In February 2014, porcine deltacoronavirus (PDCoV) was identified in the United States. We developed a PDCoV real-time reverse transcription PCR that identified PDCoV in 30% of samples tested. Four additional PDCoV genomes from the United States were sequenced; these had ≈99%–100% nt similarity to the other US PDCoV strains.

This paper views hiring as a contextual bandit problem: to find the best workers over time, firms must balance "exploitation" (selecting from groups with proven track records) with "exploration" (selecting from under-represented groups to learn about quality). Yet modern hiring algorithms, based on "supervised learning" approaches, are designed solely for exploitation. Instead, we build a resume screening algorithm that values exploration by evaluating candidates according to their statistical upside potential. Using data from professional services recruiting within a Fortune 500 firm, we show that this approach improves the quality (as measured by eventual hiring rates) of candidates selected for an interview, while also increasing demographic diversity, relative to the firm's existing practices. The same is not true for traditional supervised learning based algorithms, which improve hiring rates but select far fewer Black and Hispanic applicants. Together, our results highlight the importance of incorporating exploration in developing decision-making algorithms that are potentially both more efficient and equitable.

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The distinction between prevention and detection behaviors provides a useful guideline for appropriately framing health messages in terms of gains or losses. However, this guideline assumes that everyone perceives the outcomes associated with a behavior in a consistent manner, as prevention or detection. Individuals’ perceptions of a behavior vary, and so the effects of framed messages may be optimized by considering individuals’ perceptions rather than the prevention or detection function of the behavior. The authors tested this message-framing paradigm in a secondary analysis of data from a trial evaluating gain-framed smoking cessation counseling delivered through a state quitline (Toll et al., 2010). Smokers (N = 2,032) who called a state quitline received either gain-framed or standard care messages. Smokers’ beliefs about the positive consequences of stopping smoking (outcome expectancies) were evaluated at baseline. Smoking status and self-efficacy were assessed at 3 months. Outcome expectancies moderated the framing effects among men but not among women. Men in the gain-framed counseling condition who had positive outcome expectancies were more likely to quit and had more confidence in their ability to quit or to remain abstinent than men who were uncertain of the positive outcome of smoking cessation. Among men, self-efficacy mediated the moderated framing effects of the intervention on quit status. These findings suggest that it may be useful to consider sex and individual differences in outcome expectancies when delivering gain-framed smoking cessation messages in the context of a state quitline.

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