Workplace and lifestyle heterogeneity in subjective wellbeing: Latent Class Analysis of UK Time Use Survey before and during COVID-19 (preprint)

Background: Mental health in the UK had deteriorated compared with pre-pandemic trends. The impact of COVID-19 on the subjective wellbeing of working populations with distinct lifestyles is not yet studied. Methods: Combining time use surveys collected pre- and during COVID-19, latent class analysis was used to identify distinct lifestyles based on aggregated daily activity patterns and reported working modes. We provide qualitative pen portraits alongside pre-versus-during pandemic comparisons of intraday time use and wellbeing patterns. Lifestyle heterogeneity in wellbeing was quantified in relation to aggregated activity types. Results: COVID-19 impact on wellbeing varied significantly between usual working hours (6am-6pm) and rest of the day. The decline in wellbeing outside of usual working hours was significant and consistent across lifestyles. During usual working hours, the direction of impact varied in line with working modes: wellbeing of homeworkers decreased, remained relatively stable for commuters, and increased for certain hybrid workers. Magnitude of impact correlates strongly with lifestyle: those working long and dispersed hours are more sensitive, whereas non-work dominated lifestyles are more resilient. Conclusion: The direction and magnitude of impact from COVID-19 were not uniformly manifested across activity types, time of day, and latent lifestyles. Blurring work-life boundaries and general anxiety about the pandemic may be key determinants of the decline outside of usual working hours. During usual working hours, strong yet complex correlations between wellbeing and time-use changes suggested that policies aiming to enhance wellbeing of workers need to consider not only spatial flexibility but also provide wider support for temporal flexibility.

De novo design of ACE2 protein decoys to neutralize SARS-CoV-2 (preprint)

There is an urgent need for the ability to rapidly develop effective countermeasures for emerging biological threats, such as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that causes the ongoing coronavirus disease 2019 (COVID-19) pandemic. We have developed a generalized computational design strategy to rapidly engineer de novo proteins that precisely recapitulate the protein surface targeted by biological agents, like viruses, to gain entry into cells. The designed proteins act as decoys that block cellular entry and aim to be resilient to viral mutational escape. Using our novel platform, in less than ten weeks, we engineered, validated, and optimized de novo protein decoys of human angiotensin-converting enzyme 2 (hACE2), the membrane-associated protein that SARS-CoV-2 exploits to infect cells. Our optimized designs are hyperstable de novo proteins ([~]18-37 kDa), have high affinity for the SARS-CoV-2 receptor binding domain (RBD) and can potently inhibit the virus infection and replication in vitro. Future refinements to our strategy can enable the rapid development of other therapeutic de novo protein decoys, not limited to neutralizing viruses, but to combat any agent that explicitly interacts with cell surface proteins to cause disease.