Exogenous Streptomyces spp. enhance the drought resistance of naked oat (Avena nuda) seedlings by augmenting both the osmoregulation mechanisms and antioxidant capacities.

Drought is a major obstacle to the development of naked oat industry. This work investigated mechanisms by which exogenous Streptomyces albidoflavus T4 and Streptomyces rochei D74 improved drought tolerance in naked oat (Avena nuda ) seedlings. Results showed that in the seed germination experiment, germination rate, radicle and hypocotyl length of naked oat seeds treated with the fermentation filtrate of T4 or D74 under PEG induced drought stress increased significantly. In the hydroponic experiment, the shoot and root dry weights of oat seedlings increased significantly when treated with the T4 or D74 fermentation filtrate under the 15% PEG induced drought stress (S15). Simultaneously, the T4 treatment also significantly increased the surface area, volume, the number of tips and the root activity of oat seedlings. Both T4 and D74 treatments elicited significant increases in proline and soluble sugar contents, as well as the catalase and peroxidase activities in oat seedlings. The results of comprehensive drought resistance capacity (CDRC) calculation of oat plants showed that the drought resistance of oat seedlings under the T4 treatment was better than that under the D74 treatment, and the effect was better under higher drought stress (S15). Findings of this study may provide a novel and effective approach for enhancing plant defenses against drought stress.

Residual based attention-Unet combing DAC and RMP modules for automatic liver tumor segmentation in CT.

Purpose: Due to the complex distribution of liver tumors in the abdomen, the accuracy of liver tumor segmentation cannot meet the needs of clinical assistance yet. This paper aims to propose a new end-to-end network to improve the segmentation accuracy of liver tumors from CT. Method: We proposed a hybrid network, leveraging the residual block, the context encoder (CE), and the Attention-Unet, called ResCEAttUnet. The CE comprises a dense atrous convolution (DAC) module and a residual multi-kernel pooling (RMP) module. The DAC module ensures the network derives high-level semantic information and minimizes detailed information loss. The RMP module improves the ability of the network to extract multi-scale features. Moreover, a hybrid loss function based on cross-entropy and Tversky loss function is employed to distribute the weights of the two-loss parts through training iterations. Results: We evaluated the proposed method in LiTS17 and 3DIRCADb databases. It significantly improved the segmentation accuracy compared to state-of-the-art methods. Conclusions: Experimental results demonstrate the satisfying effects of the proposed method through both quantitative and qualitative analyses, thus proving a promising tool in liver tumor segmentation.