Science lives: School choices and ‘natural tendencies’

An analysis of 12 semi-structured interviews with university-based scientists and non-scientists illustrates their life journeys towards, or away from, science and the strengths and impact of life occurrences leading them to choose science or non-science professions. We have adopted narrative approaches and used Mezirow's transformative learning theory framework. The areas of discussion from the result have stressed on three main categories that include ‘smooth transition’, ‘incremental wavering transition' and ‘transformative transition’. The article concludes by discussing the key influences that shaped initial attitudes and direction in these people through natural inclination, environmental inspirations and perceptions of science

The gist of instructional leadership practised in Swaziland primary schools

A growing body of scholarship links instructional leadership to effective teaching and learning. This article looks at the ‘what’ of instructional leadership as practised in Swaziland primary schools. A qualitative investigation was undertaken based on individual and focus group interviews conducted at eight primary schools in the Hhohho region of Swaziland. The findings show that demonstrative leadership accompanied by collaborative support and recognition for achievement are important features of an effective instructional leadership programme. The main limitations to optimal learning are the collection of school fees during school hours and balancing English as the language of instruction with preserving the indigenous language. The findings emphasize the importance of mutual effort as the main component of effective teaching and learning.IS

A marine biogenic source of atmospheric ice nucleating particles

The amount of ice present in clouds can affect cloud lifetime, precipitation and radiative properties1,2. The formation of ice in clouds is facilitated by the presence of airborne ice nucleating particles1,2. Sea spray is one of the major global sources of atmospheric particles, but it is unclear to what extent these particles are capable of nucleating ice3-11. Sea spray aerosol contains large amounts of organic material that is ejected into the atmosphere during bubble bursting at the organically enriched sea-air interface or sea surface microlayer12-19. Here we show that organic material in the sea surface microlayer nucleates ice under conditions relevant for mixed-phase cloud and high-altitude ice cloud formation. The ice nucleating material is likely biogenic and less than ~0.2 μm in size. We find that exudates separated from cells of the marine diatom T. Pseudonana nucleate ice and propose that organic material associated with phytoplankton cell exudates is a likely candidate for the observed ice nucleating ability of the microlayer samples. Global model simulations of marine organic aerosol in combination with our measurements suggest that marine organic material may be an important source of ice nucleating particles in remote marine environments such as the Southern Ocean, North Pacific and North Atlantic

Lagrangian ocean analysis: fundamentals and practices

Lagrangian analysis is a powerful way to analyse the output of ocean circulation models and other ocean velocity data such as from altimetry. In the Lagrangian approach, large sets of virtual particles are integrated within the three-dimensional, time-evolving velocity fields. Over several decades, a variety of tools and methods for this purpose have emerged. Here, we review the state of the art in the field of Lagrangian analysis of ocean velocity data, starting from a fundamental kinematic framework and with a focus on large-scale open ocean applications. Beyond the use of explicit velocity fields, we consider the influence of unresolved physics and dynamics on particle trajectories. We comprehensively list and discuss the tools currently available for tracking virtual particles. We then showcase some of the innovative applications of trajectory data, and conclude with some open questions and an outlook. The overall goal of this review paper is to reconcile some of the different techniques and methods in Lagrangian ocean analysis, while recognising the rich diversity of codes that have and continue to emerge, and the challenges of the coming age of petascale computing

The Liverpool alcohol-related liver disease algorithm identifies twice as many emergency admissions compared to standard methods when applied to Hospital Episode Statistics for England

BackgroundEmergency admissions in England for alcohol-related liver disease (ArLD) have increased steadily for decades. Statistics based on administrative data typically focus on the ArLD-specific code as the primary diagnosis and are therefore at risk of excluding ArLD admissions defined by other coding combinations.AimTo deploy the Liverpool ArLD Algorithm (LAA), which accounts for alternative coding patterns (e.g., ArLD secondary diagnosis with alcohol/liver-related primary diagnosis), to national and local datasets in the context of studying trends in ArLD admissions before and during the COVID-19 pandemic.MethodsWe applied the standard approach and LAA to Hospital Episode Statistics for England (2013-21). The algorithm was also deployed at 28 hospitals to discharge coding for emergency admissions during a common 7-day period in 2019 and 2020, in which eligible patient records were reviewed manually to verify the diagnosis and extract data.ResultsNationally, LAA identified approximately 100% more monthly emergency admissions from 2013 to 2021 than the standard method. The annual number of ArLD-specific admissions increased by 30.4%. Of 39,667 admissions in 2020/21, only 19,949 were identified with standard approach, an estimated admission cost of £70 million in under-recorded cases. Within 28 local hospital datasets, 233 admissions were identified using the standard approach and a further 250 locally verified cases using the LAA (107% uplift). There was an 18% absolute increase in ArLD admissions in the seven-day evaluation period in 2020 versus 2019. There were no differences in disease severity or mortality, or in the proportion of admissions with decompensation of cirrhosis or alcoholic hepatitis.ConclusionsThe LAA can be applied successfully to local and national datasets. It consistently identifies approximately 100% more cases than the standard coding approach. The algorithm has revealed the true extent of ArLD admissions. The pandemic has compounded a long-term rise in ArLD admissions and mortality