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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: 2-HG is a competitive inhibitor of multiple α-KG-dependent dioxygenases, including histone demethylases and the TET family of 5-methlycytosine (5mC) hydroxylases, leading to genome-wide histone and DNA methylation alterations.

2,341 citations

Journal ArticleDOI
TL;DR: An integrative conceptual model of child development is presented, anchored within social stratification theory, emphasizing the importance of racism, prejudice, discrimination, oppression, and segregation on the development of minority children and families.
Abstract: In this article a conceptual model for the study of child development in minority populations in the United States is proposed. In support of the proposed model, this article includes (a) a delineation and critical analysis of mainstream theoretical frameworks in relation to their attention and applicability to the understanding of developmental processes in children of color and of issues at the intersection of social class, culture, ethnicity, and race, and (b) a description and evaluation of the conceptual frameworks that have guided the extant literature on minority children and families. Based on the above considerations, an integrative conceptual model of child development is presented, anchored within social stratification theory, emphasizing the importance of racism, prejudice, discrimination, oppression, and segregation on the development of minority children and families.

2,333 citations

Journal ArticleDOI
TL;DR: There are several risk subgroups for which the available data are insufficient to recommend for or against screening, including women with a personal history of breast cancer, carcinoma in situ, atypical hyperplasia, and extremely dense breasts on mammography.
Abstract: New evidence on breast Magnetic Resonance Imaging (MRI) screening has become available since the American Cancer Society (ACS) last issued guidelines for the early detection of breast cancer in 2003. A guideline panel has reviewed this evidence and developed new recommendations for women at different defined levels of risk. Screening MRI is recommended for women with an approximately 20-25% or greater lifetime risk of breast cancer, including women with a strong family history of breast or ovarian cancer and women who were treated for Hodgkin disease. There are several risk subgroups for which the available data are insufficient to recommend for or against screening, including women with a personal history of breast cancer, carcinoma in situ, atypical hyperplasia, and extremely dense breasts on mammography. Diagnostic uses of MRI were not considered to be within the scope of this review.

2,332 citations

Journal ArticleDOI
TL;DR: The present state of the rapidly emerging field of monolayer-protected cluster (MPC) molecules with regard to their synthesis andmonolayer functionalization, their core and monolayers structure, their composition, and their properties is evaluated.
Abstract: In this report, we evaluate the present state of the rapidly emerging field of monolayer-protected cluster (MPC) molecules with regard to their synthesis and monolayer functionalization, their core and monolayer structure, their composition, and their properties. Finally, we canvass some of the important remaining research opportunities involving MPCs.

2,326 citations

Journal ArticleDOI
26 Jul 2018-Nature
TL;DR: A future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence is envisaged.
Abstract: Here we summarize recent progress in machine learning for the chemical sciences. We outline machine-learning techniques that are suitable for addressing research questions in this domain, as well as future directions for the field. We envisage a future in which the design, synthesis, characterization and application of molecules and materials is accelerated by artificial intelligence.

2,295 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