developed models allowed us to predict genotypic performance under different environmental stresses. This will be a key factor in the development of common bean varieties adapted to future challenging conditions.

Background: The identification of geographic variation in incidence can be an important step in the delineation of disease risk factors, but has mostly been undertaken in upper-income countries. Here, we use Electronic Health Records (EHR) from a middle-income country, Colombia, to characterize geographic variation in major mental disorders. Method: We leveraged geolocated EHRs of 16,295 patients at a psychiatric hospital serving the entire state of Caldas, all of whom received a primary diagnosis of bipolar disorder, schizophrenia, or major depressive disorder at their first visit. To identify the relationship between travel time and incidence of mental illness we used a zero-inflated negative binomial regression model. We used spatial scan statistics to identify clusters of patients, stratified by diagnosis and severity: mild (outpatients) or severe (inpatients). Results: We observed a significant association between incidence and travel time for outpatients (N = 11,077, relative risk (RR) = 0.80, 95% confidence interval (0.71, 0.89)), but not inpatients (N = 5,218). We found seven clusters of severe mental illness: the cluster with the most extreme overrepresentation of bipolar disorder (RR = 5.83, p < 0.001) has an average annual incidence of 8.7 inpatients per 10,000 residents, among the highest frequencies worldwide. Conclusions: The hospital database reflects the geographic distribution of severe, but not mild, mental illness within Caldas. Each hotspot is a candidate location for further research to identify genetic or environmental risk factors for severe mental illness. Our analyses highlight how existing infrastructure from middle-income countries can be extraordinary resources for population studies.

Abstract. Using the cross-platform game engine Unity, we develop virtual laboratories for PC, consoles, mobile devices and website as an innovative tool to study physics. There is extensive uptake of ICT in the teaching of science and its impact on the learning, and considering the limited availability of laboratories for physics teaching and the difficulties this causes in the learning of school students, we design the virtual laboratories to enhance studentâĂŹs knowledge of concepts in physics. To achieve this goal, we use Unity due to provide support bump mapping, reflection mapping, parallax mapping, dynamics shadows using shadows maps, full-screen postprocessing effects and render-to-texture. Unity can use the best variant for the current video hardware and, if none are compatible, to use an alternative shader that may sacrifice features for performance. The control over delivery to mobile devices, web browsers, consoles and desktops is the main reason Unity is the best option among the same kind cross-platform. Supported platforms include Android, Apple TV, Linux, iOS, Nintendo 3DS line, macOS, PlayStation 4, Windows Phone 8, Wii but also an asset server and Nvidia's PhysX physics engine which is the most relevant tool on Unity for our PhysLab.

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