To determine patterns of mask wearing and other infection prevention behaviours in cities where mask wearing is not a cultural norm, over two time periods of the pandemic. A cross-sectional survey of masks and other preventive behaviours in adults ≥18 years was conducted in five cities (Sydney, Melbourne, London, Phoenix and New York). Data was analysed according to the epidemiology of COVID-19, mask mandates and a range of predictors of mask wearing. The most common measures used were avoiding public areas (80.4%), hand hygiene (76.4%), masks (71.8%) and distancing (67.6%). Over 40% of people avoided medical facilities. These measures decreased from March-July 2020. Pandemic fatigue was associated with younger age, low perceived severity of COVID-19 and declining COVID-19 prevalence. Predictors of mask wearing were location (US, UK), mandates, age <50 years, education, having symptoms and knowing someone with COVID-19. Negative experiences with mask wearing and low perceived severity of COVID-19 reduced mask wearing. Most respondents (98%) believed that hand washing and distancing were necessary, and 80% reported no change or stricter adherence to these measures when wearing masks. Pandemic mitigation measures were widely reported across all cities, but decreased between March and July 2020. Pandemic fatigue was more common in younger people. Cities with mandates had higher rates of mask wearing. Promotion of mask use for older people may be useful. Masks did not result in reduction of other hygiene measures.

This paper addresses the problem of determining the next set of releases in the course of software evolution. It formulates both ranking and selection of candidate software components as a series of feature subset selection problems to which search based software engineering can be applied. The approach is automated using greedy and simulated annealing algorithms and evaluated using a set of software components from the component base of a large telecommunications organization. The results are compared to those obtained by a panel of (human) experts. The results show that the two automated approaches convincingly outperform the expert judgment approach

MicroRNAs (miRNAs) are important regulators of diverse physiological and pathophysiological processes. MiRNA families and clusters are two key features in miRNA biology. Here we explore the use of CRISPR/Cas9 as a powerful tool to delineate the function and regulation of miRNA families and clusters. We focused on four miRNA clusters composed of miRNA members of the same family, homo-clusters or different families, hetero-clusters. Our results highlight different regulatory mechanisms in miRNA cluster expression. In the case of the miR-497~195 cluster, editing of miR-195 led to a significant decrease in the expression of the other miRNA in the cluster, miR-497a. Although no gene editing was detected in the miR-497a genomic locus, computational simulation revealed alteration in the three dimensional structure of the pri-miR-497~195 that may affect its processing. In cluster miR-143~145 our results imply a feed-forward regulation, although structural changes cannot be ruled out. Furthermore, in the miR-17~92 and miR-106~25 clusters no interdependency in miRNA expression was observed. Our findings suggest that CRISPR/Cas9 is a powerful gene editing tool that can uncover novel mechanisms of clustered miRNA regulation and function.

The protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search.