Purification and characterisation of Vibriophage isolated from Mangalavanam mangrove

The temperate, filamentous phage ФMV -5 isolated from Mangalavanam mangrove of Kochi, using the environmental strain of Vibrio sp. MV-5 shares many similar properties with other marine phage isolates, while also remaining unique. The study has revealed that the interaction of temperate phages and the microbial population in the marine environment may contribute significantly to microbial genetic diversity and composition by conversion and transduction and which requires greater study.Prophages contribute a substantial share of the mobile DNA of their bacterial hosts and seem to influence the short-term evolution of pathogenic bacteria. Automated methods for systematic investigation of prophages and other mobile DNA elements in the available bacterial genome sequences will be necessary to understand their role in bacterial genome evolution. In the past, phages were mainly investigated as the simplest model systems in molecular biology. Now it is increasingly realized that phage research will be instrumental in the understanding of bacterial abundance in the environment. One can predict that phage research will impact diverse areas such as geochemistry and medicine. Success will largely depend on integrative multidisciplinary approaches in this field. Clearly, further studies are required to understand how vibriophages interact with Vibrios to promote this organism's acquisition of the critical genes which alter its virulence or adaptation to its environmental niche.It is evident from this study and comparison with those reports cited above that vibriophage ФMV-5 is a previously unreported bacteriophage. It is recommended that the minimum requirement for reporting a new phage should be novel morphological markers and a description of host range, both of which have been achieved in this study.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis