Patrik K. E. Magnusson40, Sven Oskarsson41, Tune H. Pers13,14, Matthew R. Robinson10,42, Kevin Thom43, Chelsea Watson9, Christopher F. Chabris44, Michelle N. Meyer45, David I. Laibson7, Jian -
Ninety-Nine Independent Genetic Loci Influencing General Cognitive Function Include Genes
bioRxiv preprint doi: https://doi.org/10.1101/176511; this version posted August 17, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Ninety-nine independent genetic loci influencing general cognitive function include genes associated with brain health and structure (N = 280,360) Gail Davies1,182, Max Lam2,182, Sarah E Harris1,3, Joey W Trampush4, Michelle Luciano1, W David Hill1, Saskia P Hagenaars1,5, Stuart J Ritchie1, Riccardo E Marioni1,3, Chloe Fawns-Ritchie1, David CM Liewald1, Judith A Okely1, Ari V Ahola-Olli6,7, Catriona LK Barnes8, Lars Bertram9, Joshua C Bis10 Katherine E Burdick11,12,13, Andrea Christoforou14,15, Pamela DeRosse2,16, Srdjan Djurovic14,17, Thomas Espeseth18,19, Stella Giakoumaki20, Sudheer Giddaluru14,15, Daniel E Gustavson21,22, Caroline Hayward23,24, Edith Hofer25,26, M Arfan Ikram27,28,29, Robert Karlsson30, Emma Knowles31, Jari Lahti32,33, Markus Leber34, Shuo Li35, Karen A Mather36, Ingrid Melle14,18, Derek Morris37, Christopher Oldmeadow38, Teemu Palviainen39, Antony Payton40, Raha PaZoki41, Katja Petrovic25, Chandra A Reynolds42, Muralidharan Sargurupremraj43, Markus ScholZ44,45, Jennifer A Smith46,47, Albert V Smith48,49, Natalie TerZikhan27,50, Anbu Thalamuthu36, Stella Trompet51, Sven J van der Lee27, Erin B Ware47, Beverly G Windham52, Margaret J Wright53,54, Jingyun Yang55,56, Jin Yu16, David Ames57,58, Najaf Amin27, Philippe Amouyel59,60,61, Ole A Andreassen18,62, Nicola Armstrong63, -
Comparative Analysis of Target Gene Exon Sequencing by Cognitive Technology Using a Next Generation Sequencing Platform in Patients with Lung Cancer
MOLECULAR AND CLINICAL ONCOLOGY 14: 36, 2021 Comparative analysis of target gene exon sequencing by cognitive technology using a next generation sequencing platform in patients with lung cancer YU CHEN1,2, WENQING YAN1,2, ZHI XIE1,2, WEIBANG GUO1,2, DANXIA LU1,2, ZHIYI LV1,2 and XUCHAO ZHANG1,2 1Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, School of Medicine, South China University of Technology, Guangzhou, Guangdong 510080; 2Guangdong Provincial Key Laboratory of Translational Medicine in Lung Cancer, Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong 510280, P.R. China Received March 31, 2020; Accepted December 9, 2020 DOI: 10.3892/mco.2020.2198 Abstract. Next generation sequencing (NGS) technology analysis and the GLCI bioinformatician was 43.48%. In 65 is an increasingly important clinical tool for therapeutic (56.52%) samples, WfG analysis identified and interpreted, decision‑making. However, interpretation of NGS data on average, 1.54 more mutation sites in each sample than the presents challenges at the point of care, due to limitations in manual GLCI review. These mutation sites were located on understanding the clinical importance of gene variants and 27 genes, including EP300, ARID1A, STK11 and DNMT3A. efficiently translating results into actionable information for Mutations in the EP300 gene were most prevalent, and present the clinician. The present study compared two approaches for in 30.77% samples. The Tumor Mutation Burden (TMB) inter‑ annotating and reporting actionable genes and gene mutations preted by WfG analysis (1.82) was significantly higher than from tumor samples: The traditional approach of manual cura‑ the TMB (0.73) interpreted by GLCI review.