DNA Interaction and Catalytic Activity Studies of Some New Transition Metal Complexes of Schiff Bases Derived from Quinoxaline

In this thesis we report the synthsis and characterisation of new transition metal complexes of Pd(II),Cu(II),Ru(II) and Ir(III) of Schiff bases derived from quinoxaline-2-carboxaldehyde/3-hydroxyquinoxaline-2-carboxaldehyde and 5-aminoindazole.6-aminoindazole or 8-aminoquinoline.The complexes have been characterised by spectral and analytical data.Pd(II) and Cu(II) form square planar complexes and Ru(III) and Ir(III) form ctahedral complexes with these Schiff bases.The DNA binding properties of theses synthesised complexes have been studied by various methods including electronic absoption spectroscopy,cyclic voltammetry,different pulse voltammetry and circular dichroism spectra were used.Gel electrophoresis experiments were also performed to investigate the DNA cleavage of theses complexes.Furthermore Ru(III) and Ir(III) complexes find application as oxidation and hydogenation catalsts. The studies on catalytic activities has been presented.The metal complexes presented in this thesis assure significance as they contribute to the development of new DNA binding agents and antibacterial and anticancer drugs.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

Personnel Management Practices in the Kerala based Scheduled commercial Banks

The thesis entitled Personnel Management Practices in the Kerala-Based Scheduled Commercial Banks. Personnel management function is of cardinal importance, requiring a sophisticated and scientific approach. In a labour-intensive, service industry like banking. Productivity and ultimate profitability of the entire organization depend considerably on the effectiveness with which personnel management function is executed; and the prudence with which personnel problems are handle. The main objectives of the study are to understand the current status of personnel management functions in the banks and to evaluate the practices in the light of the principles and theories of personnel management so as to identify the strengths and weaknesses. The universe of this study is the eight Scheduled Commercial Banks based in Kerala. The major limitation of the study is that as State Bank of Travancore, the lone public sector bank based in Kerala did not grant permission for collection of data, this study had to be confined to private sector banks only. Almost the entire data used for this study are primary and were collected from the files and other records or the concerned banks. This report has chapters dealing with the functional areas of personnel management such as determination of human resource requirements, recruitment and selection, training and development, performance appraisal, promotions and compensation. Findings reveal that the practice of personnel management in the Kerala-based private sector scheduled commercial banks has not gained a degree of sophistication compatible with its role in modern business management.

A Compiler Integrated Assistance for Optimum Data Allocation in Banked Memory Embedded Processors

Bank switching in embedded processors having partitioned memory architecture results in code size as well as run time overhead. An algorithm and its application to assist the compiler in eliminating the redundant bank switching codes introduced and deciding the optimum data allocation to banked memory is presented in this work. A relation matrix formed for the memory bank state transition corresponding to each bank selection instruction is used for the detection of redundant codes. Data allocation to memory is done by considering all possible permutation of memory banks and combination of data. The compiler output corresponding to each data mapping scheme is subjected to a static machine code analysis which identifies the one with minimum number of bank switching codes. Even though the method is compiler independent, the algorithm utilizes certain architectural features of the target processor. A prototype based on PIC 16F87X microcontrollers is described. This method scales well into larger number of memory blocks and other architectures so that high performance compilers can integrate this technique for efficient code generation. The technique is illustrated with an example