ATR-FTIR spectroscopy detects alterations induced by organotin(IV) carboxylates in MCF-7 cells at sub-cytotoxic/-genotoxic concentrations.

The environmental impact of metal complexes such as organotin(IV) compounds is of increasing concern. Genotoxic effects of organotin(IV) compounds (0.01 μg/ml, 0.1 μg/ml or 1.0 μg/ml) were measured using the alkaline single-cell gel electrophoresis (comet) assay to measure DNA single-strand breaks (SSBs) and the cytokinesis-block micronucleus (CBMN) assay to determine micronucleus formation. Biochemical-cell signatures were also ascertained using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. In the comet assay, organotin(IV) carboxylates induced significantly-elevated levels of DNA SSBs. Elevated micronucleus-forming activities were also observed. Following interrogation using ATR-FTIR spectroscopy, infrared spectra in the biomolecular range (900 cm-1 – 1800 cm-1) derived from organotin-treated MCF-7 cells exhibited clear alterations in their biochemical-cell fingerprint compared to control-cell populations following exposures as low as 0.0001 μg/ml. Mono-, di- or tri-organotin(IV) carboxylates (0.1 μg/ml, 1.0 μg/ml or 10.0 μg/ml) were markedly cytotoxic as determined by the clonogenic assay following treatment of MCF-7 cells with ≥ 1.0 μg/ml. Our results demonstrate that ATR-FTIR spectroscopy can be applied to detect molecular alterations induced by organotin(IV) compounds at sub-cytotoxic and sub-genotoxic concentrations. This biophysical approach points to a novel means of assessing risk associated with environmental contaminants

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A New Procedure For Multiple Testing Of Econometric Models

A significant role for hypothesis testing in econometrics involves diagnostic checking. When checking the adequacy of a chosen model, researchers typically employ a range of diagnostic tests, each of which is designed to detect a particular form of model inadequacy. A major problem is how best to control the overall probability of rejecting the model when it is true and multiple test statistics are used. This paper presents a new multiple testing procedure, which involves checking whether the calculated values of the diagnostic statistics are consistent with the postulated model being true. This is done through a combination of bootstrapping to obtain a multivariate kernel density estimator of the joint density of the test statistics under the null hypothesis and Monte Carlo simulations to obtain a p value using this kernel density. We prove that under some regularity conditions, the estimated p value of our test procedure is a consistent estimate of the true p value. The proposed testing procedure is applied to tests for autocorrelation in an observed time series, for normality, and for model misspecification through the information matrix. We find that our testing procedure has correct or nearly correct sizes and good powers, particular for more complicated testing problems. We believe it is the first good method for calculating the overall p value for a vector of test statistics based on simulation.Bootstrapping, consistency, information matrix test, Markov chain Monte Carlo simulation, multivariate kernel density, normality, serial correlation, test vector

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