Research suggests that the integrative complexity of political rhetoric tends to drop during election season, but little research to date directly addresses if this drop in complexity serves to increase or decrease electoral success. The two present studies help fill this gap. Study 1 demonstrates that, during the Democratic Party primary debates in [2003][2004], the eventual winners of the party nomination showed a steeper drop in integrative complexity as the election season progressed than nonwinning candidates. Study 2 presents laboratory evidence from the most recent presidential campaign demonstrating that, while the complexity of Obama's rhetoric had little impact on college students' subsequent intentions to vote for him, the complexity of McCain's rhetoric was significantly positively correlated with their likelihood of voting for him. Taken together, this research is inconsistent with an unqualified simple is effective view of the complexity-success relationship. Rather, it is more consistent with a compensatory view: Effective use of complexity (or simplicity) may compensate for perceived weaknesses. Thus, appropriately timed shifts in complexity levels, and/or violations of negative expectations relevant to complexity, may be an effective means of winning elections. Surprisingly, mere simplicity as such seems largely ineffective.

One of the most vexing challenges that has faced the behavioral sciences over the past century has been how to optimally measure, summarize, and predict individual variability in stability and change over time. It has long been known that a multitude of advantages are associated with the collection and analysis of repeated measures data; indeed, longitudinal data have become nearly requisite in many disciplines within the behavioral sciences. The challenge of how to best empirically capture individual change cuts across every aspect of the empirical research endeavor, including study design, psychometric measurement, subject sampling, data analysis, and substantive interpretation. Although many textbooks have been devoted to each of these research dimensions, here we have the much more modest goal of exploring just one specific type of longitudinal data analytic method: the multivariate growth model.Given our love of jargon in the social sciences, our field has coined a rather large number of terms to describe patterns of intraindividual change over time. These terms include (but are not limited to) growth, curve, trajectory, and path, among many others. Whether the term is growth models, growth trajectories, growth curves, latent trajectories, developmental curves, latent curves, time paths, or latent developmental growth curve time path trajectories of growth, 1 all tend to refer to the same thing. Namely, repeated measures are collected on a This research was partially funded by a grant from the Army Research Institute awarded to Patrick Sweeney; additional research team members included Kurt Dirks and Paul Lester. Sample computer code may be obtained from http://www.unc.edu/~curran 1 OK, so we made up that last one.

In Indonesia, where stroke is the leading cause of death, we designed and tested a brief intervention to increase physician-patient conversations about stroke prevention in community health centers. The pilot study used a quasi-experimental design involving repeated cross-sectional data collection over 15 weeks to compare pre- and during-intervention differences within four centers. We conducted exit interviews with 675 patients immediately following their medical appointments to assess whether physicians discussed stroke risks and provided recommendations to modify their risk behaviors. From pre-intervention to during intervention, patients reported more frequent physician recommendations to modify their stroke risk behaviors. We also conducted interviews with eight providers (physicians and nurses) after the intervention to get their feedback on its implementation. This study demonstrated that a brief intervention to motivate physician-patient conversations about stroke prevention may improve these conversations in community health centers. While interventions to reduce risk hold considerable promise for reducing stroke burden, barriers to physician-patient conversations identified through this study need to be addressed.

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Purpose of the study: Alcohol-induced liver fibrosis/disease (ALD) is characterized by excessive deposition of extracellular matrix (ECM) components in response to chronic abuse that could lead to cirrhosis and hepatocellular carcinoma development. Sarcosine is a derivative of the amino acid glycine, formed by the enzymes glycine N-methyl transferase (GNMT) or dimethylglycine dehydrogenase (DMGDH) and converted back into glycine via sarcosine dehydrogenase (SARDH). GNMT is silenced in human alcohol-induced cirrhosis. In addition, GNMT knockout mice develop oxidative stress, liver injury, fibrosis, and HCC. SUMOylation is a post-translational modification that requires an essential E2-conjugating enzyme 9 (UBC9) to covalently bind of small ubiquitin modifier (SUMO) and plays an important role in a wide range of cellular processes. We previously demonstrated that UBC9 level is induced in intragastric ethanol-infusion (EI) treated mouse liver. We performed SUMO-proteomics of alcohol-fed mouse liver and identified altered SUMOylation of GNMT and SARDH. The goal of this work is to examine whether the dysregulated SUMOylation could regulate GNMT and SARDH enzymatic function in ethanol-induced liver fibrosis and elucidate the molecular mechanism(s). Methods: Studies were performed using mouse hepatocytes (HEP), kupffer cells (KCs) and stellate cells (HSCs) from in vivo acute and chronic ethanol-fed mouse models and primary human HSCs. Oxidative stress and metabolic markers were measured using commercial kits. Results: We found that ethanol treatment in vivo induced UBC9 expression in HSCs and HEP and inhibited its expression in KCs. This was associated with increased GNMT SUMOylation and total levels, specifically in HSCs. In contrast, SARDH SUMOylation fell in HSCs despite an increase in its total level. Ethanol feeding increased glycine and lowered sarcosine levels in mouse plasma, suggesting a potential regulation of GNMT/SARDH enzyme activity. Ethanol-treated co-culture model (HEP, HSCs and KCs) showed increase in ROS production in all liver cells and glycine/sarcosine ratio in HSCs but not in HEP and KCs compared to control. Ubc9 silencing in HSCs inhibited ethanol-mediated ROS production and induction of HSC activation. Conclusions: This data strongly suggests that oxidative stress-induced SUMOylation plays a key role in HSC activation and this may influence the transmethylation machinery. Alterations of glycine/ sarcosine ratio as a consequence of enzyme SUMOylation could be considered as novel biomarker for ALD. Purpose: Ethanol exposure can lead to significant neurodegeneration in the developing brain due to elevated endoplasmic reticulum (ER)-stress. Mesencephalic astrocyte-derived neurotrophic factor (MANF) is an ER-stress inducible protein expressed in many cell types including neurons. We hypothesize that MANF may act to maintain ER homeostasis in response to ethanol exposure and deficiency of MANF makes neurons more susceptible to ethanol-induced neurodegeneration. Methods: Cultured mouse neuro2a cells we...