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6th ACM International Conference on web Search and Data Mining, WSDM 2013

作者： Zhang, Jun Wang, Chaokun Wang, Jianmin Yu, Philip S. Department of Computer Science and Technology Tsinghua University Beijing China School of Software Tsinghua University Beijing China Tsinghua National Laboratory for Information Science and Technology China Key Laboratory for Information System Security Ministry of Education China Department of Computer Science University of Illinois at Chicago Chicago IL United States

ISBN: (纸本)9781450318693

Many patterns have been discovered to explain and analyze how people make friends. Among them is the triadic closure, supported by the principle of the transitivity of friendship, which means for an individual the friends of her friend are more likely to become her new friends. However, people's motivations under this principle haven't been well studied, and it's still unknown that how this principle works in diverse situations. In this paper, we try to study this principle deeply based on the behavior modeling. We study how one expands her egocentric network via her friends, also called intermediaries, based on the transitivity of friendship. We propose LaFT-Tree, a tree-based representation of friendship formation inspired from triadic closure. LaFT-Tree provides a hierarchical view of the flat structure of one's egocentric network by visualizing the expansion trace of one's egocentric network. We model people's friend-making behaviors using LaFT-LDA, a generative model for LaFT-Tree learning. The proposed model is evaluated on both synthetic and real-world social networks and experimental results demonstrate the effectiveness of LaFT-LDA for LaFT-Tree inference. We also present some interesting applications of the LaFT-Tree, showing that our model can be generalized and benefit other social network analysis tasks. © 2013 ACM.

关键词： Social networking (online)

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Semantics of intensional type theory extended with decidable equational theories 22

Semantics of intensional type theory extended with decidable...

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22nd Annual Conference of the European Association for Computer Science Logic EACSL, CSL 2013

作者： Wang, Qian Barras, Bruno Laboratoire D'Informatique École Polytechnique France INRIA Saclay Île de France France Key Laboratory for Information System Security Ministry of Education China China School of Software Tsinghua University China Department of Computer Science and Technology Tsinghua University China

ISBN: (纸本)9783939897606

Incorporating extensional equality into a dependent intensional type system such as the Calculus of Constructions (CC) provides with stronger type-checking capabilities and makes the proof development closer to intuition. Since strong forms of extensionality generally leads to undecidable type-checking, it seems a reasonable trade-off to extend intensional equality with a decidable first-order theory, as experimented in earlier work on CoqMTU and its implementation CoqMT. In this work, CoqMTU is extended with strong eliminations. The meta-theoretical study, particularly the part relying on semantic arguments, is more complex. A set-theoretical model of the equational theory is the key ingredient to derive the logical consistency of the formalism. Strong normalization, the main lemma from which type-decidability follows, is proved by attaching realizability information to the values of the model. The approach we have followed is to first consider an abstract notion of first-order equational theory, and then instantiate it with a particular instance, Presburger Arithmetic. These results have been formalized using Coq. © Qian Wang and Bruno Barras.

关键词： Semantics

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IdeaGraph: A Graph-Based Algorithm of Mining Latent information for Human Cognition

IdeaGraph: A Graph-Based Algorithm of Mining Latent Informat...

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IEEE International Conference on systems, Man, and Cybernetics

作者： Hao Wang Fanjiang Xu Xiaohui Hu Yukio Ohsawa Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China Department of System Innovations The University of Tokyo Tokyo Japan

ISBN: (纸本)9781479906505

Knowledge discovery in texts (KDT) has been widely applied for business data analysis, but it only reveals a common pattern based on large amounts of data. Since 2000, chance discovery (CD) as an extension of KDT has been proposed to detect rare but significant events or situations regarded as chance candidates for human decision making. KeyGraph is a useful and important algorithm as well as a tool in CD for mining and visualizing these chances. However, a scenario graph visualized by KeyGraph is machine-oriented, causing a bottleneck of human cognition. Traditional knowledge discovery also runs into the similar problem. In this paper, we propose a human-oriented algorithm called IdeaGraph which can generate a rich scenario graph for human's perception, comprehension and even innovation. IdeaGraph not only works on discovering more rare and significant chances, but also focuses on uncovering latent relationships among chances for gaining richer and deeper human insights. Our experiment has validated the advantages of IdeaGraph by comparing with KeyGraph.

关键词： IdeaGraph KeyGraph Knowledge Discovery Chance Discovery Latent information data mining KDT mining Cognition Intelligence Chance Human

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Dependency-aware quality-differentiated wireless video multicast

Dependency-aware quality-differentiated wireless video multi...

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IEEE Conference on Wireless Communications and Networking

作者： Han-Chiang Li Kate Ching-Ju Lin Kai-Lung Hua Ge-Ming Chiu Yu-Chin Tsai Shan Chin Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of CSIE National Taiwan University of Science and Technology Taipei Taiwan Cloud System Software Institute Institute for Information Industry Taipei Taiwan

ISBN: (纸本)9781467359382

Video multicast exploits the wireless broadcast nature to transmit a video stream to multiple clients with a minimum bandwidth requirement. Assigning a suitable transmission bit-rate to each scalable coded block in a video stream is however a challenging problem because clients in a wireless network have heterogeneous channel quality and experience different packet loss probability. Prior work attempts to transmit the base-layer stream at a low transmission bit-rate to ensure a high reception probability, and hence the basic visual quality. Such methods however are over-simplified for a video stream that supports multiple quality levels in a video frame and needs explicit rate assignment for each block. We propose in this paper a dependency-aware rate scheduling scheme that assigns each block a rate according to dependency between blocks. With consideration of block dependency, we can better utilize limited wireless bandwidth to deliver important blocks, and minimize the number of undecodable blocks due to the loss of their reference blocks at the receivers. The simulation results show that since our scheme reduces the number of undecodable blocks, it achieves a higher overall video quality for a multicast group than the existing schemes under different client distributions.

关键词： Streaming media Visualization Bit rate Dynamic programming Static VAr compensators Wireless communication Heuristic algorithms

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TriggerCas: Enabling wireless consrucive collisions

TriggerCas: Enabling wireless consrucive collisions

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yin Wang Yuan He Dapeng Cheng Yunhao Liu Xiang-yang Li MOE Key Laboratory for Information System Security School of Software TNLIST Tsinghua University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China Key Laboratory of Intelligent Information Processing Universities of Shandong China Department of Computer Science Illinois Institute of Technology USA

Constructive Interference (CI) proposed in the existing work (e.g., A-MAC [1], Glossy [2]) may degrade the packet reception performance in terms of Packet Reception Ratio (PRR) and Received Signal Strength Indication (RSSI). The packet reception performance of a set of nodes transmitting simultaneously might be no better than that of any single node transmitting individually. In this paper, we redefine CI and propose TriggerCast, a practical wireless architecture which ensures concurrent transmissions of an identical packet to interfere constructively rather than to interfere non-destructively. CI potentially allows orders of magnitude reductions in energy consumption and improvements in link quality. Moreover, we for the first time present a theoretical sufficient condition for generating CI with IEEE 802.15.4 radio: concurrent transmissions with an identical packet should be synchronized at chip level. Meanwhile, co-senders participating in concurrent transmissions should be carefully selected, and the starting instants for the concurrent transmissions should be aligned. Based on the sufficient condition, we propose practical techniques to effectively compensate propagation and radio processing delays. TriggerCast has 95 th percentile synchronization errors of at most 250ns. Extensive experiments in practical testbeds reveal that TriggerCast significantly improves PRR (from 5% to 70% with 7 concurrent senders, from 50% to 98.3% with 6 senders) and RSSI (about 6dB with 5 senders).

关键词： Delays Synchronization Signal to noise ratio Receivers Wireless sensor networks IEEE 802.15 Standards

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10.4.0 Panel 10.4.0: U.S. OSD systems of systems Engineering Guide: Status Report & INCOSE Support

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INCOSE International Symposium 2014年第1期17卷

作者： Kristen Baldwin Dr. Charles Dickerson Dr. Abe Meilich John Osterholz Malcolm Touchin Dr. Steve Bratt Panelist Deputy Director Office of the Secretary of Defense (OSD) Software Engineering and System Assurance Office of the Deputy Under Secretary of Defense Acquisition and Technology Chair INCOSE Architecture Working Group (AWG) INCOSE AWG Leadership Team former Director BAE Systems Architecture and Interoperability for the DoD Chief Information Officer Technical Manager Systems Engineering Innovation Centre (SEIC) Loughborough University CEO W3C (invited) World Wide Web Consortium (W3C)

In May 2006, a task was issued by the U.S. Deputy Under Secretary of Defense for Acquisition and technology (DUSD (A&T)) to develop a systems of systems Engineering Guidebook. The Office of the Secretary of Defense (OSD) convened a small group of Government, Industry and Academia, and selected the Stevens Institute of technology to lead the project. During the summer and autumn of 2006, the International Council on systems Engineering (INCOSE) was honored to be invited to provide a global representation of the systems engineering community in support of the OSD writing of a systems of systems, systems Engineering (SoS SE) Guide. On 22 December 2006 the draft SoS SE Guide, version 0.9, was released for review and use by the systems engineering community. Industry and academia have been asked to submit their comments as part of the effort to pilot the recommendations in the Guide. In 2007, OSD has been actively engaging a sample of DoD SoS programs to pilot the Guide, especially with respect to the 16 OSD SE processes. The responses will become part of the first update to the Guide. At this panel, OSD will present the background and motivation for writing the Guide, along with an update on the pilot. Both OSD and INCOSE will discuss their insights. As part of the broad SE community review, the Guide will also be viewed from the UK perspective of Capabilities Based Acquisition. Looking to the future, the concepts and practices of Net Centricity, Network Enablement of Capabilities, and Semantic web will be considered and how they might be more deeply integrated into future versions of the Guide. This panel will provide opportunities for INCOSE to continue to work with OSD and collaborate on SoS SE.

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Proceedings of the 8th Annual Conference on the Science of Dissemination and Implementation : Washington, DC, USA. 14-15 December 2015

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Implementation science : IS 2016年第2期11 Suppl 2卷 100页

作者： Chambers, David Simpson, Lisa Hill-Briggs, Felicia Neta, Gila Vinson, Cynthia Beidas, Rinad Marcus, Steven Aarons, Gregory Hoagwood, Kimberly Schoenwald, Sonja Evans, Arthur Hurford, Matthew Rubin, Ronnie Hadley, Trevor Barg, Frances Walsh, Lucia Adams, Danielle Mandell, David Martin, Lindsey Mignogna, Joseph Mott, Juliette Hundt, Natalie Kauth, Michael Kunik, Mark Naik, Aanand Cully, Jeffrey McGuire, Alan White, Dominique Bartholomew, Tom McGrew, John Luther, Lauren Rollins, Angie Salyers, Michelle Cooper, Brittany Funaiole, Angie Richards, Julie Lee, Amy Lapham, Gwen Caldeiro, Ryan Lozano, Paula Gildred, Tory Achtmeyer, Carol Ludman, Evette Addis, Megan Marx, Larry Bradley, Katharine VanDeinse, Tonya Wilson, Amy Blank Stacey, Burgin Powell, Byron Bunger, Alicia Cuddeback, Gary Barnett, Miya Stadnick, Nicole Brookman-Frazee, Lauren Lau, Anna Dorsey, Shannon Pullmann, Michael Mitchell, Shannon Schwartz, Robert Kirk, Arethusa Dusek, Kristi Oros, Marla Hosler, Colleen Gryczynski, Jan Barbosa, Carolina Dunlap, Laura Lounsbury, David O’Grady, Kevin Brown, Barry Damschroder, Laura Waltz, Thomas Ritchie, Mona Atkins, David Imel, Zac E. Xiao, Bo Can, Doğan Georgiou, Panayiotis Narayanan, Shrikanth Berkel, Cady Gallo, Carlos Sandler, Irwin Brown, C. Hendricks Wolchik, Sharlene Mauricio, Anne Marie Mehrotra, Sanjay Chandurkar, Dharmendra Bora, Siddhartha Das, Arup Tripathi, Anand Saggurti, Niranjan Raj, Anita Hughes, Eric Jacobs, Brian Kirkendall, Eric Loeb, Danielle Trinkley, Katy Yang, Michael Sprowell, Andrew Nease, Donald Lyon, Aaron Lewis, Cara Boyd, Meredith Melvin, Abigail Nicodimos, Semret Liu, Freda Jungbluth, Nathanial Flynn, Allen Landis-Lewis, Zach Sales, Anne Baloh, Jure Ward, Marcia Zhu, Xi Bennett, Ian Unutzer, Jurgen Mao, Johnny Proctor, Enola Vredevoogd, Mindy Chan, Ya-Fen Williams, Nathaniel Green, Phillip Bernstein, Steven Rosner, June-Marie DeWitt, Michelle Tetrault, Jeanette Dziura, James Hsiao, Allen Sussman, Scott O’Connor, Patrick Toll, Benjamin Jones, Michael Gassaway, Julie Tobin, Jonathan Zatzic Division of Cancer Control and Population Sciences National Cancer Institute National Institutes of Health Rockville USA AcademyHealth Washington USA Department of Medicine and Welch Center for Prevention Epidemiology and Clinical Research Johns Hopkins Medical Institutions Baltimore USA Psychiatry University of Pennsylvania Philadelphia USA Family Medicine and Community Health University of Pennsylvania Philadelphia USA School of Social Policy and Practice University of Pennsylvania Philadelphia USA Psychiatry UC San Diego La Jolla USA Child & Adolescent Psychiatry The Child Study Center at NYU Langone Medical Center New York NY New York USA Psychiatry and Behavioral Sciences MUSC Charleston USA DBHIDS City of Philadelphia Philadelphia USA CBH City of Philadelphia Philadelphia USA Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia USA Health Services Research & Development Department of Veterans Affairs & Baylor College of Medicine Houston USA Treatment Core Department of Veterans Affairs Waco USA National Center for PTSD Executive Division Department of Veterans Affairs White River Junction USA HSR&D HSR&D Center for Health Information and Communication Roudebush VA Medical Center Indianapolis USA Psychology Indiana University Purdue University Indianapolis Indianapolis USA Department of Psychiatric Rehabilitation and Counseling Rutgers University Newark USA Health Services Research & Development Richard L. Roudebush VAMC Indianapolis USA IUPUI Department of Psychology ACT Center of Indiana Indianapolis USA Human Development Washington State University Pullman USA Prevention Science Washington State University Pullman USA Group Health Research Institute Group Health Seattle USA Behavioral Health Services Group Health Seattle USA Preventive Care Group Health Seattle USA Health Services Research & Development Primary and Specialty Medical Care Service VA Puget Sound Seattle USA School of Social Work Un

A1 Introduction to the 8th Annual Conference on the Science of Dissemination and Implementation: Optimizing Personal and Population Health David Chambers, Lisa Simpson D1 Discussion forum: Population health D&I research Felicia Hill-Briggs D2 Discussion forum: Global health D&I research Gila Neta, Cynthia Vinson D3 Discussion forum: Precision medicine and D&I research David Chambers S1 Predictors of community therapists’ use of therapy techniques in a large public mental health system Rinad Beidas, Steven Marcus, Gregory Aarons, Kimberly Hoagwood, Sonja Schoenwald, Arthur Evans, Matthew Hurford, Ronnie Rubin, Trevor Hadley, Frances Barg, Lucia Walsh, Danielle Adams, David Mandell S2 Implementing brief cognitive behavioral therapy (CBT) in primary care: Clinicians' experiences from the field Lindsey Martin, Joseph Mignogna, Juliette Mott, Natalie Hundt, Michael Kauth, Mark Kunik, Aanand Naik, Jeffrey Cully S3 Clinician competence: Natural variation, factors affecting, and effect on patient outcomes Alan McGuire, Dominique White, Tom Bartholomew, John McGrew, Lauren Luther, Angie Rollins, Michelle Salyers S4 Exploring the multifaceted nature of sustainability in community-based prevention: A mixed-method approach Brittany Cooper, Angie Funaiole S5 Theory informed behavioral health integration in primary care: Mixed methods evaluation of the implementation of routine depression and alcohol screening and assessment Julie Richards, Amy Lee, Gwen Lapham, Ryan Caldeiro, Paula Lozano, Tory Gildred, Carol Achtmeyer, Evette Ludman, Megan Addis, Larry Marx, Katharine Bradley S6 Enhancing the evidence for specialty mental health probation through a hybrid efficacy and implementation study Tonya VanDeinse, Amy Blank Wilson, Burgin Stacey, Byron Powell, Alicia Bunger, Gary Cuddeback S7 Personalizing evidence-based child mental health care within a fiscally mandated policy reform Miya Barnett, Nicole Stadnick, Lauren Brookman-Frazee, Anna Lau S8 Leveraging an existing

关键词： Veteran Affair Motivational Interview Qualitative Comparative Analysis Community That Care Supplemental Nutrition Assistance Program

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Tracing significant association rules using critical least association rules model

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International Journal of Innovative Computing and Applications 2013年第1期5卷 3-17页

作者： Abdullah, Zailani Herawan, Tutut Deris, Mustafa Mat Computer Science Department Universiti Malaysia Terengganu 21030 Kuala Terengganu Terengganu Malaysia Faculty of Computer System and Software Engineering Universiti Malaysia Pahang 26300 Kuantan Pahang Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia 86400 Batu Pahat Johor Malaysia

Mining of least association rules from large databases has received a great attention in knowledge discovery. These rules are very useful especially in tracing the unexceptional events or situations. Until this moment, the ratio of studies in this area is still unbalanced as compared to mine frequent rules. The difficulties level of mining these rules as compared to frequent rules are different since it involves with the excessive in computational costs, rather complicated and entailed a dedicated measurement. Therefore, this paper proposed an efficient model called critical least association rule (CLAR) to mine the significant rules so called critical least association rules. Several experiments with real and UCI datasets has shown that the CLAR successfully in producing the critical least association rules, up to 1.5 times faster and less 96% complexity than benchmarked FP-growth algorithm. © 2013 Inderscience Enterprises Ltd.

关键词： Association rules

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Dynamic Relay Selection for MAC-level Retransmission in Vehicular Ad Hoc Networks

Dynamic Relay Selection for MAC-level Retransmission in Vehi...

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IEEE Global Communications Conference

作者： Bhaskar Das Sudip Misra Utpal Roy Mohammad S. Obaidat Department of Computer and System Sciences Siksha-Bhavana Visva-Bharati Santiniketan 731235 India School of Information Technology IIT Kharagpur Kharagpur 721302 India Fellow of IEEE and Fellow of SCS Department of Computer Science & Software Engineering Monmouth University West Long Branch NJ 07764 USA

ISBN: (纸本)9781479913510

Communication in Vehicular Ad Hoc Networks (VANETs) is frequently disrupted by highly dynamic, noisy and interference-prone links. This paper proposes a novel algorithm, named Cross-Layer Tree Formation (CLTF), for medium access. CLTF enables the formation of the tree architecture that connects the receiver node with the cooperative relay node in Medium Access Control (MAC)-level retransmission. The proposed algorithm uses cooperative communication to increase the throughput and decrease the average delay in the network. Each relay node in the network serves a neighboring destination node. The simulation results show that CLTF incurs a significant gain in terms of throughput, which is at least 50% better under bad channel conditions.

关键词： VANET CLTF MAC

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Human genome meeting 2016 : Houston, TX, USA. 28 February - 2 March 2016

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Human genomics 2016年第1期10 Suppl 1卷 12页

作者： A. K. Srivastava Y. Wang R. Huang C. Skinner T. Thompson L. Pollard T. Wood F. Luo R. Stevenson R. Polimanti J. Gelernter X. Lin I. Y. Lim Y. Wu A. L. Teh L. Chen I. M. Aris S. E. Soh M. T. Tint J. L. MacIsaac F. Yap K. Kwek S. M. Saw M. S. Kobor M. J. Meaney K. M. Godfrey Y. S. Chong J. D. Holbrook Y. S. Lee P. D. Gluckman N. Karnani A. Kapoor D. Lee A. Chakravarti C. Maercker F. Graf M. Boutros G. Stamoulis F. Santoni P. Makrythanasis A. Letourneau M. Guipponi N. Panousis M. Garieri P. Ribaux E. Falconnet C. Borel S. E. Antonarakis S. Kumar J. Curran J. Blangero S. Chatterjee J. Akiyama D. Auer C. Berrios L. Pennacchio T. R. Donti G. Cappuccio M. Miller P. Atwal A. Kennedy A. Cardon C. Bacino L. Emrick J. Hertecant F. Baumer B. Porter M. Bainbridge P. Bonnen B. Graham R. Sutton Q. Sun S. Elsea Z. Hu P. Wang Y. Zhu J. Zhao M. Xiong David A. Bennett A. Hidalgo-Miranda S. Romero-Cordoba S. Rodriguez-Cuevas R. Rebollar-Vega E. Tagliabue M. Iorio E. D’Ippolito S. Baroni B. Kaczkowski Y. Tanaka H. Kawaji A. Sandelin R. Andersson M. Itoh T. Lassmann Y. Hayashizaki P. Carninci A. R. R. Forrest C. A. Semple E. A. Rosenthal B. Shirts L. Amendola C. Gallego M. Horike-Pyne A. Burt P. Robertson P. Beyers C. Nefcy D. Veenstra F. Hisama R. Bennett M. Dorschner D. Nickerson J. Smith K. Patterson D. Crosslin R. Nassir N. Zubair T. Harrison U. Peters G. Jarvik F. Menghi K. Inaki X. Woo P. Kumar K. Grzeda A. Malhotra H. Kim D. Ucar P. Shreckengast K. Karuturi J. Keck J. Chuang E. T. Liu B. Ji A. Tyler G. Ananda G. Carter H. Nikbakht M. Montagne M. Zeinieh A. Harutyunyan M. Mcconechy N. Jabado P. Lavigne J. Majewski J. B. Goldstein M. Overman G. Varadhachary R. Shroff R. Wolff M. Javle A. Futreal D. Fogelman L. Bravo W. Fajardo H. Gomez C. Castaneda C. Rolfo J. A. Pinto K. C. Akdemir L. Chin S. Patterson C. Statz S. Mockus S. N. Nikolaev X. I. Bonilla L. Parmentier B. King F. Bezrukov G. Kaya V. Zoete V. Seplyarskiy H. Sharpe T. McKee K. Popadin N. Basset-Seguin R. Ben Chaabene M. Andrianova C. Verdan K. Grosdemange O. Sumara M. E JCSRI Greenwood Genetic Center Greenwood USA School of Computing Clemson University Clemson USA Biochemical Genetics Laboratory Greenwood Genetic Center Greenwood USA Department Psychiatry Yale Sch Med and VA CT Healthcare Center West Haven USA Department Genetics Yale Sch Med and VA CT Healthcare Center West Haven USA Department Neurobiology Yale Sch Med and VA CT Healthcare Center West Haven USA Singapore Institute for Clinical Sciences Singapore Singapore National University of Singapore Singapore Singapore University of British Columbia Vancouver Canada KK Women’s and Children’s Hospital Singapore Singapore University of Southampton and University Hospital Southampton NHS Foundation Trust Southampton UK University of Auckland Auckland New Zealand McKusick-Nathans Institute of Genetic Medicine Johns Hopkins University School of Medicine Baltimore USA Esslingen University of Applied Sciences Esslingen Germany German Cancer Research Center Heidelberg Germany Department of Genetic Medicine and Development University of Geneva Medical School Geneva Switzerland Geneva University Hospitals-HUG Service of Genetic Medicine Geneva Switzerland GE3 Institute of Genetics and Genomics of Geneva University of Geneva Medical School Geneva Switzerland South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Edinburg USA South Texas Diabetes and Obesity Institute School of Medicine University of Texas Rio-Grande Valley Brownsville USA Institute of Genetic Medicine Johns Hopkins University Baltimore USA Genomics Division Lawrence Berkeley National Laboratory Berkeley USA Molecular and Human Genetics Baylor College of Medicine Houston USA Department of Translational Medical Sciences Federico II University Naples Italy Metabolon Inc Durham USA Section of Pediatric Neurology and Neuroscience Baylor College of Medicine Houston USA Tawam Hospital Abu Dhabi United Arab Emirates Stanford Medical School Stanford USA Sch

O1 The metabolomics approach to autism: identification of biomarkers for early detection of autism spectrum disorder A. K. Srivastava, Y. Wang, R. Huang, C. Skinner, T. Thompson, L. Pollard, T. Wood, F. Luo, R. Stevenson O2 Phenome-wide association study for smoking- and drinking-associated genes in 26,394 American women with African, Asian, European, and Hispanic descents R. Polimanti, J. Gelernter O3 Effects of prenatal environment, genotype and DNA methylation on birth weight and subsequent postnatal outcomes: findings from GUSTO, an Asian birth cohort X. Lin, I. Y. Lim, Y. Wu, A. L. Teh, L. Chen, I. M. Aris, S. E. Soh, M. T. Tint, J. L. MacIsaac, F. Yap, K. Kwek, S. M. Saw, M. S. Kobor, M. J. Meaney, K. M. Godfrey, Y. S. Chong, J. D. Holbrook, Y. S. Lee, P. D. Gluckman, N. Karnani, GUSTO study group O4 High-throughput identification of specific qt interval modulating enhancers at the SCN5A locus A. Kapoor, D. Lee, A. Chakravarti O5 Identification of extracellular matrix components inducing cancer cell migration in the supernatant of cultivated mesenchymal stem cells C. Maercker, F. Graf, M. Boutros O6 Single cell allele specific expression (ASE) IN T21 and common trisomies: a novel approach to understand DOWN syndrome and other aneuploidies G. Stamoulis, F. Santoni, P. Makrythanasis, A. Letourneau, M. Guipponi, N. Panousis, M. Garieri, P. Ribaux, E. Falconnet, C. Borel, S. E. Antonarakis O7 Role of microRNA in LCL to IPSC reprogramming S. Kumar, J. Curran, J. Blangero O8 Multiple enhancer variants disrupt gene regulatory network in Hirschsprung disease S. Chatterjee, A. Kapoor, J. Akiyama, D. Auer, C. Berrios, L. Pennacchio, A. Chakravarti O9 Metabolomic profiling for the diagnosis of neurometabolic disorders T. R. Donti, G. Cappuccio, M. Miller, P. Atwal, A. Kennedy, A. Cardon, C. Bacino, L. Emrick, J. Hertecant, F. Baumer, B. Porter, M. Bainbridge, P. Bonnen, B. Graham, R. Sutton, Q. Sun, S. Elsea O10 A novel causal methylation network approach to Alzheimer’s

关键词： Pulmonary Arterial Hypertension Oral Squamous Cell Carcinoma Deep Neural Network Autosomal Recessive Polycystic Kidney Disease Whole Genome Bisulfite Sequencing

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