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.

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.

Disc electrophoresis of sorghum seed proteins in polyacrylamide gels

Electrophoretic analyses of sorghum flour protein by disc electrophoresis in polyacrylamide gels containing urea have been described. The albumin, globulin, and prolamin fractions of sorghum endosperm meal have been investigated, using pH 9.5 and 4.3 gel systems with four different buffers. Highly complex patterns were observed for all three protein fractions. It has been suggested that this method can provide a convenient tool for the analyses of seed proteins which are relatively insoluble in aqueous buffers.

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.

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.

Classification of Protein Kinases on the Basis of Both Kinase and Non-Kinase Regions

Background: Protein phosphorylation is a generic way to regulate signal transduction pathways in all kingdoms of life. In many organisms, it is achieved by the large family of Ser/Thr/Tyr protein kinases which are traditionally classified into groups and subfamilies on the basis of the amino acid sequence of their catalytic domains. Many protein kinases are multidomain in nature but the diversity of the accessory domains and their organization are usually not taken into account while classifying kinases into groups or subfamilies. Methodology: Here, we present an approach which considers amino acid sequences of complete gene products, in order to suggest refinements in sets of pre-classified sequences. The strategy is based on alignment-free similarity scores and iterative Area Under the Curve (AUC) computation. Similarity scores are computed by detecting common patterns between two sequences and scoring them using a substitution matrix, with a consistent normalization scheme. This allows us to handle full-length sequences, and implicitly takes into account domain diversity and domain shuffling. We quantitatively validate our approach on a subset of 212 human protein kinases. We then employ it on the complete repertoire of human protein kinases and suggest few qualitative refinements in the subfamily assignment stored in the KinG database, which is based on catalytic domains only. Based on our new measure, we delineate 37 cases of potential hybrid kinases: sequences for which classical classification based entirely on catalytic domains is inconsistent with the full-length similarity scores computed here, which implicitly consider multi-domain nature and regions outside the catalytic kinase domain. We also provide some examples of hybrid kinases of the protozoan parasite Entamoeba histolytica. Conclusions: The implicit consideration of multi-domain architectures is a valuable inclusion to complement other classification schemes. The proposed algorithm may also be employed to classify other families of enzymes with multidomain architecture.

Purification and characterization of three toxins and two agglutinins from Abrus precatorius seed by using lactamyl-Sepharose affinity chromatography

Three toxins, abrin-I, -II, and -III, and two agglutinins, APA-I and -II, were purified from the seeds of Abrus precatorius by lactamyl-Sepharose affinity chromatography followed by gel filtration and DEAE-Sephacel column chromatography. abrin-I did not bind on DEAE-Sephacel column chromatography and the bound abrin-II, abrin-III, APA-I, and APA-II were eluted with a sodium acetate gradient. The identity of each protein was established by sodium dodecylsulfate-polyacrylamide gel electrophoresis and isoelectric focusing. The relative molecular weights are abrin-I, 64,000; abrin-II and abrin-III, 63,000 each: APA-I, 130,000; and APA-II, 128,000. Isoelectric focusing revealed microheterogeneity due to the presence of isoforms in each protein. Toxicity and binding studies further confirmed the differences among the lectins. The time course of inhibition of protein synthesis in thymocytes by the toxins showed lag times of 78, 61, and 72 min with Ki's of 0.55, 0.99, and 0.74 ms−1 at a 0.63 nImage concentration of each of abrin-I, -II, and -III, respectively. A Scatchard plot obtained from the equilibrium measurement for the lectins binding to lactamyl-Sepharose beads showed nonlinearity, indicating a cooperative mode of binding which was not observed for APA-I binding to Sepharose 4B beads. Further, by the criterion of the isoelectric focusing profile, it was shown that the least toxic abrin-I and the highly toxic abrin-II isolated by lactamyl-Sepharose chromatography were not retained on a low-affinity Sepharose 4B matrix, which signifies the necessity of using a high-affinity matrix for the purification of the lectins.

Semi-Supervised Classification Using Sparse Gaussian Process Regression

Gaussian Processes (GPs) are promising Bayesian methods for classification and regression problems. They have also been used for semi-supervised learning tasks. In this paper, we propose a new algorithm for solving semi-supervised binary classification problem using sparse GP regression (GPR) models. It is closely related to semi-supervised learning based on support vector regression (SVR) and maximum margin clustering. The proposed algorithm is simple and easy to implement. It gives a sparse solution directly unlike the SVR based algorithm. Also, the hyperparameters are estimated easily without resorting to expensive cross-validation technique. Use of sparse GPR model helps in making the proposed algorithm scalable. Preliminary results on synthetic and real-world data sets demonstrate the efficacy of the new algorithm.

Sediment Volume Tests for Expansive Soil Identification and Classification

Equilibrium sediment volume tests are conducted on field soils to classify them based on their degree of expansivity and/or to predict the liquid limit of soils. The present technical paper examines different equilibrium sediment volume tests, critically evaluating each of them. It discusses the settling behavior of fine-grained soils during the soil sediment formation to evolve a rationale for conducting the latest version of equilibrium sediment volume test. Probable limitations of equilibrium sediment volume test and the possible solution to overcome the same have also been indicated.

A near-optimal initial seed value selection in K-means means algorithm using a genetic algorithm

The K-means algorithm for clustering is very much dependent on the initial seed values. We use a genetic algorithm to find a near-optimal partitioning of the given data set by selecting proper initial seed values in the K-means algorithm. Results obtained are very encouraging and in most of the cases, on data sets having well separated clusters, the proposed scheme reached a global minimum.

The 'neighbour effect' and its role in shaping the positional pattern of seed development in fruits: An illustration from the pods of Erythrina suberosa

Plants exhibit certain intra-fruit positional patterns in the development of seeds. These patterns have been generally interpreted to be a consequence of resource and fertilization gradients. However, such positional patterns might also be shaped by the 'neighbour effect', wherein formation and development of a seed at any position might positively or negatively influence those of other seeds in the neighbourhood. In this article, we examine the role of such neighbour effect in shaping the positional pattern of seeds in the pods of Erythrina suberosa. The results suggest the existence of a positive neighbour effect leading to a higher frequency of seeds in contiguous positions.

A classification of homogeneous operators in the Cowen-Douglas class

An explicit construction of all the homogeneous holomorphic Hermitian vector bundles over the unit disc D is given. It is shown that every such vector bundle is a direct sum of irreducible ones. Among these irreducible homogeneous holomorphic Hermitian vector bundles over D, the ones corresponding to operators in the Cowen-Douglas class B-n(D) are identified. The classification of homogeneous operators in B-n(D) is completed using an explicit realization of these operators. We also show how the homogeneous operators in B-n(D) split into similarity classes. (C) 2011 Elsevier Inc. All rights reserved.