Songnen Plain is originally one of the three major glasslands in China and has now become one of the three most concentrated distribution areas of sodic-saline soil worldwide. The soil is continuously degraded by natural and anthropogenic processes, which has a negative impact on agricultural production. The investigation of microbial diversity in this degraded ecosystem is fundamental for comprehending biological and ecological processes and harnessing the potential of microbial resources. The Illumina MiSeq sequencing method was practiced to investigate the bacterial diversity and composition in saline-alkali soil. The results from this study show that the change in pH under alkaline conditions was not the major contributor in shaping bacterial community in Songnen Plain. The electrical conductivity (EC) content of soil was the most important driving force for bacterial composition (20.83%), and the second most influencing factor was Na + content (14.17%). Bacterial communities were clearly separated in accordance with the EC. The dominant bacterial groups were Planctomycetes, Proteobacteria, and Bacteroidetes among the different salinity soil. As the salt concentration increased, the indicators changed from Planctomycetes and Bacteroidetes to Proteobacteria and Firmicutes. Our results suggest that Proteobacteria and Firmicutes were the main indicator species reflecting changes of the main microbial groups and the EC as a key factor drives the composition of the bacterial community under alkaline conditions in saline-alkali soil of Songnen Plain.

Objective: To generate synthetic CT (sCT) images with high quality from CBCT and planning CT (pCT) for dose calculation by using deep learning methods. Methods: 169 NPC patients with a total of 20926 slices of CBCT and pCT images were included. In this study the CycleGAN, Pix2pix and U-Net models were used to generate the sCT images. The Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) were used to quantify the accuracy of the proposed models in a testing cohort of 34 patients. Radiation dose were calculated on pCT and sCT following the same protocol. Dose distributions were evaluated for 4 patients by comparing the dose-volume-histogram (DVH) and 2D gamma index analysis. Results: The average MAE and RMSE values between sCT by three models and pCT reduced by 15.4 HU and 26.8 HU at least, while the mean PSNR and SSIM metrics between sCT by different models and pCT added by 10.6 and 0.05 at most, respectively. There were only slight differences for DVH of selected contours between different plans. The passing rates of 2D gamma index analysis under 3 mm/3% 3 mm/2%, 2 mm/3%and 2 mm/2% criteria were all higher than 95%. Conclusions: All the sCT had achieved better evaluation metrics than those of original CBCT, while the performance of CycleGAN model was proved to be best among three methods. The dosimetric agreement confirmed the HU accuracy and consistent anatomical structures of sCT by deep learning methods.

The present work was conducted to screen and identify biocontrol bacteria that effectively reduce the severity of corn stalk rot (CSR) and clarify the antifungal activity of secondary metabolites. The bacterial strain (BM21) was isolated from corn rhizosphere soil that effectively reduced CSR in pot experiments. On the basis of phylogenetic reconstructions, 16S rRNA sequence analysis, and biochemical and physiological reactions, BM21 was identified as Bacillus velezensis. The strain exhibited remarkable antifungal activity against Fusarium graminearum, a pathogenic fungus that causes CSR. Extracellular antifungal substances (10%) isolated from BM21 inhibited F. graminearum mycelial growth by 79.2%, conidial germination by 84.0%, and conidial production by 78.1%. In addition, the extracellular antifungal substances caused mycelial malformation and ultra-structural changes. The extracellular antifungal substances were sensitive to heat and showed a degree of resistance to ultraviolet radiation. The optimum pH for antifungal activity was 6-8. In pot experiments, irrigation with aqueous extracts from BM21 (1.0 mL/plant) reduced CSR incidence by 72.4-77.4%. B. velezensis BM21 effectively reduced CSR incidence and showed a potential as a biocontrol agent to control CSR.

Background: Accurate segmentation of tumor targets is critical for maximizing tumor control and minimizing normal tissue toxicity. We proposed a sequential and iterative U-Net (SI-Net) deep learning method to auto-segment the high-risk primary tumor clinical target volume (CTVp1) for treatment planning of nasopharyngeal carcinoma (NPC) radiotherapy. Methods: The SI-Net is a variant of the U-Net architecture. The input of SI-Net includes one CT image, the CTVp1 contour on this image, and the next CT image. The output is the predicted CTVp1 contour on the next CT image. We designed the SI-Net, using the left side to learn the volumetric features and the right to localize the contour on the next image. Two prediction directions, one from inferior to superior (forward direction) and the other from superior to inferior (backward direction), were tested. The performance was compared between the SI-Net and the U-Net using Dice similarity coefficient (DSC), Jaccard index (JI), average surface distance (ASD), and Hausdorff distance (HD) metrics. Results: The DSC and JI values from the forward direction SI-Net model were 5 and 6% higher than those from the U-Net model (0.84 ± 0.04 vs. 0.80 ± 0.05 and 0.74 ± 0.05 vs. 0.69 ± 0.05, p < 0.001). The smaller ASD and HD values also indicated a better performance (2.8 ± 1.0 vs. 3.3 ± 1.0 mm and 8.7 ± 2.5 vs. 9.7 ± 2.7 mm, p < 0.01) for the SI-Net model. For the backward direction SI-Net model, the DSC and JI values were still better than those from the U-Net model ( p < 0.01), although there were no significant differences in ASD and HD. Conclusions: The SI-Net model preserved the continuity between adjacent images and thus improved the segmentation accuracy compared with the conventional U-Net model. This model has potential of improving the efficiency and consistence of CTVp1 contouring for NPC patients.

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