Executive functioning skills and their environmental predictors among pre-school aged children in South Africa and The Gambia

Executive functions (EFs) in early childhood are predictors of later developmental outcomes and school readiness. Much of the research on EFs and their psychosocial correlates has been conducted in high-income, minority world countries, which represent a small and biased portion of children globally. The aim of this study is to examine EFs among children aged 3–5 years in two African countries, South Africa (SA) and The Gambia (GM), and to explore shared and distinct predictors of EFs in these settings. The SA sample (N = 243, 51.9% female) was recruited from low-income communities within the Cape Town Metropolitan area. In GM, participants (N = 171, 49.7% female) were recruited from the rural West Kiang region. EFs, working memory (WM), inhibitory control (IC) and cognitive flexibility (CF), were measured using tablet-based tasks. Associations between EF task performance and indicators of socioeconomic status (household assets, caregiver education) and family enrichment factors (enrichment activities, diversity of caregivers) were assessed. Participants in SA scored higher on all EF tasks, but children in both sites predominantly scored within the expected range for their age. There were no associations between EFs and household or familial variables in SA, except for a trend-level association between caregiver education and CF. Patterns were similar in GM, where there was a trend-level association between WM and enrichment activities but no other relationships. We challenge the postulation that children in low-income settings have poorer EFs, simply due to lower socioeconomic status, but highlight the need to identify predictors of EFs in diverse, global settings. Research Highlights: Assessed Executive Functioning (EF) skills and their psychosocial predictors among pre-school aged children (aged 3–5 years) in two African settings (The Gambia and South Africa). On average, children within each setting performed within the expected range for their age, although children in South Africa had higher scores across tasks. There was little evidence of any association between socioeconomic variables and EFs in either site. Enrichment activities were marginally associated with better working memory in The Gambia, and caregiver education with cognitive flexibility in South Africa, both associations were trend-level significance

Public perceptions of management priorities for the English Channel region

The English Channel region is an area of high conservational importance, as well being a contributor to economic prosperity, social well-being and quality of life of the people living around it. There is a need to incorporate societal elements into marine and coastal governance, to improve management of the Channel ecosystem. Public Perception Research (PPR) is a relatively unexplored dimension of marine science, with limited research at the scale of the Channel region. Using an online survey, this study examined the public’s use of, and funding priorities for, the Channel’s marine and coastal environment. It revealed that there are variations in how the English and French coastlines are used. Environmental issues were generally viewed as being more important than economic ones. Country-level differences were observed for public uses of, and priorities for the Channel region. Cleaner water and beaches, and improved coastal flood defences, were more highly prioritised by English respondents, while offshore renewable energy and sustainability of businesses were more highly prioritised by French respondents. The paper contributes to the debate on the value of PPR by addressing evidence gaps in the English Channel region, and to PPR literature more broadly. It provides baseline data to inform future engagement strategies for the marine and coastal governance of the Channel region specifically. It also identifies how this type of research has implications for the wider marine and coastal environment, including contributing to Sustainable Development Goal 14 on conserving and sustainably using the oceans, seas, and marine resources

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies

Magnetic Flux Density Sensor Analysis

Undergraduate Basi