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Institution

Université libre de Bruxelles

EducationBrussels, Belgium
About: Université libre de Bruxelles is a education organization based out in Brussels, Belgium. It is known for research contribution in the topics: Population & Breast cancer. The organization has 24974 authors who have published 56969 publications receiving 2084303 citations. The organization is also known as: ULB.


Papers
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Journal ArticleDOI
Edoardo Aprà1, Eric J. Bylaska1, W. A. de Jong2, Niranjan Govind1, Karol Kowalski1, T. P. Straatsma3, Marat Valiev1, H. J. J. van Dam4, Yuri Alexeev5, J. Anchell6, V. Anisimov5, Fredy W. Aquino, Raymond Atta-Fynn7, Jochen Autschbach8, Nicholas P. Bauman1, Jeffrey C. Becca9, David E. Bernholdt10, K. Bhaskaran-Nair11, Stuart Bogatko12, Piotr Borowski13, Jeffery S. Boschen14, Jiří Brabec15, Adam Bruner16, Emilie Cauet17, Y. Chen18, Gennady N. Chuev19, Christopher J. Cramer20, Jeff Daily1, M. J. O. Deegan, Thom H. Dunning21, Michel Dupuis8, Kenneth G. Dyall, George I. Fann10, Sean A. Fischer22, Alexandr Fonari23, Herbert A. Früchtl24, Laura Gagliardi20, Jorge Garza25, Nitin A. Gawande1, Soumen Ghosh20, Kurt R. Glaesemann1, Andreas W. Götz26, Jeff R. Hammond6, Volkhard Helms27, Eric D. Hermes28, Kimihiko Hirao, So Hirata29, Mathias Jacquelin2, Lasse Jensen9, Benny G. Johnson, Hannes Jónsson30, Ricky A. Kendall10, Michael Klemm6, Rika Kobayashi31, V. Konkov32, Sriram Krishnamoorthy1, M. Krishnan18, Zijing Lin33, Roberto D. Lins34, Rik J. Littlefield, Andrew J. Logsdail35, Kenneth Lopata36, Wan Yong Ma37, Aleksandr V. Marenich20, J. Martin del Campo38, Daniel Mejía-Rodríguez39, Justin E. Moore6, Jonathan M. Mullin, Takahito Nakajima, Daniel R. Nascimento1, Jeffrey A. Nichols10, P. J. Nichols40, J. Nieplocha1, Alberto Otero-de-la-Roza41, Bruce J. Palmer1, Ajay Panyala1, T. Pirojsirikul42, Bo Peng1, Roberto Peverati32, Jiri Pittner15, L. Pollack, Ryan M. Richard43, P. Sadayappan44, George C. Schatz45, William A. Shelton36, Daniel W. Silverstein46, D. M. A. Smith6, Thereza A. Soares47, Duo Song1, Marcel Swart, H. L. Taylor48, G. S. Thomas1, Vinod Tipparaju49, Donald G. Truhlar20, Kiril Tsemekhman, T. Van Voorhis50, Álvaro Vázquez-Mayagoitia5, Prakash Verma, Oreste Villa51, Abhinav Vishnu1, Konstantinos D. Vogiatzis52, Dunyou Wang53, John H. Weare26, Mark J. Williamson54, Theresa L. Windus14, Krzysztof Wolinski13, A. T. Wong, Qin Wu4, Chan-Shan Yang2, Q. Yu55, Martin Zacharias56, Zhiyong Zhang57, Yan Zhao58, Robert W. Harrison59 
Pacific Northwest National Laboratory1, Lawrence Berkeley National Laboratory2, National Center for Computational Sciences3, Brookhaven National Laboratory4, Argonne National Laboratory5, Intel6, University of Texas at Arlington7, State University of New York System8, Pennsylvania State University9, Oak Ridge National Laboratory10, Washington University in St. Louis11, Wellesley College12, Maria Curie-Skłodowska University13, Iowa State University14, Academy of Sciences of the Czech Republic15, University of Tennessee at Martin16, Université libre de Bruxelles17, Facebook18, Russian Academy of Sciences19, University of Minnesota20, University of Washington21, United States Naval Research Laboratory22, Georgia Institute of Technology23, University of St Andrews24, Universidad Autónoma Metropolitana25, University of California, San Diego26, Saarland University27, Sandia National Laboratories28, University of Illinois at Urbana–Champaign29, University of Iceland30, Australian National University31, Florida Institute of Technology32, University of Science and Technology of China33, Oswaldo Cruz Foundation34, Cardiff University35, Louisiana State University36, Chinese Academy of Sciences37, National Autonomous University of Mexico38, University of Florida39, Los Alamos National Laboratory40, University of Oviedo41, Prince of Songkla University42, Ames Laboratory43, University of Utah44, Northwestern University45, Universal Display Corporation46, Federal University of Pernambuco47, CD-adapco48, Cray49, Massachusetts Institute of Technology50, Nvidia51, University of Tennessee52, Shandong Normal University53, University of Cambridge54, Advanced Micro Devices55, Technische Universität München56, Stanford University57, Wuhan University of Technology58, Stony Brook University59
TL;DR: The NWChem computational chemistry suite is reviewed, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.
Abstract: Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the past few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach, and outlook.

342 citations

Journal ArticleDOI
TL;DR: Recombinant chemerin induced the phosphorylation of extracellular signal-regulated kinases 1/2 and lipolysis in differentiated 3T3-L1 adipocytes and likely regulates adipocyte function by autocrine/paracrine mechanisms.

342 citations

Journal ArticleDOI
Rasha Abbasi1, Y. Abdou2, T. Abu-Zayyad3, Markus Ackermann  +266 moreInstitutions (41)
18 Apr 2012-Nature
TL;DR: An upper limit on the flux of energetic neutrinos associated with GRBs that is at least a factor of 3.7 below the predictions is reported, implying either that GRBs are not the only sources of cosmic rays with energies exceeding 1018 electronvolts or that the efficiency of neutrino production is much lower than has been predicted.
Abstract: Very energetic astrophysical events are required to accelerate cosmic rays to above 10(18) electronvolts. GRBs (c-ray bursts) have been proposed as possible candidate sources(1-3). In the GRB 'fireball' model, cosmic-ray acceleration should be accompanied by neutrinos produced in the decay of charged pions created in interactions between the high-energy cosmic-ray protons and gamma-rays(4). Previous searches for such neutrinos found none, but the constraints were weak because the sensitivity was at best approximately equal to the predicted flux(5-7). Here we report an upper limit on the flux of energetic neutrinos associated with GRBs that is at least a factor of 3.7 below the predictions(4,8-10). This implies either that GRBs are not the only sources of cosmic rays with energies exceeding 10(18) electronvolts or that the efficiency of neutrino production is much lower than has been predicted.

342 citations

Journal ArticleDOI
TL;DR: A formulation of stochastic thermodynamics for systems subjected to nonequilibrium constraints and furthermore driven by external time-dependent forces is proposed, leading to a splitting of the second law leading to three second-law-like relations.
Abstract: We propose a formulation of stochastic thermodynamics for systems subjected to nonequilibrium constraints (i.e. broken detailed balance at steady state) and furthermore driven by external time-dependent forces. A splitting of the second law occurs in this description leading to three second-law-like relations. The general results are illustrated on specific solvable models. The present paper uses a master equation based approach.

340 citations

Journal ArticleDOI
01 Dec 2007-Proteins
TL;DR: Evaluating blind predictions performed during 2005–2007 as part of Rounds 6–12 of the community‐wide experiment on Critical Assessment of PRedicted Interactions shows that current scoring methods are probably not sensitive enough, indicating that the growing community of CAPRI predictors is engaged more actively than ever in the development of better scoring functions and means of modeling conformational flexibility.
Abstract: The performance of methods for predicting protein-protein interactions at the atomic scale is assessed by evaluating blind predictions performed during 2005-2007 as part of Rounds 6-12 of the community-wide experiment on Critical Assessment of PRedicted Interactions (CAPRI) These Rounds also included a new scoring experiment, where a larger set of models contributed by the predictors was made available to groups developing scoring functions These groups scored the uploaded set and submitted their own best models for assessment The structures of nine protein complexes including one homodimer were used as targets These targets represent biologically relevant interactions involved in gene expression, signal transduction, RNA, or protein processing and membrane maintenance For all the targets except one, predictions started from the experimentally determined structures of the free (unbound) components or from models derived by homology, making it mandatory for docking methods to model the conformational changes that often accompany association In total, 63 groups and eight automatic servers, a substantial increase from previous years, submitted docking predictions, of which 1994 were evaluated here Fifteen groups submitted 305 models for five targets in the scoring experiment Assessment of the predictions reveals that 31 different groups produced models of acceptable and medium accuracy-but only one high accuracy submission-for all the targets, except the homodimer In the latter, none of the docking procedures reproduced the large conformational adjustment required for correct assembly, underscoring yet again that handling protein flexibility remains a major challenge In the scoring experiment, a large fraction of the groups attained the set goal of singling out the correct association modes from incorrect solutions in the limited ensembles of contributed models But in general they seemed unable to identify the best models, indicating that current scoring methods are probably not sensitive enough With the increased focus on protein assemblies, in particular by structural genomics efforts, the growing community of CAPRI predictors is engaged more actively than ever in the development of better scoring functions and means of modeling conformational flexibility, which hold promise for much progress in the future

340 citations


Authors

Showing all 25206 results

NameH-indexPapersCitations
Karl J. Friston2171267217169
Yi Chen2174342293080
David Miller2032573204840
Jing Wang1844046202769
H. S. Chen1792401178529
Jie Zhang1784857221720
Jasvinder A. Singh1762382223370
D. M. Strom1763167194314
J. N. Butler1722525175561
Andrea Bocci1722402176461
Bradley Cox1692150156200
Marc Weber1672716153502
Hongfang Liu1662356156290
Guenakh Mitselmakher1651951164435
Yang Yang1642704144071
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023119
2022411
20213,194
20203,051
20192,751
20182,609