Emerging evidence has shown microRNAs (miRNAs) play an important role in human disease research. Identifying potential association among them is significant for the development of pathology, diagnose and therapy. However, only a tiny portion of all miRNA-disease pairs in the current datasets are experimentally validated. This prompts the development of high-precision computational methods to predict real interaction pairs. In this paper, we propose a new model of Logistic Model Tree for predicting miRNA-Disease Association (LMTRDA) by fusing multi-source information including miRNA sequences, miRNA functional similarity, disease semantic similarity, and known miRNA-disease associations. In particular, we introduce miRNA sequence information and extract its features using natural language processing technique for the first time in the miRNA-disease prediction model. In the cross-validation experiment, LMTRDA obtained 90.51% prediction accuracy with 92.55% sensitivity at the AUC of 90.54% on the HMDD V3.0 dataset. To further evaluate the performance of LMTRDA, we compared it with different classifier and feature descriptor models. In addition, we also validate the predictive ability of LMTRDA in human diseases including Breast Neoplasms, Breast Neoplasms and Lymphoma. As a result, 28, 27 and 26 out of the top 30 miRNAs associated with these diseases were verified by experiments in different kinds of case studies. These experimental results demonstrate that LMTRDA is a reliable model for predicting the association among miRNAs and diseases.

ZnSn(OH) 6 and binary-component SnO 2 -ZnSn(OH) 6 were introduced as affinity probes for phosphopeptide enrichment for the first time. Two strategies, either ZnSn(OH) 6 and SnO 2 serial enrichment or binarycomponent SnO 2 -ZnSn(OH) 6 enrichment in a single run, were proposed to enhance multi-phosphopeptide enrichment and to significantly improve global phosphopeptide detection.

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Focus group participants provided important insights into the refinement of existing items and the construction of new items for measuring patient safety culture in Chinese hospitals. This is a necessary first step in producing a culturally appropriate instrument applicable to specific local contexts.