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11th German Conference on Chemoinformatics (GCC 2015) Abstracts

JOURNAL OF CHEMINFORMATICS 2016年第SUPPL 1期8卷 1-27页

作者： [Anonymous] GDCh-CIC Division Associated Board Member Beilstein-Institut zur Förderung der Chemischen Wissenschaften Trakehner Str. 7-9 60487 Frankfurt Germany. ufechner@beilstein-institut.de. Division Medicinal Chemistry Amsterdam Institute for Molecules Medicines and Systems (AIMMS) VU University Amsterdam The Netherlands. Centre for Bioinformatics Uni Hamburg Bundesstr. 43 20146 Hamburg Germany. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. stefan.guessregen@***. Sanofi-Aventis Deutschland GmbH 65926 Frankfurt am Main Germany. GlaxoSmithKline Stevenage SG1 2NY UK. nicola.j.richmond@***. Discngine Paris 75011 France. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. marwin.segler@wwu.de. Organisch-Chemisches Institut Westfälische Wilhelms-Universität Münster Germany. Bundeskriminalamt Wiesbaden Central Analytics II 65173 Wiesbaden Germany. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. shea@chem.ucsb.edu. Department of Chemistry and Biochemistry University of California Santa Barbara CA 93111 USA. Chemistry Department Ludwig-Maximilians-Universität München Butenandtstr. 7 81377 Munich Germany. Inorganic Chemistry and Center for Nanointegration University of Duisburg-Essen Essen Germany. CAM-D Technologies Essen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. Institute for Bioinformatics and Chemoinformatics Westphalian University of Applied Sciences Recklinghausen Germany. achim.zielesny@w-hs.de. Leiden University Leiden Netherlands. j.fraaije@chem.leidenuniv.nl. Culgi BV Berlin Germany. Physikalische Chemie III TU Dortmund 44227 Dortmund Germany. stefan.kast@tu-dortmund.de. Centre for Molecular Informatics Department of Chemistry University of Cambridge Lensfield Road Cambridge CB2 1EW United Kingdom. kcb27@cam.ac.uk. Centre for Molecular Informatics Department of Chemistry

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Diffusion suppression in gel dosimetry by addition of nanoclay

Diffusion suppression in gel dosimetry by addition of nanocl...

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World Congress on Medical Physics and Biomedical engineering

作者： Maeyama, T. Fukunishi, N. Ishikawa, K.L. Fukasaku, K. Furuta, T. Takagi, S. Noda, S. Himeno, R. RIKEN Nishina Center for Accelerator-Based Science Wako Japan RIKEN Computational Science Research Program Wako Japan University of Tokyo Department of Nuclear Engineering and Management Graduate School of Engineering Tokyo Japan University of Tokyo Photon Science Center Graduate School of Engineering Tokyo Japan Himon'ya Hospital Department of Neurosurgery Tokyo Japan University of Tokyo Department of Mechanical Engineering Graduate School of Engineering Tokyo Japan RIKEN Advanced Center for Computing and Communication Wako Japan

ISBN: (纸本)9783642293047

This paper reports diffusion suppression in gel dosimetery by using nanocomposite gel. A Fricke xylenol gel (FXG) and a dichromate gel (DCG) were selected for adding a small amount of nanoclay (Laponite XLG). The diffusion coefficient and T1 distribution were evaluated by irradiating the gel dosimeter with a heavy ion beam. As a result, these gels showed improvement in the diffusion coefficient. In particular, by addition of the clay, the DCG exhibited diffusion-free characteristics. Moreover, it exhibited high stability after irradiation. © 2013 Springer-Verlag.

关键词： Nanocomposites

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A new integrated framework to advance fire probabilistic risk analysis of nuclear power plants

A new integrated framework to advance fire probabilistic ris...

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2013 Winter Meeting on Transactions and Embedded Topical Meetings: Risk Management for Complex Socio-Technical Systems, 2nd ANS SMR 2013 Conference, Nuclear Nonproliferation - 1st Fission to the Future

作者： Sakurahara, Tatsuya Mohaghegh, Zahra Kee, Ernie Rodgers, Shawn Brandyberry, Mark Kazemi, Reza Reihani, Seyed A. Dept. of Nuclear Plasma and Radiological Eng. University of Illinois Urbana-Champaign United States South Texas Project Nuclear Operating Company United States Computational Science and Engineering Program University of Illinois Urbana-Champaign United States Soteria Consultants LLC United States

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Distinct mechanisms of FAK mechanoactivation by different extracellular matrix proteins

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医用生物力学 2013年第S1期28卷 69-71页

作者： Jihye Seong Arash Tajik Jie Sun Jun-Lin Guan Martin J.Humphries Susan E.Craig Asha Shekaran Andrs J.García Ning Wang Yingxiao Wang Neuroscience Program University of Illinois Department of Mechanical Science and Engineering University of Illinois Department of Bioengineering & Beckman Institute for Advanced Science and Technology University of Illinois Division of Molecular Medicine and Genetics Departments of Internal Medicine and Cell & DevelopmentalBiologyUniversity of Michigan Medical School Wellcome Trust Centre for Cell-Matrix Research Faculty of Life SciencesUniversity of Manchester Woodruff School of Mechanical Engineering and Petit Institute for Bioengineering and Bioscience Georgia Institute of Technology Department of Biomedical Engineering School of Life Science and TechnologyHuazhong University of Science and Technology Department of Integrative and Molecular Physiology Center for Biophysics and Computational BiologyInstitute for Genomic BiologyUniversity of Illinois Department of Bioengineering University of California

Introduction Cells can sense and respond to the mechanical microenvironment by converting forces into biochemical signals inside the cells,***[1-3].Focal adhesions are the major sites of interaction between a cell and... 详细信息

Introduction Cells can sense and respond to the mechanical microenvironment by converting forces into biochemical signals inside the cells,***^[1-3].Focal adhesions are the major sites of interaction between a cell and its extracellular matrix（ECM）microenvironment,thus outside mechanical signals can be sensed at focal adhesions through transmembrane receptor *** particular,it has been shown that matrix elasticity can control the cell fate^[4]by modulating the interactions between ECM proteins and their receptor integrins^[5,6].For example,different rigidity of polyacrylamide（PA）gels can lead to different density of ECM ancho-

关键词： integrin converting transmembrane biochemical modulating elasticity rigidity polyacrylamide outside purchased

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Angular-Momentum-Dependent Orbital-Free Density Functional Theory

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Physical Review Letters 2013年第6期111卷 066402-066402页

作者： Youqi Ke Florian Libisch Junchao Xia Lin-Wang Wang Emily A. Carter Department of Mechanical and Aerospace Engineering Program in Applied and Computational Mathematics and Andlinger Center for Energy and the Environment Princeton University Princeton New Jersey 08544 USA Material Science Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA

Orbital-free (OF) density functional theory (DFT) directly solves for the electron density rather than the wave function of many electron systems, greatly simplifying and enabling large scale first principles simulations. However, the required approximate noninteracting kinetic energy density functionals and local electron-ion pseudopotentials severely restrict the general applicability of conventional OFDFT. Here, we present a new generation of OFDFT called angular-momentum-dependent (AMD)-OFDFT to harness the accuracy of Kohn-Sham DFT and the simplicity of OFDFT. The angular momenta of electrons are explicitly introduced within atom-centered spheres so that the important ionic core region can be accurately described. In addition to conventional OF total energy functionals, we introduce a crucial nonlocal energy term with a set of AMD energies to correct errors due to the kinetic energy density functional and the local pseudopotential. We find that our AMD-OFDFT formalism offers substantial improvements over conventional OFDFT, as we show for various properties of the transition metal titanium.

关键词： DENSITY functional theory ELECTRON density KINETIC energy PSEUDOPOTENTIAL method TRANSITION metal compounds ELECTRON-ion collisions

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AMUSE: Empowering Users for Cost-Aware Offloading with Throughput-Delay Tradeoffs

AMUSE: Empowering Users for Cost-Aware Offloading with Throu...

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IEEE INFOCOM Conference

作者： Youngbin Im Carlee Joe-Wong Sangtae Ha Soumya Sen Ted "Taekyoung" Kwon Mung Chiang School of Computer Science and Engineering Seoul National University Seoul Korea Program in Applied and Computational Mathematics Princeton University Princeton NJ USA Department of Electrical Engineering Princeton University Princeton NJ USA

ISBN: (纸本)9781467359443

Mobile users face a tradeoff between cost, through-put, and delay in making their offloading decisions. To navigate this tradeoff, we propose AMUSE (Adaptive bandwidth Management through USer-Empowerment), a practical, cost-aware WiFi offloading system that takes into account a user's throughput-delay tradeoffs and cellular budget constraint. Based on predicted future usage and WiFi availability, AMUSE decides which applications to offload to what times of the day. To practically enforce the assigned rate of each TCP application, we introduce a receiver-side TCP bandwidth control algorithm that adjusts the rate by controlling the TCP advertisement window from the user side. We implement AMUSE on Windows 7 tablets and evaluate its effectiveness with 3G and WiFi usage data obtained from a trial with 25 mobile users. Our results show that AMUSE improves user utility.

关键词： tradeoffs UNLOADING Wi-Fi third generation rate control

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Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

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Scientific data 2017年第1期4卷 170179页

作者： Jason Flannick Christian Fuchsberger Anubha Mahajan Tanya M Teslovich Vineeta Agarwala Kyle J Gaulton Lizz Caulkins Ryan Koesterer Clement Ma Loukas Moutsianas Davis J McCarthy Manuel A Rivas John R B Perry Xueling Sim Thomas W Blackwell Neil R Robertson N William Rayner Pablo Cingolani Adam E Locke Juan Fernandez Tajes Heather M Highland Josee Dupuis Peter S Chines Cecilia M Lindgren Christopher Hartl Anne U Jackson Han Chen Jeroen R Huyghe Martijn van de Bunt Richard D Pearson Ashish Kumar Martina Müller-Nurasyid Niels Grarup Heather M Stringham Eric R Gamazon Jaehoon Lee Yuhui Chen Robert A Scott Jennifer E Below Peng Chen Jinyan Huang Min Jin Go Michael L Stitzel Dorota Pasko Stephen C J Parker Tibor V Varga Todd Green Nicola L Beer Aaron G Day-Williams Teresa Ferreira Tasha Fingerlin Momoko Horikoshi Cheng Hu Iksoo Huh Mohammad Kamran Ikram Bong-Jo Kim Yongkang Kim Young Jin Kim Min-Seok Kwon Juyoung Lee Selyeong Lee Keng-Han Lin Taylor J Maxwell Yoshihiko Nagai Xu Wang Ryan P Welch Joon Yoon Weihua Zhang Nir Barzilai Benjamin F Voight Bok-Ghee Han Christopher P Jenkinson Teemu Kuulasmaa Johanna Kuusisto Alisa Manning Maggie C Y Ng Nicholette D Palmer Beverley Balkau Alena Stančáková Hanna E Abboud Heiner Boeing Vilmantas Giedraitis Dorairaj Prabhakaran Omri Gottesman James Scott Jason Carey Phoenix Kwan George Grant Joshua D Smith Benjamin M Neale Shaun Purcell Adam S Butterworth Joanna M M Howson Heung Man Lee Yingchang Lu Soo-Heon Kwak Wei Zhao John Danesh Vincent K L Lam Kyong Soo Park Danish Saleheen Wing Yee So Claudia H T Tam Uzma Afzal David Aguilar Rector Arya Tin Aung Edmund Chan Carmen Navarro Ching-Yu Cheng Domenico Palli Adolfo Correa Joanne E Curran Dennis Rybin Vidya S Farook Sharon P Fowler Barry I Freedman Michael Griswold Daniel Esten Hale Pamela J Hicks Chiea-Chuen Khor Satish Kumar Benjamin Lehne Dorothée Thuillier Wei Yen Lim Jianjun Liu Marie Loh Solomon K Musani Sobha Puppala William R Scott Loïc Yengo Sian-Tsung Tan Herman A Taylor Farook Thameem Gregory Wilson Tien Yin Wong Pål Rasmu Department of Molecular Biology Massachusetts General Hospital Boston Massachusetts USA. Program in Medical and Population Genetics Broad Institute Cambridge Massachusetts USA. Department of Biostatistics and Center for Statistical Genetics University of Michigan Ann Arbor Michigan USA. Wellcome Trust Centre for Human Genetics Nuffield Department of Medicine University of Oxford Oxford UK. Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge Massachusetts USA. Department of Statistics University of Oxford Oxford UK. Genetics of Complex Traits University of Exeter Medical School University of Exeter Exeter UK. MRC Epidemiology Unit Institute of Metabolic Science University of Cambridge Cambridge UK. Department of Twin Research and Genetic Epidemiology King's College London London UK. Oxford Centre for Diabetes Endocrinology and Metabolism Radcliffe Department of Medicine University of Oxford Oxford UK. Department of Human Genetics Wellcome Trust Sanger Institute Hinxton Cambridgeshire UK. School of Computer Science McGill University Montreal Quebec Canada. McGill University and Génome Québec Innovation Centre Montreal Quebec Canada. Human Genetics Center The University of Texas Graduate School of Biomedical Sciences at Houston The University of Texas Health Science Center at Houston Houston Texas USA. Department of Biostatistics Boston University School of Public Health Boston Massachusetts USA. National Heart Lung and Blood Institute's Framingham Heart Study Framingham Massachusetts USA. Medical Genomics and Metabolic Genetics Branch National Human Genome Research Institute National Institutes of Health Bethesda Maryland USA. Department of Biostatistics Harvard School of Public Health Boston Massachusetts USA. Chronic Disease Epidemiology Swiss Tropical and Public Health Institute University of Basel Basel Switzerland. Institute of Genetic Epidemiology Helmholtz Zentrum München German R

To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.

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Tracking chronically recorded single-units in cortically controlled brain machine interfaces

Tracking chronically recorded single-units in cortically con...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Ahmed Eleryan Mukta Vaidya Joshua Southerland Islam Badreldin Karthikeyan Balasubramanian Andrew Fagg Nicholas Hatsopoulos Karim Oweiss Department of Electrical and Computer Engineering Michigan State University MI USA Committee of Computational Neuroscience University of Chicago IL USA Department of Computer Science and Bioengineering University of Oklahoma OK USA Cognitive Science Program Michigan State University MI USA

Multiple single-units recorded from chronically-implanted microelectrode arrays frequently exhibit variability in their spike waveform features and firing characteristics, making it challenging to ascertain the identity of recorded neurons across days. In this study, we present a fast and efficient algorithm that tracks multiple single-units, recorded in a nonhuman primate performing brain control of a robotic arm, across days based on features extracted from units' average waveforms. Furthermore, the algorithm does not require long recording duration to perform the analysis and can be applied at the start of each recording session without requiring the subject to be engaged in a behavioral task. The algorithm achieves a classification accuracy of up to 92% compared to experts' manual tracking.

关键词： Feature extraction Support vector machines Training Neurons Measurement Educational institutions Niobium

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Selective surface functionalization at regions of high local curvature in graphene

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Chemical Communications 2013年第7期49卷 677-679页

作者： Wu, Qingzhi Wu, Yaping Hao, Yufeng Geng, Jianxin Charlton, Matthew Chen, Shanshan Ren, Yujie Ji, Hengxing Li, Huifeng Boukhvalov, Danil W. Piner, Richard D. Bielawski, Christopher W. Ruoff, Rodney S. State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Biomedical Materials and Engineering Center Wuhan University of Technology Wuhan 430070 China Department of Mechanical Engineering Department of Materials Science Department of Engineering Program The University of Texas at Austin Austin TX 78712 1 University Station C2200 United States Department of Chemistry and Biochemistry The University of Texas at Austin Austin TX 78712 1 University Station A1590 United States School of Computational Sciences Korea Institute for Advanced Study Seoul 130-722 South Korea ) School of Chemical and Biological Engineering Seoul National University Seoul 151-742 South Korea

Monolayer graphene was deposited on a Si wafer substrate decorated with SiO2 nanoparticles (NPs) and then exposed to aryl radicals that were generated in situ from their diazonium precursors. Using micro-Raman mapping...

Monolayer graphene was deposited on a Si wafer substrate decorated with SiO₂ nanoparticles (NPs) and then exposed to aryl radicals that were generated in situ from their diazonium precursors. Using micro-Raman mapping, the aryl radicals were found to selectively react with the regions of graphene that covered the NPs. The enhanced chemical reactivity was attributed to the increased strain energy induced by the local mechanical deformation of the graphene. © The Royal Society of Chemistry.

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Origin of Overstretching Transitions in Single-Stranded Nucleic Acids

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Physical Review Letters 2013年第18期111卷 188302-188302页

作者： Zackary N. Scholl Mahir Rabbi David Lee Laura Manson Hanna S-Gracz Piotr E. Marszalek Program in Computational Biology and Bioinformatics Duke University Durham North Carolina 27708 USA Department of Mechanical Engineering and Materials Science Center for Biologically Inspired Materials and Material Systems Duke University Durham North Carolina 27708 USA Department of Molecular and Structural Biochemistry North Carolina State University Raleigh North Carolina 27708 USA

We combined single-molecule force spectroscopy with nuclear magnetic resonance measurements and molecular mechanics simulations to examine overstretching transitions in single-stranded nucleic acids. In single-stranded DNA and single-stranded RNA there is a low-force transition that involves unwinding of the helical structure, along with base unstacking. We determined that the high-force transition that occurs in polydeoxyadenylic acid single-stranded DNA is caused by the cooperative forced flipping of the dihedral angle formed between four atoms, O5’-C5’-C4’-C3’ (γ torsion), in the nucleic acid backbone within the canonical B-type helix. The γ torsion also flips under force in A-type helices, where the helix is shorter and wider as compared to the B-type helix, but this transition is less cooperative than in the B type and does not generate a high-force plateau in the force spectrums of A-type helices. We find that a similar high-force transition can be induced in polyadenylic acid single-stranded RNA by urea, presumably due to disrupting the intramolecular hydrogen bonding in the backbone. We hypothesize that a pronounced high-force transition observed for B-type helices of double stranded DNA also involves a cooperative flip of the γ torsion. These observations suggest new fundamental relationships between the canonical structures of single-and double-stranded DNA and the mechanism of their molecular elasticity.

关键词： NUCLEAR magnetic resonance spectroscopy SINGLE-stranded DNA NUCLEOTIDE sequence DEOXYRIBOSE ATOMIC force microscopy

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