Pathological Investigations in penaeid Prawns

The thesis embodies the results of the studies carried out on certain diseases affecting the commercially important penaeid prawns in the capture and culture fisheries of the southwest and southeast coasts of india during october, 1981 to april 1985.initially a survey is conducted to obtain information and to understand the commom diseases and abnormalities occurring in the penaeid prawms in nature and those farmed. A result of the survey then cases of diseases and abnormalities are reported. These include tumour like growth soft prawm syndrome, tail necrosis brown spot disease, red rostrum, ciliate infestation ,helminth parasitisation,metacercarial infestation and bopyrid infestation in the penaeid prawns such as penaeus indicus p. monodon , p. semisulcatus ,Metapenaeus dobsoni and m.affinis.The symptoms ,occurrence and incidence of each of the above cases are provided along with the information on environmental factors such as salinity,dissolved oxygen,temperature and ph of the water from the collection sites. The nature of the disease,the tissuse of the host that are affected by the infection or infestation or by the pathogen ,and the actors influencing the infection in each of the ten cases are studied histopathologically and discussed. In the light of the available published information, the control measures for the different diseases of penaeid prawns are presented and discussed.

Pathological changes in Fenneropenaeus indicus experimentally infected with white spot virus and virus morphogenesis

To demonstrate pathological changes due to white spot virus infection in Fenneropenaeus indicus, a batch of hatchery bred quarantined animals was experimentally infected with the virus. Organs such as gills, foregut, mid-gut, hindgut, nerve, eye, heart, ovary and integument were examined by light and electron microscopy. Histopathological analyses revealed changes hitherto not reported in F. indicus such as lesions to the internal folding of gut resulted in syncytial mass sloughed off into lumen, thickening of hepatopancreatic connective tissue with vacuolization of tubules and necrosis of rectal pads in hindgut. Virus replication was seen in the crystalline tract region of the compound eye and eosinophilic granules infiltrated from its base. In the gill arch, dilation and disintegration of median blood vessel was observed. In the nervous tissues, encapsulation and subsequent atrophy of hypertrophied nuclei of the neurosecretory cells were found. Transmission electron microscopy showed viral replication and morphogenesis in cells of infected tissue. De novo formed vesicles covered the capsid forming a bilayered envelop opened at one end inside the virogenic stroma. Circular vesicles containing nuclear material was found fused with the envelop. Subsequent thickening of the envelop resulted in the fully formed virus. In this study, a correlation was observed between the stages of viral multiplication and the corresponding pathological changes in the cells during the WSV infection. Accordingly, gill and foregut tissues were found highly infected during the onset of clinical signs itself, and are proposed to be used as the tissues for routine disease diagnosis.

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