Material culture both reflects and causes human cognitive evolution.

Our commentary suggests that different materialities (fragile, enduring, and mixed) may influence cognitive evolution. Building on Stibbard-Hawkes, we propose that predictive brains minimise errors and seek information, actively structuring environments for epistemic benefits. This perspective complements Stibbard-Hawkes' view.

Surgical Dressing Cosmesis in the Immediate Postoperative Setting: A Crowdsourcing-based Study.

Background: Postoperative dressings expedite wound healing and decrease the rate of infection. Options for wound dressings vary based on cost, time to apply, method of wound healing, and availability at the hospital; however, a significant difference in postoperative complications between each type has not been found. As such, this study evaluates patient cosmetic preferences for various wound dressings as it relates to early postoperative satisfaction.

"Listening to understand," exploring postpartum women's perceptions of their social networks and social support in relation to their health behaviors and weight: A qualitative exploratory study.

Background: Postpartum is a critical period to interrupt weight gain across the lifespan, decrease weight-related risk in future pregnancies, promote healthy behaviors that are often adopted during pregnancy, and improve long-term health. Because the postpartum period is marked by unique challenges to a person's ability to prioritize healthy behaviors, a multi-level/domain approach to intervention beyond the individual-level factors of diet and activity is needed.

Objectives: The purpose of this study was to understand postpartum people's perceptions about the relationship between their social networks and support, and their health behaviors and weight.

Machine learning in the prediction of human wellbeing.

Subjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents' self-reported wellbeing.

Ultrasensitive detection of intact SARS-CoV-2 particles in complex biofluids using microfluidic affinity capture.

Measuring virus in biofluids is complicated by confounding biomolecules coisolated with viral nucleic acids. To address this, we developed an affinity-based microfluidic device for specific capture of intact severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our approach used an engineered angiotensin-converting enzyme 2 to capture intact virus from plasma and other complex biofluids.