1,251 research outputs found

    Preprocessing Solar Images while Preserving their Latent Structure

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    Telescopes such as the Atmospheric Imaging Assembly aboard the Solar Dynamics Observatory, a NASA satellite, collect massive streams of high resolution images of the Sun through multiple wavelength filters. Reconstructing pixel-by-pixel thermal properties based on these images can be framed as an ill-posed inverse problem with Poisson noise, but this reconstruction is computationally expensive and there is disagreement among researchers about what regularization or prior assumptions are most appropriate. This article presents an image segmentation framework for preprocessing such images in order to reduce the data volume while preserving as much thermal information as possible for later downstream analyses. The resulting segmented images reflect thermal properties but do not depend on solving the ill-posed inverse problem. This allows users to avoid the Poisson inverse problem altogether or to tackle it on each of \sim10 segments rather than on each of \sim107^7 pixels, reducing computing time by a factor of \sim106^6. We employ a parametric class of dissimilarities that can be expressed as cosine dissimilarity functions or Hellinger distances between nonlinearly transformed vectors of multi-passband observations in each pixel. We develop a decision theoretic framework for choosing the dissimilarity that minimizes the expected loss that arises when estimating identifiable thermal properties based on segmented images rather than on a pixel-by-pixel basis. We also examine the efficacy of different dissimilarities for recovering clusters in the underlying thermal properties. The expected losses are computed under scientifically motivated prior distributions. Two simulation studies guide our choices of dissimilarity function. We illustrate our method by segmenting images of a coronal hole observed on 26 February 2015

    Detecting Unspecified Structure in Low-Count Images

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    Unexpected structure in images of astronomical sources often presents itself upon visual inspection of the image, but such apparent structure may either correspond to true features in the source or be due to noise in the data. This paper presents a method for testing whether inferred structure in an image with Poisson noise represents a significant departure from a baseline (null) model of the image. To infer image structure, we conduct a Bayesian analysis of a full model that uses a multiscale component to allow flexible departures from the posited null model. As a test statistic, we use a tail probability of the posterior distribution under the full model. This choice of test statistic allows us to estimate a computationally efficient upper bound on a p-value that enables us to draw strong conclusions even when there are limited computational resources that can be devoted to simulations under the null model. We demonstrate the statistical performance of our method on simulated images. Applying our method to an X-ray image of the quasar 0730+257, we find significant evidence against the null model of a single point source and uniform background, lending support to the claim of an X-ray jet

    Mendelian randomization in family data

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    The phrase "mendelian randomization" has become associated with the use of genetic polymorphisms to uncover causal relationships between phenotypic variables. The statistical methods useful in mendelian randomization are known as instrumental variable techniques. We present an approach to instrumental variable estimation that is useful in family data and is robust to the use of weak instruments. We illustrate our method to measure the causal influence of low-density lipoprotein on high-density lipoprotein, body mass index, triglycerides, and systolic blood pressure. We use the Framingham Heart Study data as distributed to participants in the Genetics Analysis Workshop 16

    Comparison of univariate and multivariate linkage analysis of traits related to hypertension

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    Complex traits are often manifested by multiple correlated traits. One example of this is hypertension (HTN), which is measured on a continuous scale by systolic blood pressure (SBP). Predisposition to HTN is predicted by hyperlipidemia, characterized by elevated triglycerides (TG), low-density lipids (LDL), and high-density lipids (HDL). We hypothesized that the multivariate analysis of TG, LDL, and HDL would be more powerful for detecting HTN genes via linkage analysis compared with univariate analysis of SBP. We conducted linkage analysis of four chromosomal regions known to contain genes associated with HTN using SBP as a measure of HTN in univariate Haseman-Elston regression and using the correlated traits TG, LDL, and HDL in multivariate Haseman-Elston regression. All analyses were conducted using the Framingham Heart Study data. We found that multivariate linkage analysis was better able to detect chromosomal regions in which the angiotensinogen, angiotensin receptor, guanine nucleotide-binding protein 3, and prostaglandin I2 synthase genes reside. Univariate linkage analysis only detected the AGT gene. We conclude that multivariate analysis is appropriate for the analysis of multiple correlated phenotypes, and our findings suggest that it may yield new linkage signals undetected by univariate analysis

    Shear modulus of the hadron-quark mixed phase

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    Robust arguments predict that a hadron-quark mixed phase may exist in the cores of some "neutron" stars. Such a phase forms a crystalline lattice with a shear modulus higher than that of the crust due to the high density and charge separation, even allowing for the effects of charge screening. This may lead to strong continuous gravitational-wave emission from rapidly rotating neutron stars and gravitational-wave bursts associated with magnetar flares and pulsar glitches. We present the first detailed calculation of the shear modulus of the mixed phase. We describe the quark phase using the bag model plus first-order quantum chromodynamics corrections and the hadronic phase using relativistic mean-field models with parameters allowed by the most massive pulsar. Most of the calculation involves treating the "pasta phases" of the lattice via dimensional continuation, and we give a general method for computing dimensionally continued lattice sums including the Debye model of charge screening. We compute all the shear components of the elastic modulus tensor and angle average them to obtain the effective (scalar) shear modulus for the case where the mixed phase is a polycrystal. We include the contributions from changing the cell size, which are necessary for the stability of the lower-dimensional portions of the lattice. Stability also requires a minimum surface tension, generally tens of MeV/fm^2 depending on the equation of state. We find that the shear modulus can be a few times 10^33 erg/cm^3, two orders of magnitude higher than the first estimate, over a significant fraction of the maximum mass stable star for certain parameter choices.Comment: 22 pages, 12 figures, version accepted by Phys. Rev. D, with the corrections to the shear modulus computation and Table I given in the erratu

    Pervasive gene content variation and copy number variation in maize and its undomesticated progenitor

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    Individuals of the same species are generally thought to have very similar genomes. However, there is growing evidence that structural variation in the form of copy number variation (CNV) and presence-absence variation (PAV) can lead to variation in the genome content of individuals within a species. Array comparative genomic hybridization (CGH) was used to compare gene content and copy number variation among 19 diverse maize inbreds and 14 genotypes of the wild ancestor of maize, teosinte. We identified 479 genes exhibiting higher copy number in some genotypes (UpCNV) and 3410 genes that have either fewer copies or are missing in the genome of at least one genotype relative to B73 (DownCNV/PAV). Many of these DownCNV/PAV are examples of genes present in B73, but missing from other genotypes. Over 70% of the CNV/PAV examples are identified in multiple genotypes, and the majority of events are observed in both maize and teosinte, suggesting that these variants predate domestication and that there is not strong selection acting against them. Many of the genes affected by CNV/PAV are either maize specific (thus possible annotation artifacts) or members of large gene families, suggesting that the gene loss can be tolerated through buffering by redundant functions encoded elsewhere in the genome. While this structural variation may not result in major qualitative variation due to genetic buffering, it may significantly contribute to quantitative variation

    Distribution, functional impact, and origin mechanisms of copy number variation in the barley genome

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    BACKGROUND There is growing evidence for the prevalence of copy number variation (CNV) and its role in phenotypic variation in many eukaryotic species. Here we use array comparative genomic hybridization to explore the extent of this type of structural variation in domesticated barley cultivars and wild barleys. RESULTS A collection of 14 barley genotypes including eight cultivars and six wild barleys were used for comparative genomic hybridization. CNV affects 14.9% of all the sequences that were assessed. Higher levels of CNV diversity are present in the wild accessions relative to cultivated barley. CNVs are enriched near the ends of all chromosomes except 4H, which exhibits the lowest frequency of CNVs. CNV affects 9.5% of the coding sequences represented on the array and the genes affected by CNV are enriched for sequences annotated as disease-resistance proteins and protein kinases. Sequence-based comparisons of CNV between cultivars Barke and Morex provided evidence that DNA repair mechanisms of double-strand breaks via single-stranded annealing and synthesis-dependent strand annealing play an important role in the origin of CNV in barley. CONCLUSIONS We present the first catalog of CNVs in a diploid Triticeae species, which opens the door for future genome diversity research in a tribe that comprises the economically important cereal species wheat, barley, and rye. Our findings constitute a valuable resource for the identification of CNV affecting genes of agronomic importance. We also identify potential mechanisms that can generate variation in copy number in plant genomes.This work was financially supported by the following grants: project GABI-BARLEX, German Federal Ministry of Education and Research (BMBF), #0314000 to MP, US, KFXM and NS; Triticeae Coordinated Agricultural Project, USDA-NIFA #2011-68002-30029 to GJM; and Agriculture and Food Research Initiative Plant Genome, Genetics and Breeding Program of USDA’s Cooperative State Research and Extension Service, #2009-65300- 05645 to GJM
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