Studies on the prevalence of algal blooms along Kerala coast, India

Algal blooms are naturally occurring phenomena in the aquatic environment. These blooms cause mass mortalities of wild and farmed fish and shellfish, human intoxications which sometimes result in death, alteration of marine trophic structure through adverse effects on larvae and other life history stages of commercially important species and death of marine animals. Occurrences of harmful algal blooms and associated mortality have been reported along the coastal waters of India since the early period of the last century. The present study was taken up to study the dynamics of major phytoplankton blooms, which occur along the Kerala coast. The results of quantitative and qualitative analysis of phytoplankton in the coastal waters of Vizhinjam and Chombala, their species diversity and community structure is presented and the major algal blooms recorded along the coast of Kerala during the study period is described and their occurrence is related to the hydrographic and meteorological variations. There is a clear evident from these works in the Indian region that the fishes avoid areas where these harmful algae bloom, either due to the toxicity or due to some irritant property of the chemicals secreted by the algae. Taxonomic diversity studies indicated a change in the community structure of commercial finfishes, crustaceans and molluscs due to the bloom of C.marina and funnel plots indicated the deviation in taxonomic distinctness during the bloom period from theoretical mean for the region.

Studies on the digestive enzymes of the Indian white prawn Penaeus indicus H. Milne Edwards

The efficiency of a diet not only depends on its nutrient composition and nutrient balance but also on the effective utilization by the animal. In the utilization of dietary nutrients, the digestive enzymes play the crucial role of catalysing the hydrolytic reactions, splitting the macromolecules into simple absorbable molecules. The activity of these biocatalysts is regulated by alterations in pH, temperature, substrate type and concentrations, and also by the presence of activators and inhibitors. Thus any shift from the optimum conditions necessary for these enzymes may affect their activity, thereby correspondingly modify the digestibility of the nutrients supplied to the animals. Thus, investigations on the important digestive enzymes and their preferential conditions of activity are essential, so that the results obtained could be used in rationally adjusting the quality and quantity of feed supplied to the different stages of prawns In India, directed research on nutritional physiology and biochemical approaches to digestion in commercially important prawns is taken up_ only recently, and the field is still in an infant stage. In view of its emerging importance it is identified as an area of priority and the present investigation has been carried out on the Indian white prawn Penaeus indicus

“Improved Feature Extraction and Classification Techniques for Multispectral Brain Magnetic Resonance Images”

Magnetic Resonance Imaging (MRI) is a multi sequence medical imaging technique in which stacks of images are acquired with different tissue contrasts. Simultaneous observation and quantitative analysis of normal brain tissues and small abnormalities from these large numbers of different sequences is a great challenge in clinical applications. Multispectral MRI analysis can simplify the job considerably by combining unlimited number of available co-registered sequences in a single suite. However, poor performance of the multispectral system with conventional image classification and segmentation methods makes it inappropriate for clinical analysis. Recent works in multispectral brain MRI analysis attempted to resolve this issue by improved feature extraction approaches, such as transform based methods, fuzzy approaches, algebraic techniques and so forth. Transform based feature extraction methods like Independent Component Analysis (ICA) and its extensions have been effectively used in recent studies to improve the performance of multispectral brain MRI analysis. However, these global transforms were found to be inefficient and inconsistent in identifying less frequently occurred features like small lesions, from large amount of MR data. The present thesis focuses on the improvement in ICA based feature extraction techniques to enhance the performance of multispectral brain MRI analysis. Methods using spectral clustering and wavelet transforms are proposed to resolve the inefficiency of ICA in identifying small abnormalities, and problems due to ICA over-completeness. Effectiveness of the new methods in brain tissue classification and segmentation is confirmed by a detailed quantitative and qualitative analysis with synthetic and clinical, normal and abnormal, data. In comparison to conventional classification techniques, proposed algorithms provide better performance in classification of normal brain tissues and significant small abnormalities.