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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.


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Journal ArticleDOI
TL;DR: Recent findings are described that provide insight into ways that the regulation, structure, and localization of MAPKs and the participation of adapters and scaffolds can help determine biological outcomes.
Abstract: Signal transduction networks allow cells to perceive changes in the extracellular environment and to mount an appropriate response. Mitogen-activated protein kinase (MAPK) cascades are among the most thoroughly studied of signal transduction systems and have been shown to participate in a diverse array of cellular programs, including cell differentiation, cell movement, cell division, and cell death. A key question in studies of this cascade is, how does a ubiquitously activated regulatory enzume generate a specific and biologically appropriate cellular response? In this review we describe recent findings that provide insight into ways that the regulation, structure, and localization of MAPKs and the participation of adapters and scaffolds can help determine biological outcomes. MAPK cascades are evolutionarily conserved in all eucaryotes and play a key role in the regulation of gene expression as well as cytoplasmic activities. They typically are organized in a three-kinase architecture consisting of a MAPK, a MAPK activator (MEK, MKK, or MAPK kinase), and a MEK activator (MEK kinase [MEKK] or MAPK kinase kinase). Transmission of signals is achieved by sequential phosphorylation and activation of the components specific to a respective cascade. In the yeast Saccharomyces cerevisiae, five MAPK modules have been described; they regulate mating, filamentation, high-osmolarity responses, cell wall remodeling, and sporulation (Fig. ​(Fig.1A)1A) (reviewed in references 56 and 77). In mammalian systems five distinguishable MAPK modules have been identified so far (Fig. ​(Fig.1B).1B). These include the extracellular signal-regulated kinase 1 and 2 (ERK1/2) cascade, which preferentially regulates cell growth and differentiation, as well as the c-Jun N-terminal kinase (JNK) and p38 MAPK cascades, which function mainly in stress responses like inflammation and apoptosis (reviewed in references 57, 74, and 103). Moreover, MAPK pathways control several developmental programs, such as morphogenesis and spatial patterning in Dictyostelium amoebae (17, 45), eye development in Drosophila melanogaster (124), vulva induction in Caenorhabditis elegans (113), and T-cell development in mammals (31). FIG. 1 Schematic overview of MAPK modules. (A) In S. cerevisiae, five MAPK modules regulate mating, filamentation, high-osmolarity responses, cell wall remodeling, and sporulation. (B) Mammalian MAPK modules regulate cell growth, differentiation, stress responses, ... Individual MAPK modules generally can signal independently from each other, and this specificity is manifested in distinct physiologic responses. This is most obvious when studying MAPK signaling in S. cerevisiae. Here a particular extracellular event characteristically activates a specific MAPK module and initiates a unique cellular program (reviewed in references 56 and 77). For example, stimulation of cells with pheromone leads to the activation of the pheromone response pathway (STE11, STE7, and FUS3) (Fig. ​(Fig.2),2), which ultimately results in cell cycle arrest and the induction of mating-specific genes. However, related MAPKs whose modules share some components with the pheromone response pathway are not affected by pheromone stimulation but are activated only in response to the appropriate stimulus. For example, under conditions of high osmolarity Ste11 can lead to activation of Hog1 but does not induce mating-specific genes. Conversely, conditions that activate the filamentation pathway (which utilizes STE11 and STE7) induce only genes that regulate filamentous growth without triggering pheromone responses or responses to high osmolarity. These observations suggest that yeast cells have developed efficient mechanisms to generate pathway specificity and to successfully suppress cross talk, even when individual components participate in more than one signaling pathway. FIG. 2 Scaffold and adapter molecules in MAPK pathways. MAPK scaffolds and adapters (gray shading) are thought to promote the formation of oligomeric protein complexes with components that function in a specific MAPK module. Scaffolds have been identified in ... In metazoan cells the problem is more complex because each cell is simultaneously exposed to multiple extracellular signals and must integrate these inputs to choose an appropriate response. Thus, the biological context of a signal plays a determinative role in the way that MAPK activation is interpreted. For example, although ERKs generally regulate cell growth and cell differentiation and JNKs participate in a stress response, this is not always the case and in certain cell types activation of JNKs can induce proliferation (110). This indicates that in mammalian systems physiologic responses associated with a certain MAPK module can be cell type specific. Moreover, in PC12 cells, transient stimulation of the ERK cascade leads to proliferation whereas sustained stimulation leads to differentiation, as measured by neurite outgrowth (81). Thus, activation of the ERK cascade can lead to contrasting physiological responses in the same cellular context, suggesting that signal specificity is also determined by regulatory mechanisms other than the selective activation of a MAPK module. In this short review, we outline recent advances in understanding of this signaling system that help to explain how MAPK cascades are regulated and how specificity can be generated. Because of the power of yeast genetics, understanding of MAPK signaling in S. cerevisiae is at an advanced level, and thus many examples that utilize this organism are given. However, analogous mechanisms appear to be operative in metazoans as well. We discuss in turn the role of enzyme-substrate interactions, scaffolding proteins, subcellular targeting and localization, temporal regulation, and signal integration in determining the biological outcome of MAPK activation.

1,597 citations

Journal ArticleDOI
23 Sep 2005-Science
TL;DR: The first global assessment of amphibians provides new context for the well-publicized phenomenon of amphibian declines and shows declines are nonrandom in terms of species' ecological preferences, geographic ranges, and taxonomic associations and are most prevalent among Neotropical montane, stream-associated species.
Abstract: Using information on Brazilian species, Pimenta et al . assert that we overestimated the number of threatened amphibians. This claim, based on a misunderstanding of the IUCN-The World Conservation Union Red List criteria and a strongly evidentiary attitude to listing species, almost certainly

1,594 citations

Journal ArticleDOI
TL;DR: The task group report includes a review of the literature to identify reported clinical findings and expected outcomes for this treatment modality.
Abstract: Task Group 101 of the AAPM has prepared this report for medical physicists, clinicians, and therapists in order to outline the best practice guidelines for the external-beam radiation therapy technique referred to as stereotactic body radiation therapy (SBRT). The task group report includes a review of the literature to identify reported clinical findings and expected outcomes for this treatment modality. Information is provided for establishing a SBRT program, including protocols, equipment, resources, and QA procedures. Additionally, suggestions for developing consistent documentation for prescribing, reporting, and recording SBRT treatment delivery is provided.

1,586 citations

Journal ArticleDOI
Daniel J. Benjamin1, James O. Berger2, Magnus Johannesson1, Magnus Johannesson3, Brian A. Nosek4, Brian A. Nosek5, Eric-Jan Wagenmakers6, Richard A. Berk7, Kenneth A. Bollen8, Björn Brembs9, Lawrence D. Brown7, Colin F. Camerer10, David Cesarini11, David Cesarini12, Christopher D. Chambers13, Merlise A. Clyde2, Thomas D. Cook14, Thomas D. Cook15, Paul De Boeck16, Zoltan Dienes17, Anna Dreber3, Kenny Easwaran18, Charles Efferson19, Ernst Fehr20, Fiona Fidler21, Andy P. Field17, Malcolm R. Forster22, Edward I. George7, Richard Gonzalez23, Steven N. Goodman24, Edwin J. Green25, Donald P. Green26, Anthony G. Greenwald27, Jarrod D. Hadfield28, Larry V. Hedges14, Leonhard Held20, Teck-Hua Ho29, Herbert Hoijtink30, Daniel J. Hruschka31, Kosuke Imai32, Guido W. Imbens24, John P. A. Ioannidis24, Minjeong Jeon33, James Holland Jones34, Michael Kirchler35, David Laibson36, John A. List37, Roderick J. A. Little23, Arthur Lupia23, Edouard Machery38, Scott E. Maxwell39, Michael A. McCarthy21, Don A. Moore40, Stephen L. Morgan41, Marcus R. Munafò42, Shinichi Nakagawa43, Brendan Nyhan44, Timothy H. Parker45, Luis R. Pericchi46, Marco Perugini47, Jeffrey N. Rouder48, Judith Rousseau49, Victoria Savalei50, Felix D. Schönbrodt51, Thomas Sellke52, Betsy Sinclair53, Dustin Tingley36, Trisha Van Zandt16, Simine Vazire54, Duncan J. Watts55, Christopher Winship36, Robert L. Wolpert2, Yu Xie32, Cristobal Young24, Jonathan Zinman44, Valen E. Johnson18, Valen E. Johnson1 
University of Southern California1, Duke University2, Stockholm School of Economics3, University of Virginia4, Center for Open Science5, University of Amsterdam6, University of Pennsylvania7, University of North Carolina at Chapel Hill8, University of Regensburg9, California Institute of Technology10, New York University11, Research Institute of Industrial Economics12, Cardiff University13, Northwestern University14, Mathematica Policy Research15, Ohio State University16, University of Sussex17, Texas A&M University18, Royal Holloway, University of London19, University of Zurich20, University of Melbourne21, University of Wisconsin-Madison22, University of Michigan23, Stanford University24, Rutgers University25, Columbia University26, University of Washington27, University of Edinburgh28, National University of Singapore29, Utrecht University30, Arizona State University31, Princeton University32, University of California, Los Angeles33, Imperial College London34, University of Innsbruck35, Harvard University36, University of Chicago37, University of Pittsburgh38, University of Notre Dame39, University of California, Berkeley40, Johns Hopkins University41, University of Bristol42, University of New South Wales43, Dartmouth College44, Whitman College45, University of Puerto Rico46, University of Milan47, University of California, Irvine48, Paris Dauphine University49, University of British Columbia50, Ludwig Maximilian University of Munich51, Purdue University52, Washington University in St. Louis53, University of California, Davis54, Microsoft55
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
Abstract: We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.

1,586 citations

Journal ArticleDOI
TL;DR: It was found that management support and resources help to address organizational issues that arise during warehouse implementations; resources, user participation, and highly-skilled project team members increase the likelihood that warehousing projects will finish on-time, on-budget, with the right functionality; and diverse, unstandardized source systems and poor development technology will increase the technical issues that project teams must overcome.
Abstract: The IT implementation literature suggests that various implementation factors play critical roles in the success of an information system; however, there is little empirical research about the implementation of data warehousing projects. Data warehousing has unique characteristics that may impact the importance of factors that apply to it. In this study, a cross-sectional survey investigated a model of data warehousing success. Data warehousing managers and data suppliers from 111 organizations completed paired mail questionnaires on implementation factors and the success of the warehouse. The results from a Partial Least Squares analysis of the data identified significant relationships between the system quality and data quality factors and perceived net benefits. It was found that management support and resources help to address organizational issues that arise during warehouse implementations; resources, user participation, and highly-skilled project team members increase the likelihood that warehousing projects will finish on-time, on-budget, with the right functionality; and diverse, unstandardized source systems and poor development technology will increase the technical issues that project teams must overcome. The implementation's success with organizational and project issues, in turn, influence the system quality of the data warehouse; however, data quality is best explained by factors not included in the research model.

1,579 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