Selective use of wine yeast strains having different volatile phenols production

Among Saccharomyces cerevisiae wine yeasts, we found a high frequency of strains having the ability to decarboxylate 4-hydroxycinnamic acid and 3-methoxy-4-hydroxy-cinnamic acid. From Gewurztraminer juices fermented by S. cerevisiae wine strains with and without such character, we obtained wines with considerably different levels of volatile phenols and some interesting evidences of the likely precursors of 4-vinylguaiacol and 4-vinylphenol. The identification of yeast strains by electrophoretic karyotyping gave us the possibility of evaluating the effective contribution of the yeast in the organoleptic characteristic of Traminer wines associated with the concentration of such volatile phenols

Ampelometric evaluation of wild grape (Vitis vinifera L. ssp. sylvestris (C.C. Gmel.) Hegi) accessions in the germplasm collection of FEM-IASMA, Italy

In this paper, 45 wild grapevine accessions collected during two consecutive years were compared for 36 ampelometric traits using digital image analysis. The sample set contained male and female individuals from different geographic regions: Germany, North Italy, Central Italy, South Italy, Sardinia and Turkey. The leaf morphological data from the collected samples suggest that geographic origin, gender and vintage could have an effect on ampelometric traits in this species

Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network

The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In this paper, we show that GNNs are capable of multitask learning, which can be naturally enforced by training the model to refine a single set of multidimensional embeddings $\in \mathbb{R}^d$ and decode them into multiple outputs by connecting MLPs at the end of the pipeline. We demonstrate the multitask learning capability of the model in the relevant relational problem of estimating network centrality measures, focusing primarily on producing rankings based on these measures, i.e. is vertex $v_1$ more central than vertex $v_2$ given centrality $c$?. We then show that a GNN can be trained to develop a \emph{lingua franca} of vertex embeddings from which all relevant information about any of the trained centrality measures can be decoded. The proposed model achieves $89\%$ accuracy on a test dataset of random instances with up to 128 vertices and is shown to generalise to larger problem sizes. The model is also shown to obtain reasonable accuracy on a dataset of real world instances with up to 4k vertices, vastly surpassing the sizes of the largest instances with which the model was trained ($n=128$). Finally, we believe that our contributions attest to the potential of GNNs in symbolic domains in general and in relational learning in particular.Comment: Published at ICANN2019. 10 pages, 3 Figure

Design of the new electromagnetic measurement system for RFX-mod upgrade

A major modification of the RFX-mod toroidal load assembly has been decided in order to improve passive MHD control and to minimize the braking torque on the plasma, thus extending the operational space in both RFP and Tokamak configurations. With the removal of the vacuum vessel, the support structure will be modified in order to obtain a new vacuum-tight chamber and the first wall tiles will be directly in front of the passive stabilizing shell inside of it, so increasing both the poloidal cross section and the plasma-shell proximity. This implies the design of a new vacuum fit electromagnetic measurement system. The new local probes will be installed in vacuum onto the copper shell, behind the graphite tiles, and shall operate up to a maximum temperature of 180\ub0C to allow for baking cycles for first wall conditioning. Because of the reduced room available, tri-axial pickup probes have been designed, with the additional advantage of allowing the minimization of alignment errors. The paper describes the detailed design of the new probe set, in particular highlighting advantages and effectiveness of different probe solutions. Preliminary tests carried out on local probe prototypes to characterize their electromagnetic behaviour are also reported

Differentially expressed genes between drought-tolerant and drought-sensitive barley genotypes in response to drought stress during the reproductive stage

Drought tolerance is a key trait for increasing and stabilizing barley productivity in dry areas worldwide. Identification of the genes responsible for drought tolerance in barley (Hordeum vulgare L.) will facilitate understanding of the molecular mechanisms of drought tolerance, and also facilitate the genetic improvement of barley through marker-assisted selection or gene transformation. To monitor the changes in gene expression at the transcriptional level in barley leaves during the reproductive stage under drought conditions, the 22K Affymetrix Barley 1 microarray was used to screen two drought-tolerant barley genotypes, Martin and Hordeum spontaneum 41-1 (HS41-1), and one drought-sensitive genotype Moroc9-75. Seventeen genes were expressed exclusively in the two drought-tolerant genotypes under drought stress, and their encoded proteins may play significant roles in enhancing drought tolerance through controlling stomatal closure via carbon metabolism (NADP malic enzyme, NADP-ME, and pyruvate dehydrogenase, PDH), synthesizing the osmoprotectant glycine-betaine (C-4 sterol methyl oxidase, CSMO), generating protectants against reactive-oxygen-species scavenging (aldehyde dehydrogenase,ALDH, ascorbate-dependent oxidoreductase, ADOR), and stabilizing membranes and proteins (heat-shock protein 17.8, HSP17.8, and dehydrin 3, DHN3). Moreover, 17 genes were abundantly expressed in Martin and HS41-1 compared with Moroc9-75 under both drought and control conditions. These genes were possibly constitutively expressed in drought-tolerant genotypes. Among them, seven known annotated genes might enhance drought tolerance through signalling [such as calcium-dependent protein kinase (CDPK) and membrane steroid binding protein (MSBP)], anti-senescence (G2 pea dark accumulated protein, GDA2), and detoxification (glutathione S-transferase, GST) pathways. In addition, 18 genes, including those encoding Δl-pyrroline-5-carboxylate synthetase (P5CS), protein phosphatase 2C-like protein (PP2C), and several chaperones, were differentially expressed in all genotypes under drought; thus they were more likely to be general drought-responsive genes in barley. These results could provide new insights into further understanding of drought-tolerance mechanisms in barley

Disentangling the diversity of small farm business models in Euro-Mediterranean contexts: A resilience perspective

With growing concern for the unsustainability of food systems, the international research community has turned its attention to small farms as key actors to potentially face the global food crisis. This study aims to support a policy design that values the diversity of small farms business models vis-à-vis environmental, economic, social and institutional challenges affecting European farming systems. Building on the existing classification of five small farm types in the EU, our analysis targets the business model dynamics of small farms in four Euro-Mediterranean countries: Greece, Italy, Portugal and Spain. For this analysis, we applied resilience thinking to the Business Model Canvas framework. This innovative conceptual framework allows us to depict the architecture of small farms business models and their role in farming systems. The diversity of small farms business models and their continuous adaptation to changing conditions allows for the identification of a strongly heterogeneous assemblage of farms that contribute to the resilience of food systems at local, regional and multiple other scales