lac Repressor Is an Antivirulence Factor of Salmonella enterica: Its Role in the Evolution of Virulence in Salmonella

The genus Salmonella includes many pathogens of great medical and veterinary importance. Bacteria belonging to this genus are very closely related to those belonging to the genus Escherichia. lacZYA operon and lacI are present in Escherichia coli, but not in Salmonella enterica. It has been proposed that Salmonella has lost lacZYA operon and lacI during evolution. In this study, we have investigated the physiological and evolutionary significance of the absence of lacI in Salmonella enterica. Using murine model of typhoid fever, we show that the expression of Lacl causes a remarkable reduction in the virulence of Salmonella enterica. Lacl also suppresses the ability of Salmonella enterica to proliferate inside murine macrophages. Microarray analysis revealed that Lacl interferes with the expression of virulence genes of Salmonella pathogenicity island 2. This effect was confirmed by RT-PCR and Western blot analysis. Interestingly, we found that SBG0326 of Salmonella bongori is homologous to lacI of Escherichia coli. Salmonella bongori is the only other species of the genus Salmonella and it lacks the virulence genes of Salmonella pathogenicity island 2. Overall, our results demonstrate that Lacl is an antivirulence factor of Salmonella enterica and suggest that absence of lacI has facilitated the acquisition of virulence genes of Salmonella pathogenicity island 2 in Salmonella enterica making it a successful systemic pathogen.

Regulation of human CD4(+) alphabeta T-cell-receptor-positive (TCR(+)) and gammadelta TCR(+) T-cell responses to Mycobacterium tuberculosis by interleukin-10 and transforming growth factor beta.

Mycobacterium tuberculosis is the etiologic agent of human tuberculosis and is estimated to infect one-third of the world's population. Control of M. tuberculosis requires T cells and macrophages. T-cell function is modulated by the cytokine environment, which in mycobacterial infection is a balance of proinflammatory (interleukin-1 [IL-1], IL-6, IL-8, IL-12, and tumor necrosis factor alpha) and inhibitory (IL-10 and transforming growth factor beta [TGF-beta]) cytokines. IL-10 and TGF-beta are produced by M. tuberculosis-infected macrophages. The effect of IL-10 and TGF-beta on M. tuberculosis-reactive human CD4(+) and gammadelta T cells, the two major human T-cell subsets activated by M. tuberculosis, was investigated. Both IL-10 and TGF-beta inhibited proliferation and gamma interferon production by CD4(+) and gammadelta T cells. IL-10 was a more potent inhibitor than TGF-beta for both T-cell subsets. Combinations of IL-10 and TGF-beta did not result in additive or synergistic inhibition. IL-10 inhibited gammadelta and CD4(+) T cells directly and inhibited monocyte antigen-presenting cell (APC) function for CD4(+) T cells and, to a lesser extent, for gammadelta T cells. TGF-beta inhibited both CD4(+) and gammadelta T cells directly and had little effect on APC function for gammadelta and CD4(+) T cells. IL-10 down-regulated major histocompatibility complex (MHC) class I, MHC class II, CD40, B7-1, and B7-2 expression on M. tuberculosis-infected monocytes to a greater extent than TGF-beta. Neither cytokine affected the uptake of M. tuberculosis by monocytes. Thus, IL-10 and TGF-beta both inhibited CD4(+) and gammadelta T cells but differed in the mechanism used to inhibit T-cell responses to M. tuberculosis.

Metamodeling approach to predict friction factor of alluvial channel

The flow resistance of an alluvial channel flow is not only affected by the Reynolds number and the roughness conditions but also the Froude number. Froude number is the most basic parameter in the case of the alluvial channel, thus effect of Froude number on resistance to flow should be considered in the formulation of the friction factor, which is not in the case of present available resistance equations. At present, no generally acceptable quantitative description of the effects of the Froude number on hydraulic resistance has been developed. Metamodeling technique, which is particularly useful in modeling a complex processes or where knowledge of the physics is limited, is presented as a tool complimentary to modeling friction factor in alluvial channels. Present work uses, a radial basis metamodel, which is a type of neural network modeling, to find the effect of Froude number on the flow resistance. Based on the experimental data taken from different sources, it has been found that the predicting capability of the present model is on acceptable level. Present work also tries in formulating an empirical equation for resistance in alluvial channel comprising all the three majorm, parameters, namely, roughness parameter, Froude number and Reynolds number. (C) 2009 Elsevier B.V. All rights reserved.

A Single Mammalian Mitochondrial Translation Initiation Factor Functionally Replaces Two Bacterial Factors

The mechanism of translation in eubacteria and organelles is thought to be similar. In eubacteria, the three initiation factors IF1, IF2, and IF3 are vital. Although the homologs of IF2 and IF3 are found in mammalian mitochondria, an IF1 homolog has never been detected. Here, we show that bovine mitochondrial IF2 (IF2mt) complements E. coli containing a deletion of the IF2 gene (E. coli ΔinfB). We find that IF1 is no longer essential in an IF2mt-supported E. coli ΔinfB strain. Furthermore, biochemical and molecular modeling data show that a conserved insertion of 37 amino acids in the IF2mt substitutes for the function of IF1. Deletion of this insertion from IF2mt supports E. coli for the essential function of IF2. However, in this background, IF1 remains essential. These observations provide strong evidence that a single factor (IF2mt) in mammalian mitochondria performs the functions of two eubacterial factors, IF1 and IF2.

Gaussian mixture model based switched split vector quantization of LSF parameters

We address the issue of rate-distortion (R/D) performance optimality of the recently proposed switched split vector quantization (SSVQ) method. The distribution of the source is modeled using Gaussian mixture density and thus, the non-parametric SSVQ is analyzed in a parametric model based framework for achieving optimum R/D performance. Using high rate quantization theory, we derive the optimum bit allocation formulae for the intra-cluster split vector quantizer (SVQ) and the inter-cluster switching. For the wide-band speech line spectrum frequency (LSF) parameter quantization, it is shown that the Gaussian mixture model (GMM) based parametric SSVQ method provides 1 bit/vector advantage over the non-parametric SSVQ method.

Computationally efficient optimum weighting function for vector quantization of LSF parameters

We propose a new weighting function which is computationally simple and an approximation to the theoretically derived optimum weighting function shown in the literature. The proposed weighting function is perceptually motivated and provides improved vector quantization performance compared to several weighting functions proposed so far, for line spectrum frequency (LSF) parameter quantization of both clean and noisy speech data.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

OCR Prediction Using Support Vector Machine Based on Piezocone Data

The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance (qt), vertical total stress (sigmav), hydrostatic pore pressure (u0), pore pressure at the cone tip (u1), and the pore pressure just above the cone base (u2). Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that qt=primary in situ data influenced by OCR followed by sigmav, u0, u2, and u1. Comparison between SVM and some of the traditional interpretation methods is also presented. The results of this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR.

Multilevel Dodecagonal Space Vector Generation for Open-end Winding Induction Motor Drive Using Conventional Three Level Inverters

A novel dodecagonal space vector structure for induction motor drive is presented in this paper. It consists of two dodecagons, with the radius of the outer one twice the inner one. Compared to existing dodecagonal space vector structures, to achieve the same PWM output voltage quality, the proposed topology lowers the switching frequency of the inverters and reduces the device ratings to half. At the same time, other benefits obtained from existing dodecagonal space vector structure are retained here. This includes the extension of the linear modulation range and elimination of all 6+/-1 harmonics (n=odd) from the phase voltage. The proposed structure is realized by feeding an open-end winding induction motor with two conventional three level inverters. A detailed calculation of the PWM timings for switching the space vector points is also presented. Simulation and experimental results indicate the possible application of the proposed idea for high power drives.

Support vector machine applied to settlement of shallow foundations on cohesionless soils

The determination of settlement of shallow foundations on cohesionless soil is an important task in geotechnical engineering. Available methods for the determination of settlement are not reliable. In this study, the support vector machine (SVM), a novel type of learning algorithm based on statistical theory, has been used to predict the settlement of shallow foundations on cohesionless soil. SVM uses a regression technique by introducing an ε – insensitive loss function. A thorough sensitive analysis has been made to ascertain which parameters are having maximum influence on settlement. The study shows that SVM has the potential to be a useful and practical tool for prediction of settlement of shallow foundation on cohesionless soil.

Selective dissemination of XML documents based on genetically learned user model and Support Vector Machines

Extensible Markup Language ( XML) has emerged as a medium for interoperability over the Internet. As the number of documents published in the form of XML is increasing, there is a need for selective dissemination of XML documents based on user interests. In the proposed technique, a combination of Adaptive Genetic Algorithms and multi class Support Vector Machine ( SVM) is used to learn a user model. Based on the feedback from the users, the system automatically adapts to the user's preference and interests. The user model and a similarity metric are used for selective dissemination of a continuous stream of XML documents. Experimental evaluations performed over a wide range of XML documents, indicate that the proposed approach significantly improves the performance of the selective dissemination task, with respect to accuracy and efficiency.

Vector evaluated particle swarm optimization (VEPSO) for multi-objective design optimization of composite structures

We present a generic method/model for multi-objective design optimization of laminated composite components, based on vector evaluated particle swarm optimization (VEPSO) algorithm. VEPSO is a novel, co-evolutionary multi-objective variant of the popular particle swarm optimization algorithm (PSO). In the current work a modified version of VEPSO algorithm for discrete variables has been developed and implemented successfully for the, multi-objective design optimization of composites. The problem is formulated with multiple objectives of minimizing weight and the total cost of the composite component to achieve a specified strength. The primary optimization variables are - the number of layers, its stacking sequence (the orientation of the layers) and thickness of each layer. The classical lamination theory is utilized to determine the stresses in the component and the design is evaluated based on three failure criteria; failure mechanism based failure criteria, Maximum stress failure criteria and the Tsai-Wu failure criteria. The optimization method is validated for a number of different loading configurations - uniaxial, biaxial and bending loads. The design optimization has been carried for both variable stacking sequences, as well fixed standard stacking schemes and a comparative study of the different design configurations evolved has been presented. (C) 2007 Elsevier Ltd. All rights reserved.

Host factor Ebp1 inhibits rinderpest virus transcription in vivo

ErbB3 binding protein Ebp1 has been shown to downregulate ErbB3 receptor-mediated signaling to inhibit cell proliferation. Rinderpest virus belongs to the family Paramyxoviridae and is characterized by the presence of a non-segmented negative-sense RNA genome. In this work, we show that rinderpest virus infection of Vero cells leads to the down-regulation of the host factor Ebp1, at both the mRNA and protein levels. Ebp1 protein has been shown to co-localize with viral inclusion bodies in infected cells, and it is packaged into virions, presumably through its interaction with the N protein or the N-RNA itself. Overexpression of Ebp1 inhibits viral transcription and multiplication in infected cells, suggesting that a mutual antagonism operates between host factor Ebp1 and the virus.

Vector Evaluated Particle Swarm Optimization (VEPSO) of Supersonic Ejector for Hydrogen Fuel Cells

Fuel cells are emerging as alternate green power producers for both large power production and for use in automobiles. Hydrogen is seen as the best option as a fuel; however, hydrogen fuel cells require recirculation of unspent hydrogen. A supersonic ejector is an apt device for recirculation in the operating regimes of a hydrogen fuel cell. Optimal ejectors have to be designed to achieve best performances. The use of the vector evaluated particle swarm optimization technique to optimize supersonic ejectors with a focus on its application for hydrogen recirculation in fuel cells is presented here. Two parameters, compression ratio and efficiency, have been identified as the objective functions to be optimized. Their relation to operating and design parameters of ejector is obtained by control volume based analysis using a constant area mixing approximation. The independent parameters considered are the area ratio and the exit Mach number of the nozzle. The optimization is carried out at a particularentrainment ratio and results in a set of nondominated solutions, the Pareto front. A set of such curves can be used for choosing the optimal design parameters of the ejector.

Space-Vector-Based Hybrid Pulsewidth Modulation Techniques for Reduced Harmonic Distortion and Switching Loss

Novel switching sequences can be employed in spacevector-based pulsewidth modulation (PWM) of voltage source inverters. Differentswitching sequences are evaluated and compared in terms of inverter switching loss. A hybrid PWM technique named minimum switching loss PWM is proposed, which reduces the inverter switching loss compared to conventional space vector PWM (CSVPWM) and discontinuous PWM techniques at a given average switching frequency. Further, four space-vector-based hybrid PWM techniques are proposed that reduce line current distortion as well as switching loss in motor drives, compared to CSVPWM. Theoretical and experimental results are presented.