Biochemical effects of the pesticide Chlorpyrifos on the fish Oreochromis mossambicus (Peters)

Man uses a variety of synthetic material for his comfortable materialistic life. Thus human interactions may become harmful for various terrestrial and aquatic lives. This is by contaminating their habitat and by becoming a threat to organisms itself. Thus the application and dispersal of several organic pollutants can lead to the development of several mutated forms of the species when exposed to sublethal concentrations of the pollutants. Otherwise, a decrease in number or extinction of these exposed species from earth's face may happen. Pesticides, we use for the benefit of crop yield, but its persistence may become havoc to non-target organism. Pesticides reaching a reservoir can subsequently enter the higher trophic levels. Organophosphorus compounds have replaced all other pesticides, due to its acute toxicity and non-persistent nature.Hence the present study has concentrated on the toxicity of the largest market-selling and multipurpose pesticide, chlorpyrifos on the commonly edible aquatic organism, fish. The euryhaline cichlid Oreochromis mossambicus was selected as animal model. The study has concentrated on investigating biochemical parameters like tissue-specific enzymes, antioxidant and lipid-peroxidation parameters, haematological and histological observations and pesticide residue analysis.Major findings of this work have indicated the possibility of aquatic toxicity to the fish on exposure to the insecticide chlorpyrifos. The insecticide was found as effective to induce structural alteration, depletion in protein content, decrease in different metabolic enzyme levels and to progress lipid peroxidation on a prolonged exposure of 21 days. The ion-transport mechanism was found to be adversely affected. Electrophoretic analysis revealed the disappearance of several protein bands after 21days of exposure to chlorpyrifos. Residue, analysis by gas chromatography explored the levels of chlorpyrifos retaining on the edible tissue portions during exposure period of 21days and also on a recovery period of 10 days.

"|mpact of trawling on sea bottom and its living communities along the inshore waters of southwest coast of India'’

Mechanized fishing started in Indian waters in mid —fifties and large-scale operation of trawl fishing began in the mid sixties by the surfeit of individual entrepreneurs. The southwest coast of India especially the coastal waters of Kerala are the most productive area in the subcontinent and the state has been in the forefront in marine fish production (Kurup, 2001a). Though the coastline of Kerala is one tenth of the coastline of India, the state occupies the foremost position in the marine fish production of the country, accounting for more than 30% of the marine fish landings (Thomas, 2000). The coastal waters of Kerala have rich and diversified fishery resources, which are prone to heavy exploitation by a unprecedently high number of fishing gears, among them, mechanized bottom trawlers with a numerical strength of 4550 (Kurup, 2001a) against the permissible number of 1145 (Kalawar, et al., 1985) are the most destructive. Trawling operations during monsoon periods in Kerala has been a subject of controversy between traditional fishermen and trawl fishers on a subject that trawl fishing destroys large amount of juveniles and young ones of fishes since this period is the major breeding season of most of the fish and prawns (John, 1996). Therefore Government of Kerala imposed a ban on bottom trawling activities from 1988 onwards for a period varying from 21-70 days, which usually commences from June 15th. Though many studies revealed that large amount of non-target groups were destroyed in the commercial trawl fishing in the Indian waters, no concerted study has been conducted so far to evaluate the real impact of bottom trawling on the sea bottom and its living communities. The present study was conducted to assess the impact of excessive bottom trawling exerted on the sea bottom habitat and its living communities, which would be useful in impressing up on the seriousness of habitat degradation and biotic devastation, enabling the concerned to adopt relevant conservation and management steps to conserve the resources for sustainable exploitation

Soft Computing Approach for Optimization of siRNA Efficiency Prediction for Post-transcriptional Gene Silencing

Post-transcriptional gene silencing by RNA interference is mediated by small interfering RNA called siRNA. This gene silencing mechanism can be exploited therapeutically to a wide variety of disease-associated targets, especially in AIDS, neurodegenerative diseases, cholesterol and cancer on mice with the hope of extending these approaches to treat humans. Over the recent past, a significant amount of work has been undertaken to understand the gene silencing mediated by exogenous siRNA. The design of efficient exogenous siRNA sequences is challenging because of many issues related to siRNA. While designing efficient siRNA, target mRNAs must be selected such that their corresponding siRNAs are likely to be efficient against that target and unlikely to accidentally silence other transcripts due to sequence similarity. So before doing gene silencing by siRNAs, it is essential to analyze their off-target effects in addition to their inhibition efficiency against a particular target. Hence designing exogenous siRNA with good knock-down efficiency and target specificity is an area of concern to be addressed. Some methods have been developed already by considering both inhibition efficiency and off-target possibility of siRNA against agene. Out of these methods, only a few have achieved good inhibition efficiency, specificity and sensitivity. The main focus of this thesis is to develop computational methods to optimize the efficiency of siRNA in terms of “inhibition capacity and off-target possibility” against target mRNAs with improved efficacy, which may be useful in the area of gene silencing and drug design for tumor development. This study aims to investigate the currently available siRNA prediction approaches and to devise a better computational approach to tackle the problem of siRNA efficacy by inhibition capacity and off-target possibility. The strength and limitations of the available approaches are investigated and taken into consideration for making improved solution. Thus the approaches proposed in this study extend some of the good scoring previous state of the art techniques by incorporating machine learning and statistical approaches and thermodynamic features like whole stacking energy to improve the prediction accuracy, inhibition efficiency, sensitivity and specificity. Here, we propose one Support Vector Machine (SVM) model, and two Artificial Neural Network (ANN) models for siRNA efficiency prediction. In SVM model, the classification property is used to classify whether the siRNA is efficient or inefficient in silencing a target gene. The first ANNmodel, named siRNA Designer, is used for optimizing the inhibition efficiency of siRNA against target genes. The second ANN model, named Optimized siRNA Designer, OpsiD, produces efficient siRNAs with high inhibition efficiency to degrade target genes with improved sensitivity-specificity, and identifies the off-target knockdown possibility of siRNA against non-target genes. The models are trained and tested against a large data set of siRNA sequences. The validations are conducted using Pearson Correlation Coefficient, Mathews Correlation Coefficient, Receiver Operating Characteristic analysis, Accuracy of prediction, Sensitivity and Specificity. It is found that the approach, OpsiD, is capable of predicting the inhibition capacity of siRNA against a target mRNA with improved results over the state of the art techniques. Also we are able to understand the influence of whole stacking energy on efficiency of siRNA. The model is further improved by including the ability to identify the “off-target possibility” of predicted siRNA on non-target genes. Thus the proposed model, OpsiD, can predict optimized siRNA by considering both “inhibition efficiency on target genes and off-target possibility on non-target genes”, with improved inhibition efficiency, specificity and sensitivity. Since we have taken efforts to optimize the siRNA efficacy in terms of “inhibition efficiency and offtarget possibility”, we hope that the risk of “off-target effect” while doing gene silencing in various bioinformatics fields can be overcome to a great extent. These findings may provide new insights into cancer diagnosis, prognosis and therapy by gene silencing. The approach may be found useful for designing exogenous siRNA for therapeutic applications and gene silencing techniques in different areas of bioinformatics.