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Ephemeral gullies are widely distributed in the hilly and gully region of the Loess Plateau and play a unique role in the slope gully erosion system. Rapid and accurate identification of ephemeral gullies impacts the distribution law and development trend of soil erosion on the Loess Plateau. Deep learning algorithms can quickly and accurately process large data samples that recognize ephemeral gullies from remote sensing images. Here, we investigated ephemeral gullies in the Zhoutungou watershed in the hilly and gully region of the Loess Plateau in China using satellite and unmanned aerial vehicle images and combined a deep learning image semantic segmentation model to realize automatic recognition and feature extraction. Using Accuracy, Precision, Recall, F1value, and AUC, we compared the ephemeral gully recognition results and accuracy evaluation of U-Net, R2U-Net, and SegNet image semantic segmentation models. The SegNet model was ranked first, followed by the R2U-Net and U-Net models, for ephemeral gully recognition in the hilly and gully region of the Loess Plateau. The ephemeral gully length and width between predicted and measured values had RMSE values of 6.78 m and 0.50 m, respectively, indicating that the model has an excellent recognition effect. This study identified a fast and accurate method for ephemeral gully recognition in the hilly and gully region of the Loess Plateau based on remote sensing images to provide an academic reference and practical guidance for soil erosion monitoring and slope and gully management in the Loess Plateau region.

CRISPR-Cas9 is a common tool for gene editing, and appropriate sgRNAs are the key factor for successful editing. In this study, the effect of sgRNA length and number on editing efficiency was analyzed in rice using CYP81A6 as the target gene. A series of CRISPR-Cas9 plant expression vectors containing single sgRNAs with different lengths (17, 18, 19, 20, 21, 22, 23 nt) or two sgRNAs were constructed and introduced into rice cultivar Zhonghua11 by Agrobacterium-mediated transformation. Analysis of the editing status of 1283 transgenic rice plants showed that 371 were successfully edited with base preference. Single A or T insertions were the most frequent among the six edited types. The editing efficiency of transgenic rice with two sgRNAs was higher than that with a single sgRNA. Editing efficiency and sgRNA length showed a normal distribution with 20 nt sgRNA (25%) being the most efficient. The editing efficiency decreased slightly with decreases of 1–2 bases (19 nt 20%, 18 nt 21%), but decreased significantly with a decrease of 3 bases (17 nt 4.5%). Editing efficiency was significantly reduced by adding 1 to 3 bases (21 nt 16.8%, 22 nt 13%, 23 nt 13%) to the sgRNA. These results provide data for successful gene editing or rice by CRISPR-Cas9.

With the widespread cultivation of transgenic crops, there is increasing concern about unintended effects of these crops on soil environmental quality. In this study, we used the Biolog method and ELISA to evaluate the possible effects of OsrHSA transgenic rice on soil microbial utilization of carbon substrates under field conditions. There were no significant differences in average well-color development (AWCD) values, Shannon–Wiener diversity index (H), Simpson dominance indices (D) and Shannon–Wiener evenness indices (E) of microbial communities in rhizosphere soils at eight samplings between OsrHSA transgenic rice and its non-transgenic counterpart. The main carbon sources utilized by soil microbes were carbohydrates, carboxylic acids, amino acids and polymers. The types, capacities and patterns of carbon source utilization by microbial communities in rhizosphere soils were similar throughout the detection period. We detected no OsrHSA protein in the roots of OsrHSA transgenic rice. We concluded that OsrHSA transgenic rice and the rHSA protein it produced did not alter the functional diversity of microbial communities in the rhizosphere.