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Mishra S.,Banaras Hindu University | Bhargava P.,Birla Institute of Scientific Research | Adhikary S.P.,Visva Bharati University | Pradeep A.,University of Kentucky | Rai L.C.,Banaras Hindu University
Protoplasma | Year: 2014

The classification of order Nostocales (Cyanobacteria) and inter relationships of morphologically similar taxa is still debatable due to ever changing morphological features. No attempt has been made to improve the morphological taxonomy despite the fact that it is the morphology that represents the totality of genes. To test the validity of morphological taxonomy and fine tune the phylogenetic relationships within the order Nostocales a new weighted morphology approach was applied by using 76 isolates and their 16S rRNA gene sequences. Further, the study was extended with morphological data set of the remaining 232 taxa for which no molecular data are yet available. Trichome aggregation, heterocyst shape, and akinete shape are suggested as important and stable features for identification. At 30 % weight assignment to the selected morphological characters, morphological taxonomy found 36 % compatible with 16S tree. Adding weight to the morphological characters considerably improved the congruence between the morphology and 16S rRNA-based phylogenetic trees of the order Nostocales. When the weighting procedure was extended to all the Nostocalean members irrespective of molecular data availability, it was found that Nostoc sphaericum and Nostoc microscopicum closely assembled in a single clade. Closer arrangement of Aulosira and Nodularia represent the subfamily aulosirae (Bornet and Flahault Ann Sci Nat Bot 7:223–224, 1888) while taxonomic affiliation of Cylindrospermum with Nostoc, Anabaena, and Raphidiopsis representing the subfamily anabaenae (Bornet and Flahault Ann Sci Nat Bot 7:223–224, 1888) was resolved. © 2014, Springer-Verlag Wien. Source

Lalwani S.,Birla Institute of Scientific Research | Lalwani S.,Malaviya National Institute of Technology, Jaipur | Kumar R.,Malaviya National Institute of Technology, Jaipur | Gupta N.,Malaviya National Institute of Technology, Jaipur
Swarm and Evolutionary Computation | Year: 2016

This paper introduces a set-basedtwo-level particle swarm optimization algorithm (TL-PSOfold) with multiple swarmsfor finding secondary structure of RNA with prediction accuracy. First objectiveis concerned with maximizing number of stacked loops at hydrogen bond, whereas, second objective deals with minimum free energy (MFE) at standard nearest neighbor database (NNDB). First level of the algorithm works on theen tire search space for the best solution of each swarm, where as, these cond level worksatthe gbest solution of each swarm.The set based PSO approach has been applied at both levels to represent and up date these to for dered pairs of the folded RNA sequence. Improved weight parameters chemes with mutation operators are implemented for better convergence and to over come the stagnation problem. Bi-objectives nature of TL-PSO fold enables the algorithm to achieve maximum matching pairs as well as optimum structure a trespective levels. The performance of TL-PSO fold is compared with a family of PSO based a lgorithmsi. e. Helix PSO v1, Helix PSO v2, PSO fold, Set PSO,IPSO, FPSO, popular secondary structure prediction software RNA fold, mfold and other metaheuristics RNA-Predict, SARNA-Predict at the criteria of sensitivity, specificity and F-measure. Simulation results for TL-PSO fold show that it yield shigher prediction accuracy than all the compared approaches. The claimis supported by the non-parametric statistical significance testing using Kruskala-Wallistest followed by post-hoc analysis. © 2015 Elsevier B.V. All rights reserved. Source

Ghosh P.,Birla Institute of Scientific Research
Proceedings of the Indian National Science Academy | Year: 2015

The second generation biofuel technologies are evolving rapidly to provide solutions for the partial replacement of fossil fuels. Both bioethanol and biodiesel have great potential in India. Both the technologies, however, have to overcome various bottlenecks before they become commercial technologies. In this regard, several critical questions, besides science and technology, need to be resolved. This will require new ways of thinking about agriculture, energy infrastructure and rural economic development. © Printed in India. Source

Dadheech P.K.,Government of Rajasthan | Dadheech P.K.,Leibniz Institute of Freshwater Ecology and Inland Fisheries | Abed R.M.M.,Sultan Qaboos University | Mahmoud H.,Kuwait University | And 2 more authors.
Phycologia | Year: 2012

Four new cyanobacterial strains isolated from biological desert crusts in Thar Desert, India were characterized using a polyphasic approach. The strains were designated to two mophotypes, but all strains exhibited identical 16S ribosomal RNA (rRNA) gene sequences. On the basis of 16S rRNA phylogenetic reconstruction, the strains belonged to the Oscillatoriales order and formed a coherent cluster in the phylogenetic tree, with more than 5% sequence divergence to the closest relative belonging to a species of Microcoleus Desmazières ex Gomont. Our strains were different from the genus Microcoleus in phenotypic characters such as organization of thallus, trichome width, cell shape, gas vesicle, thylakoid arrangement and habitat. Although the strains shared some morphological similarities with members of Phormidiaceae, they differed in thylakoid pattern and 16S rRNA gene sequences. The type strain (PD2001/TDC17T) grew up to 45°C with optimum growth between 30 and 35°C. The sharp decrease in growth of that strain at 1% salinity indicates its sensitivity to salts. Phylogenetic, morphological, ultrastuctural and physiological analyses demonstrated that the investigated strains represented a novel cyanobacterial genus, for which the name Desertifilum tharense gen. et sp. nov. is proposed, with PD2001/TDC17T (5 CCAP 1420/4T 5 BISR/CYANO/61T) as the type strain. Source

Lalwani S.,Birla Institute of Scientific Research | Lalwani S.,Malaviya National Institute of Technology, Jaipur | Kumar R.,Malaviya National Institute of Technology, Jaipur | Gupta N.,Malaviya National Institute of Technology, Jaipur
Swarm and Evolutionary Computation | Year: 2015

A two-level particle swarm optimization (TL-PSO) algorithm is proposed for training stochastic context-sensitive hidden Markov model (cs-HMM), that addresses a thrust area of bioinformatics i.e. structural alignment of pseudoknotted non-coding RNAs (ncRNAs). Due to the well-conserved sequences and corresponding secondary structures of ncRNAs, the structural information becomes imperative for performing their alignments. Proposed approach is unique in the sense: it is the first idea so far which works on optimization of the model length; also it is the first swarm intelligence technique that is proposed for training csHMM. The two-level strategy with training and cross training sets helps in increasing the diversity of the particles so as to avoid trapping in local optima, yields more accurate estimation parameters, preserves the structure of the model and provides the best compression from the model. TL-PSO yields a trained stochastic model with position-dependent probabilities that achieves high prediction ratios than the compared non-stochastic scoring matrix based csHMM approaches. TL-PSO is also tested solely for sequence alignment of proteins, by training the conventional HMMs. TLPSO-HMM produces an effective framework for sequence alignment in terms of alignment quality and prediction accuracy than the competitive state-of-the-art and family of PSO based algorithms. Conjointly, TLPSO-csHMM finds higher prediction measures than competitive RNA structural alignment techniques for pseudoknotted and non-pseudoknotted RNA structures of diverse complexities. © 2014 Elsevier B.V. All rights reserved. Source

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