Grape Cold Hardiness Prediction via Multi-Task Learning

Cold temperatures during fall and spring have the potential to cause frost damage to grapevines and other fruit plants, which can significantly decrease harvest yields. To help prevent these losses, farmers deploy expensive frost mitigation measures, such as, sprinklers, heaters, and wind machines, when they judge that damage may occur. This judgment, however, is challenging because the cold hardiness of plants changes throughout the dormancy period and it is difficult to directly measure. This has led scientists to develop cold hardiness prediction models that can be tuned to different grape cultivars based on laborious field measurement data. In this paper, we study whether deep-learning models can improve cold hardiness prediction for grapes based on data that has been collected over a 30-year time period. A key challenge is that the amount of data per cultivar is highly variable, with some cultivars having only a small amount. For this purpose, we investigate the use of multi-task learning to leverage data across cultivars in order to improve prediction performance for individual cultivars. We evaluate a number of multi-task learning approaches and show that the highest performing approach is able to significantly improve over learning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars

Propionicicella superfundia gen. nov., sp. nov., a chlorosolvent-tolerant propionate-forming, facultative anaerobic bacterium isolated from contaminated groundwater

A novel strain, designated as BL-10T, was characterized using a polyphasic approach after isolation from groundwater contaminated by a mixture of chlorosolvents that included 1,1,2-trichloroethane, 1,2-dichloroethane, and vinyl chloride. Stain BL-10T is a facultatively anaerobic bacterium able to ferment glucose to form propionate, acetate, formate, lactate, and succinate. Fermentation occurred in the presence of 1,2-dichloroethane and 1,1,2-trichloroethane at concentrations to at least 9.8 and 5.9 mM, respectively. Cells are Gram-positive, rod-shaped, non-motile, and do not form spores. Oxidase and catalase are not produced and nitrate reduction did not occur in PYG medium. Menaquinone MK-9 is the predominant respiratory quinone and meso-diaminopimelic acid is present in the cell wall peptidoglycan layer. Major cellular fatty acids are C15:0, iso C16:0, and anteiso C15:0. Genomic DNA G+C content is 69.9 mol%. Phylogenetic analysis based on 16S rRNA gene sequence comparisons showed strain BL-10T to fall within the radiation of genera Propionicimonas and Micropruina. On the basis of the results obtained in this study, it is proposed that strain BL-10T should be classified as a novel taxon, for which the name Propionicicella superfundia gen. nov., sp. nov. is proposed. The type strain of Propionicicella superfundia is BL-10T (=ATCC BAA-1218T, =LMG 23096T).1http://www.sciencedirect.com/science/article/B7GVX-4HS3BT8-1/1/395e77acd1f8349190753e165cda022