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High levels of negative urgency imply risk for impulsive and potentially self-destructive behavior. The social consequences of these impulsive states remain poorly understood. In the present study, we examined how state-like fluctuation in negative urgency provokes day-to-day interpersonal stressors using experience sampling methods. We recruited 119 adults with a history of recurrent self-injury to complete surveys of negative urgency and interpersonal stress (i.e., exposure to criticism and rejection) 4 times per day over 14 days. Results from hierarchical linear models showed that when people experienced more negative urgency, relative to their personal norms, they were more likely than usual to encounter interpersonal stress over the next few hours. There was some evidence to suggest that this within-person connection was more pronounced for people who tended to have higher negative urgency levels in general across the experience sampling period. We interpret these findings in the context of stress generation theory, and we conclude that within-person variation in negative urgency may represent a clinically useful model of near-term risk for interpersonal dysfunction.

Most alcohol intervention research focuses on program efficacy, yet few studies have investigated the acceptability of a program’s design and implementation to the target population or adapting existing alcohol interventions to different populations. To address these gaps in the literature, we (1) examined participant responsiveness to and implementation quality of FITSTART+, a web-app delivered parent-based alcohol intervention designed for incoming first-year college students in the United States, and (2) gathered feedback on how this intervention could be adapted to other populations of parents. A sample of U.S. parents of 17–20-year-old first-year college students (N = 109) participated in FITSTART+ during their child’s first year of college and completed a survey about parents’ responsiveness to the app and its quality. Next, a sample of non-U.S. parents of adolescents aged 13 to 19 (N = 44) participated in one of 11 focus groups in which they briefly explored the app and then discussed how it could be adapted to be applicable and culturally relevant for them and their context. Results revealed that U.S. parents rated the intervention’s quality as high and parents were responsive to the web-app’s content, but some did not visit one of the most critical aspects of the intervention (i.e., alcohol-related parenting resources). Non-U.S. participants provided a range of suggestions for adapting the intervention to their context, which varied by culture. Results identify areas for improvement, particularly regarding the use of alcohol-related parenting resources, in this intervention and for web-delivered PBIs more broadly.

This research expanded on prior work exploring the relationship between social media use, social support, and mental health by including the usage of social virtual reality (VR). In Study 1 (undergraduate students; n = 448) we examined divergent relationships between problematic social media use (e.g., Facebook, TikTok), total use, and users’ mental health indicators (e.g., depression, anxiety, social isolation). To determine whether problematic social media use patterns extended to immersive 3-D environments, we sampled active social VR users (e.g., Rec Room) in Study 2 (n = 464). Problematic social VR use was related to decreased real-life social support (β = -.62, 95%CI [-.80, -.44]), but not to VR social support (β = -.06, 95%CI [-.25, .14]). Conversely, the amount of social VR use was only related to increased social VR (β = .06, 95%CI [.04, .15]) but not to real-life social support (β = -.02, 95%CI [-.05, .04]). Study 2 also revealed a finding that may be unique to the 3-D immersive environment: the amount of social VR use facilitated better mental health for VR users, but only through stronger perceived social support on social VR but not in real life. This result highlights the potential of immersive media to promote mental well-being by facilitating engaging and meaningful social interactions.

Background West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. Methods We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. Results Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. Conclusions Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases). Graphical Abstract

Acute decompensated heart failure (ADHF) is a leading cause of hospitalization in older persons in the United States. Reduced physical function and frailty are major determinants of adverse outcomes in older patients with hospitalized ADHF. However, these are not addressed by current heart failure (HF) management strategies and there has been little study of exercise training in older, frail HF patients with recent ADHF. Targeting physical frailty with a multi-domain structured physical rehabilitation intervention will improve physical function and reduce adverse outcomes among older patients experiencing a HF hospitalization. REHAB-HF is a multi-center clinical trial in which 360 patients ≥60 years hospitalized with ADHF will be randomized either to a novel 12-week multi-domain physical rehabilitation intervention or to attention control. The goal of the intervention is to improve balance, mobility, strength and endurance utilizing reproducible, targeted exercises administered by a multi-disciplinary team with specific milestones for progression. The primary study aim is to assess the efficacy of the REHAB-HF intervention on physical function measured by total Short Physical Performance Battery score. The secondary outcome is 6-month all-cause rehospitalization. Additional outcome measures include quality of life and costs. REHAB-HF is the first randomized trial of a physical function intervention in older patients with hospitalized ADHF designed to determine if addressing deficits in balance, mobility, strength and endurance improves physical function and reduces rehospitalizations. It will address key evidence gaps concerning the role of physical rehabilitation in the care of older patients, those with ADHF, frailty, and multiple comorbidities.

To provide practical principles and examples to help GRADE users make optimal choices regarding their ratings of certainty of evidence using a minimally or partially contextualized approach. Based on the GRADE clarification of certainty of evidence in 2017, a project group within the GRADE Working Group conducted iterative discussions and presentations at GRADE Working Group meetings to refine this construct and produce practical guidance. Systematic review and health technology assessment authors need to clarify what it is in which they are rating their certainty of evidence (i.e., the target of their certainty rating). The decision depends on the degree of contextualization (partially or minimally contextualized), thresholds (null, small, moderate or large effect threshold), and where the point estimate lies in relation to the chosen threshold(s). When the 95% confidence interval crosses multiple possible thresholds (i.e., including both large benefit and large harm), it is not worthwhile for authors to determine the target of certainty rating. GRADE provides practical principles to help systematic review and health technology assessment authors specify the target of their certainty of evidence rating.

Schizophrenia is a strongly heritable disorder, and identification of potential candidate genes has accelerated in recent years. Genomewide scans have identified multiple large linkage regions across the genome, with fine-mapping studies and other investigations of biologically plausible targets demonstrating several promising candidate genes of modest effect. The recent introduction of technological platforms for whole-genome association (WGA) studies can provide an opportunity to rapidly identify novel targets, although no WGA studies have been reported in the psychiatric literature to date. We report results of a case–control WGA study in schizophrenia, examining ∼500 000 markers, which revealed a strong effect ( P =3.7 × 10 −7 ) of a novel locus (rs4129148) near the CSF2RA (colony stimulating factor, receptor 2 alpha) gene in the pseudoautosomal region. Sequencing of CSF2RA and its neighbor, IL3RA (interleukin 3 receptor alpha) in an independent case–control cohort revealed both common intronic haplotypes and several novel, rare missense variants associated with schizophrenia. The presence of cytokine receptor abnormalities in schizophrenia may help explain prior epidemiologic data relating the risk for this illness to altered rates of autoimmune disorders, prenatal infection and familial leukemia.

A changing generation mix and growing demand for carbon-free electricity will almost certainly require dramatic changes in the infrastructure and topology of the electricity system. Rather than build new lines, one way to minimize social opposition and regulatory obstacles is to increase the capacity of existing transmission corridors. In addition to upgrading the capacity of highvoltage alternating current (HVAC) lines, we identify a number of situations in which conversion from HVAC to high-voltage direct current (HVDC) is the least-cost strategy to increase the capacity of the corridor. If restricted to the existing right-of-way (ROW), we find DC conversion to be the least-cost, and in some cases the only, option for distances of >200 km or for increases of >50% capacity. Across all configurations analyzed, we assess HVDC conversion to be the lower-cost option at >350 km and >50% capacity increases. While we recognize that capacity expansion through HVDC conversion may be the optimal solution in only some situations, with future improvements in the cost and performance of solid-state power electronics, conversion to HVDC could be attractive in a growing set of circumstances.

The Materials Genome Initiative (MGI) advanced a new paradigm for materials discovery and design, namely that the pace of new materials deployment could be accelerated through complementary efforts in theory, computation, and experiment. Along with numerous successes, new challenges are inviting researchers to refocus the efforts and approaches that were originally inspired by the MGI. In May 2017, the National Science Foundation sponsored the workshop “Advancing and Accelerating Materials Innovation Through the Synergistic Interaction among Computation, Experiment, and Theory: Opening New Frontiers” to review accomplishments that emerged from investments in science and infrastructure under the MGI, identify scientific opportunities in this new environment, examine how to effectively utilize new materials innovation infrastructure, and discuss challenges in achieving accelerated materials research through the seamless integration of experiment, computation, and theory. This article summarizes key findings from the workshop and provides perspectives that aim to guide the direction of future materials research and its translation into societal impacts.

A number of analyses, meta-analyses, and assessments, including those performed by the Intergovernmental Panel on Climate Change, the National Oceanic and Atmospheric Administration, the National Renewable Energy Laboratory, and the International Energy Agency, have concluded that deployment of a diverse portfolio of clean energy technologies makes a transition to a low-carbon-emission energy system both more feasible and less costly than other pathways. In contrast, Jacobson et al. [Jacobson MZ, Delucchi MA, Cameron MA, Frew BA (2015) Proc Natl Acad Sci USA 112(49):15060–15065] argue that it is feasible to provide “low-cost solutions to the grid reliability problem with 100% penetration of WWS [wind, water and solar power] across all energy sectors in the continental United States between 2050 and 2055”, with only electricity and hydrogen as energy carriers. In this paper, we evaluate that study and find significant shortcomings in the analysis. In particular, we point out that this work used invalid modeling tools, contained modeling errors, and made implausible and inadequately supported assumptions. Policy makers should treat with caution any visions of a rapid, reliable, and low-cost transition to entire energy systems that relies almost exclusively on wind, solar, and hydroelectric power.