Population genetic structure and predominance of cyclical parthenogenesis in the bird cherry-oat aphid Rhopalosiphum padi in England

Genetic diversity is the determinant for pest species' success and vector competence. Understanding the ecological and evolutionary processes that determine the genetic diversity is fundamental to help identify the spatial scale at which pest populations are best managed. In the present study, we present the first comprehensive analysis of the genetic diversity and evolution of Rhopalosiphum padi, a major pest of cereals and a main vector of the barley yellow dwarf virus (BYDV), in England. We have used a genotyping-by-sequencing approach to study whether (a) there is any underlying population genetic structure at a national and regional scale in this pest that can disperse long distances; (b) the populations evolve as a response to environmental change and selective pressures; and (c) the populations comprise anholocyclic lineages. Individual R. padi were collected using the Rothamsted Insect Survey's suction-trap network at several sites across England between 2004 and 2016 as part of the RIS long-term nationwide surveillance. Results identified two genetic clusters in England that mostly corresponded to a North-South division, although gene flow is ongoing between the two subpopulations. These genetic clusters do not correspond to different life cycle types, and cyclical parthenogenesis is predominant in England. Results also show that there is dispersal with gene flow across England, although there is a reduction between the northern and southern sites with the south-western population being the most genetically differentiated. There is no evidence for isolation by distance and other factors such as primary host distribution, uncommon in the south and absent in the south-west, could influence the dispersal patterns. Finally, results also show no evidence for the evolution of the R. padi population, and it is demographically stable despite the ongoing environmental change. These results are discussed in view of their relevance to pest management and the transmission of BYDV.

Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform

Diabetes is a high-prevalence disease that leads to an alteration in the patient’s blood glucose (BG) values. Several factors influence the subject’s BG profile over the day, including meals, physical activity, and sleep. Wearable devices are available for monitoring the patient’s BG value around the clock, while activity trackers can be used to record his/her sleep and physical activity. However, few tools are available to jointly analyze the collected data, and only a minority of them provide functionalities for performing advanced and personalized analyses. In this paper, we present AID-GM, a web application that enables the patient to share with his/her diabetologist both the raw BG data collected by a flash glucose monitoring device, and the information collected by activity trackers, including physical activity, heart rate, and sleep. AID-GM provides several data views for summarizing the subject’s metabolic control over time, and for complementing the BG profile with the information given by the activity tracker. AID-GM also allows the identification of complex temporal patterns in the collected heterogeneous data. In this paper, we also present the results of a real-world pilot study aimed to assess the usability of the proposed system. The study involved 30 pediatric patients receiving care at the Fondazione IRCCS Policlinico San Matteo Hospital in Pavia, Italy.

AID-GM: An Advanced System Supporting Continuous Monitoring of T1DM Patients

In this paper, we present AID-GM, a web application for the analysis and summarization of continuous blood glucose monitoring data in patients with Type 1 Diabetes. AID-GM allows patients and their care providers to share data and analyze them thanks to a set of advanced and personalized intelligent data analysis tools. Such tools enable the extraction of complex multivariate temporal patterns from data. We present a preliminary evaluation of the functionalities of the tool, both on a set of healthy volunteers, and on real patients' data in the pediatric context.