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      The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight

      1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 1 , 9 , 6 , 5 , 6 , 10 , 3 , 11 , 8 , 12 , 13 , 1 , 9 , 6 , 6 , 7 , 1 , 9 , 10 , 6 , 1 , 4 , 6 , 14 , 14 , 15 , 5 , 7 , 10 , 1 , 7 , 10 , 1 , 11 , 16 , 6 , 17 , 11 , 10 , 12 , 18 , 7 , 6 , 19 , 1 , 4 , 1 , 5 , 1 , 11 , 7 , 6 , 11 , 6 , 11 , 11 , 1 , 14 , 10 , 6 , 17 , 11 , 6 , 11 , 20 , 14 , 21 , 22 , 8 , 5 , 10 , 7 , 1 , 9 , 23 , 24 , 6 , 11 , 14 , 6 , 12
      Science
      American Association for the Advancement of Science (AAAS)

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          Abstract

          To understand the health impact of long-duration spaceflight, one identical twin astronaut was monitored before, during, and after a 1-year mission onboard the International Space Station; his twin served as a genetically matched ground control. Longitudinal assessments identified spaceflight-specific changes, including decreased body mass, telomere elongation, genome instability, carotid artery distension and increased intima-media thickness, altered ocular structure, transcriptional and metabolic changes, DNA methylation changes in immune and oxidative stress–related pathways, gastrointestinal microbiota alterations, and some cognitive decline postflight. Although average telomere length, global gene expression, and microbiome changes returned to near preflight levels within 6 months after return to Earth, increased numbers of short telomeres were observed and expression of some genes was still disrupted. These multiomic, molecular, physiological, and behavioral datasets provide a valuable roadmap of the putative health risks for future human spaceflight.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

            In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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              Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

              The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                April 11 2019
                April 12 2019
                April 11 2019
                April 12 2019
                : 364
                : 6436
                : eaau8650
                Affiliations
                [1 ]Weill Cornell Medicine, New York, NY, USA.
                [2 ]University of Virginia School of Medicine, Charlottesville, VA, USA.
                [3 ]Center for Renal Precision Medicine, University of Texas Health, San Antonio, TX, USA.
                [4 ]University of Illinois at Chicago, Chicago, IL, USA.
                [5 ]University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
                [6 ]Stanford University School of Medicine, Palo Alto, CA, USA.
                [7 ]KBRwyle, Houston, TX, USA.
                [8 ]Colorado State University, Fort Collins, CO, USA.
                [9 ]The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, New York, NY, USA.
                [10 ]Johns Hopkins University, Baltimore, MD, USA.
                [11 ]University of California, San Diego, La Jolla, CA, USA.
                [12 ]Northwestern University, Evanston, IL, USA.
                [13 ]University of California, Davis, Davis, CA, USA.
                [14 ]National Aeronautics and Space Administration (NASA), Houston, TX, USA.
                [15 ]Harvard T.H. Chan School of Public Health, Boston, MA, USA.
                [16 ]University of Bonn, Bonn, Germany.
                [17 ]University of Washington, Seattle, WA, USA.
                [18 ]Rush University Medical Center, Chicago, IL, USA.
                [19 ]MEI Technologies, Houston, TX, USA.
                [20 ]University of Texas Medical Branch, Galveston, TX, USA.
                [21 ]Space Life and Physical Sciences Division, NASA Headquarters, Washington, DC, USA.
                [22 ]National Space Biomedical Research Institute, Baylor College of Medicine, Houston, TX, USA.
                [23 ]The Feil Family Brain and Mind Research Institute, New York, NY, USA.
                [24 ]The WorldQuant Initiative for Quantitative Prediction, New York, NY, USA.
                Article
                10.1126/science.aau8650
                7580864
                30975860
                3531fd24-412f-41a3-a13a-1154105150f7
                © 2019

                http://www.sciencemag.org/about/science-licenses-journal-article-reuse

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