Survival of Staphylococcus aureus ATCC 13565 in Intermediate Moisture Foods is Highly Variable

Staphylococcus aureus is a gram-positive, enterotoxin-producing coccus. It is a hardy organism and known to survive over a wide range of water activities, pH values, and temperatures. The objective of this study was to model the survival or gradual inactivation of S. aureus ATCC 13565 in intermediate moisture foods (IMFs). Various initial concentrations (approximately 10(1), 10(2), 10(3), and 10(4) CFU/g) were used to inoculate three different IMFs (beefsteak, bread, and chicken pockets). Viable counts were determined up to 60 days using tryptic soy agar. Inoculum size did not influence the survival or gradual inactivation of S. aureus in these foods. The rate of change (increase or decrease) in log CFU/day was calculated for every consecutive pair of data points and by linear regression for each inactivation curve. Both consecutive pair and linear regression rates of change were fit to logistic distributions (with parameters alpha and beta) for each food. Based on the distribution parameters, survival or gradual inactivation of S. aureus was predicted by computer simulation. The simulations indicated an overall decline in S. aureus population over time, although a small fraction of samples in the consecutive pair simulation showed a slight population increase even after 60 days, consistent with the observed data. Simulation results were compared to predictions from other computer models. The models of Stewart et al., were fail-safe, predicting the possibility of significant growth only after > 3,000 days. The USDA pathogen modeling program predictions were found to be fail-dangerous, predicting declines at least four times faster than observed.     

Career Techniques and Interventions: Themes From an International Conversation

The need for appropriate, timely, and increasingly comprehensive career development and education programs continues to escalate. It is interesting that despite the differences in cultures, religions, economies, political systems, and education structures, many countries face similar challenges when designing and implementing career development programs. Sharing and adapting career development interventions and techniques cross‐culturally appear to be viable strategies for implementing or enhancing programs, provided the concepts and materials are tailored to the countries' unique requirements.     

The Powers of Australian Teachers in the Political System of School Government

The views of a sample of Australian teachers were obtained regarding the powers exercised by teachers and administrators in decisions relevant to internal school government. The teachers perceived themselves as playing a delegated-leadership role within the classroom and a subordinate role in decision areas external to classroom management. On the other hand, they expressed a desire to extend their delegated-leadership role to important matters concerning the instructional organization and administration of the school. The evidence suggested that teachers wished to change the perceived balance of power in schools in the direction of a more ‘democratic’ style of school government.     

Easy-to-construct confidence bands for comparing two simple linear regression lines

This paper shows how to construct confidence bands for the difference between two simple linear regression lines. These confidence bands provide directly the information on the magnitude of the difference between the regression lines over an interval of interest and, as a by-product, can be used as a formal test of the difference between the two regression lines. Various different shapes of confidence bands are illustrated, and particular attention is paid towards confidence bands whose construction only involves critical points from standard distributions so that they are consequently easy to construct.     

On the power of durbin-watson statistic against fractionally integrated processes

This paper provides the theoretical explanation and Monte Carlo experiments of using a modified version of Durbin-Watson ( D W ) statistic to test an 1 ( 1 ) process against I ( d ) alternatives, that is, integrated process of order d, where d is a fractional number. We provide the exact order of magnitude of the modified D W test when the data generating process is an I ( d ) process with d E (0. 1.5). Moreover, the consistency of the modified DW statistic as a unit root test against I ( d ) alternatives with d E ( 0 , l ) U ( 1 , 1.5) is proved in this paper. In addition to the theoretical analysis, Monte Carlo experiments show that the performance of the modified D W statistic reveals that it can be used as a unit root test against I ( d ) alternatives.     

Statisticians in stained glass houses

Statistics and stained glass may seem an odd combination, but the windows of Gonville & Caius College, Cambridge, say otherwise. Anthony Edwards explains.     

Local information and the design of sequential hypothesis tests

Various results on sequential hypotheses testing are reviewed. Optimal stopping rules are related to a local measure of statistical information. In some cases, local information can be approximated by L-numbers discovered by Lorden, and simple rules based on these approximations are asymptotically optimal to better order than the cost for a single observation.     

A Mixed Model Approach for Geoadditive Hazard Regression

Abstract.  Mixed model based approaches for semiparametric regression have gained much interest in recent years, both in theory and application. They provide a unified and modular framework for penalized likelihood and closely related empirical Bayes inference. In this article, we develop mixed model methodology for a broad class of Cox-type hazard regression models where the usual linear predictor is generalized to a geoadditive predictor incorporating non-parametric terms for the (log-)baseline hazard rate, time-varying coefficients and non-linear effects of continuous covariates, a spatial component, and additional cluster-specific frailties. Non-linear and time-varying effects are modelled through penalized splines, while spatial components are treated as correlated random effects following either a Markov random field or a stationary Gaussian random field prior. Generalizing existing mixed model methodology, inference is derived using penalized likelihood for regression coefficients and (approximate) marginal likelihood for smoothing parameters. In a simulation we study the performance of the proposed method, in particular comparing it with its fully Bayesian counterpart using Markov chain Monte Carlo methodology, and complement the results by some asymptotic considerations. As an application, we analyse leukaemia survival data from northwest England.