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This study investigates two levels of dietary selenium (Se) and vitamin E in combination on their status in sows and their progeny, and influence on antioxidant status and immunological responses of the piglets at weaning. Female pigs ( n = 6) were provided LOW or HIGH antioxidant nutrition (Se and vitamin E) from mating until weaning of their off-spring. The HIGH treatment elevated the concentration of Se ( p = 0.015) and α-tocopherol ( p = 0.023) in plasma of piglets compared with piglets of the LOW treatment. Treatments also affected the concentrations of milk and sow plasma immunoglobulins. Piglets from sows on the HIGH treatment had increased ( p < 0.001) activity of glutathione peroxidase, lower serum levels of C-reactive protein ( p = 0.005), haptoglobin ( p = 0.05) and albumin ( p = 0.05), and the number of white blood cells ( p = 0.023) and the ratio of NEU to LYM was lower ( p = 0.025) than in piglets from sows on the LOW group. Furthermore, the dietary antioxidant level influenced responses of cytokines (interleukine (IL) 6 ( p = 0.007), 12 ( p = 0.01) and 18 ( p = 0.01)) in piglets' plasma. In conclusion, improved antioxidant status via dietary maternal provision improves the robustness of the offspring via immunomodulatory mechanisms.

We considered experiments where animals chose one of all possible simultaneously presented options. The animals might be observed at repeated occasions. In the ethological literature the analysis is often focused on testing the global hypothesis of no difference in preferences by non-parametric methods. This fails to address the estimation of a ranking. Often this approach cannot adequately reflect the experimental setting and the repeated measurement structure. Therefore, we propose to model the choice probabilities for the options with a multinomial logistic model. The correlation induced by repeated measurements is incorporated by animal specific random intercepts. The ranking of the options is taken as the order of the choice probabilities. Adopting a Bayesian approach samples from the posterior distribution of the choice probabilities provide directly samples from the posterior of the rankings. Based on this an estimate of the ranking and description of its variability can be derived. The computation was performed via Markov chain Monte Carlo sampling and was implemented using WinBUGS. We illustrate our approach with an experiment to determine the preference of pigs for three different rooting materials. The proposed method allowed deriving an overall ranking for different combinations of the materials and the spatial positioning., ((c) 2007 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Latent class analysis to assess the sensitivity and specificity of a diagnostic test can be carried out under different assumptions. An often applied set of assumptions is known as the Hui-Walter paradigm, which essentially states that: (i) the population is divided into two or more populations in which two or more tests are evaluated under assumption that (ii) sensitivity and specificity of the tests are the same in all populations; and (iii) the tests are conditionally independent given the disease status. This study explores the implications of these assumptions. Through simulation studies, it is shown how the size of the difference between disease prevalences within the populations influences the precision of the estimates. It is also illustrated by a simulation study how a difference in a test sensitivity between populations may result in estimates that are biased towards the sensitivity of the test in the population with highest disease prevalence, since that population estimate is supported by most of the data. It is shown that the assumption of conditional independence between tests in general cannot be ignored in latent class models. Failure to impose conditional independence will result in a model that lacks identifiability in a way that cannot be handled by adding more tests or dividing the sample into more populations.

In veterinary practice the clinician often evaluates and predicts herd health status over time according to clinical criteria. In this paper, we modeled three different clinical signs among pigs based on longitudinal clinical observations in 15 pig herds. We compared and discussed the outputs from two different approaches for making clinical forecasts in a herd: a naive approach using a simple time series model with previous disease observations as predictors and a Bayesian state space models approach, in which the time lag variable entered into the random component of the model. We used the Markov chain Monte Carlo technique to calculate posterior distributions of the forecasts. For the herd specific forecasts the results showed that there were only minor differences between the forecasts from the simple time series model and the median forecasts from the Bayesian model. However, the credibility intervals from the Bayesian model were wider than the forecasts from the simple model and, therefore the Bayesian model encompassed the variability in the forecasts better. Compared to the statistical model, the simple time series would be easier to implement in a practical setting. However, the latter lacks the inherent "generality" from the statistical model that allows the user to make statements about the distribution of the herds and to predict disease status based on the "average" correlation among the herds. The applicability of the Bayesian approach within a clinical decision-making framework was discussed, with special emphasis on the use of prior information and clinical forecasting.