No abstract

Raffinose family oligosaccharides (RFOs) accumulate under stress conditions in many plants and have been suggested to act as stress protectants. To elucidate the metabolic process of RFOs under cold stress, levels of RFOs, and related carbohydrates, the expression and activities of main metabolic enzymes and their subcellular compartments were investigated during low-temperature treatment and during the recovery period in cucumber leaves. Cold stress induced the accumulation of stachyose in vacuoles, galactinol in vacuoles and cytosol, and sucrose and raffinose in vacuoles, cytosol, and chloroplasts. After cold stress removal, levels of these sugars decreased gradually in the respective compartments. Among four galactinol synthase genes (CsGS), CsGS1 was not affected by cold stress, while the other three CsGSs were up-regulated by low temperature. RNA levels of acid-α-galactosidase (GAL) 3 and alkaline-α-galactosidase (AGA) 2 and 3, and the activities of GAL and AGA, were up-regulated after cold stress removal. GAL3 protein and GAL activity were exclusively located in vacuoles, whereas AGA2 and AGA 3 proteins were found in cytosol and chloroplasts, respectively. The results indicate that RFOs, which accumulated during cold stress in different subcellular compartments in cucumber leaves, could be catabolized in situ by different galactosidases after stress removal.

To demonstrate the identification of corneal diseases using a novel deep learning algorithm. A novel hierarchical deep learning network, which is composed of a family of multi-task multi-label learning classifiers representing different levels of eye diseases derived from a predefined hierarchical eye disease taxonomy was designed. Next, we proposed a multi-level eye disease-guided loss function to learn the fine-grained variability of eye diseases features. The proposed algorithm was trained end-to-end directly using 5,325 ocular surface images from a retrospective dataset. Finally, the algorithm’s performance was tested against 10 ophthalmologists in a prospective clinic-based dataset with 510 outpatients newly enrolled with diseases of infectious keratitis, non-infectious keratitis, corneal dystrophy or degeneration, and corneal neoplasm. The area under the ROC curve of the algorithm for each corneal disease type was over 0.910 and in general it had sensitivity and specificity similar to or better than the average values of all ophthalmologists. Confusion matrices revealed similarities in misclassification between human experts and the algorithm. In addition, our algorithm outperformed over all four previous reported methods in identified corneal diseases. The proposed algorithm may be useful for computer-assisted corneal disease diagnosis.