The impact of black carbon (BC) on mode-specific galvanic skin response (GSR) as a measure of stress in urban environments.

Previous research has shown that walking and cycling could help alleviate stress in cities, however there is poor knowledge on how specific microenvironmental conditions encountered during daily journeys may lead to varying degrees of stress experienced at that moment. We use objectively measured data and a robust causal inference framework to address this gap. Using a Bayesian Doubly Robust (BDR) approach, we find that black carbon exposure statistically significantly increases stress, as measured by Galvanic Skin Response (GSR), while cycling and while walking. Augmented Outcome Regression (AOR) models indicate that greenspace exposure and the presence of walking or cycling infrastructure could reduce stress. None of these effects are statistically significant for people in motorized transport. These findings add to a growing evidence-base on health benefits of policies aimed at decreasing air pollution, improving active travel infrastructure and increasing greenspace in cities.

Interstellar space biology via Project Starlight.

Our ability to explore the cosmos by direct contact has been limited to a small number of lunar and interplanetary missions. However, the NASA Starlight program points a path forward to send small, relativistic spacecraft far outside our solar system via standoff directed-energy propulsion. These miniaturized spacecraft are capable of robotic exploration but can also transport seeds and organisms, marking a profound change in our ability to both characterize and expand the reach of known life. Here we explore the biological and technological challenges of interstellar space biology, focusing on radiation-tolerant microorganisms capable of cryptobiosis. Additionally, we discuss planetary protection concerns and other ethical considerations of sending life to the stars.

Poor Self-Reported Sleep is Associated with Prolonged White Matter T2 Relaxation in Psychotic Disorders.

Background: Schizophrenia (SZ) and bipolar disorder (BD) are characterized by white matter (WM) abnormalities, however, their relationship with illness presentation is not clear. Sleep disturbances are common in both disorders, and recent evidence suggests that sleep plays a critical role in WM physiology. Therefore, it is plausible that sleep disturbances are associated with impaired WM integrity in these disorders. To test this hypothesis, we examined the association of self-reported sleep disturbances with WM transverse (T2) relaxation times in patients with SZ spectrum disorders and BD with psychotic features. Methods: 28 patients with psychosis (17 BD-I, with psychotic features and 11 SZ spectrum disorders) were included. Metabolite and water T2 relaxation times were measured in the anterior corona radiata at 4T. Sleep was evaluated using the Pittsburgh Sleep Quality Index. Results: PSQI total score showed a moderate to strong positive correlation with water T2 (r = 0.64, p<0.001). Linear regressions showed that this association was specific to sleep disturbance but was not a byproduct of exacerbation in depressive, manic, or psychotic symptoms. In our exploratory analysis, sleep disturbance was correlated with free water percentage, suggesting that increased extracellular water may be a mechanism underlying the association of disturbed sleep and prolonged water T2 relaxation. Conclusion: Our results highlight the connection between poor sleep and WM abnormalities in psychotic disorders. Future research using objective sleep measures and neuroimaging techniques suitable to probe free water is needed to further our insight into this relationship.

Using a latent Hawkes process for epidemiological modelling.

Understanding the spread of COVID-19 has been the subject of numerous studies, highlighting the significance of reliable epidemic models. Here, we introduce a novel epidemic model using a latent Hawkes process with temporal covariates for modelling the infections. Unlike other models, we model the reported cases via a probability distribution driven by the underlying Hawkes process. Modelling the infections via a Hawkes process allows us to estimate by whom an infected individual was infected. We propose a Kernel Density Particle Filter (KDPF) for inference of both latent cases and reproduction number and for predicting the new cases in the near future. The computational effort is proportional to the number of infections making it possible to use particle filter type algorithms, such as the KDPF. We demonstrate the performance of the proposed algorithm on synthetic data sets and COVID-19 reported cases in various local authorities in the UK, and benchmark our model to alternative approaches.

The effects of traveling in different transport modes on galvanic skin response (GSR) as a measure of stress: An observational study.

BACKGROUND: Stress is one of many ailments associated with urban living, with daily travel a potential major source. Active travel, nevertheless, has been associated with lower levels of stress compared to other modes. Earlier work has relied on self-reported measures of stress, and on study designs that limit our ability to establish causation. OBJECTIVES: To evaluate effects of daily travel in different modes on an objective proxy measure of stress, the galvanic skin response (GSR). METHODS: We collected data from 122 participants across 3 European cities as part of the Physical Activity through Sustainable Transport Approaches (PASTA) study, including: GSR measured every minute alongside confounders (physical activity, near-body temperature) during three separate weeks covering 3 seasons; sociodemographic and travel information through questionnaires. Causal relationships between travel in different modes (the "treatment") and stress were established by using a propensity score matching (PSM) approach to adjust for potential confounding and estimating linear mixed models (LMM) with individuals as random effects to account for repeated measurements. In three separate analyses, we compared GSR while cycling to not cycling, then walking to not walking then motorized (public or private) travel to any activity other than motorized travel. RESULTS: Depending on LMM formulations used, cycling reduces 1-minute GSR by 5.7% [95% CI: 2.0-16.9%] to 11.1% [95% CI: 5.0-24.4%] compared to any other activity. Repeating the analysis for other modes we find that: walking is also beneficial, reducing GSR by 3.9% [95% CI: 1.4-10.7%] to 5.7% [95% CI: 2.6-12.3%] compared to any other activity; motorized mode (private or public) in reverse increases GSR by up to 1.1% [95% CI: 0.5-2.9%]. DISCUSSION: Active travel offers a welcome way to reduce stress in urban dwellers' daily lives. Stress can be added to the growing number of evidence-based reasons for promoting active travel in cities.

Do speed cameras reduce road traffic collisions?

This paper quantifies the effect of speed cameras on road traffic collisions using an approximate Bayesian doubly-robust (DR) causal inference estimation method. Previous empirical work on this topic, which shows a diverse range of estimated effects, is based largely on outcome regression (OR) models using the Empirical Bayes approach or on simple before and after comparisons. Issues of causality and confounding have received little formal attention. A causal DR approach combines propensity score (PS) and OR models to give an average treatment effect (ATE) estimator that is consistent and asymptotically normal under correct specification of either of the two component models. We develop this approach within a novel approximate Bayesian framework to derive posterior predictive distributions for the ATE of speed cameras on road traffic collisions. Our results for England indicate significant reductions in the number of collisions at speed cameras sites (mean ATE = -15%). Our proposed method offers a promising approach for evaluation of transport safety interventions.