Reinforcement is the process by which prezygotic isolation is strengthened as a response to selection against hybridisation. Most empirical support for reinforcement comes from the observation of its possible phenotypic signature: an accentuated degree of prezygotic isolation in the hybrid zone as compared to allopatry. Here, we implemented a novel approach to this question by seeking for the signature of reinforcement at the genetic level. In the house mouse, selection against hybrids and enhanced olfactory-based assortative mate preferences are observed in a hybrid zone between the two European subspecies Mus musculus musculus and M. m. domesticus, suggesting a possible recent reinforcement event. To test for the genetic signature of reinforcing selection and identify genes involved in sexual isolation, we adopted a hitchhiking mapping approach targeting genomic regions containing candidate genes for assortative mating in mice. We densely scanned these genomic regions in hybrid zone and allopatric samples using a large number of fast evolving microsatellite loci that allow the detection of recent selection events. We found a handful of loci showing the expected pattern of significant reduction of variability in populations close to the hybrid zone and showing assortative odour preference in mate choice experiments as compared to populations further away and displaying no such preference. These loci lie close to genes that we pinpoint as testable candidates for further investigation.

Encouraging people to walk rather than using other means of transportation is an important factor towards personal health and environmental sustainability. However, given the large number of pedestrian accidents recorded every year, the need for safe urban environments is increasing. Taking advantage of the potential of citizen-science for crowdsourcing data and creating awareness, we developed a smartphone application for enhancing the safety of pedestrians while walking in cities. Using the application, citizens will monitor the urban sidewalks and update a crowdsourcing platform with the detected barriers and damages that hinder safe walking, along with their location on a city map. To help users assign the correct type of obstacle, and authorities to assess the urgency, a Convolutional Neural Network (CNN) model for barrier and damage recognition is embedded in the application. The results of a user evaluation, based on a group of volunteers who used the application in real conditions, demonstrate the potential of using the application in conjunction with a smart city framework.