Analysis of Population Genetic Diversity and Genetic Structure of Schizothorax biddulphi Based on 20 Newly Developed SSR Markers.

To protect the germplasm resources of Schizothorax biddulphi, we developed and used 20 pairs of polymorphic microsatellite primers to analyze the genetic diversity and structure of populations. A total of 126 samples were collected from the Qarqan River (CEC), Kizil River (KZL), and Aksu River (AKS) in Xinjiang, China. The results showed that 380 alleles were detected in 20 pairs of primers and the average number of alleles was 19.0. The effective allele numbers and Nei's gene diversity ranged from 1.1499 to 1.1630 and 0.0962 to 0.1136, respectively. The Shannon index range suggested low levels of genetic diversity in all populations. The genetic distance between the CEC and AKS populations was the largest, and the genetic similarity was the smallest. There was a significant genetic differentiation between CEC and the other two populations. The UPGMA clustering tree was constructed based on population genetic distance, and the clustering tree constructed by individuals showed that the AKS population and KZL population were clustered together, and the CEC population was clustered separately. Also, the group structure analysis also got the same result. It can be seen that although the three populations of S. biddulphi do not have high genetic diversity, the differentiation between the populations was high and the gene flow was limited, especially the differentiation between the CEC population and the other two populations. This study not only provided genetic markers for the research of S. biddulphi but the results of this study also suggested the need for enhanced management of S. biddulphi populations.

Lazy collaborative filtering for data sets with missing values.

As one of the biggest challenges in research on recommender systems, the data sparsity issue is mainly caused by the fact that users tend to rate a small proportion of items from the huge number of available items. This issue becomes even more problematic for the neighborhood-based collaborative filtering (CF) methods, as there are even lower numbers of ratings available in the neighborhood of the query item. In this paper, we aim to address the data sparsity issue in the context of neighborhood-based CF. For a given query (user, item), a set of key ratings is first identified by taking the historical information of both the user and the item into account. Then, an auto-adaptive imputation (AutAI) method is proposed to impute the missing values in the set of key ratings. We present a theoretical analysis to show that the proposed imputation method effectively improves the performance of the conventional neighborhood-based CF methods. The experimental results show that our new method of CF with AutAI outperforms six existing recommendation methods in terms of accuracy.