4 resultados para INSECTICIDE RESIDUE

em Cochin University of Science


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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.

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Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

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Poisoning by pesticides from agricultural fields is a serious water pollution problem and its environmental long-term effect may result in the incidence of poisoning of fish and other aquatic life forms (jyothi and Narayan, 1999). Fishes like Heteropneustesfbssilis and C/arius batrac/nus are especially prone to serious pesticide pollution as their habitat is mostly the agriculture area. Though only few studies are conducted in this area, it can be assessed from the local information that, population of such fish is on the verge of vulnerability due to extensive use of pesticides. The knowledge of sublethal effects of xenobiotic compounds on hematological parameters, enzyme activities and metabolite concentrations is very important to delineate the fish health status and provide a future understanding of ecological impacts. These pesticides act by causing inhibition of cholinesterase enzymes (ChE) by formation of enzyme inhibitor complex (O'Brien, 1976) and damaging the nervous system. These effects may result in metabolic disorders. Associated to cholinesterase activities, a study of other enzymes such as phosphatases and aminotransferases close to intermediary metabolite determination provides a wider view of metabolism. Interest in toxicological aspects has grown in recent years and research is now increasingly focused on mechanistic aspects of oxidative damage and cellular responses in biological system. The term ‘biomarker’ is generally used in a broad sense to include almost any measurement reflecting an interaction between a biological system and a potential hazard, which may be chemical, physical or biological (WHO, 1993). As biomarker stands for immediate responses, they are used as early warning signals of biological effects caused by environmental pollutants. The present work attempts to assess the toxicity of organophosphorus insecticide monocrotophos on the experimental organism selected for this study namely stinging catfish (Heteropneustesfossi/is) (Bloch), and to probe into the stress responses of the organism

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Animportant step in the residue number system(RNS) based signal processing is the conversion of signal into residue domain. Many implementations of this conversion have been proposed for various goals, and one of the implementations is by a direct conversion from an analogue input. A novel approach for analogue-to-residue conversion is proposed in this research using the most popular Sigma–Delta analogue-to-digital converter (SD-ADC). In this approach, the front end is the same as in traditional SD-ADC that uses Sigma–Delta (SD) modulator with appropriate dynamic range, but the filtering is doneby a filter implemented usingRNSarithmetic. Hence, the natural output of the filter is an RNS representation of the input signal. The resolution, conversion speed, hardware complexity and cost of implementation of the proposed SD based analogue-to-residue converter are compared with the existing analogue-to-residue converters based on Nyquist rate ADCs