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Institution

North Carolina State University

EducationRaleigh, North Carolina, United States
About: North Carolina State University is a education organization based out in Raleigh, North Carolina, United States. It is known for research contribution in the topics: Population & Thin film. The organization has 44161 authors who have published 101744 publications receiving 3456774 citations. The organization is also known as: NCSU & North Carolina State University at Raleigh.
Topics: Population, Thin film, Silicon, Gene, Poison control


Papers
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Journal ArticleDOI
TL;DR: It is shown that TAK1 is key in the cellular response to a variety of stimuli and was indispensable for cellular responses to Toll-like receptor ligands, CD40 and B cell receptor crosslinking.
Abstract: Transforming growth factor-beta-activated kinase 1 (TAK1) has been linked to interleukin 1 receptor and tumor necrosis factor receptor signaling. Here we generated mouse strains with conditional expression of a Map3k7 allele encoding part of TAK1. TAK1-deficient embryonic fibroblasts demonstrated loss of responses to interleukin 1beta and tumor necrosis factor. Studies of mice with B cell-specific TAK1 deficiency showed that TAK1 was indispensable for cellular responses to Toll-like receptor ligands, CD40 and B cell receptor crosslinking. In addition, antigen-induced immune responses were considerably impaired in mice with B cell-specific TAK1 deficiency. TAK1-deficient cells failed to activate transcription factor NF-kappaB and mitogen-activated protein kinases in response to interleukin 1beta, tumor necrosis factor and Toll-like receptor ligands. However, TAK1-deficient B cells were able to activate NF-kappaB but not the kinase Jnk in response to B cell receptor stimulation. These results collectively indicate that TAK1 is key in the cellular response to a variety of stimuli.

914 citations

Journal ArticleDOI
TL;DR: This work argues that the Cox proportional hazards regression model method is superior to naive methods where one maximizes the partial likelihood of the Cox model using the observed covariate values and improves on two-stage methods where empirical Bayes estimates of the covariate process are computed and then used as time-dependent covariates.
Abstract: The relationship between a longitudinal covariate and a failure time process can be assessed using the Cox proportional hazards regression model We consider the problem of estimating the parameters in the Cox model when the longitudinal covariate is measured infrequently and with measurement error We assume a repeated measures random effects model for the covariate process Estimates of the parameters are obtained by maximizing the joint likelihood for the covariate process and the failure time process This approach uses the available information optimally because we use both the covariate and survival data simultaneously Parameters are estimated using the expectation-maximization algorithm We argue that such a method is superior to naive methods where one maximizes the partial likelihood of the Cox model using the observed covariate values It also improves on two-stage methods where, in the first stage, empirical Bayes estimates of the covariate process are computed and then used as time-dependent covariates in a second stage to find the parameters in the Cox model that maximize the partial likelihood

911 citations

Journal ArticleDOI
TL;DR: A comprehensive survey of all issues associated with PageRank, covering the basic PageRank model, available and recommended solution methods, storage issues, existence, uniqueness, and convergence properties, possible alterations to the basic model, and suggested alternatives to the traditional solution methods.
Abstract: This paper serves as a companion or extension to the "Inside PageRank" paper by Bianchini et al. [Bianchini et al. 03]. It is a comprehensive survey of all issues associated with PageRank, covering the basic PageRank model, available and recommended solution methods, storage issues, existence, uniqueness, and convergence properties, possible alterations to the basic model, suggested alternatives to the traditional solution methods, sensitivity and conditioning, and finally the updating problem. We introduce a few new results, provide an extensive reference list, and speculate about exciting areas of future research.

910 citations

Journal ArticleDOI
TL;DR: Simulations help confirm previous suggestions that silent sites are saturated, leaving no evidence of heterogeneity in synonymous substitution rates, and confirm previous findings that substitution rates in the chloroplast genome are subject to both lineage-specific and locus-specific effects.
Abstract: A model of DNA sequence evolution applicable to coding regions is presented. This represents the first evolutionary model that accounts for dependencies among nucleotides within a codon. The model uses the codon, as opposed to the nucleotide, as the unit of evolution, and is parameterized in terms of synonymous and nonsynonymous nucleotide substitution rates. One of the model's advantages over those used in methods for estimating synonymous and nonsynonymous substitution rates is that it completely corrects for multiple hits at a codon, rather than taking a parsimony approach and considering only pathways of minimum change between homologous codons. Likelihood-ratio versions of the relative-rate test are constructed and applied to data from the complete chloroplast DNA sequences of Oryza sativa, Nicotiana tabacum, and Marchantia polymorpha. Results of these tests confirm previous findings that substitution rates in the chloroplast genome are subject to both lineage-specific and locus-specific effects. Additionally, the new tests suggest tha the rate heterogeneity is due primarily to differences in nonsynonymous substitution rates. Simulations help confirm previous suggestions that silent sites are saturated, leaving no evidence of heterogeneity in synonymous substitution rates.

909 citations

Journal ArticleDOI
TL;DR: This discussion aims to complement the presentation of the authors by elaborating on the view from the vantage point of semi-parametric theory, focusing on the assumptions embedded in the statistical models leading to different “types” of estimators rather than on the forms of the estimators themselves.
Abstract: We congratulate Drs. Kang and Schafer (KS henceforth) for a careful and thought-provoking contribution to the literature regarding the so-called “double robustness” property, a topic that still engenders some confusion and disagreement. The authors’ approach of focusing on the simplest situation of estimation of the population mean μ of a response y when y is not observed on all subjects according to a missing at random (MAR) mechanism (equivalently, estimation of the mean of a potential outcome in a causal model under the assumption of no unmeasured confounders) is commendable, as the fundamental issues can be explored without the distractions of the messier notation and considerations required in more complicated settings. Indeed, as the article demonstrates, this simple setting is sufficient to highlight a number of key points. As noted eloquently by Molenberghs (2005), in regard to how such missing data/causal inference problems are best addressed, two “schools” may be identified: the “likelihood-oriented” school and the “weighting-based” school. As we have emphasized previously (Davidian, Tsiatis and Leon, 2005), we prefer to view inference from the vantage point of semi-parametric theory, focusing on the assumptions embedded in the statistical models leading to different “types” of estimators (i.e., “likelihood-oriented” or “weighting-based”) rather than on the forms of the estimators themselves. In this discussion, we hope to complement the presentation of the authors by elaborating on this point of view. Throughout, we use the same notation as in the paper.

908 citations


Authors

Showing all 44525 results

NameH-indexPapersCitations
Yi Cui2201015199725
Jing Wang1844046202769
Rodney S. Ruoff164666194902
Carlos Bustamante161770106053
David W. Johnson1602714140778
Joseph Wang158128298799
David Tilman158340149473
Jay Hauser1552145132683
James M. Tour14385991364
Joseph T. Hupp14173182647
Bin Liu138218187085
Rudolph E. Tanzi13563885376
Richard C. Boucher12949054509
David B. Allison12983669697
Robert W. Heath128104973171
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023160
2022652
20215,262
20205,458
20194,888
20184,522