Bacillus paralicheniformis MDJK30 was isolated from the rhizosphere of a peony. It could control the pathogen of peony root rot. Here, we report the complete genome sequence of B. paralicheniformis MDJK30. Eleven secondary metabolism gene clusters were predicted.
Bacillus atrophaeus GQJK17 was isolated from the rhizosphere of Lycium barbarum L. in China, which was shown to be a plant growth-promoting rhizobacterium as a new biological agent against pathogenic fungi and gram-positive bacteria. We present its biological characteristics and complete genome sequence, which contains a 4,325,818 bp circular chromosome with 4,181 coding DNA sequences and a G+C content of 43.3%. A genome analysis revealed a total of 8 candidate gene clusters for producing antimicrobial secondary metabolites, including surfactin, bacillaene, fengycin, and bacillibactin. Some other antimicrobial and plant growth-promoting genes were also discovered. Our results provide insights into the genetic and biological basis of B. atrophaeus strains as a biocontrol agent for application in agriculture.
In order to get RS method to extract soil salinity of the Yellow River Delta, we set Kenli County as typical Yellow River Delta to be research area and get data of soil salinity through field investigation. By using RS image of Landsat-8 of March 14, 2014 and analyzing information features of each band and surface spectral features of research areas, we select out sensitive bands and build Soil Salinity Information Extraction (SSIE) model and vegetation index NDVI model for comparison. And then, we accordingly classify grades of soil salinity and get soil salinity information by decision tree approach based on expert knowledge. The results show that overall accuracy of SSIE model is 93.04% and coefficient of Kappa is 0.7869, while overall accuracy of NDVI model is 83.67% and coefficient of Kappa is 0.7017 respectively. By comparing with measured proportions of each class, we see that results from SSIE model is more accurate, which indicates significant advantage for soil salinity information extraction. This research provides scientific basis to get and monitoring soil salinity of the Yellow River Delta region quickly and accurately.
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