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Institution

University of Texas at Dallas

EducationRichardson, Texas, United States
About: University of Texas at Dallas is a education organization based out in Richardson, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 14986 authors who have published 35589 publications receiving 1293714 citations. The organization is also known as: UT-Dallas & UT Dallas.


Papers
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Journal ArticleDOI
02 Aug 2002-Science
TL;DR: Many potential applications have been proposed for carbon nanotubes, including conductive and high-strength composites; energy storage and energy conversion devices; sensors; field emission displays and radiation sources; hydrogen storage media; and nanometer-sized semiconductor devices, probes, and interconnects.
Abstract: Many potential applications have been proposed for carbon nanotubes, including conductive and high-strength composites; energy storage and energy conversion devices; sensors; field emission displays and radiation sources; hydrogen storage media; and nanometer-sized semiconductor devices, probes, and interconnects. Some of these applications are now realized in products. Others are demonstrated in early to advanced devices, and one, hydrogen storage, is clouded by controversy. Nanotube cost, polydispersity in nanotube type, and limitations in processing and assembly methods are important barriers for some applications of single-walled nanotubes.

9,693 citations

Journal ArticleDOI
Georges Aad1, T. Abajyan2, Brad Abbott3, Jalal Abdallah4  +2964 moreInstitutions (200)
TL;DR: In this article, a search for the Standard Model Higgs boson in proton-proton collisions with the ATLAS detector at the LHC is presented, which has a significance of 5.9 standard deviations, corresponding to a background fluctuation probability of 1.7×10−9.

9,282 citations

Journal ArticleDOI
TL;DR: Principal component analysis (PCA) as discussed by the authors is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables, and its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and display the pattern of similarity of the observations and of the variables as points in maps.
Abstract: Principal component analysis PCA is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross-validation techniques such as the bootstrap and the jackknife. PCA can be generalized as correspondence analysis CA in order to handle qualitative variables and as multiple factor analysis MFA in order to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen-decomposition of positive semi-definite matrices and upon the singular value decomposition SVD of rectangular matrices. Copyright © 2010 John Wiley & Sons, Inc.

6,398 citations

Journal ArticleDOI
28 Feb 2008-Nature
TL;DR: Understanding autophagy may ultimately allow scientists and clinicians to harness this process for the purpose of improving human health, and to play a role in cell death.
Abstract: Autophagy, or cellular self-digestion, is a cellular pathway involved in protein and organelle degradation, with an astonishing number of connections to human disease and physiology. For example, autophagic dysfunction is associated with cancer, neurodegeneration, microbial infection and ageing. Paradoxically, although autophagy is primarily a protective process for the cell, it can also play a role in cell death. Understanding autophagy may ultimately allow scientists and clinicians to harness this process for the purpose of improving human health.

5,831 citations

Journal ArticleDOI
TL;DR: A conceptual framework and operational research criteria are proposed, based on the prevailing scientific evidence to date, to test and refine these models with longitudinal clinical research studies and it is hoped that these recommendations will provide a common rubric to advance the study of preclinical AD.
Abstract: The pathophysiological process of Alzheimer's disease (AD) is thought to begin many years before the diagnosis of AD dementia. This long "preclinical" phase of AD would provide a critical opportunity for therapeutic intervention; however, we need to further elucidate the link between the pathological cascade of AD and the emergence of clinical symptoms. The National Institute on Aging and the Alzheimer's Association convened an international workgroup to review the biomarker, epidemiological, and neuropsychological evidence, and to develop recommendations to determine the factors which best predict the risk of progression from "normal" cognition to mild cognitive impairment and AD dementia. We propose a conceptual framework and operational research criteria, based on the prevailing scientific evidence to date, to test and refine these models with longitudinal clinical research studies. These recommendations are solely intended for research purposes and do not have any clinical implications at this time. It is hoped that these recommendations will provide a common rubric to advance the study of preclinical AD, and ultimately, aid the field in moving toward earlier intervention at a stage of AD when some disease-modifying therapies may be most efficacious.

5,671 citations


Authors

Showing all 15148 results

NameH-indexPapersCitations
Eugene Braunwald2301711264576
Younan Xia216943175757
Eric N. Olson206814144586
Thomas C. Südhof191653118007
Scott M. Grundy187841231821
Jing Wang1844046202769
Eric Boerwinkle1831321170971
Eric J. Nestler178748116947
John D. Minna169951106363
Elliott M. Antman161716179462
Adi F. Gazdar157776104116
Bruce D. Walker15577986020
R. Kowalewski1431815135517
Joseph Izen137143398900
James A. Richardson13636375778
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202371
2022217
20212,151
20202,227
20192,192