Cytogenetic identification and molecular marker development of a novel wheat-Leymus mollis 4Ns(4D) alien disomic substitution line with resistance to stripe rust and Fusarium head blight.

Leymus mollis (Trin.) Pilg. (2n = 4x = 28, NsNsXmXm) potentially harbours useful genes that might contribute to the improvement of wheat. We describe M862 as a novel wheat-L. mollis alien disomic substitution line from a cross between wheat cv. 7182 and octoploid Tritileymus M47. Cytological observations indicate that M862 has a chromosome constitution of 2n = 42 = 21II. Two 4D chromosomes of wheat substituted by two L. mollis Ns chromosomes were observed, using the GISH and ND-FISH analyses. Molecular marker, 55K SNP array and wheat-P. huashanica liquid array (GenoBaits®WheatplusPh) analyses further indicate that the alien chromosomes are L. mollis 4Ns. Therefore, it was deduced that M862 was a wheat-L. mollis 4Ns(4D) alien disomic substitution line. There were also changes in chromosomes 1A, 1D, 2B and 5A detected by ND-FISH analysis. Transcriptome sequencing showed that the structural variation of 1D, 1A and 5A may have smaller impact on gene expression than that for 2B. In addition, a total of 16 markers derived from Lm#4Ns were developed from transcriptome sequences, and these proved to be highly effective for tracking the introduced chromosome. M862 showed reduced height, larger grains (weight and width), and was highly resistance to CYR32 and CYR34 stripe rust races at the seedling stage and mixed stripe rust races (CYR32, CYR33 and CYR34) at the adult stage. It was also resistance to Fusarium head blight (FHB). This alien disomic substitution line M862 may be exploited as an important genetic material in the domestication of stipe rust and FHB resistance wheat varieties.

Enhancing the scalability of distance-based link prediction algorithms in recommender systems through similarity selection.

Slope One algorithm and its descendants measure user-score distance and use the statistical score distance between users to predict unknown ratings, as opposed to the typical collaborative filtering algorithm that uses similarity for neighbor selection and prediction. Compared to collaborative filtering systems that select only similar neighbors, algorithms based on user-score distance typically include all possible related users in the process, which needs more computation time and requires more memory. To improve the scalability and accuracy of distance-based recommendation algorithm, we provide a user-item link prediction approach that combines user distance measurement with similarity-based user selection. The algorithm predicts unknown ratings based on the filtered users by calculating user similarity and removing related users with similarity below a threshold, which reduces 26 to 29 percent of neighbors and improves prediction error, ranking, and prediction accuracy overall.

Identification and DNA Marker Development for a Wheat-Leymus mollis 2Ns (2D) Disomic Chromosome Substitution.

Leymus mollis (2n = 4x = 28, NsNsXmXm), a wild relative of common wheat (Triticum aestivum L.), carries numerous loci which could potentially be used in wheat improvement. In this study, line 17DM48 was isolated from the progeny of a wheat and L. mollis hybrid. This line has 42 chromosomes forming 21 bivalents at meiotic metaphase I. Genomic in situ hybridization (GISH) demonstrated the presence of a pair chromosomes from the Ns genome of L. mollis. This pair substituted for wheat chromosome 2D, as shown by fluorescence in situ hybridization (FISH), DNA marker analysis, and hybridization to wheat 55K SNP array. Therefore, 17DM48 is a wheat-L. mollis 2Ns (2D) disomic substitution line. It shows longer spike and a high level of stripe rust resistance. Using specific-locus amplified fragment sequencing (SLAF-seq), 13 DNA markers were developed to identify and trace chromosome 2Ns of L. mollis in wheat background. This line provides a potential bridge germplasm for genetic improvement of wheat stripe rust resistance.

Link prediction in recommender systems with confidence measures.

The link prediction aims at predicting missing or future links in networks, which provides theoretical significance and extensive applications in the related field. However, the degree of confidence in the prediction results has not been fully discussed in related works. In this article, we propose a similarity confidence coefficient and a confidence measure for link prediction. The former is used to balance the reliability of similarity calculation results, which might be untrustworthy due to the information asymmetry in the calculation, and also makes it easier to achieve the optimal accuracy with a smaller number of neighbors. The latter is used to quantify our confidence in the prediction results of each prediction. The experimental results based on the Movie-Lens data set show that prediction accuracy is improved when the similarity between the nodes is corrected by the similarity confidence coefficient. Second, the experiments also confirm that the confidence degree of the link prediction results can be measured quantitatively. Our research indicates that the confidence level on each prediction is determined by the amount of data used in the corresponding calculation, which can be measured quantitatively.