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

CBIR Using Local and Global Properties of Image Sub-blocks

This paper proposes a content based image retrieval (CBIR) system using the local colour and texture features of selected image sub-blocks and global colour and shape features of the image. The image sub-blocks are roughly identified by segmenting the image into partitions of different configuration, finding the edge density in each partition using edge thresholding, morphological dilation and finding the corner density in each partition. The colour and texture features of the identified regions are computed from the histograms of the quantized HSV colour space and Gray Level Co- occurrence Matrix (GLCM) respectively. A combined colour and texture feature vector is computed for each region. The shape features are computed from the Edge Histogram Descriptor (EHD). Euclidean distance measure is used for computing the distance between the features of the query and target image. Experimental results show that the proposed method provides better retrieving result than retrieval using some of the existing methods