Genetic procedures for identification of enterotoxigenic strains of Staphylococcus aureus from three food poisoning outbreaks

Three food poisoning restaurant outbreaks due to Staphylococcus aureus, occurring during June-October 2002 in the Principality of Asturias (PA), Spain, provided the basis for investigating some aspects of the molecular epidemiology of this organism. The methods applied to identify strains and lineages included multiplex-polymerase chain reaction (PCR) to detect nine enterotoxin (se) genes, and three DNA fingerprinting procedures: pulsed-field gel electrophoresis (PFGE) with SmaI, randomly amplified polymorphic DNA (RAPD) with two selected primers, and plasmid restriction analysis with HindIII. Thirty-two isolates were differentiated into three non-se and 12 se strains, which were outbreak-specific, except for one that was represented in two of the outbreaks. In outbreak 1, the 16 food isolates analyzed had sec, seg and sei genes and generated a distinctive DNA fingerprint, being assigned to a single strain. This strain could be categorized as endemic in the PA and associated to manually handled dairy products and nasal carriers. In outbreak 2, the four food isolates analyzed fell into three strains, each one displaying a different se-gene profile (sea, sec and seg-seh-sei) and a distinctive DNA fingerprint. In outbreak 3, the five food isolates tested fell into four seg-sei strains generating identical RAPD but different PFGE and plasmid profiles, and one sea strain also collected from two nasal carriers. This last strain had also been found in manually handled vegetables in outbreak 2, and it belongs to a not very frequently found sea lineage in the PA. Multiplex-PCR to detect se genes together with the three applied DNA fingerprint typing procedures proved therefore to be useful tools in subclassifying S. aureus for epidemiological purposes.     

Modeling coagulation kinetics incorporating fractal theories: comparison with observed data

There are currently four possible approaches in modeling coagulation kinetics: the traditional Euclidean rectilinear; the Euclidean curvilinear; the fractal rectilinear; and the fractal curvilinear. The fractal model includes the Euclidean case as a subset. The primary purpose of this research is to investigate which of the rectilinear models among these best predicts the evolution of experimental observed particle size distribution (PSD). Using a fractal rectilinear model previously developed by the authors, model predictions were compared with a series of observed PSD data obtained from estuarine sediment particles in a 2m settling column, where the average velocity gradient (G) was 20 or 40s(-1). Nonlinear parameter estimation was performed to estimate two free parameters for the fractal model (the fractal dimension, DF, and the collision efficiency factor, a), and one free parameter (the collision efficiency factor, alpha) for the Euclidean model. Compared with the observed PSD, the simulation showed that the fractal rectilinear model was best, and that this model fit better for the larger size particles. The estimated DF was between 2.6 and 3.0. The research demonstrated that the alpha's have multiple values for the same observed data, depending on the coagulation model used. This finding is significant because a is currently used as a single value based on the conventional Euclidean rectilinear model.     

Automated prediction of sensory scores for color and appearance in canned black beans (Phaseolus vulgaris L.) using machine vision

Evaluation of canning quality of beans is commonly carried out by simple visual inspection that is time-consuming, resource intensive, and biased by the experience of the panelist. Moreover, there is not a standard scale to rate visual quality traits of canned beans. In this research, a machine vision system was implemented and tested for automatic inspection of color (COL) and appearance (APP) in canned black beans. Various color and textural image features (average, standard deviation, contrast, correlation, energy, and homogeneity from red, green, blue, lightness, red/green, yellow/blue, hue, saturation and value color scales) were extracted from beans and brine images, and evaluated to predict the quality rates for COL and APP of a group of bean panelists using multivariate statistics. Sixty-nine commercial canned black bean samples from different brands and markets were used for analysis. In spite of the “fair” agreement among the sensory panelists for COL and APP, as determined by multi-rater Kappa analysis, machine vision data based on partial least squares regression model showed high predictive performance for both COL and APP with correlation coefficients of 0.937 and 0.871, and standard errors of 0.26 and 0.38, respectively. When a classification was performed based on both COL and APP traits, a support vector machine model was able to sort the samples into two sensory quality categories of “acceptable” and “unacceptable” with an accuracy of 89.7%. Using simple color and texture image data, a machine vision system showed potential for the automatic evaluation of canned black beans by COL and/or appearance as a professional visual inspection.     

Improving the effectiveness of computer-delivered personalized drinking feedback interventions for college students.

This study evaluated methods of enhancing college students' retention of information provided to them in a computer-delivered personalized drinking feedback intervention and whether enhanced retention reduced alcohol consumption during the two-week period following the intervention. Participants were 98 college students who reported at least one heavy drinking episode in the past two weeks. After participating in an online, personalized drinking feedback intervention, students were randomly assigned to one of three experimental conditions: 1) typical, in which they were simply sent home, 2) reading, in which they were asked to spend the next 20 minutes re-reading the feedback, and 3) recall, in which they were asked to spend the next 20 minutes writing down as much of the information from the feedback as they could remember. Two weeks following the intervention, participants completed a recall test and provided information on their alcohol use during the previous two weeks. Results indicated that participants in the reading and recall conditions retained more of the feedback information than did participants in the typical condition. In addition, participants in the reading and recall conditions reported reduced alcohol consumption in the two-week period following the intervention, compared to those in the typical condition. Information retention partially mediated the effects of the reading and recall conditions on drinking outcomes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Prevalence of Salmonella in two Botswana abattoir environments

A 1-year study was carried out to investigate the prevalence of Salmonella in two abattoir environments coded "A" and "B" in Gaborone, Botswana. The total number of environmental samples collected from abattoirs A and B was 250 and 300, respectively. The samples were taken from soils in the corrals, knife blades, saw blades, cattle-drinking water, cattle feces, and feed. Preenrichment, enrichment, and selective/differential media, which enabled the favorable growth of Salmonella, were used in the study. Salmonellae were present in all sampled environments. The most common serotypes found in the environment at abattoir A were E1, C1, C2, and B. Serotypes B, C1, C2, C3, and E1 were common in abattoir B. Antigenic characterization of the salmonellae isolates showed that Salmonella Anatum, Salmonella Azteca, Salmonella Saintpaul, Salmonella Cerro, and Salmonella Westhampton were predominant in abattoir A, whereas Salmonella Anatum, Salmonella Mbandaka, Salmonella Molade, Salmonella Reading, and Salmonella Oranienburg were dominant in abattoir B. Implementing hazard analysis critical control point principles in work procedures would definitely reduce the gross contamination taking place in abattoirs.     

Generative design in architecture using an expert system

The mathematician-architect Christopher Alexander has devised a theory of objective architectural design. He believes that all architectural forms can be described as interacting patterns, all possible relationships of which are governed by generative rules. These form a ‘pattern language’ capable of generating forms appropriate for a given environmental context. The complexity of interaction among these rules leads to difficulties in their representation by conventional methods. This paper presents a Prolog-based expert system which implements Alexander's design methodology to produce perspective views of partially and fully differentiated 3-dimensional architectural forms.