Genetic polymorphisms in vitamin D receptor, vitamin D-binding protein, Toll-like receptor 2, nitric oxide synthase 2, and interferon-γ genes and its association with susceptibility to tuberculosis

Mycobacterium tuberculosis kills more people than any other single pathogen, with an estimated one-third of the world's population being infected. Among those infected, only 10% will develop the disease. There are several demonstrations that susceptibility to tuberculosis is linked to host genetic factors in twins, family and associated-based case control studies. In the past years, there has been dramatic improvement in our understanding of the role of innate and adaptive immunity in the human host defense to tuberculosis. To date, attention has been paid to the role of genetic host and parasitic factors in tuberculosis pathogenesis mainly regarding innate and adaptive immune responses and their complex interactions. Many studies have focused on the candidate genes for tuberculosis susceptibility ranging from those expressed in several cells from the innate or adaptive immune system such as Toll-like receptors, cytokines (TNF-α, TGF-β, IFN-γ, IL-1b, IL-1RA, IL-12, IL-10), nitric oxide synthase and vitamin D, both nuclear receptors and their carrier, the vitamin D-binding protein (VDBP). The identification of possible genes that can promote resistance or susceptibility to tuberculosis could be the first step to understanding disease pathogenesis and can help to identify new tools for treatment and vaccine development. Thus, in this mini-review, we summarize the current state of investigation on some of the genetic determinants, such as the candidate polymorphisms of vitamin D, VDBP, Toll-like receptor, nitric oxide synthase 2 and interferon-γ genes, to generate resistance or susceptibility to M. tuberculosis infection.

Genetic variability in Larus argentatus and Sterna hirundo

Blood serum and egg-white protein samples from individuals representing seven colonies of Larusargentatus, and four colonies of Sterna hirundo were electrophoretically analysed to determine levels of genetic variability and to assess the utility of polymorphic loci as genetic markers. Variability occurred at five co-dominant autosomal loci. S. hirundo protein polymorphism occurred at the Est-5 and the Oest-l loci, while nineteen loci were monomorphic. L. argentatus samples were monomorphic at seventeen loci and polymorphic at the Ldh-A and the Alb loci. Intergeneric differences existed at the Oalb and the Ldh-A loci. Although LDH-A100 from both species possessed identical electrophoretLc mobilities, the intergeneric differences were expressed as a difference in enzyme the'ITIlostabilities. Geographical distribution of alleles and genetic divergence estimates suggest ~ hirundo population panmixis,at least at the sampled locations. The h argentatus gene pool appears relatively heterogeneous with a discreet Atlantic Coast population and a Great Lakes demic population. These observed population structures may be maintained by the relative amount of gene flow occurring within and among populations. Mass ringing data coupled to reproductive success information and analysis of dispersal trends appear to validate this assumption. Similar results may be generated by either selection or both small organism and low locus sample sizes. To clarify these results and to detect the major factor(s) affecting the surveyed portions of the genome, larger sample sizes in conjunction with precise eco-demographic data are required.

Polyglutamine monomer structure and its implications for molecular self-assembly

Polyglutamine is a naturally occurring peptide found within several proteins in neuronal cells of the brain, and its aggregation has been implicated in several neurodegenerative diseases, including Huntington's disease. The resulting aggregates have been demonstrated to possess ~-sheet structure, and aggregation has been shown to start with a single misfolded peptide. The current project sought to computationally examine the structural tendencies of three mutant poly glutamine peptides that were studied experimentally, and found to aggregate with varying efficiencies. Low-energy structures were generated for each peptide by simulated annealing, and were analyzed quantitatively by various geometry- and energy-based methods. According to the results, the experimentally-observed inhibition of aggregation appears to be due to localized conformational restraint placed on the peptide backbone by inserted prolines, which in tum confines the peptide to native coil structure, discouraging transition towards the ~sheet structure required for aggregation. Such knowledge could prove quite useful to the design of future treatments for Huntington's and other related diseases.

Bio-inspired optimization & sampling technique for side-chain packing in MCCE

The prediction of proteins' conformation helps to understand their exhibited functions, allows for modeling and allows for the possible synthesis of the studied protein. Our research is focused on a sub-problem of protein folding known as side-chain packing. Its computational complexity has been proven to be NP-Hard. The motivation behind our study is to offer the scientific community a means to obtain faster conformation approximations for small to large proteins over currently available methods. As the size of proteins increases, current techniques become unusable due to the exponential nature of the problem. We investigated the capabilities of a hybrid genetic algorithm / simulated annealing technique to predict the low-energy conformational states of various sized proteins and to generate statistical distributions of the studied proteins' molecular ensemble for pKa predictions. Our algorithm produced errors to experimental results within .acceptable margins and offered considerable speed up depending on the protein and on the rotameric states' resolution used.

Disease-Gene Association Using a Genetic Algorithm

Understanding the relationship between genetic diseases and the genes associated with them is an important problem regarding human health. The vast amount of data created from a large number of high-throughput experiments performed in the last few years has resulted in an unprecedented growth in computational methods to tackle the disease gene association problem. Nowadays, it is clear that a genetic disease is not a consequence of a defect in a single gene. Instead, the disease phenotype is a reflection of various genetic components interacting in a complex network. In fact, genetic diseases, like any other phenotype, occur as a result of various genes working in sync with each other in a single or several biological module(s). Using a genetic algorithm, our method tries to evolve communities containing the set of potential disease genes likely to be involved in a given genetic disease. Having a set of known disease genes, we first obtain a protein-protein interaction (PPI) network containing all the known disease genes. All the other genes inside the procured PPI network are then considered as candidate disease genes as they lie in the vicinity of the known disease genes in the network. Our method attempts to find communities of potential disease genes strongly working with one another and with the set of known disease genes. As a proof of concept, we tested our approach on 16 breast cancer genes and 15 Parkinson's Disease genes. We obtained comparable or better results than CIPHER, ENDEAVOUR and GPEC, three of the most reliable and frequently used disease-gene ranking frameworks.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics

The technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] provides an attractive method of building exact tests from statistics whose finite sample distribution is intractable but can be simulated (provided it does not involve nuisance parameters). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing the method to statistics whose null distributions involve nuisance parameters (maximized MC tests, MMC). Simplified asymptotically justified versions of the MMC method are also proposed and it is shown that they provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics (e.g., unit root asymptotics). Parametric bootstrap tests may be interpreted as a simplified version of the MMC method (without the general validity properties of the latter).

Array pattern nulling by element position perturbations using a genetic algorithm

A genetic algorithm has been used for null steering in phased and adaptive arrays . It has been shown that it is possible to steer the array null s precisely to the required interference directions and to achieve any prescribed null depths . A comparison with the results obtained from the analytic solution shows the advantages of using the genetic algorithm for null steering in linear array patterns

Use of Novel Algorithms MAJE4 and MACJER-320 for Achieving Confidentiality and Message Authentication in SSL & TLS

Extensive use of the Internet coupled with the marvelous growth in e-commerce and m-commerce has created a huge demand for information security. The Secure Socket Layer (SSL) protocol is the most widely used security protocol in the Internet which meets this demand. It provides protection against eaves droppings, tampering and forgery. The cryptographic algorithms RC4 and HMAC have been in use for achieving security services like confidentiality and authentication in the SSL. But recent attacks against RC4 and HMAC have raised questions in the confidence on these algorithms. Hence two novel cryptographic algorithms MAJE4 and MACJER-320 have been proposed as substitutes for them. The focus of this work is to demonstrate the performance of these new algorithms and suggest them as dependable alternatives to satisfy the need of security services in SSL. The performance evaluation has been done by using practical implementation method.

Use of Novel Algorithms MAJE4 and MACJER-320 for Achieving Confidentiality and Message Authentication in SSL & TLS.

Extensive use of the Internet coupled with the marvelous growth in e-commerce and m-commerce has created a huge demand for information security. The Secure Socket Layer (SSL) protocol is the most widely used security protocol in the Internet which meets this demand. It provides protection against eaves droppings, tampering and forgery. The cryptographic algorithms RC4 and HMAC have been in use for achieving security services like confidentiality and authentication in the SSL. But recent attacks against RC4 and HMAC have raised questions in the confidence on these algorithms. Hence two novel cryptographic algorithms MAJE4 and MACJER-320 have been proposed as substitutes for them. The focus of this work is to demonstrate the performance of these new algorithms and suggest them as dependable alternatives to satisfy the need of security services in SSL. The performance evaluation has been done by using practical implementation method.

Design of a Compact Genetic Microstrip Antenna with improved performance

In this paper the design issues of compact genetic microstrip antennas for mobile applications has been investigated. The antennas designed using Genetic Algorithms (GA) have an arbitrary shape and occupies less area (compact) compared to the traditionally designed antenna for the same frequency but with poor performance. An attempt has been made to improve the performance of the genetic microstrip antenna by optimizing the ground plane (GP) to have a fish bone like structure. The genetic antenna with the GP optimized is even better compared to the traditional and the genetic antenna.

A Reinforcement Learning Approach to Economic Dispatch using Neural Networks

This paper presents a Reinforcement Learning (RL) approach to economic dispatch (ED) using Radial Basis Function neural network. We formulate the ED as an N stage decision making problem. We propose a novel architecture to store Qvalues and present a learning algorithm to learn the weights of the neural network. Even though many stochastic search techniques like simulated annealing, genetic algorithm and evolutionary programming have been applied to ED, they require searching for the optimal solution for each load demand. Also they find limitation in handling stochastic cost functions. In our approach once we learn the Q-values, we can find the dispatch for any load demand. We have recently proposed a RL approach to ED. In that approach, we could find only the optimum dispatch for a set of specified discrete values of power demand. The performance of the proposed algorithm is validated by taking IEEE 6 bus system, considering transmission losses

A Faster 2D Technique for the design of Combinational Digital Circuits Using Genetic Algorithm

Combinational digital circuits can be evolved automatically using Genetic Algorithms (GA). Until recently this technique used linear chromosomes and and one dimensional crossover and mutation operators. In this paper, a new method for representing combinational digital circuits as 2 Dimensional (2D) chromosomes and suitable 2D crossover and mutation techniques has been proposed. By using this method, the convergence speed of GA can be increased significantly compared to the conventional methods. Moreover, the 2D representation and crossover operation provides the designer with better visualization of the evolved circuits. In addition to this, a technique to display automatically the evolved circuits has been developed with the help of MATLAB

Genetic divergence in lobsters (Crustacea: Palinuridae and Scyllaridae) from the Indian EEZ

The management of exploited species requires the identification of demographically isolated populations that can be considered as independent management units (MUs), failuring in which can lead to over -fishing and depletion of less productive stocks. By characterizing the distribution of genetic variation, population sub structuring can be detected and the degree of connectivity among populations can be estimated. The genetic variation can be observed using identified molecular markers of both nuclear and mitochondrial origin. Hence, the present work was undertaken to study the genetic diversity and population/stock structure in P. homarus homarus and T. unimaculatus from different landing centres along the Indian coast using nuclear (RAPD) and mitochondrial DNA marker tools which will help towards developing management strategies for management and conservation of these declining resources.To make consistent conservation and fisheries management decisions, accurate species identifications are needed. It is also suggested that it is not always desirable to rely on a single sequence for taxonomic identification. Thus, the feasibility of using partial sequences of additional mitochondrial genes like 16SrRNA, 12SrRNA and nuclear 18SrRNA has also been explored in our study. Phylogenies provide a sound foundation for establishing taxonomy. The present work also attempts to reconstruct the phylogeny of eleven species of commercially important lobsters from the Indian EEZ using molecular markers

Modelling the growth and movement of cyanobacteria in river systems

Bloom-forming and toxin-producing cyanobacteria remain a persistent nuisance across the world. Modelling of cyanobacteria in freshwaters is an important tool for understanding their population dynamics and predicting the location and timing of the bloom events in lakes and rivers. In this article, a new deterministic model is introduced which simulates the growth and movement of cyanobacterial blooms in river systems. The model focuses on the mathematical description of the bloom formation, vertical migration and lateral transport of colonies within river environments by taking into account the four major factors that affect the cyanobacterial bloom formation in freshwaters: light, nutrients, temperature and river flow. The model consists of two sub-models: a vertical migration model with respect to growth of cyanobacteria in relation to light, nutrients and temperature; and a hydraulic model to simulate the horizontal movement of the bloom. This article presents the model algorithms and highlights some important model results. The effects of nutrient limitation, varying illumination and river flow characteristics on cyanobacterial movement are simulated. The results indicate that under high light intensities and in nutrient-rich waters colonies sink further as a result of carbohydrate accumulation in the cells. In turbulent environments, vertical migration is retarded by vertical velocity component generated by turbulent shear stress. (c) 2006 Elsevier B.V. All rights reserved.