Relationship between sublethal injury and microbial inactivation by the combination of high hydrostatic pressure and citral or tert-butyl hydroquinone

The aim was to investigate (i) the occurrence of sublethal injury in Listeria monocytogenes, Escherichia coli, and Saccharomyces cerevisiae after high hydrostatic pressure (HHP) treatment as a function of the treatment medium pH and composition and (ii) the relationship between the occurrence of sublethal injury and the inactivating effect of a combination of HHP and two antimicrobial compounds, tert-butyl hydroquinone (TBHQ) and citral. The three microorganisms showed a high proportion of sublethally injured cells (up to 99.99% of the surviving population) after HHP. In E. coli and L. monocytogenes, the extent of inactivation and sublethal injury depended on the pH and the composition of the treatment medium, whereas in S. cerevisiae, inactivation and sublethal injury were independent of medium pH or composition under the conditions tested. TBHQ alone was not lethal to E. coli or L. monocytogenes but acted synergistically with HHP and 24-h refrigeration, resulting in a viability decrease of >5 log(10) cycles of both organisms. The antimicrobial effect of citral depended on the microorganism and the treatment medium pH. Acting alone for 24 h under refrigeration, 1,000 ppm of citral caused a reduction of 5 log(10) cycles of E. coli at pH 7.0 and almost 3 log(10) cycles of L. monocytogenes at pH 4.0. The combination of citral and HHP also showed a synergistic effect. Our results have confirmed that the detection of sublethal injury after HHP may contribute to the identification of those treatment conditions under which HHP may act synergistically with other preserving processes.

Effect of small, acid-soluble proteins on spore resistance and germination under a combination of pressure and heat treatment

To find the range of pressure required for effective high-pressure inactivation of bacterial spores and to investigate the role of alpha/beta-type small, acid-soluble proteins (SASP) in spores under pressure treatment, mild heat was combined with pressure (room temperature to 65 degrees C and 100 to 500 MPa) and applied to wild-type and SASP-alpha(-/)beta(-) Bacillus subtilis spores. On the one hand, more than 4 log units of wild-type spores were reduced after pressurization at 100 to 500 MPa and 65 degrees C, On the other hand, the number of surviving mutant spores decreased by 2 log units at 100 MPa and by more than 5 log units at 500 MPa. At 500 MPa and 65 degrees C, both wild-type and mutant spore survivor counts were reduced by 5 log units. Interestingly, pressures of 100, 200, and 300 MPa at 65 degrees C inactivated wild-type SASP-alpha(+)/beta(+) spores more than mutant SASP-alpha(-)/beta(-) spores, and this was attributed to less pressure-induced germination in SASP-alpha(-)/beta(-) spores than in wild-type SASP-alpha(+)/beta(+) spores. However, there was no difference in the pressure resistance between SASP-alpha(+)/beta(+) and SASP-alpha(-)/beta(-) spores at 100 MPa and ambient temperature (approximately 22 degrees C) for 30 min. A combination of high pressure and high temperature is very effective for inducing spore germination, and then inactivation of the germinated spore occurs because of the heat treatment. This study showed that alpha/beta-type SASP play a role in spore inactivation by increasing spore germination under 100 to 300 MPa at high temperature.

Design and evaluation of intelligent classifier based intrusion detection system

We have discovered a novel approach of intrusion detection system using an intelligent data classifier based on a self organizing map (SOM). We have surveyed all other unsupervised intrusion detection methods, different alternative SOM based techniques and KDD winner IDS methods. This paper provides a robust designed and implemented intelligent data classifier technique based on a single large size (30x30) self organizing map (SOM) having the capability to detect all types of attacks given in the DARPA Archive 1999 the lowest false positive rate being 0.04 % and higher detection rate being 99.73% tested using full KDD data sets and 89.54% comparable detection rate and 0.18% lowest false positive rate tested using corrected data sets.

A new blind equalization algorithm based on convex combination

The convex combination is a mathematic approach to keep the advantages of its component algorithms for better performance. In this paper, we employ convex combination in the blind equalization to achieve better blind equalization. By combining the blind constant modulus algorithm (CMA) and decision directed algorithm, the combinative blind equalization (CBE) algorithm can retain the advantages from both. Furthermore, the convergence speed of the CBE algorithm is faster than both of its component equalizers. Simulation results are also given to verify the proposed algorithm.

Kernel classifier construction using orthogonal forward selection and boosting with Fisher ratio class separability measure

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers. that generalize well.

Fast kernel classifier construction using orthogonal forward selection to minimise leave-one-out misclassification rate

We propose a simple yet computationally efficient construction algorithm for two-class kernel classifiers. In order to optimise classifier's generalisation capability, an orthogonal forward selection procedure is used to select kernels one by one by minimising the leave-one-out (LOO) misclassification rate directly. It is shown that the computation of the LOO misclassification rate is very efficient owing to orthogonalisation. Examples are used to demonstrate that the proposed algorithm is a viable alternative to construct sparse two-class kernel classifiers in terms of performance and computational efficiency.

A kernel-based two-class classifier for imbalanced data sets

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.

A fast linear-in-the-parameters classifier construction algorithm using orthogonal forward selection to minimize leave-one-out misclassification rate

We propose a simple and computationally efficient construction algorithm for two class linear-in-the-parameters classifiers. In order to optimize model generalization, a forward orthogonal selection (OFS) procedure is used for minimizing the leave-one-out (LOO) misclassification rate directly. An analytic formula and a set of forward recursive updating formula of the LOO misclassification rate are developed and applied in the proposed algorithm. Numerical examples are used to demonstrate that the proposed algorithm is an excellent alternative approach to construct sparse two class classifiers in terms of performance and computational efficiency.

Self-assembled healable materials through a combination of pi-pi stacking and hydrogen bonding

The discovery of polymers with stimuli responsive physical properties is a rapidly expanding area of research. At the forefront of the field are self-healing polymers, which, when fractured can regain the mechanical properties of the material either autonomically, or in response to a stimulus. It has long been known that it is possible to promote healing in conventional thermoplastics by heating the fracture zone above the Tg of the polymer under pressure. This process requires reptation and subsequent re-entanglement of macromolecules across the fracture void, which serves to bridge, and ‘heal’ the crack. The timescale for this mechanism is highly dependent on the molecular weight of the polymer being studied. This process is in contrast to that required to affect healing in supramolecular polymers such as the plasticised, hydrogen bonded elastomer reported by Leibler et al. The disparity in bond energies between the non-covalent and covalent bonds within supramolecular polymers results in fractures propagating through scission of the comparatively weak supramolecular interactions, rather than through breaking the stronger, covalent bonds. Thus, during the healing process the macromolecules surrounding the fracture site only need sufficient energy to re-engage their supramolecular interactions in order to regenerate the strength of the pristine material. Herein we describe the design, synthesis and optimization of a new class of supramolecular polymer blends that harness the reversible nature of pi-pi stacking and hydrogen bonding interactions to produce self-supporting films with facile healable characteristics.

Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression

We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixednode RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation.

A combined SMOTE and PSO based RBF classifier for two-class imbalanced problems

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier's structure and the parameters of RBF kernels are determined using a PSO algorithm based on the criterion of minimising the leave-one-out misclassification rate. The experimental results obtained on a simulated imbalanced data set and three real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.