Prediction of protein-protein interactions between human host and a pathogen and its application to three pathogenic bacteria

Molecular understanding of disease processes can be accelerated if all interactions between the host and pathogen are known. The unavailability of experimental methods for large-scale detection of interactions across host and pathogen organisms hinders this process. Here we apply a simple method to predict protein-protein interactions across a host and pathogen organisms. We use homology detection approaches against the protein-protein interaction databases. DIP and iPfam in order to predict interacting proteins in a host-pathogen pair. In the present work, we first applied this approach to the test cases involving the pairs phage T4 - Escherichia coli and phage lambda - E. coli and show that previously known interactions could be recognized using our approach. We further apply this approach to predict interactions between human and three pathogens E. coli, Salmonella enterica typhimurium and Yersinia pestis. We identified several novel interactions involving proteins of host or pathogen that could be thought of as highly relevant to the disease process. Serendipitously, many interactions involve hypothetical proteins of yet unknown function. Hypothetical proteins are predicted from computational analysis of genome sequences with no laboratory analysis on their functions yet available. The predicted interactions involving such proteins could provide hints to their functions. (C) 2011 Elsevier B.V. All rights reserved.

Parasite abundance and diversity in mammals: correlates with host ecology

Fecally dispersed parasites of 12 wild mammal species in Mudumalai Sanctuary, southern India, were studied, Fecal propagule densities and parasite diversity measures were correlated with host ecological variables. Host species with higher predatory pressure had lower parasite loads and parasite diversity. Host body weight, home range, population density, gregariousness, and diet did not show predicted effects on parasite loads. Measures of a! diversity were positively correlated with parasite abundance and were negatively correlated with beta diversity, Based on these data, hypotheses regarding determinants of parasite community are discussed.

The algebra and geometry of SU(3) matrices

We give an elementary treatment of the defining representation and Lie algebra of the three-dimensional unitary unimodular group SU(3). The geometrical properties of the Lie algebra, which is an eight dimensional real Linear vector space, are developed in an SU(3) covariant manner. The f and d symbols of SU(3) lead to two ways of 'multiplying' two vectors to produce a third, and several useful geometric and algebraic identities are derived. The axis-angle parametrization of SU(3) is developed as a generalization of that for SU(2), and the specifically new features are brought out. Application to the dynamics of three-level systems is outlined.

Robust Formulations for Handling Uncertainty in Kernel Matrices

We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we derive a formulation which is robust to such noise. The resulting formulation applies when the noise is Gaussian, or has finite support. The formulation in general is non-convex, but in several cases of interest it reduces to a convex program. The problem of uncertainty in kernel matrix is motivated from the real world problem of classifying proteins when the structures are provided with some uncertainty. The formulation derived here naturally incorporates such uncertainty in a principled manner leading to significant improvements over the state of the art. 1.

Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions

In this paper we consider the problem of learning an n × n kernel matrix from m(1) similarity matrices under general convex loss. Past research have extensively studied the m = 1 case and have derived several algorithms which require sophisticated techniques like ACCP, SOCP, etc. The existing algorithms do not apply if one uses arbitrary losses and often can not handle m > 1 case. We present several provably convergent iterative algorithms, where each iteration requires either an SVM or a Multiple Kernel Learning (MKL) solver for m > 1 case. One of the major contributions of the paper is to extend the well knownMirror Descent(MD) framework to handle Cartesian product of psd matrices. This novel extension leads to an algorithm, called EMKL, which solves the problem in O(m2 log n 2) iterations; in each iteration one solves an MKL involving m kernels and m eigen-decomposition of n × n matrices. By suitably defining a restriction on the objective function, a faster version of EMKL is proposed, called REKL,which avoids the eigen-decomposition. An alternative to both EMKL and REKL is also suggested which requires only an SVMsolver. Experimental results on real world protein data set involving several similarity matrices illustrate the efficacy of the proposed algorithms.

Superconductivity of embedded lead nano particles in metallic and amorphous matrices

Nanodispersed lead in metallic and amorphous matrices was synthesized by rapid solidification processing. The optimum microstructure was tailored to avoid percolation of the particles. With these embedded particles it is possible to study quantitatively the effect of size on the superconducting transition temperature by carrying out quantitative microstructural characterization and magnetic measurements. Our results suggest the role of the matrices in enhancement or depression of superconducting transition temperature of lead. The origin of this difference in behavior with respect to different matrices and sizes is discussed.

The effect of host glass on optical absorption and fluorescence of Nd3+ in xNa(2)O-(30-x)K2O-70B(2)O(3) glasses

The effect of host glass composition on the optical absorption and fluorescence spectra of Nd3+ has been studied in mixed alkali borate glasses of the type xNa(2)O-(30-x)K2O-69.5B(2)O(3)-0.5Nd(2)O(3) (X = 5,10,15,20 and 25). Various spectroscopic parameters such as Racah (E-1, E-2 and E-3), spin-orbit (xi(4f)) and configuration interaction (alpha, beta) parameters have been calculated. The Judd-Ofelt intensity parameters (Omega(lambda)) have been calculated and the radiative transition probabilities (A(rad)), radiative lifetimes (tau(r)), branching ratios (beta) and integrated absorption cross sections (Sigma) have been obtained for certain excited states of the Nd3+, ion and are discussed with respect to x. From the fluorescence spectra, the effective fluorescence line widths (Deltalambda(eff)) and stimulated emission cross sections (sigma(p)) have been obtained for the three transitions F-4(3/2) --> I-4(9/2), F-4(3/2) --> I-4(11/2) and F-4(3/2) --> I-4(13/2) of Nd3+. The stimulated emission cross section (sigma(p)) values are found to be in the range (2.0-4.8) x 10(-2)0 cm(2) and they are large enough to indicate that the mixed alkali borate glasses could be potential laser host materials.

Closed form solutions for element equilibrium and flexibility matrices of eight node rectangular plate bending element using integrated force method

Closed form solutions for equilibrium and flexibility matrices of the Mindlin-Reissner theory based eight-node rectangular plate bending element (MRP8) using integrated Force Method (IFM) are presented in this paper. Though these closed form solutions of equilibrium and flexibility matrices are applicable to plate bending problems with square/rectangular boundaries, they reduce the computational time significantly and give more exact solutions. Presented closed form solutions are validated by solving large number of standard square/rectangular plate bending benchmark problems for deflections and moments and the results are compared with those of similar displacement-based eight-node quadrilateral plate bending elements available in the literature. The results are also compared with the exact solutions.

Cyclodextrin functionalized magnetic iron oxide nanocrystals: a host-carrier for magnetic separation of non-polar molecules and arsenic from aqueous media

A single-step magnetic separation procedure that can remove both organic pollutants and arsenic from contaminated water is clearly a desirable goal. Here we show that water dispersible magnetite nanoparticles prepared by anchoring carboxymethyl-beta-cyclodextrin (CMCD) cavities to the surface of magnetic nanoparticles are suitable host carriers for such a process. Monodisperse, 10 nm, spherical magnetite, Fe3O4, nanocrystals were prepared by the thermal decomposition of FeOOH. Trace amounts of antiferromagnet, FeO, present in the particles provides an exchange bias field that results in a high superparamagnetic blocking temperature and appreciable magnetization values that facilitate easy separation of the nanocrystals from aqueous dispersions on application of modest magnetic fields. We show here that small molecules like naphthalene and naphthol can be removed from aqueous media by forming inclusion complexes with the anchored cavities of the CMCD-Fe3O4 nanocrystals followed by separation of the nanocrystals by application of a magnetic field. The adsorption properties of the iron oxide surface towards As ions are unaffected by the CMCD capping so it too can be simultaneously removed in the separation process. The CMCD-Fe3O4 nanocrystals provide a versatile platform for magnetic separation with potential applications in water remediation.

Salmonella enterica serovars Typhimurium and Typhi as model organisms Revealing paradigm of host-pathogen interactions

The lifestyle of intracellular pathogens has always questioned the skill of a microbiologist in the context of finding the permanent cure to the diseases caused by them. The best tool utilized by these pathogens is their ability to reside inside the host cell, which enables them to easily bypass the humoral immunity of the host, such as the complement system. They further escape from the intracellular immunity, such as lysosome and inflammasome, mostly by forming a protective vacuole-bound niche derived from the host itself. Some of the most dreadful diseases are caused by these vacuolar pathogens, for example, tuberculosis by Mycobacterium or typhoid fever by Salmonella. To deal with such successful pathogens therapeutically, the knowledge of a host-pathogen interaction system becomes primarily essential, which further depends on the use of a model system. A well characterized pathogen, namely Salmonella, suits the role of a model for this purpose, which can infect a wide array of hosts causing a variety of diseases. This review focuses on various such aspects of research on Salmonella which are useful for studying the pathogenesis of other intracellular pathogens.

Efficient Methods for Robust Classification Under Uncertainty in Kernel Matrices

In this paper we study the problem of designing SVM classifiers when the kernel matrix, K, is affected by uncertainty. Specifically K is modeled as a positive affine combination of given positive semi definite kernels, with the coefficients ranging in a norm-bounded uncertainty set. We treat the problem using the Robust Optimization methodology. This reduces the uncertain SVM problem into a deterministic conic quadratic problem which can be solved in principle by a polynomial time Interior Point (IP) algorithm. However, for large-scale classification problems, IP methods become intractable and one has to resort to first-order gradient type methods. The strategy we use here is to reformulate the robust counterpart of the uncertain SVM problem as a saddle point problem and employ a special gradient scheme which works directly on the convex-concave saddle function. The algorithm is a simplified version of a general scheme due to Juditski and Nemirovski (2011). It achieves an O(1/T-2) reduction of the initial error after T iterations. A comprehensive empirical study on both synthetic data and real-world protein structure data sets show that the proposed formulations achieve the desired robustness, and the saddle point based algorithm outperforms the IP method significantly.

Fast Likelihood Computation in Speech Recognition using Matrices

Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech processing problems. Computing likelihoods against a large set of Gaussians is required as a part of many speech processing systems and it is the computationally dominant phase for Large Vocabulary Continuous Speech Recognition (LVCSR) systems. We express the likelihood computation as a multiplication of matrices representing augmented feature vectors and Gaussian parameters. The computational gain of this approach over traditional methods is by exploiting the structure of these matrices and efficient implementation of their multiplication. In particular, we explore direct low-rank approximation of the Gaussian parameter matrix and indirect derivation of low-rank factors of the Gaussian parameter matrix by optimum approximation of the likelihood matrix. We show that both the methods lead to similar speedups but the latter leads to far lesser impact on the recognition accuracy. Experiments on 1,138 work vocabulary RM1 task and 6,224 word vocabulary TIMIT task using Sphinx 3.7 system show that, for a typical case the matrix multiplication based approach leads to overall speedup of 46 % on RM1 task and 115 % for TIMIT task. Our low-rank approximation methods provide a way for trading off recognition accuracy for a further increase in computational performance extending overall speedups up to 61 % for RM1 and 119 % for TIMIT for an increase of word error rate (WER) from 3.2 to 3.5 % for RM1 and for no increase in WER for TIMIT. We also express pairwise Euclidean distance computation phase in Dynamic Time Warping (DTW) in terms of matrix multiplication leading to saving of approximately of computational operations. In our experiments using efficient implementation of matrix multiplication, this leads to a speedup of 5.6 in computing the pairwise Euclidean distances and overall speedup up to 3.25 for DTW.

Field extension code based dispersion matrices for coherently detected space-time shift keying

Motivated by the recent Coherent Space-Time Shift Keying (CSTSK) philosophy, we construct new dispersion matrices for rotationally invariant PSK signaling sets. Given a specific PSK signal constellation, the dispersion matrices of the existing CSTSK scheme were chosen by maximizing the mutual information over randomly generated sets of dispersion matrices. In this contribution we propose a general method for constructing a set of structured dispersion matrices for arbitrary PSK signaling sets using Field Extension (FE) codes and then study the attainable Symbol Error Rate (SER) performance of some example constructions. We demonstrate that the proposed dispersion scheme is capable of outperforming the existing dispersion arrangement at medium to high SNRs.

Antimony-resistant but not antimony-sensitive Leishmania donovani up-regulates host IL-10 to overexpress multidrug-resistant protein 1

The molecular mechanism of antimony-resistant Leishmania donovani ((SbLD)-L-R)-driven up-regulation of IL-10 and multidrug-resistant protein 1 (MDR1) in infected macrophages (M phi s) has been investigated. This study showed that both promastigote and amastigote forms of (SbLD)-L-R, but not the antimony-sensitive form of LD, express a unique glycan with N-acetylgalactosamine as a terminal sugar. Removal of it either by enzyme treatment or by knocking down the relevant enzyme, galactosyltransferase in (SbLD)-L-R (KD (SbLD)-L-R), compromises the ability to induce the above effects. Infection of M phi s with KD (SbLD)-L-R enhanced the sensitivity toward antimonials compared with infection with (SbLD)-L-R, and infection of BALB/c mice with KD (SbLD)-L-R caused significantly less organ parasite burden compared with infection induced by (SbLD)-L-R. The innate immune receptor, Toll-like receptor 2/6 heterodimer, is exploited by (SbLD)-L-R to activate ERK and nuclear translocation of NF-kappa B involving p50/c-Rel leading to IL-10 induction, whereas MDR1 up-regulation is mediated by PI3K/Akt and the JNK pathway. Interestingly both recombinant IL-10 and (SbLD)-L-R up-regulate MDR1 in M. with different time kinetics, where phosphorylation of PI3K was noted at 12 h and 48 h, respectively, but M phi s derived from IL-10(-/-) mice are unable to show MDR1 up-regulation on infection with (SbLD)-L-R. Thus, it is very likely that an IL-10 surge is a prerequisite for MDR1 up-regulation. The transcription factor important for IL-10-driven MDR1 up-regulation is c-Fos/c-Jun and not NF-kappa B, as evident from studies with pharmacological inhibitors and promoter mapping with deletion constructs.