Participatory selection—exploiting specific adaptation traits to target environments—helps to guarantees yield stability in a changing climate, in particular under low-input or organic production. The purpose of the present study was to identify reliable, low-cost, fast and easy-to-use tools to complement traditional selection for an effective participatory improvement of maize populations for drought resistance/tolerance. The morphological and eco-physiological responses to progressive water deprivation of four maize open-pollinated populations were assessed in both controlled and field conditions. Thermography and Chl a fluorescence, validated by gas exchange indicated that the best performing populations under water-deficit conditions were ‘Fandango’ and to a less extent ‘Pigarro’ (both from participatory breeding). These populations showed high yield potential under optimal and reduced watering. Under moderate water stress, ‘Bilhó’, originating from an altitude of 800 m, is one of the most resilient populations. The experiments under chamber conditions confirmed the existence of genetic variability within ‘Pigarro’ and ‘Fandango’ for drought response relevant for future populations breeding. Based on the easiness to score and population discriminatory power, the performance index (PIABS) emerges as an integrative phenotyping tool to use as a refinement of the common participatory maize selection especially under moderate water deprivation.
Background A number of Bluetooth wellness and fitness devices were analysed to explore ways to access their raw data, using Bluetooth and the Generic Attribute Profile specification, while developing a Rest API using the .Net Core framework to provide a way to easily access said data in real time, in order to provide it to other uses, like dashboards or data science applications. Methods Two Bluetooth Low Energy devices were chosen for this experiment for their ability to record and transmit heart rate data in real-time, and as such our primary focus during development would be in obtaining this raw data. After collecting all the services and characteristics of the device, a method was implemented to read a value from a particular characteristic uuid, in our case the heart rate measurement. The resulting values obtained from the API follows the format specified in the corresponding XML file of the Bluetooth-SIG specification. Results The developed API worked as expected and provided useful information to advance its development to cover more devices, services, and features. We were able to obtain data not only from Heart Rate Monitoring, but also from the Battery Service and Device Information. Conclusions With these results it will be possible to expand the compatibility of the API to other devices, and also to other services present on the devices analysed, which were not considered in this preliminary analysis. The ultimate goal is the integration of this API on a rehabilitation oriented device being developed for this project.
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