Recognition of Posed and Spontaneous Dynamic Smiles in Younger and Older Adults

In two studies, we investigated age effects in the ability to recognize dynamic posed and spontaneous smiles. Study 1 found that both younger and older adult participants were above-chance in their ability to distinguish between posed and spontaneous younger adult smiles. Study 2 found that younger adult participant performance declined when judging a combination of both younger and older adult target smiles, while older adult participants outperformed younger adult participants in distinguishing between posed and spontaneous smiles. A synthesis of results across the two studies showed a small-to-medium age effect (d = −0.40) suggesting an older adult advantage when discriminating between smile types. Mixed stimuli (i.e., a mixture of younger and older adult faces) may impact accurate smile discrimination. Future research should investigate both the sources (cues, etc.) and behavioral effects of age-related differences in the discrimination of positive expressions

Genetic and Physiological Analysis of Iron Biofortification in Maize Kernels

BACKGROUND: Maize is a major cereal crop widely consumed in developing countries, which have a high prevalence of iron (Fe) deficiency anemia. The major cause of Fe deficiency in these countries is inadequate intake of bioavailable Fe, where poverty is a major factor. Therefore, biofortification of maize by increasing Fe concentration and or bioavailability has great potential to alleviate this deficiency. Maize is also a model system for genomic research and thus allows the opportunity for gene discovery. Here we describe an integrated genetic and physiological analysis of Fe nutrition in maize kernels, to identify loci that influence grain Fe concentration and bioavailability. METHODOLOGY: Quantitative trait locus (QTL) analysis was used to dissect grain Fe concentration (FeGC) and Fe bioavailability (FeGB) from the Intermated B73 × Mo17 (IBM) recombinant inbred (RI) population. FeGC was determined by ion coupled argon plasma emission spectroscopy (ICP). FeGB was determined by an in vitro digestion/Caco-2 cell line bioassay. CONCLUSIONS: Three modest QTL for FeGC were detected, in spite of high heritability. This suggests that FeGC is controlled by many small QTL, which may make it a challenging trait to improve by marker assisted breeding. Ten QTL for FeGB were identified and explained 54% of the variance observed in samples from a single year/location. Three of the largest FeGB QTL were isolated in sister derived lines and their effect was observed in three subsequent seasons in New York. Single season evaluations were also made at six other sites around North America, suggesting the enhancement of FeGB was not specific to our farm site. FeGB was not correlated with FeGC or phytic acid, suggesting that novel regulators of Fe nutrition are responsible for the differences observed. Our results indicate that iron biofortification of maize grain is achievable using specialized phenotyping tools and conventional plant breeding techniques

Performance of propensity score weighting in structural equation modeling

In order to make causal statements based on the results of a research study, one must eliminate any possible confounding variables. When a study does not randomize its participants into groups, the different treatment and control groups cannot be treated as equivalent at baseline, and a statistical correction must be undertaken to justify any subsequent causal statements. This dissertation evaluated the use of propensity score weighting in structural equation modeling to correct for non-equivalence by incorporating the propensity score weights into the loglikelihood equation and formulas for standard errors and test statistics. The use of propensity score weights together with structural equation models is appropriate for any non-randomized treatment study with latent variable outcomes. A series of simulations were conducted to assess the impact of several data-related and model-fitting-related variables. It was found that the method studied in this dissertation is a large-sample technique, requiring a sample size of at least 500 in order to produce unbiased treatment effect estimates; however, it was difficult to attain the nominal Type-I error rate even with a sample size of 5,000. A large amount of propensity score distribution overlap (~70%) is needed. Correctly specified propensity score models, or models with extra predictors, performed better than models that omitted confounding variables. An illustrative data analysis was used to show how to conduct this type of analysis on real data. It was shown that ignoring the non-equivalence of the groups led to an overestimation of the treatment effect estimate