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Institution

University of Virginia

EducationCharlottesville, Virginia, United States
About: University of Virginia is a education organization based out in Charlottesville, Virginia, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 52543 authors who have published 113268 publications receiving 5220506 citations. The organization is also known as: U of V & UVa.


Papers
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Journal ArticleDOI
TL;DR: A domain within Dock180 is identified that specifically recognizes nucleotide-free Rac and can mediate GTP loading of Rac in vitro and it is proposed that the Dock180–ELMO complex functions as an unconventional two-part exchange factor for Rac.
Abstract: Mammalian Dock180 and ELMO proteins, and their homologues in Caenorhabditis elegans and Drosophila melanogaster, function as critical upstream regulators of Rac during development and cell migration. The mechanism by which Dock180 or ELMO mediates Rac activation is not understood. Here, we identify a domain within Dock180 (denoted Docker) that specifically recognizes nucleotide-free Rac and can mediate GTP loading of Rac in vitro. The Docker domain is conserved among known Dock180 family members in metazoans and in a yeast protein. In cells, binding of Dock180 to Rac alone is insufficient for GTP loading, and a Dock180 ELMO1 interaction is required. We can also detect a trimeric ELMO1 Dock180 Rac1 complex and ELMO augments the interaction between Dock180 and Rac. We propose that the Dock180 ELMO complex functions as an unconventional two-part exchange factor for Rac.

579 citations

Journal ArticleDOI
TL;DR: It is very likely that interactions among these hormonal axes are more important than their main effects, and that alterations in body composition and the regional distribution of body fat actually are signals to alter the neuroendocrine and peripheral hormone axes.

578 citations

Journal ArticleDOI
TL;DR: It is suggested that miR-34a could serve as a potential therapeutic agent for brain tumors after being shown to suppresses brain tumor growth by targeting c-Met and Notch.
Abstract: MicroRNA-34a (miR-34a) is a transcriptional target of p53 that is down-regulated in some cancer cell lines. We studied the expression, targets, and functional effects of miR-34a in brain tumor cells and human gliomas. Transfection of miR-34a down-regulated c-Met in human glioma and medulloblastoma cells and Notch-1, Notch-2, and CDK6 protein expressions in glioma cells. miR-34a expression inhibited c-Met reporter activities in glioma and medulloblastoma cells and Notch-1 and Notch-2 3'-untranslated region reporter activities in glioma cells and stem cells. Analysis of human specimens showed that miR-34a expression is down-regulated in glioblastoma tissues as compared with normal brain and in mutant p53 gliomas as compared with wild-type p53 gliomas. miR-34a levels in human gliomas inversely correlated to c-Met levels measured in the same tumors. Transient transfection of miR-34a into glioma and medulloblastoma cell lines strongly inhibited cell proliferation, cell cycle progression, cell survival, and cell invasion, but transfection of miR-34a into human astrocytes did not affect cell survival and cell cycle status. Forced expression of c-Met or Notch-1/Notch-2 transcripts lacking the 3'-untranslated region sequences partially reversed the effects of miR-34a on cell cycle arrest and cell death in glioma cells and stem cells, respectively. Also, transient expression of miR-34a in glioblastoma cells strongly inhibited in vivo glioma xenograft growth. Together, these findings represent the first comprehensive analysis of the role of miR-34a in gliomas. They show that miR-34a suppresses brain tumor growth by targeting c-Met and Notch. The results also suggest that miR-34a could serve as a potential therapeutic agent for brain tumors.

578 citations

Journal ArticleDOI
TL;DR: Paired conditioning of ipsi- and contralateral inputs by nearly simultaneous conditioning stimulation of the EC bilaterally results in LTP in the crossing system, and this associatively induced LTP of the crossed system can be reversed by subsequent conditioning of the ipsilateral system alone.

578 citations

Journal ArticleDOI
TL;DR: In this paper, the authors reported a monolithically grown germanium/silicon avalanche photodetector with a gain-bandwidth product of 340 GHz, a keff of 0.09 and a sensitivity of −28 dBm at 10Gb s−1.
Abstract: Significant progress has been made recently in demonstrating that silicon photonics is a promising technology for low-cost optical detectors, modulators and light sources1,2,3,4,5,6,7,8,9,10,11,12. It has often been assumed, however, that their performance is inferior to InP-based devices. Although this is true in most cases, one of the exceptions is the area of avalanche photodetectors, where silicon's material properties allow for high gain with less excess noise than InP-based avalanche photodetectors and a theoretical sensitivity improvement of 3 dB or more. Here, we report a monolithically grown germanium/silicon avalanche photodetector with a gain–bandwidth product of 340 GHz, a keff of 0.09 and a sensitivity of −28 dB m at 10 Gb s−1. This is the highest reported gain–bandwidth product for any avalanche photodetector operating at 1,300 nm and a sensitivity that is equivalent to mature, commercially available III–V compound avalanche photodetectors. This work paves the way for the future development of low-cost, CMOS-based germanium/silicon avalanche photodetectors operating at data rates of 40 Gb s−1 or higher. A monolithically grown Ge/Si avalanche photodetectors (APD) with a gain–bandwidth product of 340 GHz, the highest value for any APDs operating at 1,300 nm, and a sensitivity equivalent to commercially available III-V compound APDs is reported. The excellent performance paves the way to achieving low-cost, CMOS-based, Ge/Si APDs operating at data rates of 40 Gb s−1 or higher, where the performance of III-V APDs is severely limited.

577 citations


Authors

Showing all 53083 results

NameH-indexPapersCitations
Joan Massagué189408149951
Michael Rutter188676151592
Gordon B. Mills1871273186451
Ralph Weissleder1841160142508
Gonçalo R. Abecasis179595230323
Jie Zhang1784857221720
John R. Yates1771036129029
John A. Rogers1771341127390
Bradley Cox1692150156200
Mika Kivimäki1661515141468
Hongfang Liu1662356156290
Carl W. Cotman165809105323
Ralph A. DeFronzo160759132993
Elio Riboli1581136110499
Dan R. Littman157426107164
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023189
2022783
20215,566
20205,600
20195,001
20184,586