440 research outputs found

    The evolutionary patterns of barley pericentromeric chromosome regions, as shaped by linkage disequilibrium and domestication

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    The distribution of recombination events along large cereal chromosomes is uneven and is generally restricted to gene-rich telomeric ends. To understand how the lack of recombination affects diversity in the large pericentromeric regions, we analysed deep exome capture data from a final panel of 815 Hordeum vulgare (barley) cultivars, landraces and wild barleys, sampled from across their eco-geographical ranges. We defined and compared variant data across the pericentromeric and non-pericentromeric regions, observing a clear partitioning of diversity both within and between chromosomes and germplasm groups. Dramatically reduced diversity was found in the pericentromeres of both cultivars and landraces when compared with wild barley. We observed a mixture of completely and partially differentiated single-nucleotide polymorphisms (SNPs) between domesticated and wild gene pools, suggesting that domesticated gene pools were derived from multiple wild ancestors. Patterns of genome-wide linkage disequilibrium, haplotype block size and number, and variant frequency within blocks showed clear contrasts among individual chromosomes and between cultivars and wild barleys. Although most cultivar chromosomes shared a single major pericentromeric haplotype, chromosome 7H clearly differentiated the two-row and six-row types associated with different geographical origins. Within the pericentromeric regions we identified 22 387 non-synonymous SNPs, 92 of which were fixed for alternative alleles in cultivar versus wild accessions. Surprisingly, only 29 SNPs found exclusively in the cultivars were predicted to be 'highly deleterious'. Overall, our data reveal an unconventional pericentromeric genetic landscape among distinct barley gene pools, with different evolutionary processes driving domestication and diversification.</p

    Transcriptomic atlas and interaction networks of brain cells in mouse CNS demyelination and remyelination

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    Demyelination is a hallmark of multiple sclerosis, leukoencephalopathies, cerebral vasculopathies, and several neurodegenerative diseases. The cuprizone mouse model is widely used to simulate demyelination and remyelination occurring in these diseases. Here, we present a high-resolution single-nucleus RNA sequencing (snRNA-seq) analysis of gene expression changes across all brain cells in this model. We define demyelination-associated oligodendrocytes (DOLs) and remyelination-associated MAF

    A search for spectral hysteresis and energy-dependent time lags from X-ray and TeV gamma-ray observations of Mrk 421

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    Blazars are variable emitters across all wavelengths over a wide range of timescales, from months down to minutes. It is therefore essential to observe blazars simultaneously at different wavelengths, especially in the X-ray and gamma-ray bands, where the broadband spectral energy distributions usually peak. In this work, we report on three "target-of-opportunity" (ToO) observations of Mrk 421, one of the brightest TeV blazars, triggered by a strong flaring event at TeV energies in 2014. These observations feature long, continuous, and simultaneous exposures with XMM-Newton (covering X-ray and optical/ultraviolet bands) and VERITAS (covering TeV gamma-ray band), along with contemporaneous observations from other gamma-ray facilities (MAGIC and Fermi-LAT) and a number of radio and optical facilities. Although neither rapid flares nor significant X-ray/TeV correlation are detected, these observations reveal subtle changes in the X-ray spectrum of the source over the course of a few days. We search the simultaneous X-ray and TeV data for spectral hysteresis patterns and time delays, which could provide insight into the emission mechanisms and the source properties (e.g. the radius of the emitting region, the strength of the magnetic field, and related timescales). The observed broadband spectra are consistent with a one-zone synchrotron self-Compton model. We find that the power spectral density distribution at 4×104\gtrsim 4\times 10^{-4} Hz from the X-ray data can be described by a power-law model with an index value between 1.2 and 1.8, and do not find evidence for a steepening of the power spectral index (often associated with a characteristic length scale) compared to the previously reported values at lower frequencies.Comment: 45 pages, 15 figure

    Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

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    Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

    The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey

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    The Sloan Digital Sky Survey III (SDSS-III) presents the first spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS). This ninth data release (DR9) of the SDSS project includes 535,995 new galaxy spectra (median z=0.52), 102,100 new quasar spectra (median z=2.32), and 90,897 new stellar spectra, along with the data presented in previous data releases. These spectra were obtained with the new BOSS spectrograph and were taken between 2009 December and 2011 July. In addition, the stellar parameters pipeline, which determines radial velocities, surface temperatures, surface gravities, and metallicities of stars, has been updated and refined with improvements in temperature estimates for stars with T_eff<5000 K and in metallicity estimates for stars with [Fe/H]>-0.5. DR9 includes new stellar parameters for all stars presented in DR8, including stars from SDSS-I and II, as well as those observed as part of the SDSS-III Sloan Extension for Galactic Understanding and Exploration-2 (SEGUE-2). The astrometry error introduced in the DR8 imaging catalogs has been corrected in the DR9 data products. The next data release for SDSS-III will be in Summer 2013, which will present the first data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) along with another year of data from BOSS, followed by the final SDSS-III data release in December 2014.Comment: 9 figures; 2 tables. Submitted to ApJS. DR9 is available at http://www.sdss3.org/dr
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