Chemistry and biology of DNA methyltransferases

Recognition of a specific DNA sequence by a protein is probably the best example of macromolecular interactions leading to various events. It is a prerequisite to understanding the basis of protein-DNA interactions to obtain a better insight into fundamental processes such as transcription, replication, repair, and recombination. DNA methyltransferases with varying sequence specificities provide an excellent model system for understanding the molecular mechanism of specific DNA recognition. Sequence comparison of cloned genes, along with mutational analyses and recent crystallographic studies, have clearly defined the functions of various conserved motifs. These enzymes access their target base in an elegant manner by flipping it out of the DNA double helix. The drastic protein-induced DNA distortion, first reported for HhaI DNA methyltransferase, appears to be a common mechanism employed by various proteins that need to act on bases. A remarkable feature of the catalytic mechanism of DNA (cytosine-5) methyltransferases is the ability of these enzymes to induce deamination of the target cytosine in the absence of S-adenosyl-L-methionine or its analogs. The enzyme-catalyzed deamination reaction is postulated to be the major cause of mutational hotspots at CpG islands responsible for various human genetic disorders. Methylation of adenine residues in Escherichia coli is known to regulate various processes such as transcription, replication, repair, recombination, transposition, and phage packaging.

Assembly of physalis mottle virus capsid protein in Escherichia coli and the role of amino and carboxy termini in the formation of the icosahedral particles

The coat protein gene of physalis mottle tymovirus (PhMV) was over expressed in Escherichia coli using pET-3d vector. The recombinant protein was found to self assemble into capsids in vivo. The purified recombinant capsids had an apparent s value of 56.5 S and a diameter of 29(±2) nm. In order to establish the role of amino and carboxy-terminal regions in capsid assembly, two amino-terminal deletions clones lacking the first 11 and 26 amino acid residues and two carboxy-terminal deletions lacking the last five and ten amino acid residues were constructed and overexpressed. The proteins lacking N-terminal 11 (PhCPN1) and 26 (PhCPN2) amino acid residues self assembled into T = 3 capsids in vivo, as evident from electron microscopy, ultracentrifugation and agarose gel electrophoresis. The recombinant, PhCPN1 and PhCPN2 capsids were as stable as the empty capsids formed in vivo and encapsidated a small amount of mRNA. The monoclonal antibody PA3B2, which recognizes the epitope within region 22 to 36, failed to react with PhCPN2 capsids while it recognized the recombinant and PhCPN1 capsids. Disassembly of the capsids upon treatment with urea showed that PhCPN2 capsids were most stable. These results demonstrate that the N-terminal 26 amino acid residues are not essential for T = 3 capsid assembly in PhMV. In contrast, both the proteins lacking the C-terminal five and ten amino acid residues were present only in the insoluble fraction and could not assemble into capsids, suggesting that these residues are crucial for folding and assembly of the particles.

Subspace intersection method of high-resolution bearing estimation in shallow ocean using acoustic vector sensors

The subspace intersection method (SIM) provides unbiased bearing estimates of multiple acoustic sources in a range-independent shallow ocean using a one-dimensional search without prior knowledge of source ranges and depths. The original formulation of this method is based on deployment of a horizontal linear array of hydrophones which measure acoustic pressure. In this paper, we extend SIM to an array of acoustic vector sensors which measure pressure as well as all components of particle velocity. Use of vector sensors reduces the minimum number of sensors required by a factor of 4, and also eliminates the constraint that the intersensor spacing should not exceed half wavelength. The additional information provided by the vector sensors leads to performance enhancement in the form of lower estimation error and higher resolution.

Prediction of Candidate Primary Immunodeficiency Disease Genes Using a Support Vector Machine Learning Approach

Screening and early identification of primary immunodeficiency disease (PID) genes is a major challenge for physicians. Many resources have catalogued molecular alterations in known PID genes along with their associated clinical and immunological phenotypes. However, these resources do not assist in identifying candidate PID genes. We have recently developed a platform designated Resource of Asian PDIs, which hosts information pertaining to molecular alterations, protein-protein interaction networks, mouse studies and microarray gene expression profiling of all known PID genes. Using this resource as a discovery tool, we describe the development of an algorithm for prediction of candidate PID genes. Using a support vector machine learning approach, we have predicted 1442 candidate PID genes using 69 binary features of 148 known PID genes and 3162 non-PID genes as a training data set. The power of this approach is illustrated by the fact that six of the predicted genes have recently been experimentally confirmed to be PID genes. The remaining genes in this predicted data set represent attractive candidates for testing in patients where the etiology cannot be ascribed to any of the known PID genes.

HDAC inhibitor valproic acid enhances tumor cell kill in adenovirus-HSVtk mediated suicide gene therapy in HNSCC xenograft mouse model

Safety, efficacy and enhanced transgene expression are the primary concerns while using any vector for gene therapy. One of the widely used vectors in clinical. trials is adenovirus which provides a safe way to deliver the therapeutic gene. However, adenovirus has poor transduction efficiency in vivo since most tumor cells express low coxsackie and adenovirus receptors. Similarly transgene expression remains low, possibly because of the chromatization of adenoviral genome upon infection in eukaryotic cells, an effect mediated by histone deacetylases (HDACs). Using a recombinant adenovirus (Ad-HSVtk) carrying the herpes simplex thymidine kinase (HSVtk) and GFP genes we demonstrate that HDAC inhibitor valproic acid can bring about an increase in CAR expression on host cells and thereby enhanced Ad-HSVtk infectivity. It also resulted in an increase in transgene (HSVtk and GFP) expression. This, in turn, resulted in increased cell kill of HNSCC cells, following ganciclovir treatment in vitro as well as in vivo in a xenograft nude mouse model.

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.

The biology and pathogenesis of hepatitis viruses

Viral hepatitis is caused mainly by infection with one of the five hepatitis viruses, which use the liver as their primary site of replication. Each of these, known as hepatitis A through E viruses (HAV to HEV), belong to different virus families, have unique morphology, genomic organization and replication strategy. These viruses cause similar clinical manifestations during the acute phase of infection but vary in their ability to cause chronic infection. While HAV and HEV cause only acute disease with no chronic sequelae, HBV, HCV and HDV cause varying degrees of chronicity and liver injury, which can progress to cirrhosis and liver cancers. Though specific serological tests are available for the known hepatitis viruses, nearly 20% of all hepatitis cases show no markers. Antiviral therapy is also recommended for some hepatitis viruses and a preventive vaccine is available only for hepatitis B. More research and public awareness programmes are needed to control the disease. This review will provide an overview of the hepatitis viruses and the disease they cause.

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.