scispace - formally typeset
Search or ask a question
Institution

Research Triangle Park

NonprofitDurham, North Carolina, United States
About: Research Triangle Park is a nonprofit organization based out in Durham, North Carolina, United States. It is known for research contribution in the topics: Population & Environmental exposure. The organization has 24961 authors who have published 35800 publications receiving 1684504 citations. The organization is also known as: RTP.


Papers
More filters
Journal ArticleDOI
TL;DR: This paper explored the value of such external climate forecast information to pastoralists in southern Ethiopia and northern Kenya using data collected using both open-ended, qualitative methods to identify and understand indigenous climate forecasting methods and quantitative data collected with survey instruments.

300 citations

Journal ArticleDOI
TL;DR: Fluorescence intensity of 3 increased ∼10 times in organic solvents such as ethanol and 1,2‐propanediol compared to aqueous solutions, suggesting that fluorescence may be used to image the distribution of 1–4 in Cercospora to understand better the interactions of pyridoxine and 1O2 in the living fungus.
Abstract: Vitamin B6 (pyridoxine, 1) and its derivatives: pyridoxal (2), pyridoxal 5-phosphate (3) and pyridoxamine (4) are important natural compounds involved in numerous biological functions. Pyridoxine appears to play a role in the resistance of the filamentous fungus Cercospora nicotianae to its own abundantly produced strong photosensitizer of singlet molecular oxygen (

300 citations

Journal ArticleDOI
TL;DR: This review focuses on the emerging evidence that reactive oxygen species (ROS) derived from glucose metabolism, such as H(2)O(2), act as metabolic signaling molecules for glucose-stimulated insulin secretion (GSIS) in pancreatic beta-cells and proposed cellular adaptive response to oxidative stress challenge.

299 citations

Journal ArticleDOI
TL;DR: The application of machine learning models in the design, synthesis and characterisation of molecules at different stages in the drug discovery and development process has considerable implications for developing future therapies and their targeting.
Abstract: A variety of machine learning methods such as naive Bayesian, support vector machines and more recently deep neural networks are demonstrating their utility for drug discovery and development. These leverage the generally bigger datasets created from high-throughput screening data and allow prediction of bioactivities for targets and molecular properties with increased levels of accuracy. We have only just begun to exploit the potential of these techniques but they may already be fundamentally changing the research process for identifying new molecules and/or repurposing old drugs. The integrated application of such machine learning models for end-to-end (E2E) application is broadly relevant and has considerable implications for developing future therapies and their targeting. This Perspective describes the application of machine learning models in the design, synthesis and characterisation of molecules at different stages in the drug discovery and development process.

299 citations

Journal ArticleDOI
01 Jan 2015-Chest
TL;DR: Evidence-based interventions that prevent tobacco use and reduce the clinical complications of COPD may result in potential decreased COPD-attributable costs, which are projected to increase through 2020.

299 citations


Authors

Showing all 25006 results

NameH-indexPapersCitations
Douglas G. Altman2531001680344
Lewis C. Cantley196748169037
Ronald Klein1941305149140
Daniel J. Jacob16265676530
Christopher P. Cannon1511118108906
James B. Meigs147574115899
Lawrence Corey14677378105
Jeremy K. Nicholson14177380275
Paul M. Matthews14061788802
Herbert Y. Meltzer137114881371
Charles J. Yeo13667276424
Benjamin F. Cravatt13166661932
Timothy R. Billiar13183866133
Peter Brown12990868853
King K. Holmes12460656192
Network Information
Related Institutions (5)
University of North Carolina at Chapel Hill
185.3K papers, 9.9M citations

90% related

University of Minnesota
257.9K papers, 11.9M citations

89% related

University of Washington
305.5K papers, 17.7M citations

89% related

University of Pittsburgh
201K papers, 9.6M citations

89% related

National Institutes of Health
297.8K papers, 21.3M citations

88% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202317
202277
2021988
20201,001
20191,035
20181,051