Biochemical Effects of Anionic, Cationic and Non Ionic Surfactants on a Tropical Teleost Oreochromis Mossambicus(Peters) and a Marine Cyanobacterium Synechocystis Salina Wislouch

The present work is a base line attempt to investigate and assess the toxicities of three surfactants viz. anionic sodium dodecyl sulfate (SDS), non ionic Triton X-1OO (TX-IOO) and cationic cetyl trimethyl ammonium bromide (CTAB). These compounds represent simple members of the often neglected group of aquatic pollutants i.e. the anionic alkyl sulfates, non ionics and the cationics. These compounds are widely used In plastic industry, pesticide/herbicide formulations, detergents, oil spill dispersants, molluscicides etc. The test organisms selected for the present study are the cyanobacterium Synechocystis salina Wislouch representing a primary producer in the marine environment and a fresh water adapted euryhaline teleost Oreochromis mossambicus (peters) at the consumer level of the ecological pyramid. The fish species, though not indigenous to our country, is now found ubiquitously in fresh water systems and estuaries. Also it is highly resistant to pollutants and has been suggested as an indicator of pollution in tropical region .

Model Diagnostics in the presence of measurement error

The problem of using information available from one variable X to make inferenceabout another Y is classical in many physical and social sciences. In statistics this isoften done via regression analysis where mean response is used to model the data. Onestipulates the model Y = µ(X) +ɛ. Here µ(X) is the mean response at the predictor variable value X = x, and ɛ = Y - µ(X) is the error. In classical regression analysis, both (X; Y ) are observable and one then proceeds to make inference about the mean response function µ(X). In practice there are numerous examples where X is not available, but a variable Z is observed which provides an estimate of X. As an example, consider the herbicidestudy of Rudemo, et al. [3] in which a nominal measured amount Z of herbicide was applied to a plant but the actual amount absorbed by the plant X is unobservable. As another example, from Wang [5], an epidemiologist studies the severity of a lung disease, Y , among the residents in a city in relation to the amount of certain air pollutants. The amount of the air pollutants Z can be measured at certain observation stations in the city, but the actual exposure of the residents to the pollutants, X, is unobservable and may vary randomly from the Z-values. In both cases X = Z+error: This is the so called Berkson measurement error model.In more classical measurement error model one observes an unbiased estimator W of X and stipulates the relation W = X + error: An example of this model occurs when assessing effect of nutrition X on a disease. Measuring nutrition intake precisely within 24 hours is almost impossible. There are many similar examples in agricultural or medical studies, see e.g., Carroll, Ruppert and Stefanski [1] and Fuller [2], , among others. In this talk we shall address the question of fitting a parametric model to the re-gression function µ(X) in the Berkson measurement error model: Y = µ(X) + ɛ; X = Z + η; where η and ɛ are random errors with E(ɛ) = 0, X and η are d-dimensional, and Z is the observable d-dimensional r.v.