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The Arabidopsis TMS1 encodes a heat shock protein identical to the Hsp40 protein AtERdj3A and plays important roles in the thermotolerance of pollen tubes and other plant tissues. Despite its importance to plant growth and reproduction, little has been known about its mechanisms underlying thermotolerance of plants. In this study, the relationship between TMS1 and the Hsp70 proteins, Binding Immunoglobulin Proteins (BiPs) was explored to understand the molecular mechanisms of TMS1 in thermotolerance of plants. The expression of TMS1 was induced not only by heat shock, but also by dithiothreitol (DTT) and L-azetidine-2-carboxylic acid (AZC), similarly to the three BiP genes, indicating that TMS1 may be involved in unfolded protein response (UPR). The firefly luciferase complementary imaging (LCI), GST pull-down and ATPase enzyme activity assays demonstrated that the DnaJ domain of TMS1 could interact with BiP1 and BiP3, and could stimulate their ATPase enzyme activities. In addition, the expression level of TMS1 was reduced in the bzip28 bzip60 double mutant. These results suggest that TMS1 may function at the downstream of bZIP28 and bZIP60 and be involved in termotolerance of plants, possibly by participating in refolding or degradation of unfolded and misfolded proteins through interaction with the BiPs.

Collaborative filtering generates recommendations for target users by summarizing the preferences of neighbor users, so it does not need to analyze the content of digital resources in the Internet, which makes it a more successful recommendation technology. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. In this study, a novel collaborative filtering recommendation algorithm is proposed. Firstly, non-negative matrix factorization is adopted to factor the original rating matrix, and the nearest neighbors are searched on the low-rank user feature matrix, which improves the speed of the collaborative filtering algorithm. Then a weight determination method is designed and applied to collaborative filtering. Compared with the previous collaborative filtering algorithms that only consider rating values, the proposed algorithm takes both rating values and feature weights into account when calculating user similarities, which can more accurately express user relationships, thereby resulting in high quality recommendations. The MAE and F1 values of different collaborative filtering algorithms are compared based on two actual datasets. The proposed algorithm performs better than the popular collaborative filtering algorithms. The complexity values of different collaborative filtering algorithms are also compared. The computational complexity of the proposed algorithm MFFW-CF is O(m), which is the lowest among all the algorithms compared. [ABSTRACT FROM AUTHOR]

Recommendation systems provide personalized products or services to online users by mining their past preferences. Collaborative filtering is a popular recommendation technique because it is easy to implement. However, with the rapid growth of the number of users in recommendation systems, collaborative filtering suffers from serious scalability and sparsity problems. To address these problems, a novel collaborative filtering recommendation algorithm is proposed. The proposed algorithm partitions the users using affinity propagation clustering, and searches for k nearest neighbors in the partition where active user belongs, which can reduce the range of searching and improve real-time performance. When predicting the ratings of active user's unrated items, mean deviation method is used to impute values for neighbors' missing ratings, thus the sparsity can be decreased and the recommendation quality can be ensured. Experiments based on two different datasets show that the proposed algorithm is excellent both in terms of real-time performance and recommendation quality. [ABSTRACT FROM AUTHOR]