Branch-branch connections in trees analogous to hyphal fusions in fungal colonies

the leopard tree Caesalpinia ferrea (Leguminosae) a native of eastern Brazil-some of the leader branches connect to and fuse with neighbouring branches of the same tree. The bridge initials project out as pegs or protuberances and apparently extend in a coordinated manner, connecting branches up to 4 ft apart. The fusion of two branches of the same tree implies intra-plant communication involving signaling factor(s). The bridges resemble fusions between hyphae in a fungal colony. Whereas hyphal fusions are common and the process is apparently completed in <1 h, branch fusions in C. ferrea tree are limited and a slow process, apparently requiring several months to years to complete. Branch fusions in C. ferrea are in accord with Claus Mattheck's analysis that tree branches actually seek contact rather than avoid contacts.

Inferring molecular function: contributions from functional linkages

In the current era of high-throughput sequencing and structure determination, functional annotation has become a bottleneck in biomedical science. Here, we show that automated inference of molecular function using functional linkages among genes increases the accuracy of functional assignments by >= 8% and enriches functional descriptions in >= 34% of top assignments. Furthermore, biochemical literature supports >80% of automated inferences for previously unannotated proteins. These results emphasize the benefit of incorporating functional linkages in protein annotation.

Urban satellite image classification using biologically inspired techniques

This paper focuses on optimisation algorithms inspired by swarm intelligence for satellite image classification from high resolution satellite multi- spectral images. Amongst the multiple benefits and uses of remote sensing, one of the most important has been its use in solving the problem of land cover mapping. As the frontiers of space technology advance, the knowledge derived from the satellite data has also grown in sophistication. Image classification forms the core of the solution to the land cover mapping problem. No single classifier can prove to satisfactorily classify all the basic land cover classes of an urban region. In both supervised and unsupervised classification methods, the evolutionary algorithms are not exploited to their full potential. This work tackles the land map covering by Ant Colony Optimisation (ACO) and Particle Swarm Optimisation (PSO) which are arguably the most popular algorithms in this category. We present the results of classification techniques using swarm intelligence for the problem of land cover mapping for an urban region. The high resolution Quick-bird data has been used for the experiments.

Functional characterization of lysophosphatidic acid phosphatase from Arabidopsis thaliana

Lysophosphatidic acid (LPA) acts as a signaling molecule that regulates diverse cellular processes and it can rapidly be metabolized by phosphatase and acyltransferase LPA phosphatase gene has not been identified and characterized in plants so far The BLAST search revealed that the At3g03520 is similar to phospholipase family. and distantly related to bacterial phosphatases The conserved motif. (J)4XXXNXSFD, was identified in both At3g03520 like phospholipases and acid phosphatases In silico expression analysis of At3g03520 revealed a high expression during phosphate starvation and abiotic stresses. This gene was overexpressed in Escherichia coli and shown to posses LPA specific phosphatase activity These results Suggest that this gene possibly plays a role in signal transduction and storage lipid synthesis.

The chemical ecology of seed dispersal in monoecious and dioecious figs

In the nursery pollination system of figs (Ficus, Moraceae), flower-bearing receptacles called syconia breed pollinating wasps and are units of both pollination and seed dispersal. Pollinators and mammalian seed dispersers are attracted to syconia by volatile organic compounds (VOCs). In monoecious figs, syconia produce both wasps and seeds, while in (gyno)dioecious figs, male (gall) fig trees produce wasps and female (seed) fig trees produce seeds. VOCs were collected using dynamic headspace adsorption methods on freshly collected figs from different trees using Super Q® collection traps. VOC profiles were determined using gas chromatography–mass spectrometry (GC–MS).The VOC profile of receptive and dispersal phase figs were clearly different only in the dioecious mammal-dispersed Ficus hispida but not in dioecious bird-dispersed F. exasperata and monoecious bird-dispersed F. tsjahela. The VOC profile of dispersal phase female figs was clearly different from that of male figs only in F. hispida but not in F. exasperata, as predicted from the phenology of syconium production which only in F. hispida overlaps between male and female trees. Greater difference in VOC profile in F. hispida might ensure preferential removal of seed figs by dispersal agents when gall figs are simultaneously available.The VOC profile of only mammal-dispersed female figs of F. hispida had high levels of fatty acid derivatives such as amyl-acetates and 2-heptanone, while monoterpenes, sesquiterpenes and shikimic acid derivatives were predominant in the other syconial types. A bird- and mammal-repellent compound methyl anthranilate occurred only in gall figs of both dioecious species, as expected, since gall figs containing wasp pollinators should not be consumed by dispersal agents.

Evolution of domain combinations in protein kinases and its implications for functional diversity

Protein kinases phosphorylating Ser/Thr/Tyr residues in several cellular proteins exert tight control over their biological functions. They constitute the largest protein family in most eukaryotic species. Protein kinases classified based on sequence similarity in their catalytic domains, cluster into subfamilies, which share gross functional properties. Many protein kinases are associated or tethered covalently to domains that serve as adapter or regulatory modules,naiding substrate recruitment, specificity, and also serve as scaffolds. Hence the modular organisation of the protein kinases serves as guidelines to their functional and molecular properties. Analysis of genomic repertoires of protein kinases in eukaryotes have revealed wide spectrum of domain organisation across various subfamilies of kinases. Occurrence of organism-specific novel domain combinations suggests functional diversity achieved by protein kinases in order to regulate variety of biological processes. In addition, domain architecture of protein kinases revealed existence of hybrid protein kinase subfamilies and their emerging roles in the signaling of eukaryotic organisms. In this review we discuss the repertoire of non-kinase domains tethered to multi-domain kinases in the metazoans. Similarities and differences in the domain architectures of protein kinases in these organisms indicate conserved and unique features that are critical to functional specialization. (C) 2009 Elsevier Ltd. All rights reserved.

Existence and approximations of solutions to some fractional order functional integral equations

In this paper we shall study a fractional order functional integral equation. In the first part of the paper, we proved the existence and uniqueness of mile and global solutions in a Banach space. In the second part of the paper, we used the analytic semigroups theory oflinear operators and the fixed point method to establish the existence, uniqueness and convergence of approximate solutions of the given problem in a separable Hilbert space. We also proved the existence and convergence of Faedo-Galerkin approximate solution to the given problem. Finally, we give an example.

Charge density based classification of intermolecular interactions in molecular crystals

Evaluation of intermolecular interactions in terms of both experimental and theoretical charge density analyses has produced a unified picture with which to classify strong and weak hydrogen bonds, along with van der Waals interactions, into three regions.

A Framework for Classification of Prokaryotic Protein Kinases

Background:Overwhelming majority of the Serine/Threonine protein kinases identified by gleaning archaeal and eubacterial genomes could not be classified into any of the well known Hanks and Hunter subfamilies of protein kinases. This is owing to the development of Hanks and Hunter classification scheme based on eukaryotic protein kinases which are highly divergent from their prokaryotic homologues. A large dataset of prokaryotic Serine/Threonine protein kinases recognized from genomes of prokaryotes have been used to develop a classification framework for prokaryotic Ser/Thr protein kinases. Methodology/Principal Findings: We have used traditional sequence alignment and phylogenetic approaches and clustered the prokaryotic kinases which represent 72 subfamilies with at least 4 members in each. Such a clustering enables classification of prokaryotic Ser/Thr kinases and it can be used as a framework to classify newly identified prokaryotic Ser/Thr kinases. After series of searches in a comprehensive sequence database we recognized that 38 subfamilies of prokaryotic protein kinases are associated to a specific taxonomic level. For example 4, 6 and 3 subfamilies have been identified that are currently specific to phylum proteobacteria, cyanobacteria and actinobacteria respectively. Similarly subfamilies which are specific to an order, sub-order, class, family and genus have also been identified. In addition to these, we also identify organism-diverse subfamilies. Members of these clusters are from organisms of different taxonomic levels, such as archaea, bacteria, eukaryotes and viruses.Conclusion/Significance: Interestingly, occurrence of several taxonomic level specific subfamilies of prokaryotic kinases contrasts with classification of eukaryotic protein kinases in which most of the popular subfamilies of eukaryotic protein kinases occur diversely in several eukaryotes. Many prokaryotic Ser/Thr kinases exhibit a wide variety of modular organization which indicates a degree of complexity and protein-protein interactions in the signaling pathways in these microbes.

X-ray Crystallographic Analysis of the Complexes of Enoyl Acyl Carrier Protein Reductase of Plasmodium falciparum with Triclosan Variants to Elucidate the Importance of Different Functional Groups in Enzyme Inhibition

Triclosan, a well-known inhibitor of Enoyl Acyl Carrier Protein Reductase (ENR) from several pathogenic organisms, is a promising lead compound to design effective drugs. We have solved the X-ray crystal structures of Plasmodium falciparum ENR in complex with triclosan variants having different substituted and unsubstituted groups at different key functional locations. The structures revealed that 4 and 2' substituted compounds have more interactions with the protein, cofactor, and solvents when compared with triclosan. New water molecules were found to interact with some of these inhibitors. Substitution at the 2' position of triclosan caused the relocation of a conserved water molecule, leading to an additional hydrogen bond with the inhibitor. This observation can help in conserved water-based inhibitor design. 2' and 4' unsubstituted compounds showed a movement away from the hydrophobic pocket to compensate for the interactions made by the halogen groups of triclosan. This compound also makes additional interactions with the protein and cofactor which compensate for the lost interactions due to the unsubstitution at 2' and 4'. In cell culture, this inhibitor shows less potency, which indicates that the chlorines at 2' and 4' positions increase the ability of the inhibitor to cross multilayered membranes. This knowledge helps us to modify the different functional groups of triclosan to get more potent inhibitors. (C) 2010 IUBMB IUBMB Life, 62(6): 467-476.

A Geometric Algorithm for Learning Oblique Decision Trees

In this paper we present a novel algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess goodness of hyperplanes at each node. These impurity measures do not properly capture the geometric structures in the data. Motivated by this, our algorithm uses a strategy, based on some recent variants of SVM, to assess the hyperplanes in such a way that the geometric structure in the data is taken into account. We show through empirical studies that our method is effective.

Comparison of Multiclass SVM Classification Methods to Use in a Supportive System for Distance Relay Coordination

This paper aims at evaluating the methods of multiclass support vector machines (SVMs) for effective use in distance relay coordination. Also, it describes a strategy of supportive systems to aid the conventional protection philosophy in combating situations where protection systems have maloperated and/or information is missing and provide selective and secure coordinations. SVMs have considerable potential as zone classifiers of distance relay coordination. This typically requires a multiclass SVM classifier to effectively analyze/build the underlying concept between reach of different zones and the apparent impedance trajectory during fault. Several methods have been proposed for multiclass classification where typically several binary SVM classifiers are combined together. Some authors have extended binary SVM classification to one-step single optimization operation considering all classes at once. In this paper, one-step multiclass classification, one-against-all, and one-against-one multiclass methods are compared for their performance with respect to accuracy, number of iterations, number of support vectors, training, and testing time. The performance analysis of these three methods is presented on three data sets belonging to training and testing patterns of three supportive systems for a region and part of a network, which is an equivalent 526-bus system of the practical Indian Western grid.

Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network

In this paper. we propose a novel method using wavelets as input to neural network self-organizing maps and support vector machine for classification of magnetic resonance (MR) images of the human brain. The proposed method classifies MR brain images as either normal or abnormal. We have tested the proposed approach using a dataset of 52 MR brain images. Good classification percentage of more than 94% was achieved using the neural network self-organizing maps (SOM) and 98% front support vector machine. We observed that the classification rate is high for a Support vector machine classifier compared to self-organizing map-based approach.