Post-concussion symptom burden and dynamics: Insights from a digital health intervention and machine learning.

Individuals who sustain a concussion can experience a range of symptoms which can significantly impact their quality of life and functional outcome. This study aims to understand the nature and recovery trajectories of post-concussion symptomatology by applying an unsupervised machine learning approach to data captured from a digital health intervention (HeadOn). As part of the 35-day program, patients complete a daily symptom diary which rates 8 post-concussion symptoms.

Combined effect of confinement and dielectric exclusion on ion adsorption in slits, pores, and cavities.

We present simulation results for the Donnan equilibrium between a homogeneous bulk reservoir and inhomogeneous confining geometries with varying number of restricted dimensions, . Planar slits ( = 1), cylindrical pores ( = 2), and spherical cavities ( = 3) are considered. The walls have a negative surface charge density.

The Precise Synthesis of Ultradense Bottlebrush Polymers Unearths Unique Trends in Lyotropic Ordering.

Biomacromolecular networks with multiscale fibrillar structures are characterized by exceptional mechanical properties, making them attractive architectures for synthetic materials. However, there is a dearth of synthetic polymeric building blocks capable of forming similarly structured networks. Bottlebrush polymers (BBPs) are anisotropic graft polymers with the potential to mimic and replace biomacromolecules such as tropocollagen for the fabrication of synthetic fibrillar networks; however, a longstanding limitation of BBPs has been the lack of rigidity necessary to access the lyotropic ordering that underpins the formation of collagenous networks.

Comparing neural networks against click train detectors to reveal temporal trends in passive acoustic sperm whale detections.

Passive acoustic monitoring (PAM) is an increasingly popular tool to study vocalising species. The amount of data generated by PAM studies calls for robust automatic classifiers. Deep learning (DL) techniques have been proven effective in identifying acoustic signals in challenging datasets, but due to their black-box nature their underlying biases are hard to quantify.

Teaching Early Reading Skills to Adults With Intellectual Disabilities Using a Support Worker/Family Carer Mediated Online Reading Programme: A Feasibility Randomised Controlled Trial.

Background: There is a paucity of research into interventions that help people with intellectual disabilities learn to read. This feasibility study examines whether an online reading programme, Headsprout, with additional support strategies and supervision (the intervention), can be delivered by support workers/family carers and the feasibility of conducting a later large-scale effectiveness trial.

Methods: The study used a 2-arm randomised controlled trial (RCT) design with an embedded process evaluation using a mixed methods approach.