Competing meanings of childhood and the social construction of child sexual abuse in the Caribbean

This article examines the dynamic interplay between competing meanings of childhood and the social construction of sexual abuse in the Caribbean. Drawing on qualitative data from a study undertaken in six Caribbean countries, the article suggests that Caribbean childhoods are neither wholly global nor local but hybrid creations of the region’s complex historical, social and cultural specificities, real or imagined. As childhood is a concept that lies at the intersection of multiple frames of reference, context-specific definitions of childhood – what it means to be a child – have a direct impact on the way in which the issue of child sexual abuse is constructed and understood

Sodium/Na β″ Alumina Interface:Effect of Pressure on Voids

Three-electrode studies coupled with tomographic imaging of the Na/Na-β″-alumina interface reveal that voids form in the Na metal at the interface on stripping and they accumulate on cycling, leading to increasing interfacial current density, dendrite formation on plating, short circuit, and cell failure. The process occurs above a critical current for stripping (CCS) for a given stack pressure, which sets the upper limit on current density that avoids cell failure, in line with results for the Li/solid-electrolyte interface. The pressure required to avoid cell failure varies linearly with current density, indicating that Na creep rather than diffusion per se dominates Na transport to the interface and that significant pressures are required to prevent cell death, &gt;9 MPa at 2.5</p

A foundation model for generalizable disease detection from retinal images

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders 1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications 2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.</p

A foundation model for generalizable disease detection from retinal images

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders 1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications 2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.</p