64 research outputs found

    The role of CSF1R-dependent macrophages in control of the intestinal stem cell niche

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    Colony-stimulating factor 1 (CSF1) controls the growth and differentiation of macrophages.CSF1R signaling has been implicated in the maintenance of the intestinal stem cell niche and differentiation of Paneth cells, but evidence of expression of CSF1R within the crypt is equivocal. Here we show that CSF1R-dependent macrophages influence intestinal epithelial differentiation and homeostasis. In the intestinal lamina propria CSF1R mRNA expression is restricted to macrophages which are intimately associated with the crypt epithelium, and is undetectable in Paneth cells. Macrophage ablation following CSF1R blockade affects Paneth cell differentiation and leads to a reduction of Lgr5 intestinal stem cells. The disturbances to the crypt caused by macrophage depletion adversely affect the subsequent differentiation of intestinal epithelial cell lineages. Goblet cell density is enhanced, whereas the development of M cells in Peyer's patches is impeded. We suggest that modification of the phenotype or abundance of macrophages in the gut wall alters the development of the intestinal epithelium and the ability to sample gut antigens

    Kinesiology of the Shoulder Complex

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    Impact of OVL variation on AUC bias estimated by non-parametric methods

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    The area under the ROC curve (AUC) is the most commonly used index in the ROC methodology to evaluate the performance of a classifier that discriminates between two mutually exclusive conditions. The AUC can admit values between 0.5 and 1, where values close to 1 indicate that the model of classification has high discriminative power. The overlap coefficient (OVL) between two density functions is defined as the common area between both functions. This coefficient is used as a measure of agreement between two distributions presenting values between 0 and 1, where values close to 1 reveal total overlapping densities. These two measures were used to construct the arrow plot to select differential expressed genes. A simulation study using the bootstrap method is presented in order to estimate AUC bias and standard error using empirical and kernel methods. In order to assess the impact of the OVL variation on the AUC bias, samples from various continuous distributions were simulated considering different values for its parameters and for fixed OVL values between 0 and 1. Samples of dimensions 15, 30, 50, and 100, and 1000 bootstrap replicate for each scenario were considered.info:eu-repo/semantics/publishedVersio

    Bounds for the Bayes Error in Classification: A Bayesian Approach Using Discriminant Analysis

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    Overlapping coefficient, Discriminant analysis, Misclassification, Lissack and Fu bounds, Bhattacharyya bounds, Hypergeometric functions, Bernoulli, Beta distribution,

    AD-LIBS: inferring ancestry across hybrid genomes using low-coverage sequence data

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    Abstract Background Inferring the ancestry of each region of admixed individuals’ genomes is useful in studies ranging from disease gene mapping to speciation genetics. Current methods require high-coverage genotype data and phased reference panels, and are therefore inappropriate for many data sets. We present a software application, AD-LIBS, that uses a hidden Markov model to infer ancestry across hybrid genomes without requiring variant calling or phasing. This approach is useful for non-model organisms and in cases of low-coverage data, such as ancient DNA. Results We demonstrate the utility of AD-LIBS with synthetic data. We then use AD-LIBS to infer ancestry in two published data sets: European human genomes with Neanderthal ancestry and brown bear genomes with polar bear ancestry. AD-LIBS correctly infers 87–91% of ancestry in simulations and produces ancestry maps that agree with published results and global ancestry estimates in humans. In brown bears, we find more polar bear ancestry than has been published previously, using both AD-LIBS and an existing software application for local ancestry inference, HAPMIX. We validate AD-LIBS polar bear ancestry maps by recovering a geographic signal within bears that mirrors what is seen in SNP data. Finally, we demonstrate that AD-LIBS is more effective than HAPMIX at inferring ancestry when preexisting phased reference data are unavailable and genomes are sequenced to low coverage. Conclusions AD-LIBS is an effective tool for ancestry inference that can be used even when few individuals are available for comparison or when genomes are sequenced to low coverage. AD-LIBS is therefore likely to be useful in studies of non-model or ancient organisms that lack large amounts of genomic DNA. AD-LIBS can therefore expand the range of studies in which admixture mapping is a viable tool
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