Isolation of a pathogenic strain of Vibrio alginolyticus from necrotic larvae of Macrobrachium rosenbergii (de Man)

Giant freshwater prawn, Macrobrachium rosenbergii (de Man), is an important commercial species with considerable export value, ideal for cultivation under low saline conditions and in freshwater zones (Kurup 1994). However, despite more than a decade of research on its larval production systems, vibriosis still hampers seed production resulting in high mortality rates. Among the different species of vibrios, Vibrio alginolyticus has been isolated frequently from diseased shrimp as the aetiological agent of vibriosis and has been described as a principal pathogen of both penaeids and nonpenaeids (Lightner 1988; Baticados, Cruz-Lacierda, de la Cruz, Duremdez-Fernandez, Gacutan, Lavilla- Pitogo & Lio-Po 1990; Mohney, Lightner & Bell 1994; Lee, Yu, Chen, Yang & Liu 1996). Vibrio fluvialis, V. alginolyticus, V. cholerae non-O1 (Fujioka & Greco 1984), Aeromonas liquifaciens and V. anguillarum (Colorni 1985) have been isolated from the larvae of M. rosenbergii. A profound relationship between the abundance of members of the family Vibrionaceae and larval mortality (Singh 1990) and the predominance of Vibrio in eggs, larvae and post-larvae of M. rosenbergii (Hameed, Rahaman, Alagan & Yoganandhan 2003) was reported. The present paper reports the isolation, characterization, pathogenicity and antibiotic sensitivity of V. alginolyticus associated with M. rosenbergii larvae during an occurrence of severe mass mortality at the ninth larval stage.

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Area and Volume Calculation of Necrotic Tissue regions of heart using Interpolation

This paper attempts to develop an improved tool, which would read two dimensional(2D) cardiac MRI images and compute areas and volume of the scar tissue. Here the computation would be done on the cardiac MR images to quantify the extent of damage inflicted by myocardial infarction on the cardiac muscle (myocardium) using Interpolation