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Background: Drinking identity (the extent to which one links the self with drinking alcohol) is a unique risk factor for college students' hazardous drinking that is not directly targeted by existing interventions. We conducted a study that aimed to decrease drinking identity among college students with hazardous drinking. We adapted a writing task about the future self and tested whether three writing sessions could decrease drinking identity and change drinking. We also investigated whether two additional factors (writing perspective and inclusion of participants' social networks) would enhance task impact. The present study evaluated whether posited proximal cognitive and motivational outcomes (drinking identity, self‐efficacy, readiness to change, and drinking intentions) changed immediately after each writing session. Method: The study is a randomized clinical trial in which hypotheses and analyses were pre‐registered. Participants were 328 college students who met hazardous drinking criteria. The study had a 2 (narrative writing topic: low‐risk drinker vs. reduced smartphone use) × 2 (writing perspective: first‐person vs. non‐first‐person) × 2 (social network instruction: instructed to include vs. not) factorial design. Proximal outcomes were drinking identity, self‐efficacy, readiness to change, and drinking intentions. The clinical outcome was alcohol consumption. Participants completed three laboratory sessions at weekly intervals that included the writing task and pre‐ and post‐task assessments. Results: Results were largely null, except that readiness to reduce drinking was higher in the low‐risk drinker condition and increased over the lab sessions. Time effects indicated that reductions in drinking identity, drinking intentions, and alcohol consumption, and increases in self‐efficacy were observed but did not change above and beyond control conditions. Conclusions: Findings indicate the need to strengthen the writing task and select a more appropriate control task to target proposed proximal outcomes. Future studies might try personalizing the task, evaluating its efficacy with individuals motivated to change their drinking, and using a control task that does not involve imagining a future self. [ABSTRACT FROM AUTHOR]

We examined the error-related negativity (ERN) as an endophenotype of ASD by comparing the ERN in families of ASD probands to control families. We hypothesized that ASD probands and families would display reduced-amplitude ERN relative to controls. Participants included 148 individuals within 39 families consisting of a mother, father, sibling, and proband. Robust ANOVAs revealed non-significant differences in ERN amplitude and behavioral performance among ASD probands relative to control youth. In subsequent multiple regression analyses group and kinship (proband, sibling, mother, father) did not significantly predict ?ERN (error minus correct ERN) or behavioral performance. Results do not provide evidence for the ERN as an endophenotype of ASD. Future research is needed to examine state- or trait-related factors influencing ERN amplitudes in ASD.

Abstract Background The Ras association domain family 1 (RASSF1) gene is a Ras effector encoding two major mRNA forms, RASSF1A and RASSF1C, derived by alternative promoter selection and alternative mRNA splicing. RASSF1A is a tumor suppressor gene. However, very little is known about the function of RASSF1C both in normal and transformed cells. Methods Gene silencing and over-expression techniques were used to modulate RASSF1C expression in human breast cancer cells. Affymetrix-microarray analysis was performed using T47D cells over-expressing RASSF1C to identify RASSF1C target genes. RT-PCR and western blot techniques were used to validate target gene expression. Cell invasion and apoptosis assays were also performed. Results In this article, we report the effects of altering RASSF1C expression in human breast cancer cells. We found that silencing RASSF1C mRNA in breast cancer cell lines (MDA-MB231 and T47D) caused a small but significant decrease in cell proliferation. Conversely, inducible over-expression of RASSF1C in breast cancer cells (MDA-MB231 and T47D) resulted in a small increase in cell proliferation. We also report on the identification of novel RASSF1C target genes. RASSF1C down-regulates several pro-apoptotic and tumor suppressor genes and up-regulates several growth promoting genes in breast cancer cells. We further show that down-regulation of caspase 3 via overexpression of RASSF1C reduces breast cancer cells' sensitivity to the apoptosis inducing agent, etoposide. Furthermore, we found that RASSF1C over-expression enhances T47D cell invasion/migration in vitro. Conclusion Together, our findings suggest that RASSF1C, unlike RASSF1A, is not a tumor suppressor, but instead may play a role in stimulating metastasis and survival in breast cancer cells.

Understanding the efficacy and relative effectiveness of a brief alcohol intervention (BAI) relies on obtaining a credible intervention effect estimate. Outcomes in BAI trials are often count variables, such as the number of drinks consumed, which may be overdispersed (i.e., greater variability than expected based on a given model) and zero‐inflated (i.e., greater probability of zeros than expected based on a given model). Ignoring such distribution characteristics can lead to biased estimates and invalid statistical conclusions. In this critical review, we identified and reviewed 64 articles that reported count outcomes from a systematic review of BAI trials for adolescents and young adults from 2013 to 2018. Given many statistical models to choose from when analyzing count outcomes, we reviewed the models used and reporting practices in the BAI trial literature. A majority (61.3%) of analyses with count outcomes used linear models despite violations of normality assumptions; 75.6% of outcome variables demonstrated clear overdispersion. We provide an overview of available count models (Poisson, negative binomial, zero‐inflated or hurdle, and marginalized zero‐inflated Poisson regression) and formulate practical guidelines for reporting outcomes of BAIs. We provide a visual step‐by‐step decision guide for selecting appropriate statistical models and reporting results for count outcomes. We list accessible resources to help researchers select an appropriate model with which to analyze their data. Recent advances in count distribution‐based models hold promise for evaluating count outcomes to gauge the efficacy and effectiveness of BAIs and identify critical covariates in alcohol epidemiologic research. We recommend that researchers report the distributional properties of count outcomes, such as the proportion of zero counts, and select an appropriate statistical analysis for count outcomes using the provided decision tree. By following these recommendations, future research may yield more accurate, transparent, and reproducible results. [ABSTRACT FROM AUTHOR]

Clinical studies of adaptive control emphasize the role disruptions in control play in psychopathology. However, many studies used confound‐laden designs and examined only one type of psychopathology. Recent studies of event‐related potentials (ERPs) suggest that robust congruency sequence effects (CSEs)—a popular index of adaptive control—appear in confound‐minimized designs. Thus, the present study sought to determine whether a confound‐minimized CSE paradigm could identify adaptive control dysfunction in people with major depressive disorder (MDD), generalized anxiety disorder (GAD), and obsessive‐compulsive disorder (OCD). We predicted that participants with MDD and GAD would show smaller ERP CSEs and that participants with OCD would show larger ERP CSEs than healthy controls. Data from 44 people with GAD, 51 people with MDD, 31 people with OCD, and 56 healthy comparison participants revealed normal CSEs as indexed by response times (RTs) and ERPs in the psychopathology groups. Moreover, psychiatric symptoms did not moderate these CSEs. Finally, we observed a strong mean–variance relationship in RT CSEs, such that participants with stronger post‐recruitment of control in mean RT scores showed the most consistent post‐conflict responses (i.e., the least intraindividual variability). These findings suggest that prior findings from confound‐laden tasks indicating altered CSEs in psychopathology stem from processes that are unrelated to adaptive control. Future research should employ experimental designs that isolate these processes to advance our understanding of abnormal CSEs in psychopathology. This study investigates adaptive control dysfunction in people with major depressive disorder (MDD), generalized anxiety disorder (GAD), and obsessive‐compulsive disorder (OCD) and healthy comparison participants using a confound‐minimized congruency sequence effects (CSEs) paradigm. The present findings show normal CSEs across groups, suggesting that altered CSEs in psychopathology may stem from unrelated processes. The study emphasizes the need to use confound‐minimized CSE designs to isolate control‐related processes to understand abnormal CSEs in psychopathology. [ABSTRACT FROM AUTHOR]

Meta-analysis using individual participant data (IPD) is an important methodology in intervention research because it (a) increases accuracy and precision of estimates, (b) allows researchers to investigate mediators and moderators of treatment effects, and (c) makes use of extant data. IPD meta-analysis can be conducted either via a one-step approach that uses data from all studies simultaneously, or a two-step approach, which aggregates data for each study and then combines them in a traditional meta-analysis model. Unfortunately, there are no evidence-based guidelines for how best to approach IPD meta-analysis for count outcomes with many zeroes, such as alcohol use. We used simulation to compare the performance of four hurdle models (3 one-step and 1 two-step models) for zero-inflated count IPD, under realistic data conditions. Overall, all models yielded adequate coverage and bias for the treatment effect in the count portion of the model, across all data conditions. However, in the zero portion, the treatment effect was underestimated in most models and data conditions, especially when there were fewer studies. The performance of both one- and two-step approaches depended on the formulation of the treatment effects, suggesting a need to carefully consider model assumptions and specifications when using IPD. [ABSTRACT FROM AUTHOR]