Predilection Muscles and Physical Condition of Raccoon Dogs (Nyctereutes procyonoides) Experimentally Infected with Trichinella spiralis and Trichinella nativa

The predilection muscles of Trichinella spiralis and T. nativa were studied in 2 experimental groups of 6 raccoon dogs (Nyctereutes procyonoides), the third group serving as a control for clinical signs. The infection dose for both parasites was 1 larva/g body weight. After 12 weeks, the animals were euthanized and 13 sampling sites were analysed by the digestion method. Larvae were found in all sampled skeleton muscles of the infected animals, but not in the specimens from the heart or intestinal musculature. Both parasite species reproduced equally well in the raccoon dog. The median density of infection in positive tissues was 353 larvae per gram (lpg) with T. spiralis and 343 lpg with T. nativa. All the infected animals had the highest larvae numbers in the carpal flexors (M. flexor carpi ulnaris). Also tongue and eye muscles had high infection levels. There were no significant differences in the predilection sites between these 2 parasite species. Trichinellosis increased the relative amount of fat, but not the body weight in the captive raccoon dogs. Thus, Trichinella as a muscle parasite might have catabolic effect on these animals

A TV-Gaussian prior for infinite-dimensional Bayesian inverse problems and its numerical implementations

Many scientific and engineering problems require to perform Bayesian inferences in function spaces, in which the unknowns are of infinite dimension. In such problems, choosing an appropriate prior distribution is an important task. In particular we consider problems where the function to infer is subject to sharp jumps which render the commonly used Gaussian measures unsuitable. On the other hand, the so-called total variation (TV) prior can only be defined in a finite dimensional setting, and does not lead to a well-defined posterior measure in function spaces. In this work we present a TV-Gaussian (TG) prior to address such problems, where the TV term is used to detect sharp jumps of the function, and the Gaussian distribution is used as a reference measure so that it results in a well-defined posterior measure in the function space. We also present an efficient Markov Chain Monte Carlo (MCMC) algorithm to draw samples from the posterior distribution of the TG prior. With numerical examples we demonstrate the performance of the TG prior and the efficiency of the proposed MCMC algorithm

Low-loss singlemode PECVD silicon nitride photonic wire waveguides for 532-900 nm wavelength window fabricated within a CMOS pilot line

PECVD silicon nitride photonic wire waveguides have been fabricated in a CMOS pilot line. Both clad and unclad single mode wire waveguides were measured at lambda = 532, 780, and 900 nm, respectively. The dependence of loss on wire width, wavelength, and cladding is discussed in detail. Cladded multimode and singlemode waveguides show a loss well below 1 dB/cm in the 532-900 nm wavelength range. For singlemode unclad waveguides, losses < 1 dB/cm were achieved at lambda = 900 nm, whereas losses were measured in the range of 1-3 dB/cm for lambda = 780 and 532 nm, respectively

Fast Gibbs sampling for high-dimensional Bayesian inversion

Solving ill-posed inverse problems by Bayesian inference has recently attracted considerable attention. Compared to deterministic approaches, the probabilistic representation of the solution by the posterior distribution can be exploited to explore and quantify its uncertainties. In applications where the inverse solution is subject to further analysis procedures, this can be a significant advantage. Alongside theoretical progress, various new computational techniques allow to sample very high dimensional posterior distributions: In [Lucka2012], a Markov chain Monte Carlo (MCMC) posterior sampler was developed for linear inverse problems with $\ell_1$-type priors. In this article, we extend this single component Gibbs-type sampler to a wide range of priors used in Bayesian inversion, such as general $\ell_p^q$ priors with additional hard constraints. Besides a fast computation of the conditional, single component densities in an explicit, parameterized form, a fast, robust and exact sampling from these one-dimensional densities is key to obtain an efficient algorithm. We demonstrate that a generalization of slice sampling can utilize their specific structure for this task and illustrate the performance of the resulting slice-within-Gibbs samplers by different computed examples. These new samplers allow us to perform sample-based Bayesian inference in high-dimensional scenarios with certain priors for the first time, including the inversion of computed tomography (CT) data with the popular isotropic total variation (TV) prior.Comment: submitted to "Inverse Problems

A modelling framework for the assessment of the impacts of alternative policy and management options on the sustainability of Finnish agrifood systems

Recently, a new project focussing on integrated assessment modelling of agrifood systems (IAM-Tools) has been launched at MTT Agrifood Research Finland to gather, evaluate, refine and develop these component models and to link tem in an IAM framework for Finnish conditions

Relationship between value added capital employed, value added human capital, structural capital value added and financial performance

Companies that can survive are companies that need to quickly change its strategy from a business based on labor towards knowledge-based business, so that the main characteristics of the company are changed towards a science-based company. This study examines the relationship of value added capital employed, value-added human capital, structural capital value added and financial performance. The method of this research is purposive sampling with a total of 34 samples analyzed by using Eviews version 9. The result stated that value added capital employed has no effect on return on asset, value added human capital has an effect on return on asset, structural capital value added has an effect on return on asset