Surface physicochemical analysis of natural Lactococcus lactis strains reveals the existence of hydrophobic and low charged strains with altered adhesive properties

The cell surface physicochemical properties of 50 Lactococcus lactis strains of different subspecies and isolated from different origins (dairy, vegetal and animal) were examined. Cell surface hydrophobicity and Lewis acid-base properties were evaluated by affinity measurements to solvents in a partitioning test, while the global electrical charge of the cells was assessed by micro-electrophoresis using a laser zeta-meter. A global multivariate analysis of the results revealed a high natural diversity of L. lactis cell surface properties. While 52% of the strains present a hydrophilic and electronegative cell wall surface, a group of strikingly hydrophobic strains (12% of the strains) and a group of strains with unusual low charged surface (18%) were identified. Adhesion on polystyrene microtitre plates was evaluated for twelve strains selected from the multivariate analysis as representatives of the various observed cell wall surface physicochemical patterns. A significant correlation between adhesion, hydrophobicity and low electronegativity was observed when adhesion was performed in a low ionic strength suspending medium. The most adhesive strains were hydrophobic or low charged. The presence of repulsive electrostatic interactions led to a decrease in adhesion of the most negatively charged hydrophilic strains. The present study highlights the diversity of L. lactis cell surface physicochemical properties, diversity that could not be connected to the origin or to the subspecies of the strains.     

A novel spectral microscope system: application in quantitative pathology

In this paper, a novel spectral microscope system is presented together with a method for the quantitative assessment of the uptake by histologic samples of stains used in pathology to label tissue features of diagnostic importance. The critical component of the microscope is a variable interference filter-based monochromator. The system is capable of performing real-time spectral imaging in a plurality of spectral bands and micro-spectroscopy in any image pixel, in the spectral range 400-1000 nm. The wavelength-tuning step is 2.4-2.6 nm, while the full-width at half maximum in each step is about 1.5% of the operating central wavelength. The developed system integrates algorithms and calibration procedures for the calculation of the stain-uptake by the tissue. The acquired spectra from both stained tissue and calibration stain solutions enable the calculation of the concentration maps of the stains, even if the latter are multiple and overlap spatially and spectrally. The system was used for the quantitative mapping of the expression of estrogen and progesterone receptors in breast cancer cells. In this particular case, model validation shows that although two stains are employed, capturing of their transmittance at more than ten wavelengths is required in order to obtain an acceptable accuracy. These findings highlight the need for the development and implementation of spectral microscopy in pathology and its potential to introduce novel more reliable diagnostic criteria.     

Partial‐state stabilization and optimal feedback control

In this paper, we develop a unified framework to address the problem of optimal nonlinear analysis and feedback control for partial stability and partial‐state stabilization. Partial asymptotic stability of the closed‐loop nonlinear system is guaranteed by means of a Lyapunov function that is positive definite and decrescent with respect to part of the system state, which can clearly be seen to be the solution to the steady‐state form of the Hamilton–Jacobi–Bellman equation and hence guaranteeing both partial stability and optimality. The overall framework provides the foundation for extending optimal linear‐quadratic controller synthesis to nonlinear nonquadratic optimal partial‐state stabilization. Connections to optimal linear and nonlinear regulation for linear and nonlinear time‐varying systems with quadratic and nonlinear nonquadratic cost functionals are also provided. Finally, we also develop optimal feedback controllers for affine nonlinear systems using an inverse optimality framework tailored to the partial‐state stabilization problem and use this result to address polynomial and multilinear forms in the performance criterion. Copyright © 2015 John Wiley & Sons, Ltd.     

Supporting multilinguality in library automation systems using ai tools

Language barriers present a major problem in the effectiveness of resource sharing and in common access to the resources of libraries. In this paper we present the TRANSLIB system, which consists of an integration of both new and existing multilingual information tools. This system takes full advantage of some AI-based methods in order to provide multilingual access to library catalogues. Its main features include functionalities for searching in multiple languages, multilingual presentation of the query results, and localization of the user interface. TRANSLIB has currently been tested in existing medium-sized bibliographic databases. Evaluation results show a remarkable improvement in the search process and report high user friendliness and easy and low-cost maintenance and upgrade of the system.     

An authoring tool for democratizing the creation of high-quality VR experiences

Virtual Reality - The contribution of this paper is toward three directions, namely (a) in identifying the advantages and disadvantages of the current state-of-the-art methods that allow laymen in...     

Testing equality of autocovariance operators for functional time series

We consider strictly stationary stochastic processes of Hilbert space-valued random variables and focus on fully functional tests for the equality of the lag-zero autocovariance operators of several independent functional time series. A moving block bootstrap (MBB)-based testing procedure is proposed which generates pseudo random elements that satisfy the null hypothesis of interest. It is based on directly bootstrapping the time series of tensor products which overcomessome common difficulties associated with applications of the bootstrap to related testing problems. The suggested methodology can be potentially applied to a broad range of test statistics of the hypotheses of interest. As an example, we establish validity for approximating the distribution under the null of a test statistic based on the Hilbert–Schmidt distance of the corresponding sample lag-zero autocovariance operators, and show consistency under the alternative. As a prerequisite, we prove a central limit theorem for the MBB procedure applied to the sample autocovariance operator which is of interest on its own. The finite sample size and power performance of the suggested MBB-based testing procedure is illustrated through simulations and an application to a real-life dataset is discussed.     

Application of neural networks to simulate the growth profile of lactic acid bacteria in green olive fermentation

The growth profile of five strains of lactic acid bacteria (Lactobacillus plantarum ACA-DC 287, L. plantarum ACA-DC 146, Lactobacillus paracasei ACA-DC 4037, Lactobacillus sakei LQC 1378, and Leuconostoc mesenteroides LQC 1398) was investigated in controlled fermentation of cv. Conservolea green olives with a multilayer perceptron network, a combined logistic-Fermi function, and a two-term Gompertz function. Neural network training was based on the steepest-descent gradient learning algorithm. Model performance was compared with the experimental data with five statistical indices, namely coefficient of determination (R2), root mean square error (RMSE), mean relative percentage error (MRPE), mean absolute percentage error (MAPE), and standard error of prediction (SEP). The experimental data set consisted of 125 counts (CFU per milliliter) of lactic acid bacteria during the green olive fermentation process for up to 38 days (5 strains x 25 sampling days). For model development, a standard methodology was followed, dividing the data set into training (120 data) and validation (25 data) subsets. Our results demonstrated that the developed network was able to model the growth and survival profile of all the strains of lactic acid bacteria during fermentation equally well with the statistical models. The performance indices for the training subset of the multilayer perceptron network were R2 = 0.987, RMSE = 0.097, MRPE = 0.069, MAPE = 0.933, and SEP = 1.316. The relevant mean values for the logistic-Fermi and two-term Gompertz functions were R2 = 0.981 and 0.989, RMSE = 0.109 and 0.083, MRPE = 0.026 and 0.030, MAPE = 1.430 and 1.076, and SEP = 1.490 and 1.127, respectively. For the validation subset, the network also gave good predictions (R2 = 0.968, RMSE = 0.149, MRPE = 0.100, MAPE = 1.411, and SEP = 2.009).     

Types of traditional Greek foods and their safety

Greek traditional foods have been evolved over centuries in the country according to local culture and artisanal practices in small scale family-owned installations and constitute a major part of the so-called Mediterranean diet. Consumer concern for food safety and high demand for traditional food products is becoming an important challenge for the food industry. To this end, safety issues about the hygienic condition and the prevalence of foodborne pathogens for a diversity of popular traditional Greek foods including fermented meat products, cheese, table olives, and emulsion-type appetizers is being reported. Until recently, the production of these food commodities presented heterogeneity from area to area and practice to practice resulting in a final product with diverse microbiological, physicochemical and sensory characteristics. The condition has drastically changed over the last years where the production has shifted from artisanal practice to industrial level under strict processing and hygienic conditions. Nowadays, it can be considered that Greek traditional foods have a good safety record.     

Sensory and microbiological quality assessment of beef fillets using a portable electronic nose in tandem with support vector machine analysis

The performance of a portable quartz microbalance based electronic nose has been evaluated in monitoring aerobically packaged beef fillet spoilage at different storage temperatures (0, 4, 8, 12, and 16 °C). Electronic nose data were collected from the headspace of meat samples in parallel with data from microbiological analysis for the enumeration of the population dynamics of total viable counts, Pseudomonas spp., Brochothrix thermosphacta, lactic acid bacteria and Enterobacteriaceae. Qualitative interpretation of electronic nose data was based on sensory evaluation discriminating samples in three quality classes (fresh, semi-fresh, and spoiled). Support Vector Machine (SVM) classification and regression models using radial basis kernel function were developed to classify beef fillet samples in the respective quality class, and correlate the population dynamics of the microbial association with electronic nose sensor responses. The obtained results demonstrated good performance in discriminating meat samples in one of the three pre-defined quality classes. Overall classification accuracies of prediction above 89% were obtained for the three sensory classes regardless of storage temperature. For SVM regression model development, correlations above 0.96 and 0.86 were obtained between observed and predicted microbial counts for the training and test data sets, respectively.