Automated Motion Analysis of Adherent Cells in Monolayer Culture

This paper presents a novel method for tracking and characterizing adherent cells in monolayer culture. A system of cell tracking employing computer vision techniques was applied to time-lapse videos of replicate normal human uro-epithelial cell cultures exposed to different concentrations of adenosine triphosphate (ATP), acquired over a 20 h period. Subsequent analysis, comprising feature extraction, demonstrated the ability of the technique to successfully separate the modulated classes of cell

A mechanistic account of bodily resonance and implicit bias

Implicit social biases play a critical role in shaping our attitudes towards other people. Such biases are thought to arise, in part, from a comparison between features of one's own self-image and those of another agent, a process known as 'bodily resonance'. Recent data have demonstrated that implicit bias can be remarkably plastic, being modulated by brief immersive virtual reality experiences that place participants in a virtual body with features of an out-group member. Here, we provide a mechanistic account of bodily resonance and implicit bias in terms of a putative self-image network that encodes associations between different features of an agent. When subsequently perceiving another agent, the output of this self-image network is proportional to the overlap between their respective features, providing an index of bodily resonance. By combining the self-image network with a drift diffusion model of decision making, we simulate performance on the implicit association test (IAT) and show that the model captures the ubiquitous implicit bias towards in-group members. We subsequently demonstrate that this implicit bias can be modulated by a simulated illusory body ownership experience, consistent with empirical data; and that the magnitude and plasticity of implicit bias correlates with self-esteem. Hence, we provide a simple mechanistic account of bodily resonance and implicit bias which could contribute to the development of interventions for reducing the negative evaluation of social out-groups

Computational approaches for understanding the diagnosis and treatment of Parkinson's disease

This study describes how the application of evolutionary algorithms (EAs) can be used to study motor function in humans with Parkinson's disease (PD) and in animal models of PD. Human data is obtained using commercially available sensors via a range of non-invasive procedures that follow conventional clinical practice. EAs can then be used to classify human data for a range of uses, including diagnosis and disease monitoring. New results are presented that demonstrate how EAs can also be used to classify fruit flies with and without genetic mutations that cause Parkinson's by using measurements of the proboscis extension reflex. The case is made for a computational approach that can be applied across human and animal studies of PD and lays the way for evaluation of existing and new drug therapies in a truly objective way