Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement, and negative emotions—especially anger—emerged as risk factors; positive emotions and psychological engagement emerged as protective factors. Most correlations remained significant after controlling for income and education. A cross-sectional regression model based only on Twitter language predicted AHD mortality significantly better than did a model that combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity. Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level.

Dating violence is a prevalent problem among female college students. Several researchers have called for a continued investigation of risk and protective factors for aggression that can be modified through prevention programming. Mindfulness, the ability to be aware and open to the present moment in a nonjudgmental manner (Kabat-Zinn, 1994), may be one such protective factor. However, we are unaware of research that has examined whether individuals higher in mindfulness report less dating violence perpetration. This study investigated this question within a sample of female undergraduate students (N = 379). Findings demonstrated that several facets of mindfulness, particularly describing, acting with awareness, and nonreactivity, were associated with less psychological or physical aggression perpetration in the previous year. Moreover, several mindfulness facets were able to differentiate individuals with a history of perpetration relative to individuals without a history of perpetration. These findings provide preliminary evidence that mindfulness may play an important role in dating violence. Directions for future research on the relation between mindfulness and dating violence are discussed.

A recent preprint by Brown and Coyne titled, "No Evidence That Twitter Language Reliably Predicts Heart Disease: A Reanalysis of Eichstaedt et al." asserts to re-analyze our 2015 article published in Psychological Science, “Twitter Language Predicts Heart Disease Mortality”, disputing its primary findings. While we welcome scrutiny of the study, Brown and Coyne’s paper does not in fact report on a reanalysis, but rather presents a new analysis relating Twitter language to suicide instead of heart disease mortality. In our original article, we showed that Twitter language, fed into standard machine learning algorithms, was able to predict (i.e., estimate cross-sectionally) the out-of-sample heart disease rates of U.S. counties. Further, in a separate analysis, we found that the dictionaries and topics (i.e., sets of related words) which best predicted county atherosclerotic heart disease mortality rates included language related to education and income (e.g., “management,” “ideas,” “conference”) as well as negative social relationships (“hate”, “alone,” “jealous”), disengagement (“tired, “bored,” “sleepy”), negative emotions (“sorry,” “mad,” “sad”) as well as positive emotions (“great,” “happy,” “cool”) and psychological engagement (“learn,” “interesting,” “awake”). Beyond conducting a new analysis (correlating Twitter language with suicide rates), Brown and Coyne also detail a number of methodological limitations of group-level and social media-based studies. We discussed most of these limitations in our original article, but welcome this opportunity to emphasize some of the key aspects and qualifiers of our findings, considering each of their critiques and how they relate to our findings. Of particular note, even though we discuss our findings in the context of what is known about the etiology of heart disease at the individual level, we reiterate here a point made in our original paper: that individual-level causal inferences cannot be made from the cross-sectional and group-level analyses we presented. Our findings are intended to provide a new epidemiological tool to take advantage of large amounts of public data, and to complement, not replace, definitive health data collected through other means.We offer preliminary comments on the suicide language correlations: Previous studies have suggested that county-level suicides are relatively strongly associated with living in rural areas (Hirsch et al., 2006; Searles et al., 2014) and with county elevation (Kim et al., 2011; Brenner et al., 2011). When we control for these two confounds, we find the dictionary associations reported by Brown and Coyne are no longer significant. We conclude that their analysis is largely unrelated to our study and does not invalidate the findings of our original paper. In addition, we offer a replication of our original findings across more years, with a larger Twitter data set. We find that (a) Twitter language still predicts county atherosclerotic heart disease mortality with the same accuracy, and (b) the specific dictionary correlations we reported are largely unchanged on the new data set. To facilitate the reproduction by other researchers of our original work, we also re-release the data and code with which to reproduce our original findings, making it more user-friendly. We will do the same for this replication upon publication.

This paper summarizes key information on topics of contemporary interest in human and ecological per‐ and polyfluoroalkyl substance (PFAS) risk assessment, which were discussed at the PFAS Experts Symposium 2. For human health, the discussion focused on the toxicologic and epidemiologic endpoints and exposure assumptions that contribute to differences in PFAS regulatory criteria. For ecological risk, the discussion assessed the current state of the science available to support ecological screening levels and identified key data gaps and uncertainties in our understanding of ecological exposure and toxicity. Finally, the paper summarizes a panel discussion that addressed the challenges and uncertainties of regulating PFAS as a class.

1.A transgenic 'knock-in' mouse model expressing a human UGT1 locus (Tg-UGT1) was recently developed and validated. Although these animals express mouse UGT1A proteins, UGT1A4 is a pseudo-gene in mice. Therefore, Tg-UGT1 mice serve as a 'humanized' UGT1A4 animal model. Lamotrigine (LTG) is primarily metabolized to its N-glucuronide (LTGG) by hUGT1A4.This investigation aimed at examining the impact of pregnane X receptor (PXR), constitutive androstane receptor (CAR) and peroxisome proliferator-activated receptor (PPAR) activators on LTG glucuronidation in vivo and in vitro. Tg-UGT1 mice were administered the inducers phenobarbital (CAR), pregnenolone-16α-carbonitrile (PXR), WY-14643 (PPAR-α), ciglitazone (PPAR-γ), or L-165041 (PPAR-β), once daily for 3 or 4 days. Thereafter, LTG was administered orally and blood samples were collected over 24 h. LTG was measured in blood and formation of LTGG was measured in pooled microsomes made from the livers of treated animals. 3.A three-fold increase in in vivo LTG clearance was seen after phenobarbital administration. In microsomes prepared from phenobarbital-treated Tg-UGT1 animals, 13-fold higher CL int (V max /K m ) value was observed as compared with the untreated transgenic mice. A trend toward induction of catalytic activity in vitro and in vivo was also observed following pregnenolone-16α-carbonitrile and WY-14643 treatment. This study demonstrates the successful application of Tg-UGT1 mice as a novel tool to study the impact of induction and regulation on metabolism of UGT1A4 substrates.