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https://www.selleckchem.com/products/gsk2656157.html Due to the emergence of the coronavirus disease (COVID 19), education systems in most countries have adapted and quickly changed their teaching strategy to online teaching. This paper presents the design and implementation of a novel Internet of Things (IoT) device, called MEIoT weather station, which incorporates an exogenous disturbance input, within the National Digital Observatory of Smart Environments (OBNiSE) architecture. The exogenous disturbance input involves a wind blower based on a DC brushless motor. It can be controlled, via Node-RED platform, manually through a sliding bar, or automatically via different predefined profile functions, modifying the wind speed and the wind vane sensor variables. An application to Engineering Education is presented with a case study that includes the instructional design for the least-squares regression topic for linear, quadratic, and cubic approximations within the Educational Mechatronics Conceptual Framework (EMCF) to show the relevance of this proposal. This work's main contribution to the state-of-the-art is to turn a weather monitoring system into a hybrid hands-on learning approach thanks to the integrated exogenous disturbance input.Neofusicoccum parvum is a fungal plant-pathogen belonging to the family Botryosphaeriaceae, and is considered one of the most aggressive causal agents of the grapevine trunk disease (GTD) Botryosphaeria dieback. In this study, the mycovirome of a single strain of N. parvum (COLB) was characterized by high throughput sequencing analysis of total RNA and subsequent bioinformatic analyses. Contig annotations, genome completions, and phylogenetic analyses allowed us to describe six novel mycoviruses belonging to four different viral families. The virome is composed of two victoriviruses in the family Totiviridae, one alphaendornavirus in the family Endornaviridae, two mitoviruses in the family Mitoviridae, and one narnavirus belonging

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