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Soilborne plant pathogens are an increasing problem in modern agriculture, and their ability to survive long periods in soil as persistent sclerotia makes control and treatment particularly challenging. To develop new control strategies, we explored bacteria associated with sclerotia of Sclerotinia sclerotiorum and Rhizoctonia solani, two soilborne fungi causing high yield losses. We combined different methodological approaches to get insights into the indigenous microbiota of sclerotia, to compare it to bacterial communities of the surrounding environment, and to identify novel biocontrol agents and antifungal volatiles. Analysis of 16S rRNA gene fragment amplicons revealed significant compositional differences in the bacterial microbiomes of Rhizoctonia sclerotia, the unaffected tuber surface and surrounding soil. Moreover, distinctive bacterial lineages were associated with specific sample types. Flavobacteriaceae and Caulobacteraceae were primarily found in unaffected areas, while Phyllobacteriaceae and Bradyrhizobiaceae were associated with sclerotia of R. solani. In parallel, we studied a strain collection isolated from sclerotia of the pathogens for emission of bioactive volatile compounds. Isolates of Bacillus, Pseudomonas, and Buttiauxella exhibited high antagonistic activity toward both soilborne pathogens and were shown to produce novel, not yet described volatiles. Differential imaging showed that volatiles emitted by the antagonists altered the melanized sclerotia surface of S. sclerotiorum. Interestingly, combinations of bacterial antagonists increased inhibition of mycelial growth up to 60% when compared with single isolates. Our study showed that fungal survival structures are associated with a specific microbiome, which is also a reservoir for new biocontrol agents.

This paper presents development of Artificial Neural Network (ANN) for prediction of the size of nanoparticles (NP) and microspore surface area (MSA). Developed neural network architecture has the following three inputs: the concentration of the biodegradable polymer in the organic phase, surfactant concentration in the aqueous phase and the homogenizing pressure. Two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer is trained, using Levenberg-Marquardt training algorithm. For training of this network, as well as for subsequent validation, 36 samples were used. From 36 samples which were used for subsequent validation in this ANN, 80,5% of them had highest accuracy while 19,5% of output data had insignificant differences comparing to experimental values.

This paper presents development of Artificial Neural Network (ANN) for prediction of the size of nanoparticles (NP) and microspore surface area (MSA). Developed neural network architecture has the following three inputs: the concentration of the biodegradable polymer in the organic phase, surfactant concentration in the aqueous phase and the homogenizing pressure. Two-layer feedforward network with a sigmoid transfer function in the hidden layer and a linear transfer function in the output layer is trained, using Levenberg-Marquardt training algorithm. For training of this network, as well as for subsequent validation, 36 samples were used. From 36 samples which were used for subsequent validation in this ANN, 80,5% of them had highest accuracy while 19,5% of output data had insignificant differences comparing to experimental values.