Institution
Research Triangle Park
Nonprofit•Durham, 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.
Topics: Population, Environmental exposure, Receptor, Poison control, Agonist
Papers published on a yearly basis
Papers
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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
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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
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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
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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
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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
Name | H-index | Papers | Citations |
---|---|---|---|
Douglas G. Altman | 253 | 1001 | 680344 |
Lewis C. Cantley | 196 | 748 | 169037 |
Ronald Klein | 194 | 1305 | 149140 |
Daniel J. Jacob | 162 | 656 | 76530 |
Christopher P. Cannon | 151 | 1118 | 108906 |
James B. Meigs | 147 | 574 | 115899 |
Lawrence Corey | 146 | 773 | 78105 |
Jeremy K. Nicholson | 141 | 773 | 80275 |
Paul M. Matthews | 140 | 617 | 88802 |
Herbert Y. Meltzer | 137 | 1148 | 81371 |
Charles J. Yeo | 136 | 672 | 76424 |
Benjamin F. Cravatt | 131 | 666 | 61932 |
Timothy R. Billiar | 131 | 838 | 66133 |
Peter Brown | 129 | 908 | 68853 |
King K. Holmes | 124 | 606 | 56192 |