Studies on isolation and molecular characterization of plasmids from Vibrios

While the seriousness of the problem of antibiotic resistance is now recognized, the complex web of resistance linking humans, animals, and the environment is getting realized. More often, antibiotics are used as a preventive measure against diseases. Antibiotic use for agriculture leads to the increased resistance in the environment since antibiotics are inevitable element during agriculture/aquaculture and antibiotic residues are excreted as waste that is frequently spread onto farmland as organic fertilizer. Fecal bacteria survive long periods in the environment and spread through runoff into groundwater, rivers, and marine ecosystems.However, horizontal gene transfer occurs in the animals and guts of humans and in a variety of ecosystems, creating a pool of resistance in the rice fields and open waters. Even if people are not in direct contact with resistant disease through food animals, there are chances of contact with resistant fecal pathogens from the environment. Additionally, pathogens that are autochthonous to the environment can acquire resistance genes from the environment. Our study revealed that autochthonous , bacteria Vibrio spp gained antibiotic resistance in the environment. Further, it was evident that horizontal gene transfer occurs in Vibrio by means of plasmids, which further augments the gravity of the problem. Non-pathogenic bacteria may also acquire resistance genes and serve as a continuing source of resistance for other bacteria, both in the environment, and in the human gut. As the effectiveness of antibiotics for medical applications decline, the indiscriminate use of in aquaculture and in humans can have disastrous conditions in future due to horizontal gene transfer and the spread of resistant organisms: We must recognize and deal with the threat posed by overuse of antibiotics.

Wave-vector dependence of spin and density multipole excitations in quantum dots

We have employed time-dependent local-spin density-functional theory to analyze the multipole spin and charge density excitations in GaAs-AlxGa1-xAs quantum dots. The on-plane transferred momentum degree of freedom has been taken into account, and the wave-vector dependence of the excitations is discussed. In agreement with previous experiments, we have found that the energies of these modes do not depend on the transferred wave vector, although their intensities do. Comparison with a recent resonant Raman scattering experiment [C. Schüller et al., Phys. Rev. Lett. 80, 2673 (1998)] is made. This allows us to identify the angular momentum of several of the observed modes as well as to reproduce their energies

Unconstrained Handwritten Malayalam Character Recognition using Wavelet Transform and Support vector Machine Classifier

This paper presents the application of wavelet processing in the domain of handwritten character recognition. To attain high recognition rate, robust feature extractors and powerful classifiers that are invariant to degree of variability of human writing are needed. The proposed scheme consists of two stages: a feature extraction stage, which is based on Haar wavelet transform and a classification stage that uses support vector machine classifier. Experimental results show that the proposed method is effective

Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

Characterization and applications of two protease enzymes obtained by culture dependent and independent approaches from mangrove sediments

Soil community genomics or metagenomics is employed in this study to analyze the evolutionary related - ness of mangrove microbial community. The metagenomic DNA was isolated from mangrove sediment and 16SrDNA was amplified using universal primers. The amplicons were ligated into pTZ57R/T cloning vector and transformed onto E. coli JM109 host cells. The recombinant plasmids were isolated from positive clones and the insert was confirmed by its reamplification. The amplicons were subjected to Amplified Ribosomal DNA Restriction Analysis (ARDRA) using three different tetra cutter restriction enzymes namely Sau3A1, Hha1 and HpaII. The 16SrDNA insert were sequenced and their identity was determined. The sequences were submitted to NCBI database and accession numbers obtained. The phylo - genetic tree was constructed based on Neighbor-Joining technique. Clones belonged to two major phyla of the bacterial domain, namely Firmicutes and Proteobacteria, with members of Firmicutes predominating. The microbial diversity of the mangrove sediment was explored in this manner.

Nuclear organisation and epigenetic regulation of gene expression in Dictyostelium discoideum

A series of vectors for the over-expression of tagged proteins in Dictyostelium were designed, constructed and tested. These vectors allow the addition of an N- or C-terminal tag (GFP, RFP, 3xFLAG, 3xHA, 6xMYC and TAP) with an optimized polylinker sequence and no additional amino acid residues at the N or C terminus. Different selectable markers (Blasticidin and gentamicin) are available as well as an extra chromosomal version; these allow copy number and thus expression level to be controlled, as well as allowing for more options with regard to complementation, co- and super-transformation. Finally, the vectors share standardized cloning sites, allowing a gene of interest to be easily transfered between the different versions of the vectors as experimental requirements evolve. The organisation and dynamics of the Dictyostelium nucleus during the cell cycle was investigated. The centromeric histone H3 (CenH3) variant serves to target the kinetochore to the centromeres and thus ensures correct chromosome segregation during mitosis and meiosis. A number of Dictyostelium histone H3-domain containing proteins as GFP-tagged fusions were expressed and it was found that one of them functions as CenH3 in this species. Like CenH3 from some other species, Dictyostelium CenH3 has an extended N-terminal domain with no similarity to any other known proteins. The targeting domain, comprising α-helix 2 and loop 1 of the histone fold is required for targeting CenH3 to centromeres. Compared to the targeting domain of other known and putative CenH3 species, Dictyostelium CenH3 has a shorter loop 1 region. The localisation of a variety of histone modifications and histone modifying enzymes was examined. Using fluorescence in situ hybridisation (FISH) and CenH3 chromatin-immunoprecipitation (ChIP) it was shown that the six telocentric centromeres contain all of the DIRS-1 and most of the DDT-A and skipper transposons. During interphase the centromeres remain attached to the centrosome resulting in a single CenH3 cluster which also contains the putative histone H3K9 methyltransferase SuvA, H3K9me3 and HP1 (heterochromatin protein 1). Except for the centromere cluster and a number of small foci at the nuclear periphery opposite the centromeres, the rest of the nucleus is largely devoid of transposons and heterochromatin associated histone modifications. At least some of the small foci correspond to the distal telomeres, suggesting that the chromosomes are organised in a Rabl-like manner. It was found that in contrast to metazoans, loading of CenH3 onto Dictyostelium centromeres occurs in late G2 phase. Transformation of Dictyostelium with vectors carrying the G418 resistance cassette typically results in the vector integrating into the genome in one or a few tandem arrays of approximately a hundred copies. In contrast, plasmids containing a Blasticidin resistance cassette integrate as single or a few copies. The behaviour of transgenes in the nucleus was examined by FISH, and it was found that low copy transgenes show apparently random distribution within the nucleus, while transgenes with more than approximately 10 copies cluster at or immediately adjacent to the centromeres in interphase cells regardless of the actual integration site along the chromosome. During mitosis the transgenes show centromere-like behaviour, and ChIP experiments show that transgenes contain the heterochromatin marker H3K9me2 and the centromeric histone variant H3v1. This clustering, and centromere-like behaviour was not observed on extrachromosomal transgenes, nor on a line where the transgene had integrated into the extrachromosomal rDNA palindrome. This suggests that it is the repetitive nature of the transgenes that causes the centromere-like behaviour. A Dictyostelium homolog of DET1, a protein largely restricted to multicellular eukaryotes where it has a role in developmental regulation was identified. As in other species Dictyostelium DET1 is nuclear localised. In ChIP experiments DET1 was found to bind the promoters of a number of developmentally regulated loci. In contrast to other species where it is an essential protein, loss of DET1 is not lethal in Dictyostelium, although viability is greatly reduced. Loss of DET1 results in delayed and abnormal development with enlarged aggregation territories. Mutant slugs displayed apparent cell type patterning with a bias towards pre-stalk cell types.

A Vector Signal Processing Approach to Color

Surface (Lambertain) color is a useful visual cue for analyzing material composition of scenes. This thesis adopts a signal processing approach to color vision. It represents color images as fields of 3D vectors, from which we extract region and boundary information. The first problem we face is one of secondary imaging effects that makes image color different from surface color. We demonstrate a simple but effective polarization based technique that corrects for these effects. We then propose a systematic approach of scalarizing color, that allows us to augment classical image processing tools and concepts for multi-dimensional color signals.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

Vector-Based Integration of Local and Long-Range Information in Visual Cortex

Integration of inputs by cortical neurons provides the basis for the complex information processing performed in the cerebral cortex. Here, we propose a new analytic framework for understanding integration within cortical neuronal receptive fields. Based on the synaptic organization of cortex, we argue that neuronal integration is a systems--level process better studied in terms of local cortical circuitry than at the level of single neurons, and we present a method for constructing self-contained modules which capture (nonlinear) local circuit interactions. In this framework, receptive field elements naturally have dual (rather than the traditional unitary influence since they drive both excitatory and inhibitory cortical neurons. This vector-based analysis, in contrast to scalarsapproaches, greatly simplifies integration by permitting linear summation of inputs from both "classical" and "extraclassical" receptive field regions. We illustrate this by explaining two complex visual cortical phenomena, which are incompatible with scalar notions of neuronal integration.

Improving Multiclass Text Classification with the Support Vector Machine

We compare Naive Bayes and Support Vector Machines on the task of multiclass text classification. Using a variety of approaches to combine the underlying binary classifiers, we find that SVMs substantially outperform Naive Bayes. We present full multiclass results on two well-known text data sets, including the lowest error to date on both data sets. We develop a new indicator of binary performance to show that the SVM's lower multiclass error is a result of its improved binary performance. Furthermore, we demonstrate and explore the surprising result that one-vs-all classification performs favorably compared to other approaches even though it has no error-correcting properties.

Properties of Support Vector Machines

Support Vector Machines (SVMs) perform pattern recognition between two point classes by finding a decision surface determined by certain points of the training set, termed Support Vectors (SV). This surface, which in some feature space of possibly infinite dimension can be regarded as a hyperplane, is obtained from the solution of a problem of quadratic programming that depends on a regularization parameter. In this paper we study some mathematical properties of support vectors and show that the decision surface can be written as the sum of two orthogonal terms, the first depending only on the margin vectors (which are SVs lying on the margin), the second proportional to the regularization parameter. For almost all values of the parameter, this enables us to predict how the decision surface varies for small parameter changes. In the special but important case of feature space of finite dimension m, we also show that there are at most m+1 margin vectors and observe that m+1 SVs are usually sufficient to fully determine the decision surface. For relatively small m this latter result leads to a consistent reduction of the SV number.

Notes on PCA, Regularization, Sparsity and Support Vector Machines

We derive a new representation for a function as a linear combination of local correlation kernels at optimal sparse locations and discuss its relation to PCA, regularization, sparsity principles and Support Vector Machines. We first review previous results for the approximation of a function from discrete data (Girosi, 1998) in the context of Vapnik"s feature space and dual representation (Vapnik, 1995). We apply them to show 1) that a standard regularization functional with a stabilizer defined in terms of the correlation function induces a regression function in the span of the feature space of classical Principal Components and 2) that there exist a dual representations of the regression function in terms of a regularization network with a kernel equal to a generalized correlation function. We then describe the main observation of the paper: the dual representation in terms of the correlation function can be sparsified using the Support Vector Machines (Vapnik, 1982) technique and this operation is equivalent to sparsify a large dictionary of basis functions adapted to the task, using a variation of Basis Pursuit De-Noising (Chen, Donoho and Saunders, 1995; see also related work by Donahue and Geiger, 1994; Olshausen and Field, 1995; Lewicki and Sejnowski, 1998). In addition to extending the close relations between regularization, Support Vector Machines and sparsity, our work also illuminates and formalizes the LFA concept of Penev and Atick (1996). We discuss the relation between our results, which are about regression, and the different problem of pattern classification.

From Regression to Classification in Support Vector Machines

We study the relation between support vector machines (SVMs) for regression (SVMR) and SVM for classification (SVMC). We show that for a given SVMC solution there exists a SVMR solution which is equivalent for a certain choice of the parameters. In particular our result is that for $epsilon$ sufficiently close to one, the optimal hyperplane and threshold for the SVMC problem with regularization parameter C_c are equal to (1-epsilon)^{- 1} times the optimal hyperplane and threshold for SVMR with regularization parameter C_r = (1-epsilon)C_c. A direct consequence of this result is that SVMC can be seen as a special case of SVMR.

On the Noise Model of Support Vector Machine Regression

Support Vector Machines Regression (SVMR) is a regression technique which has been recently introduced by V. Vapnik and his collaborators (Vapnik, 1995; Vapnik, Golowich and Smola, 1996). In SVMR the goodness of fit is measured not by the usual quadratic loss function (the mean square error), but by a different loss function called Vapnik"s $epsilon$- insensitive loss function, which is similar to the "robust" loss functions introduced by Huber (Huber, 1981). The quadratic loss function is well justified under the assumption of Gaussian additive noise. However, the noise model underlying the choice of Vapnik's loss function is less clear. In this paper the use of Vapnik's loss function is shown to be equivalent to a model of additive and Gaussian noise, where the variance and mean of the Gaussian are random variables. The probability distributions for the variance and mean will be stated explicitly. While this work is presented in the framework of SVMR, it can be extended to justify non-quadratic loss functions in any Maximum Likelihood or Maximum A Posteriori approach. It applies not only to Vapnik's loss function, but to a much broader class of loss functions.

A Unified Framework for Regularization Networks and Support Vector Machines

Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples -- in particular the regression problem of approximating a multivariate function from sparse data. We present both formulations in a unified framework, namely in the context of Vapnik's theory of statistical learning which provides a general foundation for the learning problem, combining functional analysis and statistics.