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[Objectives]This paper aimed to explore the effect of heat stress on partial miRNA in myoblasts,and analyze by bioinformatics and predict the target genes of miR-23a,miR-24,miR-27a-3p and miR-30a-5p,so as to reveal the mechanism of miRNA regulating the development of skeletal muscle under heat stress. [Methods]The expression of miRNA in myoblasts under heat stress was studied by RT-qPCR. The miRBase was used to obtain the precursor sequences and mature sequences of miR-23a,miR-24,miR-27a-3p and miR-30a-5p in different species,then MEGA X software was used to construct the phylogenetic trees for similarity analysis. The target genes of the above miRNA were predicted by TargetScan,miRDB,RNA22 and miRWalk,and the predicted common targets were analyzed by DAVID for GO function and KEGG pathway enrichment. Ultimately,the correlation among miRNA,common target genes and the KEGG pathway was analyzed. [Results]Heat stress significantly decreased the expression of miRNA in myoblasts at the proliferation and differentiation stages. The miR-23a,miR-24,miR-27a and miR-30a showed high conservation among different species,and except for miR-24 of Bos taurus,the mature sequences of four miRNA were basically the same,and the seed sequences were completely the same. The predicted common target genes were mainly enriched in the GO items,such as cell proliferation,cell differentiation,cell cycle,cell apoptosis and cell migration,as well as mTOR,TGFβ,MAPK,Ras,ErbB,FoxO,and PI3K-Akt and other KEGG pathways. Additionally,the correlation analysis showed that miR-24 and miR-27a-3p may target NLK and TAOK1 to participate in the MAPK and FoxO signaling pathways to regulate skeletal muscle development. [Conclusions]The miR-23a,miR-24,miR-27a and miR-30a were highly conserved in different species and the expression of partial miRNA was down-regulated in myoblasts under heat stress,thereby it might affect the development of skeletal muscle. [ABSTRACT FROM AUTHOR]

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The traditional VSM vector space model often ignores text semantics, and the constructes text feature matrix is sparse. Based on the word vector technology of deep learning, this paper proposes a similarity calculation method that integrated the improved TextRank algorithm. This method uses the word vector embedding technology to build a text vector space, which make the vector space model possess the semantic relevance. At the same time, with the improved TextRank algorithm to extract text keywords, the expression of text feature is enhanced and a large amount of redundant information is eliminated. The text characteristic of sparse matrix is reduced, which makes the text similarity computing more efficient. The results of the simulation experiments of different models showed that the fusion of the improved TextRank algorithm with Berl word vector technology have heller performance of text similarity calculation. [ABSTRACT FROM AUTHOR]