15 research outputs found

    Structure, function and diversity of the healthy human microbiome

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    Author Posting. © The Authors, 2012. This article is posted here by permission of Nature Publishing Group. The definitive version was published in Nature 486 (2012): 207-214, doi:10.1038/nature11234.Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analysed the largest cohort and set of distinct, clinically relevant body habitats so far. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology and translational applications of the human microbiome.This research was supported in part by National Institutes of Health grants U54HG004969 to B.W.B.; U54HG003273 to R.A.G.; U54HG004973 to R.A.G., S.K.H. and J.F.P.; U54HG003067 to E.S.Lander; U54AI084844 to K.E.N.; N01AI30071 to R.L.Strausberg; U54HG004968 to G.M.W.; U01HG004866 to O.R.W.; U54HG003079 to R.K.W.; R01HG005969 to C.H.; R01HG004872 to R.K.; R01HG004885 to M.P.; R01HG005975 to P.D.S.; R01HG004908 to Y.Y.; R01HG004900 to M.K.Cho and P. Sankar; R01HG005171 to D.E.H.; R01HG004853 to A.L.M.; R01HG004856 to R.R.; R01HG004877 to R.R.S. and R.F.; R01HG005172 to P. Spicer.; R01HG004857 to M.P.; R01HG004906 to T.M.S.; R21HG005811 to E.A.V.; M.J.B. was supported by UH2AR057506; G.A.B. was supported by UH2AI083263 and UH3AI083263 (G.A.B., C. N. Cornelissen, L. K. Eaves and J. F. Strauss); S.M.H. was supported by UH3DK083993 (V. B. Young, E. B. Chang, F. Meyer, T. M. S., M. L. Sogin, J. M. Tiedje); K.P.R. was supported by UH2DK083990 (J. V.); J.A.S. and H.H.K. were supported by UH2AR057504 and UH3AR057504 (J.A.S.); DP2OD001500 to K.M.A.; N01HG62088 to the Coriell Institute for Medical Research; U01DE016937 to F.E.D.; S.K.H. was supported by RC1DE0202098 and R01DE021574 (S.K.H. and H. Li); J.I. was supported by R21CA139193 (J.I. and D. S. Michaud); K.P.L. was supported by P30DE020751 (D. J. Smith); Army Research Office grant W911NF-11-1-0473 to C.H.; National Science Foundation grants NSF DBI-1053486 to C.H. and NSF IIS-0812111 to M.P.; The Office of Science of the US Department of Energy under Contract No. DE-AC02-05CH11231 for P.S. C.; LANL Laboratory-Directed Research and Development grant 20100034DR and the US Defense Threat Reduction Agency grants B104153I and B084531I to P.S.C.; Research Foundation - Flanders (FWO) grant to K.F. and J.Raes; R.K. is an HHMI Early Career Scientist; Gordon&BettyMoore Foundation funding and institutional funding fromthe J. David Gladstone Institutes to K.S.P.; A.M.S. was supported by fellowships provided by the Rackham Graduate School and the NIH Molecular Mechanisms in Microbial Pathogenesis Training Grant T32AI007528; a Crohn’s and Colitis Foundation of Canada Grant in Aid of Research to E.A.V.; 2010 IBM Faculty Award to K.C.W.; analysis of the HMPdata was performed using National Energy Research Scientific Computing resources, the BluBioU Computational Resource at Rice University

    The fate of mercury in Arctic terrestrial and aquatic ecosystems, a review

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    A Meta-Analysis of Workaholism

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    This meta-analysis examines the relationship between workaholism and numerous work behaviors and outcomes in an attempt to a) derive a consensus regarding the current state of our understanding of this construct, and b) clarify the impact that the compulsion to work may have on an individual\u27s life. Overall, based on data from 44 studies, results indicate that there is a considerable amount of variability between workaholism and work-related outcomes. Specifically, the two most established and reputable measures of workaholism, the Work Addiction Risk Test (WART) and the Workaholism Battery (WorkBat), appear to focus on uniquely different aspects of workaholism and were subsequently found to be differentially related to various work criteria. These findings suggest that a consistent definition and operationalization of workaholism is explicitly needed before further progress can be made

    Curriculum-based measurement of oral reading (R-CBM): A diagnostic test accuracy meta-analysis of evidence supporting use in universal screening

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    A great deal of research over the past decade has examined the appropriateness of curriculum-based measurement of oral reading (R-CBM) in universal screening. Multiple researchers have meta-analyzed available correlational evidence, yielding support for the interpretation of R-CBM as an indicator of general reading proficiency. In contrast, researchers have yet to synthesize diagnostic accuracy evidence, which pertains to the defensibility of the use of R-CBM for screening purposes. The overall purpose of this research was to therefore conduct the first meta-analysis of R-CBM diagnostic accuracy research. A systematic search of the literature resulted in the identification of 34 studies, including 20 peer-reviewed articles, 7 dissertations, and 7 technical reports. Bivariate hierarchical linear models yielded generalized estimates of diagnostic accuracy statistics, which predominantly exceeded standards for acceptable universal screener performance. For instance, when predicting criterion outcomes within a school year (≤ 9 months), R-CBM sensitivity ranged between .80 and .83 and specificity ranged between .71 and .73. Multiple moderators of R-CBM diagnostic accuracy were identified, including the (a) R-CBM cut score used to define risk, (b) lag in time between R-CBM and criterion test administration, and (c) percentile rank corresponding to the criterion test cut score through which students were identified as either truly at risk or not at risk. Follow-up analyses revealed substantial variability of extracted cut scores within grade and time of year (i.e., fall, winter, and spring). This result called into question the inflexible application of a single cut score across contexts and suggested the potential necessity of local cut scores. Implications for practices, directions for future research, and limitations are discussed

    A Meta-Analysis of Workaholism

    No full text
    This meta-analysis examines the relationship between workaholism and numerous work behaviors and outcomes in an attempt to a) derive a consensus regarding the current state of our understanding of this construct, and b) clarify the impact that the compulsion to work may have on an individual\u27s life. Overall, based on data from 44 studies, results indicate that there is a considerable amount of variability between workaholism and work-related outcomes. Specifically, the two most established and reputable measures of workaholism, the Work Addiction Risk Test (WART) and the Workaholism Battery (WorkBat), appear to focus on uniquely different aspects of workaholism and were subsequently found to be differentially related to various work criteria. These findings suggest that a consistent definition and operationalization of workaholism is explicitly needed before further progress can be made
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