4,400 research outputs found

    Representation of Dormant and Active Microbial Dynamics for Ecosystem Modeling

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    Dormancy is an essential strategy for microorganisms to cope with environmental stress. However, global ecosystem models typically ignore microbial dormancy, resulting in major model uncertainties. To facilitate the consideration of dormancy in these large-scale models, we propose a new microbial physiology component that works for a wide range of substrate availabilities. This new model is based on microbial physiological states and is majorly parameterized with the maximum specific growth and maintenance rates of active microbes and the ratio of dormant to active maintenance rates. A major improvement of our model over extant models is that it can explain the low active microbial fractions commonly observed in undisturbed soils. Our new model shows that the exponentially-increasing respiration from substrate-induced respiration experiments can only be used to determine the maximum specific growth rate and initial active microbial biomass, while the respiration data representing both exponentially-increasing and non-exponentially-increasing phases can robustly determine a range of key parameters including the initial total live biomass, initial active fraction, the maximum specific growth and maintenance rates, and the half-saturation constant. Our new model can be incorporated into existing ecosystem models to account for dormancy in microbially-mediated processes and to provide improved estimates of microbial activities.Comment: 38 pages, 2 Tables, 4 Figure

    Sympathy for the devil: The desire to punish and rehabilitate sex offenders

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    Prophetic sources of the teachings of Jesus

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    This item was digitized by the Internet Archive. Thesis (M.A.)--Boston Universityhttps://archive.org/details/propheticsources00sch

    Integration of disease-specific single nucleotide polymorphisms, expression quantitative trait loci and coexpression networks reveal novel candidate genes for type 2 diabetes.

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    Aims/hypothesisWhile genome-wide association studies (GWASs) have been successful in identifying novel variants associated with various diseases, it has been much more difficult to determine the biological mechanisms underlying these associations. Expression quantitative trait loci (eQTL) provide another dimension to these data by associating single nucleotide polymorphisms (SNPs) with gene expression. We hypothesised that integrating SNPs known to be associated with type 2 diabetes with eQTLs and coexpression networks would enable the discovery of novel candidate genes for type 2 diabetes.MethodsWe selected 32 SNPs associated with type 2 diabetes in two or more independent GWASs. We used previously described eQTLs mapped from genotype and gene expression data collected from 1,008 morbidly obese patients to find genes with expression associated with these SNPs. We linked these genes to coexpression modules, and ranked the other genes in these modules using an inverse sum score.ResultsWe found 62 genes with expression associated with type 2 diabetes SNPs. We validated our method by linking highly ranked genes in the coexpression modules back to SNPs through a combined eQTL dataset. We showed that the eQTLs highlighted by this method are significantly enriched for association with type 2 diabetes in data from the Wellcome Trust Case Control Consortium (WTCCC, p = 0.026) and the Gene Environment Association Studies (GENEVA, p = 0.042), validating our approach. Many of the highly ranked genes are also involved in the regulation or metabolism of insulin, glucose or lipids.Conclusions/interpretationWe have devised a novel method, involving the integration of datasets of different modalities, to discover novel candidate genes for type 2 diabetes

    Coanalysis of GWAS with eQTLs reveals disease-tissue associations.

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    Expression quantitative trait loci (eQTL), or genetic variants associated with changes in gene expression, have the potential to assist in interpreting results of genome-wide association studies (GWAS). eQTLs also have varying degrees of tissue specificity. By correlating the statistical significance of eQTLs mapped in various tissue types to their odds ratios reported in a large GWAS by the Wellcome Trust Case Control Consortium (WTCCC), we discovered that there is a significant association between diseases studied genetically and their relevant tissues. This suggests that eQTL data sets can be used to determine tissues that play a role in the pathogenesis of a disease, thereby highlighting these tissue types for further post-GWAS functional studies

    Erprobung eines computergestützten Stimmbelastungstestes an stimmgesunden Probanden

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    Die vorliegende Dissertation trägt den Titel „Erprobung eines computergestützten Stimmbelastungstests an gesunden Probanden“. Anhand eines computerbasierten Stimmbelastungstest wird versucht, vergleichbare Daten für zukünftige Testungen zu erheben. Ebenfalls wird bewertet, ob der verwendete Test für den Praxisalltag praktikabel ist. Die gemessenen Daten stammen von stimmgesunden Probanden. Können die Daten zu Normwerten erhoben werden, können diese womöglich helfen, zwischen „gesunder“ und „kranker“ Stimme zu differenzieren
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