Taxifolin suppressed the toxicity and THP-1 cell adhesion to HUVECs induced by Cr(vi) via regulating the p38 MAPK and JNK pathways.

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Breast cancer is one of the most common cancer with high morbidity and mortality in women. This study aimed to explore the potential mechanism of costunolide inducing MCF‐7 cells apoptosis by multi‐spectroscopy, molecular docking, and cell experiments. The results manifested that costunolide interacted with calf thymus DNA (ct‐DNA) in a spontaneous manner, and the minor groove as the preferential binding mode. Furthermore, costunolide inhibited cell proliferation and colony formation. Hoechst 33258 staining showed that cell apoptosis induced by costunolide might be related to DNA damage. The apoptosis mechanism relied on regulating the protein expression of Bax, Bcl‐2, p53, Caspase‐3 and the activation of p38MAPK and nuclear factor κB (NF‐κB) pathways. This study will provide some experimental basis and potential therapeutic strategy for breast cancer treatment.

Recommendation systems are widely used in e-commerce websites as they can recommend appropriate movies, songs, books, and other items to users according to users' historical behavior. In traditional collaborative filtering algorithms, users' historical scores are usually used to predict the unknown item rating, while ignoring their textual reviews. Therefore, this paper proposes a park recommendation model based on user reviews and ratings (PRMRR). PRMRR first uses the latent Dirichlet allocation model to extract the statistical distribution of the park features. Secondly, it detects user preference distribution based on park features and user ratings. In order to measure the credibility of user ratings, user rating confidence level is considered to correct user preferences. Thirdly, it uses Kullback-Leibler divergence to calculate the similarity between different users and then predicts the unknown park rating for a specific user. Finally, the proposed algorithm is evaluated on two real park data sets, and the results on two different data sets show that the proposed approach outperforms other traditional approaches. Our recommendation algorithm thus has great potential to improve the quality of park recommendation and effectively handle the data sparsity problem.

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