Estimating Listeria monocytogenes Growth in Ready-to-Eat Chicken Salad Using a Challenge Test for Quantitative Microbial Risk Assessment

Currently, there is a growing preference for convenience food products, such as ready-to-eat (RTE) foods, associated with long refrigerated shelf-lives, not requiring a heat treatment prior to consumption. Because Listeria monocytogenes is able to grow at refrigeration temperatures, inconsistent temperatures during production, distribution, and at consumer's household may allow for the pathogen to thrive, reaching unsafe limits. L. monocytogenes is the causative agent of listeriosis, a rare but severe human illness, with high fatality rates, transmitted almost exclusively by food consumption. With the aim of assessing the quantitative microbial risk of L. monocytogenes in RTE chicken salads, a challenge test was performed. Salads were inoculated with a three-strain mixture of cold-adapted L. monocytogenes and stored at 4, 12, and 16 °C for eight days. Results revealed that the salad was able to support L. monocytogenes’ growth, even at refrigeration temperatures. The Baranyi primary model was fitted to microbiological data to estimate the pathogen's growth kinetic parameters. Temperature effect on the maximum specific growth rate (μ_max) was modeled using a square-root-type model. Storage temperature significantly influenced μ_max of L. monocytogenes (p < 0.05). These predicted growth models for L. monocytogenes were subsequently used to develop a quantitative microbial risk assessment, estimating a median number of 0.00008726 listeriosis cases per year linked to the consumption of these RTE salads. Sensitivity analysis considering different time–temperature scenarios indicated a very low median risk per portion (<−7 log), even if the assessed RTE chicken salad was kept in abuse storage conditions.     

Cognition in Situations

The symbolic interactionist tradition can contribute to advancing sociological studies of cognition by setting dual process models on more solid ground. I draw on Blumer's epistemological statements and the interactionist tradition more broadly to consider how dual process models of cognition could be applied to naturally occurring situations. I suggest that attending to the ways the past and the future are handled and modified within social interaction provides a usable inroad for the sociology of cognition to engage with situational analysis. I identify “resonance” and “iterative reprocessing” as concepts that are suitable to this end.     

Peers,proactivity, and problem-solving: A multilevel study of team impacts on stress appraisals of problem-solving demands

ABSTRACT

To date, there is a paucity of research on team-level impacts on the individual stress appraisal process despite the recognised role of teams for solving problems. Applying a multilevel approach, this study investigates the cross-level impact of team problem prevention behaviours on employee stress appraisals of problem-solving demands. It was hypothesised that team problem prevention would moderate the individual-level relationship between problem-solving demands and stress appraisals. Data were collected from 43 work teams comprised of 192 team members including all team leaders who also provided evaluations of their team’s problem prevention behaviour. Results supported the hypothesised cross-level moderating effects on challenge appraisal, but not threat appraisal. As one of the first studies to demonstrate that stress appraisals are impacted by the group, not just by individual factors, the results support a multilevel conceptualisation of stress appraisals. The findings also highlight implications for practice, broadening the scope of possibilities for stress management interventions to utilise team-level strategies such as leadership development programmes and/or team building initiatives.     

Is Marriage More Than Cohabitation? Well‐Being Differences in 30 European Countries

The study aims to assess, first, whether there is a gap in well‐being between unmarried cohabitants and the married, second, if selection factors can explain this so‐called cohabitation gap, and third, if the size of the cohabitation gap differs across countries and how this can be explained. We use pooled data from young adults (18–44) in 3 rounds of the European Social Survey (N ? 31, 500). Multilevel regression analyses show that there is a moderate cohabitation gap that can be partly explained with the selection factors material resources and religiosity. Country differences were clear and could partly be explained with the level of institutionalization: In countries where cohabitation is more accepted and more prevalent, the cohabitation gap is smaller.     

试析中国大陆高校通识教育的模式

由于对通识教育理念认识的差异，大陆高校的通识教育模式也各不相同。对目前大陆高校通识教育的模式进行了梳理，并分析了大陆高校实施通识教育的经验与启示。     

时滞交联系统的H_∞滤波设计

基于T-S模糊模型方法,针对一类时滞交联系统,提出了一种集中H∞滤波设计新方案.通过构造新的模糊线积分李雅普诺夫泛函,给出了整体滤波误差系统时滞相关的集中H∞滤波设计LMI条件,降低了性能指标的保守性.最后通过仿真例子说明了所提方法的有效性和优越性.     

Conditional Gaussian mixture modelling for dietary pattern analysis

Summary.  Free-living individuals have multifaceted diets and consume foods in numerous combinations. In epidemiological studies it is desirable to characterize individual diets not only in terms of the quantity of individual dietary components but also in terms of dietary patterns. We describe the conditional Gaussian mixture model for dietary pattern analysis and show how it can be adapted to take account of important characteristics of self-reported dietary data. We illustrate this approach with an analysis of the 2000–2001 National Diet and Nutrition Survey of adults. The results strongly favoured a mixture model solution allowing clusters to vary in shape and size, over the standard approach that has been used previously to find dietary patterns.     

A distance-based diagnostic for trans-dimensional Markov chains

Over the last decade the use of trans-dimensional sampling algorithms has become endemic in the statistical literature. In spite of their application however, there are few reliable methods to assess whether the underlying Markov chains have reached their stationary distribution. In this article we present a distance-based method for the comparison of trans-dimensional Markov chain sample output for a broad class of models. This diagnostic will simultaneously assess deviations between and within chains. Illustration of the analysis of Markov chain sample-paths is presented in simulated examples and in two common modelling situations: a finite mixture analysis and a change-point problem.     

Mixture Multi-state Markov Regression Model

Although heterogeneity across individuals may be reduced when a two-state process is extended into a multi-state process, the discrepancy between the observed and the predicted for some states may still exist owing to two possibilities, unobserved mixture distribution in the initial state and the effect of measured covariates on subsequent multi-state disease progression. In the present study, we developed a mixture Markov exponential regression model to take account of the above-mentioned heterogeneity across individuals (subject-to-subject variability) with a systematic model selection based on the likelihood ratio test. The model was successfully demonstrated by an empirical example on surveillance of patients with small hepatocellular carcinoma treated by non-surgical methods. The estimated results suggested that the model with the incorporation of unobserved mixture distribution behaves better than the one without. Complete and partial effects regarding risk factors on different subsequent multi-state transitions were identified using a homogeneous Markov model. The combination of both initial mixture distribution and homogeneous Markov exponential regression model makes a significant contribution to reducing heterogeneity across individuals and over time for disease progression.     

Model determination for categorical data with factor level merging

Summary.  We deal with contingency table data that are used to examine the relationships between a set of categorical variables or factors. We assume that such relationships can be adequately described by the cond`itional independence structure that is imposed by an undirected graphical model. If the contingency table is large, a desirable simplified interpretation can be achieved by combining some categories, or levels, of the factors. We introduce conditions under which such an operation does not alter the Markov properties of the graph. Implementation of these conditions leads to Bayesian model uncertainty procedures based on reversible jump Markov chain Monte Carlo methods. The methodology is illustrated on a 2×3×4 and up to a 4×5×5×2×2 contingency table.