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African swine fever virus (ASFV) encodes proteins that manipulate important host antiviral mechanisms. Bioinformatic analysis of the ASFV genome revealed ORF I329L, a gene without any previous functional characterization as a possible inhibitor of TLR signaling. We demonstrate that ORF I329L encodes a highly glycosylated protein expressed in the cell membrane and on its surface. I329L also inhibited dsRNA-stimulated activation of NFκB and IRF3, two key players in innate immunity. Consistent with this, expression of I329L protein also inhibited the activation of interferon-β and CCL5. Finally, overexpression of TRIF reversed I329L-mediated inhibition of both NFκB and IRF3 activation. Our results suggest that TRIF, a key MyD88-independent adaptor molecule, is a possible target of this viral host modulation gene. The demonstration of an ASFV host evasion molecule inhibiting TLR responses is consistent with the ability of this virus to infect vertebrate and invertebrate hosts, both of which deploy innate immunity controlled by conserved TLR systems.

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Abdominal aortic aneurysm (AAA) is a vascular condition where the use of a biomechanics-based assessment for patient-specific risk assessment is a promising approach for clinical management of the disease. Among various factors that affect such assessment, AAA wall thickness is expected to be an important factor. However, regionally varying patient-specific wall thickness has not been incorporated as a modeling feature in AAA biomechanics. To the best our knowledge, the present work is the first to incorporate patient-specific variable wall thickness without an underlying empirical assumption on its distribution for AAA wall mechanics estimation. In this work, we present a novel method for incorporating regionally varying wall thickness (the "PSNUT" modeling strategy) in AAA finite element modeling and the application of this method to a diameter-matched cohort of 28 AAA geometries to assess differences in wall mechanics originating from the conventional assumption of a uniform wall thickness. For the latter, we used both a literature-derived population average wall thickness (1.5 mm; the "UT" strategy) as well as the spatial average of our patient-specific variable wall thickness (the "PSUT" strategy). For the three different wall thickness modeling strategies, wall mechanics were assessed by four biomechanical parameters: the spatial maxima of the first principal stress, strain, strain-energy density, and displacement. A statistical analysis was performed to address the hypothesis that the use of any uniform wall thickness model resulted in significantly different biomechanical parameters compared to a patient-specific regionally varying wall thickness model. Statistically significant differences were obtained with the UT modeling strategy compared to the PSNUT strategy for the spatial maxima of the first principal stress (p ¼ 0.002), strain (p ¼ 0.0005), and strain-energy density (p ¼ 7.83 e-5) but not for displacement (p ¼ 0.773). Likewise, significant differences were obtained comparing the PSUT modeling strategy with the PSNUT strategy for the spatial maxima of the first principal stress (p ¼ 9.68 e-7), strain (p ¼ 1.03 e-8), strain-energy density (p ¼ 9.94 e-8), and displacement (p ¼ 0.0059). No significant differences were obtained comparing the UT and PSUT strategies for the spatial maxima of the first principal stress (p ¼ 0.285), strain (p ¼ 0.152), strain-energy density (p ¼ 0.222), and displacement (p ¼ 0.0981). This work strongly recommends the use of patient-specific regionally varying wall thickness derived from the segmentation of abdominal computed tomography (CT) scans if the AAA finite element analysis is focused on estimating peak biomechanical parameters, such as stress, strain, and strain-energy density.

Conditional autoregressive (CAR) models have been extensively used for the analysis of spatial data in diverse areas, such as demography, economy, epidemiology and geography, as models for both latent and observed variables. In the latter case, the most common inferential method has been maximum likelihood, and the Bayesian approach has not been used much. This work proposes default (automatic) Bayesian analyses of CAR models. Two versions of Jeffreys prior, the independence Jeffreys and Jeffreys-rule priors, are derived for the parameters of CAR models and properties of the priors and resulting posterior distributions are obtained. The two priors and their respective posteriors are compared based on simulated data. Also, frequentist properties of inferences based on maximum likelihood are compared with those based on the Jeffreys priors and the uniform prior. Finally, the proposed Bayesian analysis is illustrated by fitting a CAR model to a phosphate dataset from an archaeological region.