Toxoplasma gondii in Spanish commercial dry-cured meat products

Toxoplasmosis is an infection caused by Toxoplasma gondii, the transmission of which has usually been attributed to ingestion of undercooked or raw meat. Epidemiological studies also mention cured meat products as a potential risk factor for acquiring toxoplasmosis. With the aim of contributing to the risk assessment process, 552 samples of commercial dry-cured hams/shoulders and dry-cured sausages of different trademarks from different localities in Spain were randomly purchased for analysis. These were, specifically, 311 dry-cured hams/shoulders and 241 dry-cured sausages (76 samples of chorizo, 75 samples of fuet/longaniza, and 90 samples of salchichón). Additionally, data featured on labels of each meat product were gathered with the purpose of studying the influence of curing time and salt content, among other parameters, on the viability of Toxoplasma. Real-time polymerase chain reaction technique (qPCR) was performed to detect T. gondii DNA in the samples, and infectivity was determined by mouse bioassay of positive qPCR samples. The presence of T. gondii was detected in 57 samples (10.3%), with a parasite load between 28.05 and 35.66 Ct. Bioassay test showed that 47 out of the 57 meat products in which the parasite has been detected produced mice seropositive response (IFA), which represents 8.5 of the total number of samples analyzed. Of those samples, DNA of Toxoplasma gondii in mice brain was detected in 6 meat products, indicating its viability (1.1%). Ct values obtained by qPCR in the brains of seropositive mice ranged from 33.10 to 36.04. According to product type, the parasite was viable in 3 dry-cured ham/shoulder samples and in 3 salchichón samples. Statistical analysis showed that none of the variables under consideration detailed on the meat product labels had a significant influence on the viability of the parasite. In conclusion, we found a low prevalence of the infective forms of Toxoplasma gondii in cured meat products, although the risk for consumers cannot be completely excluded. However, scientific monitoring of commercial meat products continues to be necessary in order to provide data for risk assessment of Toxoplasma gondii through the meat industry's Hazard Analysis and Critical Control Point (HACCP-based self-control system). In order to ensure that consumers can make a safe choice among these ready-to-eat products, it is important for food labels to include information on those parameters which are relevant for the survival of the parasite, such as curing times, or freezing treatment of meat used as an ingredient

Influence of light intensity on plasma melatonin and locomotor activity rhythms in tench

©2005. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Chronobiology International: The Journal of Biological and Medical Rhythm Research.Melatonin production by the pineal organ is influenced by light intensity, as has been described in most vertebrate species, in which melatonin is considered a synchronizer of circadian rhythms. In the case of tench, strict nocturnal activity rhythms have been described although the role of melatonin has not been clarified. In this study we investigated daily activity and melatonin rhythms under 12:12 light-dark (LD) conditions with two different light intensities (58.6 and 1091µW/cm2 ), and the effect of one hour broad spectrum white light pulses of different intensities (3.3, 5.3, 10.5, 1091.4 µW/cm2 ) applied at mid darkness (MD) on nocturnal circulating melatonin. The results showed that plasma melatonin in tench under LD 12:12 and high light conditions displayed a rhythmic variation, where values at MD (255.8 ± 65.9 pg/ml) were higher than at mid light (ML) (70.7 ± 31.9 pg/ml). Such a difference between MD and ML values was reduced in animals exposed to LD 12:12 and low light intensity. The application of one hour light pulses at MD lowered plasma melatonin to 111.6 ± 3.2 pg/ml (in the 3.3-10.5 µW/cm2 range) and to 61.8 ± 18.3 pg/ml (with the 1091.4 µW/cm2 light pulse) and totally suppressed nocturnal locomotor activity. These results showed that melatonin rhythms persisted in tench exposed to low light intensity although the amplitude of the rhythm is affected. In addition, it was observed that light pulses applied at MD affected plasma melatonin content and locomotor activity. Such a low threshold suggests that the melatonin system is capable of transducing light even under dim conditions, which may be used by this nocturnal fish to synchronize to weak night light signals (e.g. moonlight cycles

Prior-based Bayesian information criterion

We present a new approach to model selection and Bayes factor determination, based on Laplace expansions (as in BIC), which we call Prior-based Bayes Information Criterion (PBIC). In this approach, the Laplace expansion is only done with the likelihood function, and then a suitable prior distribution is chosen to allow exact computation of the (approximate) marginal likelihood arising from the Laplace approximation and the prior. The result is a closed-form expression similar to BIC, but now involves a term arising from the prior distribution (which BIC ignores) and also incorporates the idea that different parameters can have different effective sample sizes (whereas BIC only allows one overall sample size n). We also consider a modification of PBIC which is more favourable to complex models

Recentered importance sampling with applications to Bayesian model validation

Since its introduction in the early 1990s, the idea of using importance sampling (IS) with Markov chain Monte Carlo (MCMC) has found many applications. This article examines problems associated with its application to repeated evaluation of related posterior distributions with a particular focus on Bayesian model validation. We demonstrate that, in certain applications, the curse of dimensionality can be reduced by a simple modification of IS. In addition to providing new theoretical insight into the behavior of the IS approximation in a wide class of models, our result facilitates the implementation of computationally intensive Bayesian model checks. We illustrate the simplicity, computational savings, and potential inferential advantages of the proposed approach through two substantive case studies, notably computation of Bayesian p-values for linear regression models and simulation-based model checking. Supplementary materials including the Appendix and the R code for Section 3.1.2 are available online

Sequential design of computer experiments for the estimation of a probability of failure

This paper deals with the problem of estimating the volume of the excursion set of a function $f:\mathbb{R}^d \to \mathbb{R}$ above a given threshold, under a probability measure on $\mathbb{R}^d$ that is assumed to be known. In the industrial world, this corresponds to the problem of estimating a probability of failure of a system. When only an expensive-to-simulate model of the system is available, the budget for simulations is usually severely limited and therefore classical Monte Carlo methods ought to be avoided. One of the main contributions of this article is to derive SUR (stepwise uncertainty reduction) strategies from a Bayesian-theoretic formulation of the problem of estimating a probability of failure. These sequential strategies use a Gaussian process model of $f$ and aim at performing evaluations of $f$ as efficiently as possible to infer the value of the probability of failure. We compare these strategies to other strategies also based on a Gaussian process model for estimating a probability of failure.Comment: This is an author-generated postprint version. The published version is available at http://www.springerlink.co

Designing "Successful" Replications

Replications of experiments are endlessly performed in applied research areas. However, and in spite of this widespread use, systematic studies of the goals and motivations of a "replication" are rare. As a consequence, there does not seem to be a precise notion of what a "success" when replicating means. In this paper we collect an systematize some of the possible goals for replication, which leads to different (but precise) notions of "success" when replicating. Bayesian hierarchical models allow for a flexible and explicit incorporation of the assumed relationship among the experiments into the analysis. Bayesian predictive distributions are a natural tool to compute the probability of the replication being successful, and hence to design the replication so that the probability of success is high enough. Derivations are exemplified with data coming from a Non-Central t distribution. Key-Words and Phrases: Bayesian analysis. Effect Size. Hypothesis testing. Meta-analysis. Non-Centra..