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

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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A DECAM SEARCH FOR EXPLOSIVE OPTICAL TRANSIENTS ASSOCIATED WITH ICECUBE NEUTRINOS

arXiv

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arXiv 2019年

作者： Morgan, Robert Bechtol, Keith Kessler, R. Sako, M. Herner, K. Doctor, Z. Scolnic, D. Sevilla-Noarbe, I. Franckowiak, A. Neilson, K.N. Kowalski, M. Palmese, A. Swann, E. Thomas, B.P. Vivas, A.K. Drlica-Wagner, A. Garcia, A. Brout, D. Paz-Chinchón, F. Neilsen, E. Diehl, H.T. Soares-Santos, M. Abbott, T.M.C. Avila, S. Bertin, E. Brooks, D. Buckley-Geer, E. Carnero Rosell, A. Carrasco Kind, M. Carretero, J. Cawthon, R. Costanzi, M. De Vicente, J. Desai, S. Doel, P. Flaugher, B. Fosalba, P. Frieman, J. García-Bellido, J. Gaztanaga, E. Gerdes, D.W. Gruen, D. Gruendl, R.A. Gschwend, J. Gutierrez, G. Hollowood, D.L. Honscheid, K. James, D.J. Kuropatkin, N. Lima, M. Maia, M.A.G. Marshall, J.L. Menanteau, F. Miller, C.J. Miquel, R. Plazas, A.A. Sanchez, E. Scarpine, V. Schubnell, M. Serrano, S. Smith, M. Sobreira, F. Suchyta, E. Swanson, M.E.C. Tarle, G. Vikram, V. Walker, A.R. Weller, J. Physics Department University of Wisconsin-Madison 1150 University Avenue MadisonWI53706 United States Large Synoptic Survey Telescope Corporation Data Science Fellowship Program United States Large Synoptic Survey Telescope 933 North Cherry Avenue TucsonAZ85721 United States Department of Astronomy and Astrophysics University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Fermi National Accelerator Laboratory P. O. Box 500 BataviaIL60510 United States Madrid Spain Deutsches Elektronen Synchrotron DESY Zeuthen D-15738 Germany Department of Physics Drexel University 3141 Chestnut St PhiladelphiaPA19104 United States Institute of Physics Humboldt-Universität zu Berlin Newtonstr. 15 Berlin12489 Germany Deutsches Elektronen-Synchrotron Zeuthen D-15735 Germany Institute of Cosmology and Gravitation University of Portsmouth PortsmouthPO1 3FX United Kingdom Cerro Tololo Inter-American Observatory National Optical Astronomy Observatory Casilla 603 La Serena Chile Brandeis University Physics Department 415 South Street WalthamMA02453 United States National Center for Supercomputing Applications 1205 West Clark St. UrbanaIL61801 United States Instituto de Fisica Teorica UAM CSIC Universidad Autonoma de Madrid Madrid28049 Spain CNRS UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Sorbonne Universités UPMC Univ Paris 06 UMR 7095 Institut d’Astrophysique de Paris ParisF-75014 France Department of Physics & Astronomy University College London Gower Street LondonWC1E 6BT United Kingdom Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 RJ Rio de Janeiro20921-400 Brazil Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green Street UrbanaIL61801 United States The Barcelona Insti

In this work, we investigate the likelihood of association between realtime, TeV-PeV energy neutrino alerts from IceCube and optical counterparts in the form of core-collapse supernovae (CC SNe). The optical follow-up of IceCube alerts requires two main instrumental capabilities: (1) deep imaging, since 73% of neutrinos would come from CC SNe at redshifts z > 0.3, and (2) a large field of view (FoV), since typical IceCube muon neutrino pointing accuracy is on the order of ∼ 1 deg. With Blanco/DECam (gri to 24th magnitude and 2.2 deg diameter FoV), we performed a triggered optical follow-up observation of two IceCube alerts, IC170922A and IC171106A on ∼ 6 nights during the ∼ 3 weeks following each alert. For the IC170922A (IC171106A) follow-up observations, we expect that 12.1% (9.5%) of coincident CC SNe at z 0.3 are detectable, and that on average, 0.23 (0.07) unassociated SNe in the neutrino 90% containment regions also pass our selection criteria. We find two candidate CC SNe that are temporally coincident with the neutrino alerts in the FoV, but none in the 90% containment regions, which is statistically consistent with expected rates of background CC SNe for these observations. If CC SNe are the dominant source of TeV-PeV neutrinos, we would expect an excess of coincident CC SNe to be detectable at the 3σ confidence level using DECam observations similar to those of this work for ∼ 60 (∼ 200) neutrino alerts with (without) redshift information for all candidates. Copyright © 2019, The Authors. All rights reserved.

关键词： Neutrons

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Automated, high throughput exploration of process–structure–property relationships using the MapReduce paradigm

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Materials Discovery 2015年 1卷 21-28页

作者： Wodo, Olga Zola, Jaroslaw Sarath Pokuri, Balaji Sesha Du, Pengfei Ganapathysubramanian, Baskar University at Buffalo Materials Design and Innovation Department Buffalo United States University at Buffalo Mechanical and Aerospace Engineering Department Buffalo United States University at Buffalo Computational and Data-Enabled Science and Engineering Program Buffalo United States University at Buffalo Computer Science and Engineering Department Buffalo United States University at Buffalo Biomedical Informatics Department Buffalo United States Iowa State University Mechanical Engineering Department Ames United States Iowa State University Electrical and Computer Engineering Department Ames United States

The microstructure of a material intimately affects the performance of a device made from this material. The microstructure, in turn, is affected by the processing pathway used to fabricate the device. This forms the process–structure–property triangle that is central to material science. There has been increasing interest to comprehensively understand and subsequently exploit process–structure–property (PSP) relationships to design processing pathways that result in tailored microstructures exhibiting optimal properties. However, unraveling process–structure–property relationships usually requires systematic and tedious combinatorial search of process and system variables to identify the microstructures that are produced. This is further complicated by the necessity to interrogate the properties of the huge set of corresponding microstructures. Motivated by this challenge, we focus on developing a generic methodology to establish and explore PSP pathways. We leverage recent advances in high performance computing (HPC) and high throughput computing (HTC) with the premise that a domain expert should be able to focus on domain specific PSP problems while the highly specialized HPC/HTC knowledge needed to approach such problems should be hidden from the domain expert. Our hypothesis is that PSP exploration can be naturally formulated in terms of a standard paradigm in cloud computing, namely the MapReduce programming model. We show how reformulating PSP exploration into a MapReduce workflow enables us to take advantage of advances in cloud computing while requiring minimal specialized knowledge of HPC. We illustrate this generic approach by exploring PSP relationships relevant to organic photovoltaics. We focus on identifying microstructural traits that correlate with specific properties of the photovoltaic process: exciton generation, exciton dissociation and charge generation. We integrate a graph-based microstructure characterization tool, and a microstructure-aware

关键词： Drift diffusion model High throughput analysis MapReduce paradigm Organic solar cells Process–structure–processing Thermal annealing

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A data Mining Analysis of Asthma Risk Factors

A Data Mining Analysis of Asthma Risk Factors

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International Conference on Applied Computing and Information Technology/International Conference on Computational science and Intelligence (ACIT-CSI)

作者： Alma Pochini Ben M. Williams Hasanboy M. Isomitdinov Gongzhu Hu Departments of Finance Law & Departments of Mathematics Data Mining Graduate Certificate Program Department of Economics Department of Computer Science Central Michigan University Michigan USA

Asthma is a chronic inflammatory disease of the airways. Although causes of asthma may directly related to environment factors such as dust mites, pollens, cold air, etc.,we would like to find out if demographic, social and behavior factors also play a role. In this paper, we present a study of the behavioral rick factors in relation to people getting asthma. This study applies data mining techniques to the 2013 Behavioral Risk Factor Surveillance System (BRFSS) dataset of over 490,000 observations with 336 variables. The results reveal some relationships between some of these variables and asthma that may help asthma prevention.

关键词： Logistics Diseases data mining Neural networks Regression tree analysis

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Corrigendum to "Dissecting the genetic heterogeneity of gastric cancer"

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EBioMedicine 2023年 94卷 104709页

作者： Timo Hess Carlo Maj Jan Gehlen Oleg Borisov Stephan L Haas Ines Gockel Michael Vieth Guillaume Piessen Hakan Alakus Yogesh Vashist Carina Pereira Michael Knapp Vitalia Schüller Alexander Quaas Heike I Grabsch Jessica Trautmann Ewa Malecka-Wojciesko Anna Mokrowiecka Jan Speller Andreas Mayr Julia Schröder Axel M Hillmer Dominik Heider Florian Lordick Ángeles Pérez-Aísa Rafael Campo Jesús Espinel Fernando Geijo Concha Thomson Luis Bujanda Federico Sopeña Ángel Lanas María Pellisé Claudia Pauligk Thorsten Oliver Goetze Carolin Zelck Julian Reingruber Emadeldin Hassanin Peter Elbe Sandra Alsabeah Mats Lindblad Magnus Nilsson Nicole Kreuser René Thieme Francesca Tavano Roberta Pastorino Dario Arzani Roberto Persiani Jin-On Jung Henrik Nienhüser Katja Ott Ralf R Schumann Oliver Kumpf Susen Burock Volker Arndt Anna Jakubowska Małgorzta Ławniczak Victor Moreno Vicente Martín Manolis Kogevinas Marina Pollán Justyna Dąbrowska Antonio Salas Olivier Cussenot Anne Boland-Auge Delphine Daian Jean-Francois Deleuze Erika Salvi Maris Teder-Laving Gianluca Tomasello Margherita Ratti Chiara Senti Valli De Re Agostino Steffan Arnulf H Hölscher Katharina Messerle Christiane Josephine Bruns Armands Sīviņš Inga Bogdanova Jurgita Skieceviciene Justina Arstikyte Markus Moehler Hauke Lang Peter P Grimminger Martin Kruschewski Nikolaos Vassos Claus Schildberg Philipp Lingohr Karsten Ridwelski Hans Lippert Nadine Fricker Peter Krawitz Per Hoffmann Markus M Nöthen Lothar Veits Jakob R Izbicki Adrianna Mostowska Federico Martinón-Torres Daniele Cusi Rolf Adolfsson Geraldine Cancel-Tassin Aksana Höblinger Ernst Rodermann Monika Ludwig Gisela Keller Andres Metspalu Hermann Brenner Joerg Heller Markus Neef Michael Schepke Franz Ludwig Dumoulin Lutz Hamann Renato Cannizzaro Michele Ghidini Dominik Plaßmann Michael Geppert Peter Malfertheiner Olivier Glehen Tomasz Skoczylas Marek Majewski Jan Lubiński Orazio Palmieri Stefania Boccia Anna Latiano Nuria Aragones Thomas Schmidt Mário Dinis-Ribeiro Rui Medeiros Salah-Eddin Al-Batran Mārcis Leja Juozas Institute of Human Genetics University of Marburg Marburg Germany Institute of Human Genetics University of Bonn School of Medicine & University Hospital Bonn Bonn Germany. Institute for Genomic Statistics and Bioinformatics Medical Faculty University of Bonn Bonn Germany. Institute of Human Genetics University of Marburg Marburg Germany. Department of Upper GI Diseases Karolinska University Hospital and Unit of Gastroenterology and Rheumatology Karolinska Institutet Stockholm Sweden. Department of Visceral Transplant Thoracic and Vascular Surgery University Hospital Leipzig Leipzig Germany. Institute for Pathology Friedrich-Alexander-University Erlangen-Nuernberg Klinikum Bayreuth Bayreuth Germany. Department of Digestive and Oncological Surgery Claude Huriez Hospital CHU Lille Lille France. Department of General Visceral Cancer and Transplant Surgery University of Cologne Cologne Germany. Department of General Visceral and Thoracic Surgery University Medical Center Hamburg-Eppendorf University of Hamburg Hamburg Germany Centre for Surgical Oncology Medias Klinikum Burghausen Burghausen Germany. Molecular Oncology and Viral Pathology Group IPO Porto Research Center (CI-IPOP) Portuguese Institute of Oncology of Porto (IPO Porto) 4200-072 Porto Portugal Porto Comprehensive Cancer Center & RISE @ CI-IPO University of Porto 4200-450 Porto Portugal. Institute of Medical Biometrics Informatics and Epidemiology (IMBIE) Medical Faculty University of Bonn Bonn Germany. Institute of Pathology University Hospital Cologne Medical Faculty University of Cologne Germany. Department of Pathology GROW School for Oncology and Developmental Biology Maastricht University Medical Center Maastricht the Netherlands Pathology and Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds United Kingdom. Department of Digestive Tract Diseases Medical University of Lodz Lodz Poland. Department of Mathematics and Computer Science Unive

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Emerging technologies for improving embryo selection: a systematic review

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Advanced Health Care Technologies 2015年 1卷 55-64页

作者： Sengul, Yasemin Bener, Ayse Uyar, Asli 1 Computer Programming Program Advanced Vocational Studies Dogus University Acibadem Kadikoy Istanbul Turkey 2 Data Science Laboratory Department of Mechanical and Industrial Engineering Ryerson University Toronto ON Canada 3 Department of Computer Engineering Okan University Tuzla Istanbul Turkey

Background: Embryo selection procedure is one of the critical success factors in in vitro fertilization treatment. Various embryo selection technologies have emerged within the past decade. These technologies are either used in combination with morphology or introduced to replace the conventional morphological evaluation. This review aims at investigating the effect of these novel embryo selection technologies on in vitro fertilization success rates. Methods: A systematic review of the literature was performed among full-text English articles in the PubMed database. Study selection was based on the predefined inclusion and exclusion criteria. Clinical effectiveness of the selected studies was measured in terms of implantation, pregnancy, live birth, and multiple pregnancy rates. Results: Five studies were identified that fitted the inclusion criteria. In these studies, researchers used aneuploidy screening, metabolomic profiling, and time-lapse imaging analysis as the new technologies. Among these studies, the one that conducted a randomized controlled trial of a commercial time-lapse imaging system demonstrated significant improvement in implantation rate. Conclusion: Studies using emerging technologies for embryo assessment provide promising results in retrospective analysis. On the other hand, randomized controlled trial studies that test the efficacy of novel embryo selection techniques in clinical practice failed to demonstrate a consistent improvement in the resulting success rates. This review provides a snapshot of the most recent literature on embryo assessment and embryo selection studies. Our findings show that there is a lack of comparative measurements and analyses that are able to assess benefits of the novel technologies in the field of embryo selection.

关键词： embryo selection in vitro fertilization IVF

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Nat Genet:单细胞分析解开结直肠癌细胞的神秘面纱

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现代生物医学进展 2017年第15期17卷 I0003-I0003页

作者： Huipeng Li, Elise T Courtois, Debarka Sengupta, Yuliana Tan Shyam Prabhakar Elise T Courtois, Yuliana Tan Paul Robson Debarka Sengupta Kok Hao Chen, Jolene Jie Lin Goh Paul Jongjoon Choi Say Li Kong, Axel M Hillmer Iain Beehuat Tan Clarinda Chua Iain Beehuat Tan Lim Kiat Hon Wah Siew Tan Mark Wong Lawrence J K Wee Iain Beehuat Tan Paul Robson Paul Robson Paul Robson Computational and Systems Biology Genome Institute of Singapore Singapore Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India Synthetic Biology Genome Institute of Singapore Singapore Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Department of Medical Oncology National Cancer Centre Singapore Singapore Department of Pathology Singapore General Hospital Singapore Department of Colorectal Surgery Singapore General Hospital Singapore Data Analytics Department Institute for Infocomm Research Singapore Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA Department of Biological Sciences National University of Singapore Singapore.

结合单细胞基因组学和计算机技术，一个研究团队（包括来自杰克逊实验室（JAx）单细胞生物学主任Paul Robson博士在内）鉴定出了11种结直肠癌肿瘤的癌细胞组成及邻近的非癌细胞。这对于更好的肿瘤靶向诊断和治疗很重要。”使用单细胞测... 详细信息

结合单细胞基因组学和计算机技术，一个研究团队（包括来自杰克逊实验室（JAx）单细胞生物学主任Paul Robson博士在内）鉴定出了11种结直肠癌肿瘤的癌细胞组成及邻近的非癌细胞。这对于更好的肿瘤靶向诊断和治疗很重要。”使用单细胞测序。”JAX科学家、这篇发表在Nature Genetics上的文章共同第一作者Elise Courtois说道。”我们可以根据肿瘤中的细胞组成将结直肠癌进一步分类。因为每种亚型的肿瘤病人生存率存在差别，我们的方法将为肿瘤医生提供更多的关于肿瘤预后和治疗的信息。”

关键词：单细胞分析结直肠癌癌细胞 Nat 细胞组成肿瘤病计算机技术细胞生物学

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Author Correction: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

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Nature genetics 2018年第12期50卷 1754页

作者： Huipeng Li Elise T Courtois Debarka Sengupta Yuliana Tan Kok Hao Chen Jolene Jie Lin Goh Say Li Kong Clarinda Chua Lim Kiat Hon Wah Siew Tan Mark Wong Igor Cima Min-Han Tan Lawrence J K Wee Axel M Hillmer Iain Beehuat Tan Paul Robson Shyam Prabhakar Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India. Synthetic Biology Genome Institute of Singapore Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. Department of Pathology Singapore General Hospital Singapore Singapore. Department of Colorectal Surgery Singapore General Hospital Singapore Singapore. Institute of Bioengineering and Nanotechnology Singapore Singapore. Data Analytics Department Institute for Infocomm Research Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. paul.robson@***. The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA. paul.robson@***. Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA. paul.robson@***. Department of Biological Sciences National University of Singapore Singapore Singapore. paul.robson@***. Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. prabhakars@gis.a-star.edu.sg.

In the version of the article published, the author list is not accurate. Igor Cima and Min-Han Tan should have been authors, appearing after Mark Wong in the author list, while Paul Jongjoon Choi should not have been listed as an author. Igor Cima and Min-Han Tan both have the affiliation Institute of Bioengineering and Nanotechnology, Singapore, Singapore, and their contributions should have been noted in the Author Contributions section as "I.C. preprocessed Primary Cell Atlas data with inputs from M.-H.T." The following description of the contribution of Paul Jongjoon Choi should not have appeared: "P.J.C. supported the smFISH experiments." In the 'RCA: global panel' section of the Online Methods, the following sentence should have appeared as the second sentence, "An expression atlas of human primary cells (the Primary Cell Atlas) was preprocessed similarly to in ref. 55," with new reference 55 (Cima, I. et al. Tumor-derived circulating endothelial cell clusters in colorectal cancer. science Transl. Med. 8, 345ra89, 2016).

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Integrating knowledge tracing and item response theory: A tale of two frameworks 22

Integrating knowledge tracing and item response theory: A ta...

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Workshop of the 22nd Conference on User Modeling, Adaptation, and Personalization, UMAP 2014 - Co-located with the 22nd Conference on User Modeling, Adaptation, and Personalization, UMAP 2014

作者： Khajah, Mohammad M. Huang, Yun González-Brenes, José P. Mozer, Michael C. Brusilovsky, Peter Department of Computer Science Institute of Cognitive Science University of Colorado BoulderCO80309-0430 United States Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Digital Data Analytics and Adaptive Learning Pearson Research and Innovation Network PhiladelphiaPA19103 United States

Traditionally, the assessment and learning science commu-nities rely on different paradigms to model student performance. The assessment community uses Item Response Theory which allows modeling different student abilities and problem difficulties, while the learning science community uses Knowledge Tracing, which captures skill acquisition. These two paradigms are complementary - IRT cannot be used to model student learning, while Knowledge Tracing assumes all students and problems are the same. Recently, two highly related models based on a principled synthesis of IRT and Knowledge Tracing were introduced. However, these two models were evaluated on different data sets, using different evaluation metrics and with different ways of splitting the data into training and testing sets. In this paper we reconcile the models' results by presenting a unified view of the two models, and by evaluating the models under a common evaluation metric. We find that both models are equivalent and only differ in their training procedure. Our results show that the combined IRT and Knowledge Tracing models offer the best of assessment and learning sciences - high prediction accuracy like the IRT model, and the ability to model student learning like Knowledge Tracing.

关键词： Students

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DDX5 and its associated lncRNA Rmrp modulate T_H17 cell effector functions

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Nature 2015年第7583期528卷 517-522页

作者： Huang, Wendy Thomas, Benjamin Flynn, Ryan A. Gavzy, Samuel J. Wu, Lin Kim, Sangwon V. Hall, Jason A. Miraldi, Emily R. Ng, Charles P. Rigo, Frank W. Meadows, Sarah Montoya, Nina R. Herrera, Natalia G. Domingos, Ana I. Rastinejad, Fraydoon Myers, Richard M. Fuller-Pace, Frances V. Bonneau, Richard Chang, Howard Y. Acuto, Oreste Littman, Dan R. Kimmel Center for Biology and Medicine Skirball Institute New York University School of Medicine New York 10016 NY United States Sir William Dunn School of Pathology University of Oxford Oxford OX1 3RE United Kingdom Center for Personal Dynamic Regulomes Stanford University Stanford 94305 CA United States Center for Genomics and Systems Biology Department of Biology New York University New York 10003 NY United States Courant Institute of Mathematical Sciences Computer Science Department New York University New York 10012 NY United States Simons Center for Data Analysis Simons Foundation New York 10010 NY United States Isis Pharmaceuticals Carlsbad 92010 CA United States HudsonAlpha Institute for Biotechnology Huntsville 35806 AL United States Instituto Gulbenkian de Ciencia Oeiras 2780-156 Portugal Integrative Metabolism Program Sanford Burnham Prebys Medical Discovery Institute Orlando 32827 FL United States Division of Cancer Research University of Dundee Dundee DD1 9SY United Kingdom Howard Hughes Medical Institute New York University School of Medicine New York 10016 NY United States

T helper 17 (TH17) lymphocytes protect mucosal barriers from infections, but also contribute to multiple chronic inflammatory diseases. Their differentiation is controlled by RORγt, a ligand-regulated nuclear recepto...

T helper 17 (T_H17) lymphocytes protect mucosal barriers from infections, but also contribute to multiple chronic inflammatory diseases. Their differentiation is controlled by RORγt, a ligand-regulated nuclear receptor. Here we identify the RNA helicase DEAD-box protein 5 (DDX5) as a RORγt partner that coordinates transcription of selective T_H17 genes, and is required for T_H17-mediated inflammatory pathologies. Surprisingly, the ability of DDX5 to interact with RORγt and coactivate its targets depends on intrinsic RNA helicase activity and binding of a conserved nuclear long noncoding RNA (lncRNA), Rmrp, which is mutated in patients with cartilage-hair hypoplasia. A targeted Rmrp gene mutation in mice, corresponding to a gene mutation in cartilage-hair hypoplasia patients, altered lncRNA chromatin occupancy, and reduced the DDX5-RORγt interaction and RORγt target gene transcription. Elucidation of the link between Rmrp and the DDX5-RORγt complex reveals a role for RNA helicases and lncRNAs in tissue-specific transcriptional regulation, and provides new opportunities for therapeutic intervention in T_H17-dependent diseases. © 2015 Macmillan Publishers Limited. All rights reserved.

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