Soft Synthetic Microgels as Mimics of Mycoplasma

Artificial model colloids are of special interest for the development of advanced sterile filters, able to efficiently separate pleomorphic, highly deformable and infectious bacteria such as mycoplasma, which, until now,...

Advanced Modeling for the Identification of Different Pathogen Tolerant Vines to Reduce Fungicides and Energy Consumption

The aim of this study is the application of advanced modeling techniques to identify powdery mildew tolerant cultivars and reduce fungicides and energy consumption. The energy savings resulting from the increased efficiency of the use of fungicides is an innovative aspect investigated within the project AGROENER researching on energy efficiency. In this preliminary study, investigations through phenotyping methods could represent a potential solution, especially if they are used in combination with tools and algorithms able to extract and convert a large amount of data. Twelve different grapevine cultivars were tested. The construction of an artificial model, characterized by absolute optima of response to a pathogen (i.e., low values of disease incidence and severity and first day of the pathogen appearance), allowed us to cover the potential variability of a real dataset. To identify the cultivars that tolerate powdery mildew the most, two Soft Independent Modeling of Class Analogy (SIMCA) models were built. The modeling efficiencies, indicated by sensitivity value, were equal to 100%. These statistical multivariate classifications identified some of these tolerant cultivars, as the best responding to the pathogen.