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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 & Receptor. The organization has 24961 authors who have published 35800 publications receiving 1684504 citations. The organization is also known as: RTP.


Papers
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Journal ArticleDOI
TL;DR: The NLCD 2006 dataset as discussed by the authors provides the first wall-to-wall land-cover change database for the conterminous United States from Landsat Thematic Mapper (TM) data.

324 citations

Patent
06 Mar 1995
TL;DR: In this paper, a method and system for the adaptive allocation of channels within a radio communication system, specifically a cellular network, is presented, where the allocation method takes advantage of measurements made by the mobile radiotelephone and allocates channels based on the carrier to interference ratio.
Abstract: A method and system for the adaptive allocation of channels within a radio communication system, specifically a cellular network, is presented. The allocation method takes advantage of measurements made by the mobile radiotelephone and allocates channels based on the carrier to interference ratio. Using adaptive power control, consideration is given to maintaining an acceptable carrier to interference ratio while at the same time minimizing transmit power. Exemplary embodiments consider independent allocation of the uplink and downlink as well as independent determination of the uplink and downlink power levels. Other exemplary embodiments also give consideration to an efficient method for slot allocation in a TDMA communication system.

324 citations

Journal ArticleDOI
TL;DR: The current and ongoing need for more relevant, organotypic in vitro surrogate systems of human liver and recent efforts to recreate the multicellular architecture and hemodynamic properties of the liver using novel culture platforms are described.
Abstract: Prediction of chemical-induced hepatotoxicity in humans from in vitro data continues to be a significant challenge for the pharmaceutical and chemical industries. Generally, conventional in vitro hepatic model systems (i.e. 2-D static monocultures of primary or immortalized hepatocytes) are limited by their inability to maintain histotypic and phenotypic characteristics over time in culture, including stable expression of clearance and bioactivation pathways, as well as complex adaptive responses to chemical exposure. These systems are less than ideal for longer-term toxicity evaluations and elucidation of key cellular and molecular events involved in primary and secondary adaptation to chemical exposure, or for identification of important mediators of inflammation, proliferation and apoptosis. Progress in implementing a more effective strategy for in vitro-in vivo extrapolation and human risk assessment depends on significant advances in tissue culture technology and increasing their level of biological complexity. This article describes the current and ongoing need for more relevant, organotypic in vitro surrogate systems of human liver and recent efforts to recreate the multicellular architecture and hemodynamic properties of the liver using novel culture platforms. As these systems become more widely used for chemical and drug toxicity testing, there will be a corresponding need to establish standardized testing conditions, endpoint analyses and acceptance criteria. In the future, a balanced approach between sample throughput and biological relevance should provide better in vitro tools that are complementary with animal testing and assist in conducting more predictive human risk assessment.

324 citations

Journal ArticleDOI
TL;DR: COPD and cardiovascular disease was associated with poorer quality of life, higher MRC dyspnea scores, reduced 6MWD, higher BODE index scores, and the comorbidities of heart disease, hypertension and diabetes are associated with increased systemic inflammation.

323 citations

Journal ArticleDOI
TL;DR: Computational techniques for systematic analysis of transcriptomics, side effects, and genetics data to generate new hypotheses for additional indications and data domains such as electronic health records (EHRs) and phenotypic screening that are considered promising for novel computational repositioning methods are discussed.
Abstract: Traditionally, most drugs have been discovered using phenotypic or target-based screens. Subsequently, their indications are often expanded on the basis of clinical observations, providing additional benefit to patients. This review highlights computational techniques for systematic analysis of transcriptomics (Connectivity Map, CMap), side effects, and genetics (genome-wide association study, GWAS) data to generate new hypotheses for additional indications. We also discuss data domains such as electronic health records (EHRs) and phenotypic screening that we consider promising for novel computational repositioning methods.

323 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
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202317
202277
2021988
20201,001
20191,035
20181,051