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Domestic dogs are susceptible to numerous vector-borne pathogens that are of significant importance for their health. In addition to being of veterinary importance, many of these pathogens are zoonotic and thus may pose a risk to human health. In the USA, owned dogs are commonly screened for exposure to or infection with several canine vector-borne pathogens. Although the screening data are widely available to show areas where infections are being diagnosed, testing of owned dogs is expected to underestimate the actual prevalence in dogs that have no access to veterinary care. The goal of this study was to measure the association between the widely available data from a perceived low-risk population with temporally and spatially collected data from shelter-housed dog populations. These data were then used to extrapolate the prevalence in dogs that generally lack veterinary care. The focus pathogens includedDirofilaria immitis,Ehrlichiaspp.,Anaplasmaspp., andBorrelia burgdorferi.There was a linear association between the prevalence of selected vector-borne pathogens in shelter-housed and owned dog populations and, generally, the data suggested that prevalence of heartworm (D. immitis) infection and seroprevalence ofEhrlichiaspp. andB. burgdorferiare higher in shelter-housed dogs, regardless of their location, compared with the owned population. The seroprevalence ofAnaplasmaspp. was predicted to be higher in areas that have very low to low seroprevalence, but unexpectedly, in areas of higher seroprevalence within the owned population, the seroprevalence was expected to be lower in the shelter-housed dog population. If shelters and veterinarians make decisions to not screen dogs based on the known seroprevalence of the owned group, they are likely underestimating the risk of exposure. This is especially true for heartworm. With this new estimate of the seroprevalence in shelter-housed dogs throughout the USA, shelters and veterinarians can make evidence-based informed decisions on whether testing and screening for these pathogens is appropriate for their local dog population. This work represents an important step in understanding the relationships in the seroprevalences of vector-borne pathogens between shelter-housed and owned dogs, and provides valuable data on the risk of vector-borne diseases in dogs.

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In this work, we develop a novel Bayesian regression framework that can be used to complete variable selection in high dimensional settings. Unlike existing techniques, the proposed approach can leverage side information to inform about the sparsity structure of the regression coefficients. This is accomplished by replacing the usual inclusion probability in the spike and slab prior with a binary regression model which assimilates this extra source of information. To facilitate model fitting, a computationally efficient and easy to implement Markov chain Monte Carlo posterior sampling algorithm is developed via carefully chosen priors and data augmentation steps. The finite sample performance of our methodology is assessed through numerical simulations, and we further illustrate our approach by using it to identify genetic markers associated with the nicotine metabolite ratio; a key biological marker associated with nicotine dependence and smoking cessation treatment.

 

Stalk lodging (breaking of agricultural plant stalks prior to harvest) is a multi-billion dollar a year problem. Stalk lodging occurs when high winds induce bending moments in the stalk which exceed the bending strength of the plant. Previous biomechanical models of plant stalks have investigated the effect of cross-sectional morphology on stalk lodging resistance (e.g., diameter and rind thickness). However, it is unclear if the location of stalk failure along the length of stem is determined by morphological or compositional factors. It is also unclear if the crops are structurally optimized, i.e., if the plants allocate structural biomass to create uniform and minimal bending stresses in the plant tissues. The purpose of this paper is twofold: (1) to investigate the relationship between bending stress and failure location of maize stalks, and (2) to investigate the potential of phenotyping for internode-level bending stresses to assess lodging resistance.

868 maize specimens representing 16 maize hybrids were successfully tested in bending to failure. Internode morphology was measured, and bending stresses were calculated. It was found that bending stress is highly and positively associated with failure location. A user-friendly computational tool is presented to help plant breeders in phenotyping for internode-level bending stress. Phenotyping for internode-level bending stresses could potentially be used to breed for more biomechanically optimal stalks that are resistant to stalk lodging.

Internode-level bending stress plays a potentially critical role in the structural integrity of plant stems. Equations and tools provided herein enable researchers to account for this phenotype, which has the potential to increase the bending strength of plants without increasing overall structural biomass.

 

e. With recent advances in online sensing technology and high-performance computing, structural health monitoring (SHM) has begun to emerge as an automated approach to the real-time conditional monitoring of civil infrastructure. Ideal SHM strategies detect and characterize damage by leveraging measured response data to update physics-based finite element models (FEMs). When monitoring composite structures, such as reinforced concrete (RC) bridges, the reliability of FEM based SHM is adversely affected by material, boundary, geometric, and other model uncertainties. Civil engineering researchers have adapted popular artificial intelligence (AI) techniques to overcome these limitations, as AI has an innate ability to solve complex and ill-defined problems by leveraging advanced machine learning techniques to rapidly analyze experimental data. In this vein, this study employs a novel Bayesian estimation technique to update a coupled vehicle-bridge FEM for the purposes of SHM. Unlike existing AI based techniques, the proposed approach makes intelligent use of an embedded FEM model, thus reducing the parameter space while simultaneously guiding the Bayesian model via physics-based principles. To validate the method, bridge response data is generated from the vehicle-bridge FEM given a set of “true” parameters and the bias and standard deviation of the parameter estimates are analyzed. Additionally, the mean parameter estimates are used to solve the FEM model and the results are compared against the results obtained for “true” parameter values. A sensitivity study is also conducted to demonstrate methods for properly formulating model spaces to improve the Bayesian estimation routine. The study concludes with a discussion highlighting factors that need to be considered when leveraging experimental data to update FEMs of concrete structures using AI techniques. 

Summary In screening applications involving low-prevalence diseases, pooling specimens (e.g., urine, blood, swabs, etc.) through group testing can be far more cost effective than testing specimens individually. Estimation is a common goal in such applications and typically involves modeling the probability of disease as a function of available covariates. In recent years, several authors have developed regression methods to accommodate the complex structure of group testing data but often under the assumption that covariate effects are linear. Although linearity is a reasonable assumption in some applications, it can lead to model misspecification and biased inference in others. To offer a more flexible framework, we propose a Bayesian generalized additive regression approach to model the individual-level probability of disease with potentially misclassified group testing data. Our approach can be used to analyze data arising from any group testing protocol with the goal of estimating multiple unknown smooth functions of covariates, standard linear effects for other covariates, and assay classification accuracy probabilities. We illustrate the methods in this article using group testing data on chlamydia infection in Iowa.