Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling

\ua9 2023, The Author(s).Purpose of Review: To explore the advances and future research directions in image analysis and computational modelling of human stem cells (hSCs) for ophthalmological applications. Recent Findings: hSCs hold great potential in ocular regenerative medicine due to their application in cell-based therapies and in disease modelling and drug discovery using state-of-the-art 2D and 3D organoid models. However, a deeper characterisation of their complex, multi-scale properties is required to optimise their translation to clinical practice. Image analysis combined with computational modelling is a powerful tool to explore mechanisms of hSC behaviour and aid clinical diagnosis and therapy. Summary: Many computational models draw on a variety of techniques, often blending continuum and discrete approaches, and have been used to describe cell differentiation and self-organisation. Machine learning tools are having a significant impact in model development and improving image classification processes for clinical diagnosis and treatment and will be the focus of much future research

Corrigendum to “Estimating the reproduction number, R₀, from individual-based models of tree disease spread” [Ecological Modelling 489 (2024) 110630] (Ecological Modelling (2024) 489, (S030438002400019X), (10.1016/j.ecolmodel.2024.110630))

\ua9 2024 The Authors. The authors regret that in the first publication of the manuscript affiliation b was incorrectly listed as “Faculty of Environment, University of Leeds, Leeds, LS2 9JT, UK”. The correct affiliation is “School of Food Science & Nutrition, Faculty of Environment, University of Leeds, Leeds, LS2 9JT, UK”. The authors would like to apologise for any inconvenience caused

Estimating the reproduction number, R0, from individual-based models of tree disease spread

\ua9 2024 The Author(s)Tree populations worldwide are facing an unprecedented threat from a variety of tree diseases and invasive pests. Their spread, exacerbated by increasing globalisation and climate change, has an enormous environmental, economic and social impact. Computational individual-based models are a popular tool for describing and forecasting the spread of tree diseases due to their flexibility and ability to reveal collective behaviours. In this paper we present a versatile individual-based model with a Gaussian infectivity kernel to describe the spread of a generic tree disease through a synthetic treescape. We then explore several methods of calculating the basic reproduction number R0, a characteristic measurement of disease infectivity, defining the expected number of new infections resulting from one newly infected individual throughout their infectious period. It is a useful comparative summary parameter of a disease and can be used to explore the threshold dynamics of epidemics through mathematical models. We demonstrate several methods of estimating R0 through the individual-based model, including contact tracing, inferring the Kermack–McKendrick SIR model parameters using the linear noise approximation, and an analytical approximation. As an illustrative example, we then use the model and each of the methods to calculate estimates of R0 for the ash dieback epidemic in the UK