First Detection of Atractylodes Mild Mottle Virus in Atractylodes lancea (Thunb.) DC. in Hubei Province of China.

The cultivated aromatic medicinal herb Atractylodes lancea (Thunb.) DC. is widely used in the pharmaceuticals, nutraceuticals, and cosmetics industries (Na-Bangchang et al. 2014; Zhan et al. 2023). Huanggang in Hubei Province is a major production area for A. lancea (Huang et al. 2022; Wang et al. 2023). In April 2023, more than two-thirds of the surveyed plant leaves in this region exhibited virus-like symptoms, such as curling and mosaic patterns. To identify the underlying cause, 80 symptomatic plant leaf samples were collected from four fields (20 leaves per field) in this region and pooled for virome analysis. Total RNA, including ribosomal RNA, was extracted from the pooled samples using the Plant RNA Extraction Mini Kit (Onrew Biotech, Guangdong, China), for sequencing library construction. The Illumina NovaSeq 6000 platform was used to sequence the library and generate 150 bp paired-end reads. After processing the raw data with Trimmomatic software, a total of 44,354,650 high-quality clean reads were obtained. The clean reads were aligned against ribosomal RNA using BWA software (v0.7.17) to avoid interference and eliminate corresponding sequences. After removing potential contamination, contig assembly of the clean reads was performed using Megahit software (v1.2.9). The resulting contigs were compared with the virus NT database using the BLASTn program. Sequence pairwise comparison revealed 8 contigs (574 nt to 2243 nt) with identities ranging from 81.88% to 90.77% with Atractylodes mild mottle virus (AMMV, NC_027924.1, Lim et al., 2015). Additionally, contigs mapped to Carlavirus, Pelarspovirus, and other plant viruses in our virome dataset had low coverage and pairwise identity (less than 70%), which need to be further investigated. The presence of AMMV was confirmed by aligning the clean reads to the reference sequence (NC_027924.1) using BWA and SAMtools software, resulting in a consensus sequence (8024 nt) with gaps. DNA extraction from the pooled samples was performed using the Rapid Universal Genomic DNA Extraction Kit (Simgen, Zhejiang, China). Two pairs of specific primers, 3399F (5'-AAAGAAGAACCTCCTGATACGG-3')/5924R (5'-TGAACCTGATTCTCTTGGC-3') and 1830F (5'- CTCAGGAAATCCCAATGC -3')/3640R(5'-TTTCCCAATGTTCTTCGGG-3'), were designed to amplify the complete gene sequences of polymerase and coat protein (CP), based on the consensus sequence. The PCR products with the lengths of 2521 bp and 1814 bp were cloned into the pMD18-T vector (Takara Biotech, Dalian, China) for sequencing. The BLASTn analysis showed that the polymerase and CP gene sequences shared an identity of 94.51% (1929/2041 nt) and 88.41% (1419/1605 nt) with the AMMV isolate (NC_027924.1), respectively. The sequences have been deposited in GenBank under the accession numbers OR544810 and OR544811. We collected leaves from 32 A. lancea plants (16 symptomatic and 16 asymptomatic) in the fields. RT-PCR was conducted using CPF (5'-CTGCGAATATGAAAGTGC-3') and CPR (5'-GGTGAGCTTGTCTGTTAGG-3') primers, which were designed targeting a 527bp fragment of the CP gene (OR544811). Amplicons of the expected size (527bp) were detected in 24 plants (11 symptomatic and 13 asymptomatic), three of which were sequenced by Sanger sequencing, showing a 100% match to OR544811. These findings indicate that AMMV is prevalent in the major production area of A. lancea. Further research is needed to better characterize the potential risks of AMMV to A. lancea cultivation in China as well as other countries.

Insights into the impacts of autotoxic allelochemicals from rhizosphere of Atractylodes lancea on soil microenvironments.

Atractylodes lancea suffers from continuous cropping obstacles that have become a major constraint in its cultivation, but there is still little information on the autotoxic allelochemicals and their interaction with soil microorganisms. In this study, we firstly identified the autotoxic allelochemicals from rhizosphere of A. lancea and determined their autotoxicity. Third-year continuous A. lancea cropping soils, i.e., rhizospheric soil and bulk soil, compared with control soil and one-year natural fallow soil were used to determine soil biochemical properties and microbial community. Eight allelochemicals from A. lancea roots were detected and exhibited significant autotoxicity effects on seed germination and seedling growth of A. lancea with the highest content of dibutyl phthalate in rhizospheric soil and lowest IC50 value of 2,4-di-tert-butylphenol inhibiting seed germination. The contents of soil nutrients and organic matter, pH value, and enzyme activity were altered between different soils, and the parameters of fallow soil were close to those of the unplanted soil. The PCoA analysis indicated that the community composition of both bacteria and fungi were differed significantly among the soil samples. Continuous cropping decreased OTUs numbers of bacterial and fungal communities, and natural fallow restored them. The relative abundance of Proteobacteria, Planctomycetes, and Actinobacteria decreased, and that of Acidobacteria and Ascomycota increased after three years cultivation. The LEfSe analysis identified 115 and 49 biomarkers for bacterial and fungal communities, respectively. The results suggested that natural fallow restored the structure of soil microbial community. Overall, our results revealed that autotoxic allelochemicals caused the variations of soil microenvironments and resulted in replantation problem of A. lancea, and natural fallow alleviated the soil deterioration by remodeling the rhizospheric microbial community and restoring soil biochemical properties. These findings provide important insights and clues for solving the continuous cropping problems and guiding the management of sustainable farmland.

Landslide Displacement Prediction Based on Multivariate LSTM Model.

There are many frequent landslide areas in China, which badly affect local people. Since the 1980s, there have been more than 200 landslides in China with a death toll of 30 or more people at a time, economic losses of more than CNY 10 million or significant social impact. Therefore, the study of landslide displacement prediction is very important. The traditional ARIMA and LSTM models are commonly used for forecasting time series data. In our study, a multivariable LSTM landslide displacement prediction model is proposed based on the traditional LSTM model, which integrates rainfall and reservoir water level data. Taking the Baijiabao landslide in the Three Gorges Reservoir area as an example, the data of displacement, rainfall and reservoir water level of monitoring point ZG323 from November 2006 to December 2012 were selected for this study. Our results show that the displacement prediction results of the multivariable LSTM model are more accurate than those of the ARIMA and the univariate LSTM models, and the mean square, root mean square and mean absolute errors are the smallest, which are 0.64223, 0.8014 and 0.50453 mm, respectively. Therefore, the multivariable LSTM model method has higher accuracy and better application prospects in the displacement prediction of the Baijiabao landslide, which can provide a certain reference for the displacement prediction of the same type of landslide.

Assessment of Landslide Susceptibility Based on Multiresolution Image Segmentation and Geological Factor Ratings.

Evaluating the susceptibility of regional landslides is one of the core steps in spatial landslide prediction. Starting from multiresolution image segmentation and object-oriented classification theory, this paper uses the four parameters of entropy, energy, correlation, and contrast from remote-sensing images in the Zigui-Badong section of Three Gorges Reservoir as image texture factors; the original image data for the study area were divided into 2279 objects after segmentation. According to the various indicators of the existing historical landslide database in the Three Gorges Reservoir area, combined with the classification processing steps for different types of multistructured data, the relevant geological evaluation factors, including the slope gradient, slope structure, and engineering rock group, were rated based on expert experience. From the perspective of the object-oriented segmentation of multiresolution images and geological factor rating classification, the C5.0 decision tree susceptibility classification model was constructed for the prediction of four types of landslide susceptibility units in the Zigui-Badong section. The mapping results show that the engineering rock group of a high-susceptibility unit usually develops in soft rock or soft-hard interphase rock groups, and the slope is between 15°-30°. The model results show that the average accuracy is 91.64%, and the kappa coefficients are 0.84 and 0.51, indicating that the C5.0 decision tree algorithm provides good accuracy and can clearly divide landslide susceptibility levels for a specific area, respectively. This landslide susceptibility classification, based on multiresolution image segmentation and geological factor classification, has potential applicability.