sRNAscanner: A Computational Tool for Intergenic Small RNA Detection in Bacterial Genomes

Background:Bacterial non-coding small RNAs (sRNAs) have attracted considerable attention due to their ubiquitous nature and contribution to numerous cellular processes including survival, adaptation and pathogenesis. Existing computational approaches for identifying bacterial sRNAs demonstrate varying levels of success and there remains considerable room for improvement. Methodology/Principal Findings: Here we have proposed a transcriptional signal-based computational method to identify intergenic sRNA transcriptional units (TUs) in completely sequenced bacterial genomes. Our sRNAscanner tool uses position weight matrices derived from experimentally defined E. coli K-12 MG1655 sRNA promoter and rho-independent terminator signals to identify intergenic sRNA TUs through sliding window based genome scans. Analysis of genomes representative of twelve species suggested that sRNAscanner demonstrated equivalent sensitivity to sRNAPredict2, the best performing bioinformatics tool available presently. However, each algorithm yielded substantial numbers of known and uncharacterized hits that were unique to one or the other tool only. sRNAscanner identified 118 novel putative intergenic sRNA genes in Salmonella enterica Typhimurium LT2, none of which were flagged by sRNAPredict2. Candidate sRNA locations were compared with available deep sequencing libraries derived from Hfq-co-immunoprecipitated RNA purified from a second Typhimurium strain (Sittka et al. (2008) PLoS Genetics 4: e1000163). Sixteen potential novel sRNAs computationally predicted and detected in deep sequencing libraries were selected for experimental validation by Northern analysis using total RNA isolated from bacteria grown under eleven different growth conditions. RNA bands of expected sizes were detected in Northern blots for six of the examined candidates. Furthermore, the 5'-ends of these six Northern-supported sRNA candidates were successfully mapped using 5'-RACE analysis. Conclusions/Significance: We have developed, computationally examined and experimentally validated the sRNAscanner algorithm. Data derived from this study has successfully identified six novel S. Typhimurium sRNA genes. In addition, the computational specificity analysis we have undertaken suggests that similar to 40% of sRNAscanner hits with high cumulative sum of scores represent genuine, undiscovered sRNA genes. Collectively, these data strongly support the utility of sRNAscanner and offer a glimpse of its potential to reveal large numbers of sRNA genes that have to date defied identification. sRNAscanner is available from: http://bicmku.in:8081/sRNAscanner or http://cluster.physics.iisc.ernet.in/sRNAscanner/.

Multiple contour extraction from graylevel images using an artificial neural network

For active contour modeling (ACM), we propose a novel self-organizing map (SOM)-based approach, called the batch-SOM (BSOM), that attempts to integrate the advantages of SOM- and snake-based ACMs in order to extract the desired contours from images. We employ feature points, in the form of ail edge-map (as obtained from a standard edge-detection operation), to guide the contour (as in the case of SOM-based ACMs) along with the gradient and intensity variations in a local region to ensure that the contour does not "leak" into the object boundary in case of faulty feature points (weak or broken edges). In contrast with the snake-based ACMs, however, we do not use an explicit energy functional (based on gradient or intensity) for controlling the contour movement. We extend the BSOM to handle extraction of contours of multiple objects, by splitting a single contour into as many subcontours as the objects in the image. The BSOM and its extended version are tested on synthetic binary and gray-level images with both single and multiple objects. We also demonstrate the efficacy of the BSOM on images of objects having both convex and nonconvex boundaries. The results demonstrate the superiority of the BSOM over others. Finally, we analyze the limitations of the BSOM.

DNA Clasping by Mycobacterial HU: The C-Terminal Region of HupB Mediates Increased Specificity of DNA Binding

Background: HU a small, basic, histone like protein is a major component of the bacterial nucleoid. E. coli has two subunits of HU coded by hupA and hupB genes whereas Mycobacterium tuberculosis (Mtb) has only one subunit of HU coded by ORF Rv2986c (hupB gene). One noticeable feature regarding Mtb HupB, based on sequence alignment of HU orthologs from different bacteria, was that HupB(Mtb) bears at its C-terminal end, a highly basic extension and this prompted an examination of its role in Mtb HupB function. Methodology/Principal Findings: With this objective two clones of Mtb HupB were generated; one expressing full length HupB protein (HupB(Mtb)) and another which expresses only the N terminal region (first 95 amino acid) of hupB (HupB(MtbN)). Gel retardation assays revealed that HupBMtbN is almost like E. coli HU (heat stable nucleoid protein) in terms of its DNA binding, with a binding constant (K-d) for linear dsDNA greater than 1000 nM, a value comparable to that obtained for the HU alpha alpha and HU alpha beta forms. However CTR (C-terminal Region) of HupB(Mtb) imparts greater specificity in DNA binding. HupB(Mtb) protein binds more strongly to supercoiled plasmid DNA than to linear DNA, also this binding is very stable as it provides DNase I protection even up to 5 minutes. Similar results were obtained when the abilities of both proteins to mediate protection against DNA strand cleavage by hydroxyl radicals generated by the Fenton's reaction, were compared. It was also observed that both the proteins have DNA binding preference for A: T rich DNA which may occur at the regulatory regions of ORFs and the oriC region of Mtb. Conclusions/Significance: These data thus point that HupB(Mtb) may participate in chromosome organization in-vivo, it may also play a passive, possibly an architectural role.

High-yield bacterial expression and structural characterization of recombinant human insulin-like growth factor binding protein-2

The diverse biological activities of the insulin-like growth factors (IGF-1 and IGF-2) are mediated by the IGF-1 receptor (IGF-1R). These actions are modulated by a family of six IGF-binding proteins (ICFBP-1-6; 22-31 kDa) that via high affinity binding to the IGFs (K-D similar to 300-700 pM) both protect the IGFs in the circulation and attenuate IGF action by blocking their receptor access In recent years, IGFBPs have been implicated in a variety of cancers However, the structural basis of their interaction with IGFs and/or other proteins is not completely understood A critical challenge in the structural characterization of full-length IGFBPs has been the difficulty in expressing these proteins at levels suitable for NMR/X-ray crystallography analysis Here we describe the high-yield expression of full-length recombinant human IGFBP-2 (rhIGFBP-2) in Eschericha coli Using a single step purification protocol, rhIGFBP-2 was obtained with >95% purity and structurally characterized using NMR spectroscopy. The protein was found to exist as a monomer at the high concentrations required for structural studies and to exist in a single conformation exhibiting a unique intra-molecular disulfide-bonding pattern The protein retained full biologic activity. This study represents the first high-yield expression of wild-type recombinant human IGFBP-2 in E coli and first structural characterization of a full-length IGFBP (C) 2010 Elsevier Inc. All rights reserved

Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures

In this paper, we present a generic method/model for multi-objective design optimization of laminated composite components, based on Vector Evaluated Artificial Bee Colony (VEABC) algorithm. VEABC is a parallel vector evaluated type, swarm intelligence multi-objective variant of the Artificial Bee Colony algorithm (ABC). In the current work a modified version of VEABC 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. Finally the performance is evaluated in comparison with other nature inspired techniques which includes Particle Swarm Optimization (PSO), Artificial Immune System (AIS) and Genetic Algorithm (GA). The performance of ABC is at par with that of PSO, AIS and GA for all the loading configurations. (C) 2009 Elsevier B.V. All rights reserved.

Artificial neural networks and multicriterion analysis for sustainable irrigation planning

The objective of the present paper is to select the best compromise irrigation planning strategy for the case study of Jayakwadi irrigation project, Maharashtra, India. Four-phase methodology is employed. In phase 1, separate linear programming (LP) models are formulated for the three objectives, namely. net economic benefits, agricultural production and labour employment. In phase 2, nondominated (compromise) irrigation planning strategies are generated using the constraint method of multiobjective optimisation. In phase 3, Kohonen neural networks (KNN) based classification algorithm is employed to sort nondominated irrigation planning strategies into smaller groups. In phase 4, multicriterion analysis (MCA) technique, namely, Compromise Programming is applied to rank strategies obtained from phase 3. It is concluded that the above integrated methodology is effective for modeling multiobjective irrigation planning problems and the present approach can be extended to situations where number of irrigation planning strategies are even large in number. (c) 2004 Elsevier Ltd. All rights reserved.

Improving outcome of childhood bacterial meningitis by simplified treatment : Experience from Angola

Background Acute bacterial meningitis (BM) continues to be an important cause of childhood mortality and morbidity, especially in developing countries. Prognostic scales and the identification of risk factors for adverse outcome both aid in assessing disease severity. New antimicrobial agents or adjunctive treatments - except for oral glycerol - have essentially failed to improve BM prognosis. A retrospective observational analysis found paracetamol beneficial in adult bacteraemic patients, and some experts recommend slow β-lactam infusion. We examined these treatments in a prospective, double-blind, placebo-controlled clinical trial. Patients and methods A retrospective analysis included 555 children treated for BM in 2004 in the infectious disease ward of the Paediatric Hospital of Luanda, Angola. Our prospective study randomised 723 children into four groups, to receive a combination of cefotaxime infusion or boluses every 6 hours for the first 24 hours and oral paracetamol or placebo for 48 hours. The primary endpoints were 1) death or severe neurological sequelae (SeNeSe), and 2) deafness. Results In the retrospective study, the mortality of children with blood transfusion was 23% (30 of 128) vs. without blood transfusion 39% (109 of 282; p=0.004). In the prospective study, 272 (38%) of the children died. Of those 451 surviving, 68 (15%) showed SeNeSe, and 12% (45 of 374) were deaf. Whereas no difference between treatment groups was observable in primary endpoints, the early mortality in the infusion-paracetamol group was lower, with the difference (Fisher s exact test) from the other groups at 24, 48, and 72 hours being significant (p=0.041, 0.0005, and 0.005, respectively). Prognostic factors for adverse outcomes were impaired consciousness, dyspnoea, seizures, delayed presentation, and absence of electricity at home (Simple Luanda Scale, SLS); the Bayesian Luanda Scale (BLS) also included abnormally low or high blood glucose. Conclusions New studies concerning the possible beneficial effect of blood transfusion, and concerning longer treatment with cefotaxime infusion and oral paracetamol, and a study to validate our simple prognostic scales are warranted.

Voltage and Temperature Aware Statistical Leakage Analysis Framework Using Artificial Neural Networks

Artificial neural networks (ANNs) have shown great promise in modeling circuit parameters for computer aided design applications. Leakage currents, which depend on process parameters, supply voltage and temperature can be modeled accurately with ANNs. However, the complex nature of the ANN model, with the standard sigmoidal activation functions, does not allow analytical expressions for its mean and variance. We propose the use of a new activation function that allows us to derive an analytical expression for the mean and a semi-analytical expression for the variance of the ANN-based leakage model. To the best of our knowledge this is the first result in this direction. Our neural network model also includes the voltage and temperature as input parameters, thereby enabling voltage and temperature aware statistical leakage analysis (SLA). All existing SLA frameworks are closely tied to the exponential polynomial leakage model and hence fail to work with sophisticated ANN models. In this paper, we also set up an SLA framework that can efficiently work with these ANN models. Results show that the cumulative distribution function of leakage current of ISCAS'85 circuits can be predicted accurately with the error in mean and standard deviation, compared to Monte Carlo-based simulations, being less than 1% and 2% respectively across a range of voltage and temperature values.

Artificial database for character recognition research

This paper describes a technique for artificial generation of learning and test sample sets suitable for character recognition research. Sample sets of English (Latin), Malayalam, Kannada and Tamil characters are generated easily through their prototype specifications by the endpoint co-ordinates, nature of segments and connectivity.

Generating artificial chromosomes with probability control in genetic algorithm for machine scheduling problems

In this paper, a novel genetic algorithm is developed by generating artificial chromosomes with probability control to solve the machine scheduling problems. Generating artificial chromosomes for Genetic Algorithm (ACGA) is closely related to Evolutionary Algorithms Based on Probabilistic Models (EAPM). The artificial chromosomes are generated by a probability model that extracts the gene information from current population. ACGA is considered as a hybrid algorithm because both the conventional genetic operators and a probability model are integrated. The ACGA proposed in this paper, further employs the ``evaporation concept'' applied in Ant Colony Optimization (ACO) to solve the permutation flowshop problem. The ``evaporation concept'' is used to reduce the effect of past experience and to explore new alternative solutions. In this paper, we propose three different methods for the probability of evaporation. This probability of evaporation is applied as soon as a job is assigned to a position in the permutation flowshop problem. Experimental results show that our ACGA with the evaporation concept gives better performance than some algorithms in the literature.

The Biocontrol Agent Phlebiopsis gigantea: Efficacy and Impacts on the Stump Bacterial Biota and Conifer tree Defences

Phlebiopsis gigantea has been for a long time known as a strong competitor against Heterobasidion annosum and intensively applied as a biological control agent on stump surfaces of Picea abies in Fennoscandia. However, the mechanism underlying its antagonistic activity is still unknown. A primary concern is the possible impact of P. gigantea treatment on resident non-target microbial biota of conifer stumps. Additional risk factor is the potential of P. gigantea to acquire a necrotrophic habit through adaptation to living wood tissues. This study focused on the differential screening of several P. gigantea isolates from diverse geographical sources as well as the use of breeding approach to enhance the biocontrol efficacy against H. annosum infection. The results showed a significant positive correlation between growth rate in wood and high biocontrol efficacy. Furthermore, with aid of breeding approach, several progeny strains were obtained that had better growth rate and control efficacy than parental isolates. To address the issue of the potential of P. gigantea to acquire necrotrophic capability, a combination of histochemical, molecular and transcript profiling (454 sequencing) were used to investigate the interactions between these two fungi and ten year old P. sylvestris seedlings. The results revealed that both P. gigantea and H. annosum provoked strong necrotic lesions, but after prolonged incubation, P. gigantea lesions shrank and ceased to expand further. Tree seedlings pre-treated with P. gigantea further restricted H. annosum-induced necrosis and had elevated transcript levels of genes important for lignification, cell death regulation and jasmonic acid signalling. These suggest that induced localized resistance is a contributory factor for the biocontrol efficacy of P.gigantea, and it has a comparatively limited necrotrophic capability than H. annosum. Finally, to investigate the potential impact of P. gigantea on the stump bacterial biota, 16S rDNA isolated from tissue samples from stumps of P. abies after 1-, 6- and 13-year post treatment was sequenced using bar-coded 454 Titanium pyrosequencing. Proteobacteria were found to be the most abundant at the initial stages of stump decay but were selectively replaced by Acidobacteria at advanced stages of the decay. Moreover, P. gigantea treatment significantly decreased the bacterial richness at initial decay stage in the stumps. Over time, the bacterial community in the stumps gradually recovered and the negative effects of P. gigantea was attenuated.

Bovine mastitis caused by coagulase-negative staphylococci : host response and bacterial factors

Coagulase-negative staphylococci (CNS) are the most common bacteria isolated in bovine subclinical mastitis in many countries, and also a frequent cause of clinical mastitis. The most common species isolated are Staphylococcus (S) chromogenes, S. simulans, S. epidermidis, and S. xylosus. One half of the intramammary infections (IMI) caused by CNS persist in the udder. The pathogenesis of IMI caused by CNS is poorly understood. This dissertation focuses on host response in experimental intramammary infection induced by S. chromogenes, S. epidermidis and S. simulans. Model for a mild experimental CNS infection was developed with S. chromogenes (study I). All cows were infected and most developed subclinical mastitis. In study II the innate immune response to S. epidermidis and S. simulans IMI was compared in eight cows using a crossover design. A larger dose of bacteria was used to induce clinical mastitis. All cows became infected and showed mild to moderate clinical signs of mastitis. S. simulans caused a slightly stronger innate immune response than S. epidermidis, with significantly higher concentrations of the interleukins IL-1beta and IL-8 in the milk. The spontaneous elimination rate of the 16 IMIs was 31%, with no difference between species. No significant differences were recorded between infections eliminated spontaneously or remaining persistent, although the response was stronger in IMIs eliminated spontaneously, except the concentration of TNF-α, which remained elevated in persistent infections. Lactoferrin (Lf) is a component of the humoral defence of the host and is present at low concentrations in the milk. The concentration of Lf in milk is high during the dry period, in colostrum, and in mastitic milk. The effect of an inherent, high concentration of Lf in the milk on experimental IMI induced with S. chromogenes was studied in transgenic cows that expressed recombinant human Lf in their milk. Human Lf did not prevent S. chromogenes IMI, but the host response was milder in transgenic cows than in normal cows, and the former eliminated infection faster. Biofilm production has been suggested to promote persistence of IMI. Phenotypic biofilm formation and slime producing ability of CNS isolates from bovine mastitis was investigated in vitro. One-third of mastitis isolates produced biofilm. Slime production was less frequent for isolates of the most common mastitis causing species S. chromogenes and S. simulans compared with S. epidermidis. No association was found between the phenotypic ability to form biofilm and the persistence of IMI or severity of mastitis. Slime production was associated with persistent infections, but only 8% of isolates produced slime.