Over the last ten years, Oosterhof and Todorov's valence-dominance model has emerged as the most prominent account of how people evaluate faces on social dimensions. In this model, two dimensions (valence and dominance) underpin social judgments of faces. Because this model has primarily been developed and tested in Western regions, it is unclear whether these findings apply to other regions. We addressed this question by replicating Oosterhof and Todorov's methodology across 11 world regions, 41 countries, and 11,570 participants. When we used Oosterhof and Todorov's original analysis strategy, the valence-dominance model generalized across regions. When we used an alternative methodology to allow for correlated dimensions we observed much less generalization. Collectively, these results suggest that, while the valence-dominance model generalizes very well across regions when dimensions are forced to be orthogonal, regional differences are revealed when we use different extraction methods, correlate and rotate the dimension reduction solution.

Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses. Consequently, readers and peer-reviewers alike may either overestimate or underestimate the validity and credibility of an ML-based model. Conversely, ML experts without clinical experience may present details of the analysis that are too granular for a clinical readership to assess. Overwhelming evidence has shown poor reproducibility and reporting of ML models in clinical research suggesting the need for ML analyses to be presented in a clear, concise, and comprehensible manner to facilitate understanding and critical evaluation. We present a recommendation for transparent and structured reporting of ML analysis results specifically directed at clinical researchers. Furthermore, we provide a list of key reporting elements with examples that can be used as a template when preparing and submitting ML-based manuscripts for the same audience.

Key Points Question What factors are associated with observed trends in the in-hospital mortality rates in the United States during the first 9 months of the COVID-19 pandemic? Findings In this cohort study of 20 736 patients, in-hospital mortality rates decreased in the US between March and November 2020, even after accounting for the changing mix in patient age, sex, comorbidities, and disease severity at the time of admission. Hospital and intensive care unit length of stay and use of mechanical ventilation decreased over time, whereas the use of glucocorticoids and remdesivir increased. Meaning Changes in age, sex, comorbidities, and disease severity among patients with COVID-19 do not fully explain the decrease in the in-hospital mortality rates observed during the first 9 months of the COVID-19 pandemic.

Continuous nevirapine may be associated with increased toxicity among HIV-1-infected pregnant women with CD4 cell counts greater than 250 cells/microL, as has been observed in non-pregnant women.

Subjects performed on four sensory discrimination tasks, with controlled success and failure feedback on each, and then learned how others had done on the fourth task. Systematic variations in feedback produced a factorial design varying the distinctiveness of the fourth task, consistency of performance on the fourth and the very similar first task, successful or failing performance on the fourth task, and the predominant success-failure of others (consensus). Subjects were to consider the fourth task and evaluate the importance of ability, effort, task difficulty, and luck. The results showed that successful subjects made more internal and fewer external attributions than failing subjects. In general, subjects became less "logical" and more defensive the more unrelieved their failure. Luck was the main resort as a failure explanation. Under less dire conditions, the roles of consensus and distinctiveness were in line with theoretical predictions. Inconsistency led to task attributions, a significant reversal of prediction.

Integrating the open science movement with impactful discoveries in science, velocity of technology, and raw power of cloud computing has led to an unprecedented opportunity for scientific discovery. The American Heart Association recently established the Precision Medicine Platform 1 through the efforts of multiple American Heart Association volunteers and a collaboration with Amazon Web Services. The cloud-based platform, powered by Amazon Web Services and available at https://precision.heart.org, was founded on the FAIR principles (findable, accessible, interoperable, and reusable) 2 and includes secure collaboration areas (workspaces) and an open sharing area. The goals of the platform are to democratize data, to make it easy to search across orthogonal data sets, to provide a secure workspace to leverage the power of cloud computing, and to provide a forum for users to share insights. Multiple learning tools are available, including video tutorials, templates using open interactive programming framework, and a forum for interaction among community members. 3 Keywordscloud computing; machine learning; risk factors; statistics [publication type] DATA HARMONIZATION AND SEARCHINGWhen accessing large public data sets today, researchers have to find, access, download, and interpret each data set individually. Researchers must untangle and interpret the data and then expend resources to house the data. In addition, the lack of harmonization across multiple data sets obviates the ability of researchers to combine data sources and to confirm or generate cogent findings.The platform aims to address these challenges through a transparent and explicit harmonization approach: identifying common parameters across all data sets and thus allowing users to interactively find or merge data of interest. Data harmonization on the platform is transparent, providing insight into how each data variable is defined. Filters and

The pursuit of justice increasingly relies on productive interactions between witnesses and investigators from diverse cultural backgrounds during investigative interviews. To date, the role of cultural context has largely been ignored by researchers in the field of investigative interviewing, despite repeated requests from practitioners and policymakers for evidence‐based guidance for the conduct of interviews with people from different cultures. Through examining cultural differences in human memory and communication and considering specific contextual challenges for investigative interviewing through the lens of culture, this review and associated commentaries highlight the scope for considering culture and human diversity in research on, and the practice of, investigative interviewing with victims, witnesses, and other sources. Across 11 commentaries, contributors highlight the importance of considering the role of culture in different investigative interviewing practices (e.g., rapport building, questioning techniques) and contexts (e.g., gender‐based violence, asylum seeking, child abuse), address common areas of cultural mismatch between interviewer–interviewee expectations, and identify critical future routes for research. We call for an increased focus in the investigative interviewing literature on the nature and needs of our global community and encourage constructive and collaborative discussion between researchers and practitioners from around the world to better identify specific challenges and work together towards evidence‐based solutions.

ObjectivesThis study examined patterns of sexual violence against adults and children in Kenya during the COVID-19 pandemic to inform sexual violence prevention, protection, and response efforts.DesignA prospective cross-sectional research design was used with data collected from March to August 2020.SettingKenya.Participants317 adults, 224 children.Main measuresPerpetrator and survivor demographic data, characteristics of the assault.ResultsBivariate analyses found that children were more likely than adults to be attacked during daytime (59% vs 44%, p<0.001) by a single perpetrator rather than multiple perpetrators (31% vs 13%, p<0.001) in a private as opposed to a public location (66% vs 45%, p<0.001) and by someone known to the child (76% vs 58%, p<0.001). Children were violated most often by neighbours (29%) and family members (20%), whereas adults were equally likely to be attacked by strangers (41%) and persons known to them (59%). These variables were entered as predictors into a logistic regression model that significantly predicted the age group of the survivor, χ2(5, n=541)=53.3, p<0.001.ConclusionsPatterns of sexual violence against adult and child survivors during the COVID-19 pandemic are different, suggesting age-related measures are needed in national emergency plans to adequately address sexual violence during the pandemic and for future humanitarian crises.