Dynamic modeling of Listeria monocytogenes growth in pasteurized vanilla cream after postprocessing contamination

A product-specific model was developed and validated under dynamic temperature conditions for predicting the growth of Listeria monocytogenes in pasteurized vanilla cream, a traditional milk-based product. Model performance was also compared with Growth Predictor and Sym'Previus predictive microbiology software packages. Commercially prepared vanilla cream samples were artificially inoculated with a five-strain cocktail of L. monocytogenes, with an initial concentration of 102 CFU g(-1), and stored at 3, 5, 10, and 15 degrees C for 36 days. The growth kinetic parameters at each temperature were determined by the primary model of Baranyi and Roberts. The maximum specific growth rate (mu(max)) was further modeled as a function of temperature by means of a square root-type model. The performance of the model in predicting the growth of the pathogen under dynamic temperature conditions was based on two different temperature scenarios with periodic changes from 4 to 15 degrees C. Growth prediction for dynamic temperature profiles was based on the square root model and the differential equations of the Baranyi and Roberts model, which were numerically integrated with respect to time. Model performance was based on the bias factor (B(f)), the accuracy factor (A(f)), the goodness-of-fit index (GoF), and the percent relative errors between observed and predicted growth. The product-specific model developed in the present study accurately predicted the growth of L. monocytogenes under dynamic temperature conditions. The average values for the performance indices were 1.038, 1.068, and 0.397 for B(f), A(f), and GoF, respectively for both temperature scenarios assayed. Predictions from Growth Predictor and Sym'Previus overestimated pathogen growth. The average values of B(f), A(f), and GoF were 1.173, 1.174, and 1.162, and 1.267, 1.281, and 1.756 from Growth Predictor and Sym'Previus, respectively.     

ORDER IDENTIFICATION STATISTICS IN STATIONARY AUTOREGRESSIVE MOVING-AVERAGE MODELS:VECTOR AUTOCORRELATIONS AND THE BOOTSTRAP

Abstract. In this paper we consider the vector autocorrelation approach for identifying ARMA ( p, q ) models and use a bootstrap procedure in order to evaluate the distribution of the corresponding sample statistics by means of a resampling scheme for the residuals when p and q are unknown. The asymptotic validity of the bootstrap procedure applied to the vector autocorrelation estimates is established. Some simulations and examples demonstrating the appropriateness of the proposed bootstrap procedure in comparison with large-sample Gaussian approximations are included.     

The adherence of Salmonella Enteritidis PT4 to stainless steel: the importance of the air-liquid interface and nutrient availability

Biofilm formation on stainless steel by Salmonella enterica serovar Enteritidis PT4 during growth in three different nutritious conditions was studied. The ability of micro-organisms to generate biofilms on the stainless steel surfaces was studied for a total period of 18 days at 20 degrees C, under three different experimental treatments: (i) growth medium (tryptone soy broth) was not refreshed (no further nutrients were provided) during the incubation period, (ii) growth medium was renewed every 2 days and (iii) growth medium was renewed every 2 days and at the same time the planktonic cells from the old medium were transferred to the new fresh medium. It was found that biofilms developed better and a higher number of adherent cells (ca. 10(7) cfu/cm(2)) were recovered when the organism was grown in periodically renewed nutrient medium than when the growth medium was not refreshed. Regardless of the availability of nutrients, biofilm development was better (range 2-3 logs greater) when coupons were not totally covered by the growth medium and part of the surface was exposed to the air-liquid interface, than when coupons were submerged in the medium. The results suggest that existence of air-liquid interface and adequate nutrient conditions provide the best environment for Salmonella Enteritidis PT4 biofilm formation on stainless steel. The possible role of stationary phase planktonic cells in biofilm development by sessile/attached microbial cells is also discussed.     

Advances in Vision-Based Lane Detection: Algorithms,Integration, Assessment,and Perspectives on ACP-Based Parallel Vision

Lane detection is a fundamental aspect of most current advanced driver assistance systems (ADASs). A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous visionbased lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.      

Multi-view scans alignment for 3D spherical mosaicing in large-scale unstructured environments

We are currently developing a vision-based system aiming to perform a fully automatic pipeline for in situ photorealistic three-dimensional (3D) modeling of previously unknown, complex and unstructured underground environments. Since in such environments navigation sensors are not reliable, our system embeds only passive (camera) and active (laser) 3D vision senors. Laser Range Finders are particularly well suited for generating dense 3D maps by aligning multiples scans acquired from different viewpoints. Nevertheless, nowadays Iteratively Closest Point (ICP)-based scan matching techniques rely on heavy human operator intervention during a post-processing step. Since a human operator cannot access the site, these techniques are not suitable in high-risk underground environments. This paper presents an automatic on-line scan matcher able to cope with the nowadays 3D laser scanners’ architecture and to process either intensity or depth data to align scans, providing robustness with respect to the capture device. The proposed implementation emphasizes the portability of our algorithm on either single or multi-core embedded platforms for on-line mosaicing onboard 3D scanning devices. The proposed approach addresses key issues for in situ 3D modeling in difficult-to-access and unstructured environments and solves for the 3D scan matching problem within an environment-independent solution. Several tests performed in two prehistoric caves illustrate the reliability of the proposed method.     

Histogram Preserving Image Transformations

Histograms are used to analyze and index images. They have been found experimentally to have low sensitivity to certain types of image morphisms, for example, viewpoint changes and object deformations. The precise effect of these image morphisms on the histogram, however, has not been studied. In this work we derive the complete class of local transformations that preserve or scale the magnitude of the histogram of all images. We also derive a more general class of local transformations that preserve the histogram relative to a particular image. To achieve this, the transformations are represented as solutions to families of vector fields acting on the image. The local effect of fixed points of the fields on the histograms is also analyzed. The analytical results are verified with several examples. We also discuss several applications and the significance of these transformations for histogram indexing.     

LES and RANS comparison of flow and pollutant dispersion in urban environment

This study investigates air pollution dispersion in urban areas by means of Computational Fluid Dynamics (CFD). The commercial CFD software FLUENT was used to implement two different turbulence simulation methods (RANS and LES), in domains similar to complex urban environments. Particularly, different combinations of roof shapes were studied and simulation results of pollutant (ethane) concentrations were compared against experimental data. The building height (H) to the neighbour building distance (B) ratio was also taken into consideration. Previous studies showed that both RANS and LES models are accurate enough to predict pollutant concentrations fields in B/H = 1. In the present study the incapability of RANS models to predict accurately pollutant concentration in B/H = 0.5 for all roof shapes configurations is revealed.     

Survival of Bacillus cereus vegetative cells during Spanish-style fermentation of conservolea green olives

The aim of this research was to investigate the survival of artificially inoculated Bacillus cereus during Spanish-style green olive fermentation. Olives were initially treated with lye and subjected to different fermentation procedures including (i) heat shock (85 degrees C for 10 min) and inoculation with Lactobacillus plantarum ACA-DC 287, (ii) heat shock and inoculation with L. plantarum ACA-DC 146, (iii) heat shock and inoculation with Lactobacillus pentosus isolated during previous studies, and (iv) fermentation by indigenous flora (control process). Microbial growth and survival, pH, titratable acidity, and organic acid evolution were monitored. Inactivation of B. cereus was observed during all processes. The pathogen population declined during all fermentations, but a tailing effect was observed in the brines when the population reached 2 log CFU/ml, at which point the pathogen does not pose a risk to human health. The rate of inactivation was higher in heat-shocked inoculated olives (mean of -2.21 log CFU/day) compared with control olives (-1.26 log/day), indicating an advantage of heat shock and inoculation over spontaneous fermentation. The production of organic acids (primarily lactic acid) during fermentation seemed to be the main factor that determined the behavior of the pathogen under stress conditions prevailing in the brine. Principal components analysis was useful for distinguishing among the different fermentation processes on the basis of the relevant organic acid profile.     

Hidden Markov modeling and macroscopic traffic filtering supporting location‐based services

In this study, we present a technique that combines pattern recognition techniques with cellular signaling measurements and more precisely information extracted from Abis air interface in GSM network. The pattern recognition is applied to measurement reports that mobile terminal (MT) sends to its serving base station (BS). Modeling of these reports is performed by hidden Markov model (HMM) while employing clustering large applications (CLARA) as clustering method. The accurate results during MT velocity estimation located inside a probe vehicle show the potential of the method when applied to large scale of MTs in order to estimate basic parameters for road traffic. Copyright © 2006 John Wiley & Sons, Ltd.