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Rhizosphere-associated microbes have important implications for plant health, but knowledge of the association between the pathological conditions of soil-borne virus-infected wheat and soil microbial communities, especially changes in fungal communities, remains limited. We investigated the succession of fungal communities from bulk soil to wheat rhizosphere soil in both infected and healthy plants using amplicon sequencing methods, and assessed their potential role in plant health. The results showed that the diversity of fungi in wheat rhizosphere and bulk soils significantly differed post wheat yellow mosaic virus disease onset. The structure differences in fungal community at the two wheat health states or two compartment niches were evident, soil physicochemical properties (i.e., NH4+) contribute to differences in fungal community structure and alpha diversity. Comparison analysis showed Mortierellomycetes and Dothideomycetes as dominant communities in healthy wheat soils at class level. The genus Pyronemataceae and Solicoccozyma were significantly are significantly enriched in rhizosphere soil of diseased plant, the genus Cystofilobasidium, Cladosporium, Mortierella, and Stephanonectria are significantly enriched in bulk soil of healthy plant. Co-occurrence network analysis showed that the fungi in healthy wheat soil has higher mutual benefit and connectivity compared with diseased wheat. The results of this study demonstrated that the occurrence of wheat yellow mosaic virus diseases altered both fungal community diversity and composition, and that NH4+ is the most important soil physicochemical factor influencing fungal diversity and community composition.

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Abstract:The traditional collaborative filtering recommendation algorithm relies on the user's scoring relation to the item. However, user's behavior data in the filed of tourism industry is sparsely, and the interaction between the data are few. These problems lead to the traditional algorithms are lacking of ability to acquire the users' preference , and influence the recommendation quality of tourist spots. In this paper, a collaborative filtering recommendation algorithm based on tourist spots labels and user preferences were proposed. By using the label information of the tourist spots, to extract visitors ' interest factors of spots and preferences weights, used the adaptive algorithm of neural network to optimize the users' preference feature vector, and computed the similarity between users to get the recommended result. The results show that compared with the traditional recommendation method, the method of this paper can ameliorate the accuracy of user similarity relationship and has a great improvement in the recommendation quality of tourist spots.

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