Real-time 3D tracking of swimming microbes using digital holographic microscopy and deep learning.

The three-dimensional swimming tracks of motile microorganisms can be used to identify their species, which holds promise for the rapid identification of bacterial pathogens. The tracks also provide detailed information on the cells' responses to external stimuli such as chemical gradients and physical objects. Digital holographic microscopy (DHM) is a well-established, but computationally intensive method for obtaining three-dimensional cell tracks from video microscopy data.

Medical practitioner perspectives on AI in emergency triage.

Introduction: A proposed Diagnostic AI System for Robot-Assisted Triage ("DAISY") is under development to support Emergency Department ("ED") triage following increasing reports of overcrowding and shortage of staff in ED care experienced within National Health Service, England ("NHS") but also globally. DAISY aims to reduce ED patient wait times and medical practitioner overload. The objective of this study was to explore NHS health practitioners' perspectives and attitudes towards the future use of AI-supported technologies in ED triage.

On the use of AI for Generation of Functional Music to Improve Mental Health.

Increasingly music has been shown to have both physical and mental health benefits including improvements in cardiovascular health, a link to reduction of cases of dementia in elderly populations, and improvements in markers of general mental well-being such as stress reduction. Here, we describe short case studies addressing general mental well-being (anxiety, stress-reduction) through AI-driven music generation. Engaging in active listening and music-making activities (especially for at risk age groups) can be particularly beneficial, and the practice of music therapy has been shown to be helpful in a range of use cases across a wide age range.

A Digital Repository and Execution Platform for Interactive Scholarly Publications in Neuroscience.

The CARMEN Virtual Laboratory (VL) is a cloud-based platform which allows neuroscientists to store, share, develop, execute, reproduce and publicise their work. This paper describes new functionality in the CARMEN VL: an interactive publications repository. This new facility allows users to link data and software to publications.

The CARMEN software as a service infrastructure.

The CARMEN platform allows neuroscientists to share data, metadata, services and workflows, and to execute these services and workflows remotely via a Web portal. This paper describes how we implemented a service-based infrastructure into the CARMEN Virtual Laboratory. A Software as a Service framework was developed to allow generic new and legacy code to be deployed as services on a heterogeneous execution framework.

A high performance k-NN approach using binary neural networks.

This paper evaluates a novel k-nearest neighbour (k-NN) classifier built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and numeric data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall a candidate set of matching records, which are then processed by a conventional k-NN approach to determine the k-best matches.