Shrews are small insectivorous mammals that are distributed worldwide. Similar to rodents, shrews live on the ground and are commonly found near human residences. In this study, we investigated the enteric virome of wild shrews in the genus Crocidura using a sequence-independent viral metagenomics approach. A large portion of the shrew enteric virome was composed of insect viruses, whilst novel viruses including cyclovirus, picornavirus and picorna-like virus were also identified. Several cycloviruses, including variants of human cycloviruses detected in cerebrospinal fluid and stools, were detected in wild shrews at a high prevalence rate. The identified picornavirus was distantly related to human parechovirus, inferring the presence of a new genus in this family. The identified picorna-like viruses were characterized as different species of calhevirus 1, which was discovered previously in human stools. Complete or nearly complete genome sequences of these novel viruses were determined in this study and then were subjected to further genetic characterization. Our study provides an initial view of the diversity and distinctiveness of the shrew enteric virome and highlights unique novel viruses related to human stool-associated viruses.

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We propose a new method for bipartite link prediction using matrix factorization with negative sample selection. Bipartite link prediction is a problem that aims to predict the missing links or relations in a bipartite network. One of the most popular solutions to the problem is via matrix factorization (MF), which performs well but requires reliable information on both absent and present network links as training samples. This, however, is sometimes unavailable since there is no ground truth for absent links. To solve the problem, we propose a technique called negative sample selection, which selects reliable negative training samples using formal concept analysis (FCA) of a given bipartite network in advance of the preceding MF process. We conduct experiments on two hypothetical application scenarios to prove that our joint method outperforms the raw MF-based link prediction method as well as all other previously-proposed unsupervised link prediction methods.

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