Inner necrosis in grapevine rootstock mother plants in the Cognac area (Charentes, France)

The incidence and quantification of decline-associated inner necrosis in grapevine rootstock mother plants have rarely been studied. In an experimental vineyard planted in 1991 at Saintes (Charentes), susceptibility to esca was evaluated in eleven common rootstock varieties. Fifty vines per rootstock variety were used as mother plants producing long canes which were severely pruned every year. No foliar symptoms, typical of grapevine wood diseases, were seen in field inspections conducted in the summer of 1996, 2002, 2003 and 2006. In 2007, nine trunks per variety were randomly selected and were cross-sectioned at the point of greatest diameter. All sections revealed typical esca necrosis, central and/or sector-shaped, indicating that such necrosis is very common. Every section was photographed and the percentage of necrotic area was calculated by either visual assessment or image-analysis. No significant difference was detected between these two calculating methods. Based on the mean percent necrotic area, rootstock varieties were ranked in order of susceptibility from the least susceptible, ‘1103 Paulsen’ (33%), to the most susceptible, ‘101-14 MGT’ (71%). The percent of necrotic area was correlated significantly with i) the incidence of mortality and ii) the percentage of vine sections showing white rot, a type of necrosis indicating an advanced stage of wood deterioration. This study confirmed that necrosis in grapevine wood is not always associated with foliar symptoms, but that it is related positively with grapevine mortality. Furthermore, wood necrosis in mother-plants poses a risk of disseminating associated fungi through propagation material

Sensibilite des grappes au black-rot : effet de l'interaction temperature-duree d'humectation sur la gravite de l'infection en conditions controlees

International audienc

Multi-task Representation Learning with Stochastic Linear Bandits

We study the problem of transfer-learning in the setting of stochastic linear bandit tasks. We consider that a low dimensional linear representation is shared across the tasks, and study the benefit of learning this representation in the multi-task learning setting. Following recent results to design stochastic bandit policies, we propose an efficient greedy policy based on trace norm regularization. It implicitly learns a low dimensional representation by encouraging the matrix formed by the task regression vectors to be of low rank. Unlike previous work in the literature, our policy does not need to know the rank of the underlying matrix. We derive an upper bound on the multi-task regret of our policy, which is, up to logarithmic factors, of order $\sqrt{NdT(T+d)r}$, where $T$ is the number of tasks, $r$ the rank, $d$ the number of variables and $N$ the number of rounds per task. We show the benefit of our strategy compared to the baseline $Td\sqrt{N}$ obtained by solving each task independently. We also provide a lower bound to the multi-task regret. Finally, we corroborate our theoretical findings with preliminary experiments on synthetic data

Tomosyn-1 is a gatekeeper of mast cell and basophil degranulation and is upregulated by high levels of IgE: Meeting Abstract: PD0482

International audienc

Tomosyn-1 is a gatekeeper of mast cell and basophil degranulation and is upregulated by high levels of IgE: Meeting Abstract: PD0482

International audienc