Identification of Acremonium isolates from grapevines and evaluation of their antagonism towards Plasmopara viticola

Some endophytic fungal genera in Vitis vinifera, including Acremonium, have been reported as antagonists of Plasmopara viticola. Endophytic Acremonium isolates from an asymptomatic grapevine cultivar Inzolia from Italy were identified by morphological features and multigene phylogenies of ITS, 18S and 28S genes, and their intra-specific genomic diversity was analyzed by RAPD analysis. Culture filtrates (CFs) obtained from Acremonium isolates were tested in vitro for their inhibitory activity against the P. viticola sporangia germination. Among 94 isolates, 68 belonged to the Acremonium persicinum and 26 to the Acremonium sclerotigenum. RAPD analysis grouped the A. persicinum isolates into 15 clusters and defined 31 different strains. The A. sclerotigenum isolates, instead, were clustered into 22 groups and represented 25 strains. All A. persicinum CFs inhibited sporangia germination of P. viticola, while not all those of A. sclerotigenum had inhibitory effect. A different degree of inhibition was observed between strains of the same species, while some strains of different species showed identical inhibitory effect. No correlation was found between RAPD groups and inhibitory activity in both Acremonium species.

Phrase-based statistical machine translation: explanation of its processes and statistical models and evaluation of the English to Spanish translations produced

Statistical machine translation (SMT) is an approach to Machine Translation (MT) that uses statistical models whose parameter estimation is based on the analysis of existing human translations (contained in bilingual corpora). From a translation student’s standpoint, this dissertation aims to explain how a phrase-based SMT system works, to determine the role of the statistical models it uses in the translation process and to assess the quality of the translations provided that system is trained with in-domain goodquality corpora. To that end, a phrase-based SMT system based on Moses has been trained and subsequently used for the English to Spanish translation of two texts related in topic to the training data. Finally, the quality of this output texts produced by the system has been assessed through a quantitative evaluation carried out with three different automatic evaluation measures and a qualitative evaluation based on the Multidimensional Quality Metrics (MQM).