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Institution

University of North Carolina at Chapel Hill

EducationChapel Hill, North Carolina, United States
About: University of North Carolina at Chapel Hill is a education organization based out in Chapel Hill, North Carolina, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 81393 authors who have published 185327 publications receiving 9948508 citations. The organization is also known as: University of North Carolina & North Carolina.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors propose a method for the identification of the most likely candidate species of a given species from a set of known species: a.k.a. a. nomenclature.

1,466 citations

Proceedings ArticleDOI
24 Oct 2016
TL;DR: A novel class of attacks is defined: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual, and a systematic method to automatically generate such attacks is developed through printing a pair of eyeglass frames.
Abstract: Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to attack, particularly when used in applications where physical security or safety is at risk. In this paper, we focus on facial biometric systems, which are widely used in surveillance and access control. We define and investigate a novel class of attacks: attacks that are physically realizable and inconspicuous, and allow an attacker to evade recognition or impersonate another individual. We develop a systematic method to automatically generate such attacks, which are realized through printing a pair of eyeglass frames. When worn by the attacker whose image is supplied to a state-of-the-art face-recognition algorithm, the eyeglasses allow her to evade being recognized or to impersonate another individual. Our investigation focuses on white-box face-recognition systems, but we also demonstrate how similar techniques can be used in black-box scenarios, as well as to avoid face detection.

1,466 citations

Journal ArticleDOI
Jennifer Stone1, Jennifer Stone2, Jennifer Stone3, Michael Conlon O'Donovan4, Hugh Gurling5, George Kirov4, Douglas Blackwood6, Aiden Corvin7, Nicholas John Craddock4, Michael Gill7, Christina M. Hultman8, Christina M. Hultman9, Paul Lichtenstein8, Andrew McQuillin5, Carlos N. Pato10, Douglas M. Ruderfer2, Douglas M. Ruderfer1, Douglas M. Ruderfer3, Michael John Owen4, David St Clair11, Patrick F. Sullivan12, Pamela Sklar2, Pamela Sklar3, Pamela Sklar1, Shaun Purcell3, Shaun Purcell1, Shaun Purcell2, Joshua M. Korn2, Joshua M. Korn1, Stuart MacGregor13, Derek W. Morris7, Colm O'Dushlaine7, Mark J. Daly3, Mark J. Daly2, Mark J. Daly1, Peter M. Visscher13, Peter Holmans4, Edward M. Scolnick2, Edward M. Scolnick3, Nigel Williams4, Lucy Georgieva4, Ivan Nikolov4, Nadine Norton4, Hywel Williams4, Draga Toncheva, Vihra Milanova, Emma Flordal Thelander8, Patrick Sullivan12, Elaine Kenny7, John L. Waddington14, Khalid Choudhury5, Susmita Datta5, Jonathan Pimm5, Srinivasa Thirumalai15, Vinay Puri5, Robert Krasucki5, Jacob Lawrence5, Digby Quested16, Nicholas Bass5, David Curtis17, Caroline Crombie11, Gillian Fraser11, Soh Leh Kwan11, Nicholas Walker, Walter J. Muir6, Kevin A. McGhee6, Ben S. Pickard6, P. Malloy6, Alan W Maclean6, Margaret Van Beck6, Michele T. Pato10, Helena Medeiros10, Frank A. Middleton18, Célia Barreto Carvalho10, Christopher P. Morley18, Ayman H. Fanous, David V. Conti10, James A. Knowles10, Carlos Ferreira, António Macedo19, M. Helena Azevedo19, Steve McCarroll1, Steve McCarroll2, Mark J. Daly1, Mark J. Daly3, Mark J. Daly2, Kimberly Chambert3, Kimberly Chambert2, Casey Gates2, Stacey Gabriel2, Scott Mahon2, Kristen Ardlie2 
11 Sep 2008-Nature
TL;DR: A genome-wide survey of rare CNVs in 3,391 patients with schizophrenia and 3,181 ancestrally matched controls provides strong support for a model of schizophrenia pathogenesis that includes the effects of multiple rare structural variants, both genome- wide and at specific loci.
Abstract: Schizophrenia is a severe mental disorder marked by hallucinations, delusions, cognitive deficits and apathy, with a heritability estimated at 73 - 90% ( ref. 1). Inheritance patterns are complex, and the number and type of genetic variants involved are not understood. Copy number variants ( CNVs) have been identified in individual patients with schizophrenia(2-7) and also in neurodevelopmental disorders(8-11), but large- scale genome- wide surveys have not been performed. Here we report a genome- wide survey of rare CNVs in 3,391 patients with schizophrenia and 3,181 ancestrally matched controls, using high- density microarrays. For CNVs that were observed in less than 1% of the sample and were more than 100 kilobases in length, the total burden is increased 1.15- fold in patients with schizophrenia in comparison with controls. This effect was more pronounced for rarer, single- occurrence CNVs and for those that involved genes as opposed to those that did not. As expected, deletions were found within the region critical for velo- cardio- facial syndrome, which includes psychotic symptoms in 30% of patients(12). Associations with schizophrenia were also found for large deletions on chromosome 15q13.3 and 1q21.1. These associations have not previously been reported, and they remained significant after genome- wide correction. Our results provide strong support for a model of schizophrenia pathogenesis that includes the effects of multiple rare structural variants, both genome- wide and at specific loci.

1,465 citations

Journal ArticleDOI
12 Nov 2009-Nature
TL;DR: Compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands, chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations.
Abstract: Although drugs are intended to be selective, at least some bind to several physiological targets, explaining side effects and efficacy. Because many drug-target combinations exist, it would be useful to explore possible interactions computationally. Here we compared 3,665 US Food and Drug Administration (FDA)-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested experimentally, including the antagonism of the beta(1) receptor by the transporter inhibitor Prozac, the inhibition of the 5-hydroxytryptamine (5-HT) transporter by the ion channel drug Vadilex, and antagonism of the histamine H(4) receptor by the enzyme inhibitor Rescriptor. Overall, 23 new drug-target associations were confirmed, five of which were potent (<100 nM). The physiological relevance of one, the drug N,N-dimethyltryptamine (DMT) on serotonergic receptors, was confirmed in a knockout mouse. The chemical similarity approach is systematic and comprehensive, and may suggest side-effects and new indications for many drugs.

1,465 citations

Journal ArticleDOI
TL;DR: Treatment of patients with CDI should be stratified depending on whether they have mild-to-moderate, severe, or complicated disease, and a classification of disease severity is proposed to guide therapy that is useful for clinicians.

1,464 citations


Authors

Showing all 82249 results

NameH-indexPapersCitations
Walter C. Willett3342399413322
Salim Yusuf2311439252912
David J. Hunter2131836207050
Irving L. Weissman2011141172504
Eric J. Topol1931373151025
Dennis W. Dickson1911243148488
Scott M. Grundy187841231821
Peidong Yang183562144351
Patrick O. Brown183755200985
Eric Boerwinkle1831321170971
Alan C. Evans183866134642
Anil K. Jain1831016192151
Terrie E. Moffitt182594150609
Aaron R. Folsom1811118134044
Valentin Fuster1791462185164
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023311
20221,325
202110,885
20209,949
20199,108
20188,477