Drosophila PINK1 and parkin loss-of-function mutants display a range of non-motor Parkinson's disease phenotypes.

Parkinson's disease (PD) is more commonly associated with its motor symptoms and the related degeneration of dopamine (DA) neurons. However, it is becoming increasingly clear that PD patients also display a wide range of non-motor symptoms, including memory deficits and disruptions of their sleep-wake cycles. These have a large impact on their quality of life, and often precede the onset of motor symptoms, but their etiology is poorly understood. The fruit fly Drosophila has already been successfully used to model PD, and has been used extensively to study relevant non-motor behaviours in other contexts, but little attention has yet been paid to modelling non-motor symptoms of PD in this genetically tractable organism. We examined memory performance and circadian rhythms in flies with loss-of-function mutations in two PD genes: PINK1 and parkin. We found learning and memory abnormalities in both mutant genotypes, as well as a weakening of circadian rhythms that is underpinned by electrophysiological changes in clock neurons. Our study paves the way for further work that may help us understand the mechanisms underlying these neglected aspects of PD, thus identifying new targets for treatments to address these non-motor problems specifically and perhaps even to halt disease progression in its prodromal phase.

Is it riskier to meet 100 people outdoors or 14 people indoors? Comparing public and expert perceptions of COVID-19 risk.

People have limited capacity to process and integrate multiple sources of information, so how do they integrate multiple contextual risk factors for Coronavirus disease (COVID-19) infection? In June 2020, we elicited risk perceptions from a nationally representative sample of the public (N = 800) using three psychologically-distinct tasks. Responses were compared to a sample of medical experts who completed the same tasks. Relative to experts, the public perceived lower risk associated with environmental factors (such as whether a gathering takes place indoors or outdoors) and were less inclined to treat risk factors as multiplicative. Our results are consistent with a heuristic simply to "avoid people" and with a coarse (e.g., "safe or unsafe") classification of social settings. A further task, completed only by the general public sample, generated novel evidence that when infection risk competes with risk in another domain (e.g., a different medical risk), people perceive a lower likelihood of contracting the virus. These results inform the policy response to the pandemic and have implications for understanding differences between expert and lay perception of risk. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Using decision aids to support self-isolation during the COVID-19 pandemic.

OBJECTIVE: Self-isolation is a vital element of efforts to contain COVID-19. We set out to test whether decision aids can support self-isolation. DESIGN: We conducted a pre-registered online experiment with a nationally representative sample (n = 500). Three stages tested: (i) whether decision trees help people to decide whether they need to self-isolate; (ii) whether an online planning tool increases people's confidence in their ability to self-isolate; and (iii) whether infographics help people to absorb advice on managing a household in which someone must self-isolate. MAIN OUTCOME MEASURES: (i) Accuracy of matching symptom patterns to a response scale for the need to self-isolate; (ii) self-reported confidence in coping with self-isolation; (iii) objective tests of recall and comprehension. RESULTS: Decision trees improved decisions about when self-isolation was necessary, although participants systematically underestimated the need to self-isolate with less common COVID-19 symptoms (e.g. sore throat, fatigue). The online planning tool increased confidence about coping with self-isolation only among the adults aged under 40. Infographics improved recall and comprehension of how to manage self-isolation. CONCLUSION: Decision aids can be used to support self-isolation during COVID-19. The study also demonstrates how even an emergency public health response can benefit from rapid experimental pre-testing of interventions.

Motivating social distancing during the COVID-19 pandemic: An online experiment.

RATIONALE: Maintaining social distance during the COVID-19 pandemic can save lives. We therefore set out to test communication strategies to promote social distancing. OBJECTIVE: We aimed to test two novel public health messages against a control message. The first was designed to exploit the "identifiable victim" effect by highlighting the risk of transmission to identifiable vulnerable persons. The second sought to counteract intuitive underestimation of exponential transmission. METHOD: In total, 500 Irish adults undertook a pre-registered, online experiment. They were randomly assigned to a control group or one of two treatment groups. The control group viewed a current poster that encouraged a 2-m separation between people. The two treatment groups saw posters of similar design, but with narrative messages describing how an individual had infected a specific vulnerable person or multiple other people. Later questions measured intentions to undertake three specific types of social interaction over the coming days and the stated acceptability of three other types of social interaction. Pilot work had identified these six behaviors as "marginal" - people were unsure whether they were advisable. RESULTS: Participants in the treatment conditions were more cautious about undertaking the behaviors and less accepting of them. This positive effect occurred despite participants rating the treatment posters as likely to be less effective and memorable than the control poster. CONCLUSIONS: Messages that invoke thoughts of infecting vulnerable people or large numbers of people can motivate social distancing and, hence, help to limit the spread of COVID-19. Stated public evaluations (obtained via focus groups or surveys) may underestimate the actual effectiveness of such emotional messages.

A simple algorithm for averaging spike trains.

Although spike trains are the principal channel of communication between neurons, a single stimulus will elicit different spike trains from trial to trial. This variability, in both spike timings and spike number can obscure the temporal structure of spike trains and often means that computations need to be run on numerous spike trains in order to extract features common across all the responses to a particular stimulus. This can increase the computational burden and obscure analytical results. As a consequence, it is useful to consider how to calculate a central spike train that summarizes a set of trials. Indeed, averaging responses over trials is routine for other signal types. Here, a simple method for finding a central spike train is described. The spike trains are first mapped to functions, these functions are averaged, and a greedy algorithm is then used to map the average function back to a spike train. The central spike trains are tested for a large data set. Their performance on a classification-based test is considerably better than the performance of the medoid spike trains.