Molecular structure of antibodies against small ligands

Antibodies which bind bioactive ligands can serve as a template for the generation of a second antibody which may react with the physiological receptor. This phenomenon of molecular mimicry by antibodies has been described in a variety of systems. In order to understand the chemical and molecular mechanisms involved in these interactions, monoclonal antibodies directed against two pharmacologically active alkaloids, morphine and nicotine, were carefully studied using experimental and theoretical molecular modeling techniques. The molecular characterization of these antibodies involved binding studies with ligand analogs and determination of the variable region amino acid sequence. A three-dimensional model of the anti-morphine binding site was constructed using computational and graphics display techniques. The antibody response in BALB/c mice to morphine appears relatively restricted, in that all of the antibodies examined in this study contained a $\lambda$ light chain, which is normally found in only 5% of mouse immunoglobulins. This study represents the first use of theoretical and experimental modeling techniques to describe the antigen binding site of a mouse Fv region containing a $\lambda$ light chain. The binding site model indicates that a charged glutamic acid residue and aromatic side chains are key features in ionic and hydrophobic interactions with the ligand morphine. A glutamic acid residue is found in the identical position in the anti-nicotine antibody and may play a role in binding nicotine. ^

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

HEAD OF HOUSEHOLD STRUCTURE: ITS INFLUENCE ON THE DIETARY QUALITY OF U.S. HOUSEHOLDS, 1977-78 (UNITED STATES, NATIONWIDE FOOD CONSUMPTION SURVEY)

Using analysis of variance, household data collected in the Spring portion of the 1977-78 Nationwide Food Consumption Survey conducted by the United States Department of Agriculture were analyzed to examine the relationship between household characteristics and dietary quality of the household food supply. Results indicated that head of household structure was a statistically significant variable, with female headed households having higher dietary quality.^ Further analysis indicated that neither race, degree of urbanization, regional location, the education level of the female head, nor her employment status were significant variables in influencing dietary quality. The influence of head of household structure remained significant when these variables were controlled. However, income, household size, and family life cycle stage had statistically significant effects on dietary quality, and when individually controlled, the influence of head of household structure disappeared. ^