Metamorphism, Transient Mid-Crustal Rheology, Strain Localization and the Exhumation of High-Grade Metamorphic Rocks

We present a series of three-dimensional numerical models investigating the effects of metamorphic strengthening and weakening on the geodynamic evolution of convergent orogens that are constrained by observations from an exposed mid-crustal section in the New England Appalachians. The natural mid-crustal section records evidence for spatially and temporally variable mid-crustal strength as a function of metamorphic grade during prograde polymetamorphism. Our models address changes in strain rate partitioning and topographic uplift as a function of strengthening/weakening in the middle crust, as well as the resultant changes in deformation kinematics and potential exhumation patterns of high-grade metamorphic rock. Results suggest that strengthening leads to strain rate partitioning around the zone and suppressed topographic uplift rates whereas weakening leads to strain rate partitioning into the zone and enhanced topographic uplift rates. Deformation kinematics recorded in the orogen are also affected by strengthening/weakening, with complete reversals in shear sense occurring as a function of strengthening/weakening without changes in plate boundary kinematics

The Bayesian two-sample t-test

In this article we show how the pooled-variance two-sample t-statistic arises from a Bayesian formulation of the two-sided point null testing problem, with emphasis on teaching. We identify a reasonable and useful prior giving a closed-form Bayes factor that can be written in terms of the distribution of the two-sample t-statistic under the null and alternative hypotheses respectively. This provides a Bayesian motivation for the two-sample t-statistic, which has heretofore been buried as a special case of more complex linear models, or given only roughly via analytic or Monte Carlo approximations. The resulting formulation of the Bayesian test is easy to apply in practice, and also easy to teach in an introductory course that emphasizes Bayesian methods. The priors are easy to use and simple to elicit, and the posterior probabilities are easily computed using available software, in some cases using spreadsheets

Comparison of pre- and post-vaccination ovine Johne's disease prevalence using a Bayesian approach

postprintThis study was conducted to evaluate the effectiveness of GudairTM vaccine in decreasing the prevalence of shedding of Mycobacterium avium subsp. paratuberculosis (MAP) in flocks of varying initial prevalence. Thirty seven self-replacing Merino flocks from New South Wales and Victoria (Australia) that had been vaccinating lambs with GudairTM for at least five years were enrolled in the study. These flocks had been tested prior to or at commencement of vaccination using pooled faecal culture, agar gel immunodiffusion or both tests. These pre-vaccination test results were used to estimate pre-vaccination prevalence. Post-vaccination prevalence was estimated from culture of usually 7 pools of 50 sheep collected from the enrolled flocks in 2008-2009, approximately five or more years after commencement of vaccination. A Bayesian model was developed to estimate and compare the pre- and post-vaccination prevalences for the enrolled flocks. Apparent pre- and post-vaccination prevalences for flocks were modelled as functions of the true pre- and post-vaccination prevalences, respectively, and the sensitivities and specificities of the respective diagnostic tests. Logit-normal models were specified on pre- and post-vaccination true prevalences and were then used to make inferences about the median and 90th percentile of the prevalence distributions and their differences. Priors were mostly specified based on published literature or analysis of abattoir surveillance data for this population of flocks. The analysis found a significant decline in ovine Johne’s disease prevalence from a pre-vaccination median prevalence of 2.72% [95% probability interval (PI): 1.40; 6.86%] to a post-vaccination median prevalence of 0.72% (0.39; 1.27%). However 30 of the 37 flocks still contained sheep that were shedding MAP in their faeces. The results suggest that vaccination with Gudair™ is usually effective in reducing the prevalence of faecal shedding but the response to vaccination is variable among flocks. This approach could be implemented in similar situations to compare prevalences where information from multiple diagnostic tests with varied sensitivities and specificities is available. Keywords: Ovine Johne’s disease; Gudair; Vaccination; Abattoir surveillance; Faecal culture; Agar gel immune-diffusion test

Comparison of pre- and post-vaccination ovine Johne's disease prevalence using a Bayesian approach

This study was conducted to evaluate the effectiveness of GudairTM vaccine in decreasing the prevalence of shedding of Mycobacterium avium subsp. paratuberculosis (MAP) in flocks of varying initial prevalence. Thirty seven self-replacing Merino flocks from New South Wales and Victoria (Australia) that had been vaccinating lambs with GudairTM for at least five years were enrolled in the study. These flocks had been tested prior to or at commencement of vaccination using pooled faecal culture, agar gel immunodiffusion or both tests. These pre-vaccination test results were used to estimate pre-vaccination prevalence. Post-vaccination prevalence was estimated from culture of usually 7 pools of 50 sheep collected from the enrolled flocks in 2008-2009, approximately five or more years after commencement of vaccination. A Bayesian model was developed to estimate and compare the pre- and post-vaccination prevalences for the enrolled flocks. Apparent pre- and post-vaccination prevalences for flocks were modelled as functions of the true pre- and post-vaccination prevalences, respectively, and the sensitivities and specificities of the respective diagnostic tests. Logit-normal models were specified on pre- and post-vaccination true prevalences and were then used to make inferences about the median and 90th percentile of the prevalence distributions and their differences. Priors were mostly specified based on published literature or analysis of abattoir surveillance data for this population of flocks. The analysis found a significant decline in ovine Johne’s disease prevalence from a pre-vaccination median prevalence of 2.72% [95% probability interval (PI): 1.40; 6.86%] to a post-vaccination median prevalence of 0.72% (0.39; 1.27%). However 30 of the 37 flocks still contained sheep that were shedding MAP in their faeces. The results suggest that vaccination with Gudair™ is usually effective in reducing the prevalence of faecal shedding but the response to vaccination is variable among flocks. This approach could be implemented in similar situations to compare prevalences where information from multiple diagnostic tests with varied sensitivities and specificities is available. Keywords: Ovine Johne’s disease; Gudair; Vaccination; Abattoir surveillance; Faecal culture; Agar gel immune-diffusion test

Informative g-Priors for Logistic Regression

Eliciting information from experts for use in constructing prior distributions for logistic regression coefficients can be challenging. The task is especially difficult when the model contains many predictor variables, because the expert is asked to provide summary information about the probability of “success” for many subgroups of the population. Often, however, experts are confident only in their assessment of the population as a whole. This paper is about incorporating such overall information easily into a logistic regression data analysis using g-priors. We present a version of the g-prior such that the prior distribution on the overall population logistic regression probabilities of success can be set to match a beta distribution. A simple data augmentation formulation allows implementation in standard statistical software packages.Keywords: Generalized linear model, Prior elicitation, Binomial regressio

Cryogenic Orbital Testbed (CRYOTE) Ground Test Article, Final Report

Liquid propulsion has been used since Robert Goddard started developing a liquid oxygen (LO2) and gasoline powered rocket and fired it in 1923 (Ref. 1). In the following decades engineers settled on the combination of liquid hydrogen (LH2) and LO2 as the most efficient propellant combination for in-space travel. Due to their low temperatures (LH2 at 20 K and LO2 at 90 K), they require special handling and procedures. General Dynamics began developing LO2 and LH2 upper stages in 1956 in the form of Centaur, these efforts were soon funded by the Department of Defense in conjunction with NASA (beginning in 1958) (Ref. 2). Meanwhile NASA also worked with McDonnell Douglas to develop the SIV-B stage for the Saturn V rocket. In the subsequent years, the engineers were able to push the Centaur to up to 9 hr of orbital lifetime and the SIV-B to up to 6 hr. Due to venting the resultant boil-off from the high heat loads through the foam insulation on the upper stages, both vehicles remained in a settled configuration throughout the flights, thus the two phases of propellant (liquid and vapor) were separated at a known location. The one exception to this were the Titan/Centaur missions, which thanks to the lower boil-off using three layers of multilayer insulation (MLI), were able to coast unsettled for up to 5.25 hr during direct geosynchronous orbit insertion missions. In the years since there has been a continuous effort to extend the life of these upper stages from hours to days or even months

Estimating equations for biomarker based exposure estimation under non-steady-state conditions

Unrealistic steady-state assumptions are often used to estimate toxicant exposure rates from biomarkers. A biomarker may instead be modeled as a weighted sum of historical time-varying exposures. Estimating equations are derived for a zero-inflated gamma distribution for daily exposures with a known exposure frequency. Simulation studies suggest that the estimating equations can provide accurate estimates of exposure magnitude at any reasonable sample size, and reasonable estimates of the exposure variance at larger sample sizes