Meat quality is one of the most important economic traits in pig breeding and production. Intramuscular fat (IMF) is a major factor that improves meat quality. To better understand the alternative splicing (AS) events underlying meat quality, long-read isoform sequencing (Iso-seq) was used to identify differential (D)AS events between the longissimus thoracis (LT) and semitendinosus (ST), which differ in IMF content, together with short-read RNA-seq. Through Iso-seq analysis, we identified a total of 56,789 novel transcripts covering protein-coding genes, lncRNA, and fusion transcripts that were not previously annotated in pigs. We also identified 456,965 AS events, among which 3930 were DAS events, corresponding to 2364 unique genes. Through integrative analysis of Iso-seq and RNA-seq, we identified 1174 differentially expressed genes (DEGs), among which 122 were DAS genes, i.e., DE-DAS genes. There are 12 overlapped pathways between the top 20 DEGs and DE-DAS genes, as revealed by KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis, indicating that DE-DAS genes play important roles in the differential phenotype of LT and ST. Further analysis showed that upregulated DE-DAS genes are more important than downregulated ones in IMF deposition. Fatty acid degradation and the PPAR (peroxisome proliferator-activated receptor) signaling pathway were found to be the most important pathways regulating the differential fat deposition of the two muscles. The results update the existing porcine genome annotations and provide data for the in-depth exploration of the mechanisms underlying meat quality and IMF deposition.

Chimeric RNA was considered a special marker of cancer. However, recent studies have demonstrated that chimeric RNAs also exist in non-cancerous cells and tissues. Here, we analyzed and predicted jointly 49 chimeric RNAs by Star-Fusion and FusionMap. One chimeric RNA, we named TNNI2-ACTA1, and its eight transcript variants were identified by reverse transcriptase–polymerase chain reaction. The overexpression of TNNI2-ACTA1 V1 inhibited the proliferation of porcine skeletal muscle satellite cells through down-regulating the mRNA expression levels of cell cycle–related genes cyclinD1. However, as parental genes, there is no such effect in the TNNI2 and ACTA1. To explore the underlying mechanism for this phenomenon, we used RNA-seq to profile the transcriptomes of PSCs with overexpression. Compared with the negative control group, 1,592 differentially expressed genes (DEGs) were upregulated and 1,077 DEGs downregulated in TNNI2 group; 1,226 DEGs were upregulated and 902 DEGs downregulated in ACTA1 group; and 13 DEGs were upregulated and 16 DEGs downregulated in TNNI2-ACTA1 V1 group, respectively. Compared with the parental gene groups, three specific genes were enriched in the TNNI2-ACTA1 V1 group (NCOA3, Radixin, and DDR2). These three genes may be the key to TNNI2-ACTA1 V1 regulating cell proliferation. Taken together, our study explores the role of chimeric RNAs in normal tissues. In addition, our study as the first research provides the foundation for the mechanism of chimeric RNAs regulating porcine skeletal muscle growth.

cis-Splicing of adjacent genes (cis-SAGe) has been involved in multiple physiological and pathological processes in humans. However, to the best of our knowledge, there is no report of cis-SAGe in adipogenic regulation. In this study, a cis-SAGe product, BCL2L2–PABPN1 (BP), was characterized in fat tissue of pigs with RT-PCR and RACE method. BP is an in-frame fusion product composed of 333 aa and all the functional domains of both parents. BP is highly conserved among species and rich in splicing variants. BP was found to promote proliferation and inhibit differentiation of primary porcine preadipocytes. A total of 3074/44 differentially expressed mRNAs (DEmRs)/known miRNAs (DEmiRs) were identified in porcine preadipocytes overexpressing BP through RNA-Seq analysis. Both DEmRs and target genes of DEmiRs were involved in various fat-related pathways with MAPK and PI3K-Akt being the top enriched. PPP2CB, EGFR, Wnt5A and EHHADH were hub genes among the fat-related pathways identified. Moreover, ssc-miR-339-3p was found to be critical for BP regulating adipogenesis through integrated analysis of mRNA and miRNA data. The results highlight the role of cis-SAGe in adipogenesis and contribute to further revealing the mechanisms underlying fat deposition, which will be conductive to human obesity control.

In order to solve the problems of time-consuming and frequent mistranslations in traditional translation methods, this study designed an English-Chinese machine translation method based on transfer learning. On the basis of analyzing the basic principles and specific strategies of English-Chinese machine translation, the traditional neural machine translation methods are analyzed, and then the translation process is optimized by transfer learning. On the basis of preprocessing English-Chinese translation text data, features of English-Chinese translation text are extracted, features of English-Chinese translation text are rapidly classified by feature transfer learning, and machine models of English-Chinese translation are constructed based on the classification results. The objective of feature transfer learning is to reuse the past knowledge obtained in the form of dataset to be utilized for another target data. The findings of the experiment show the effectiveness of the proposed method in achieving the design expectation. The benefit of the proposed method includes a short translation time and fewer mistranslations.

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The evaluation of English teaching is becoming increasingly popular. An English teaching evaluation approach based on machine learning is developed in order to improve the accuracy of the English teaching evaluation and reduce the inaccuracy of the English teaching evaluation. Firstly, the specific meaning of the English classroom teaching evaluation is analyzed, the selection principle of the English classroom teaching evaluation index is designed, and the selection of the English teaching evaluation index is completed. Second, the machine learning technology is examined, and the analytic hierarchy process is used to produce the judgment of the English teaching assessment index. Then, using the principal component analysis, calculate the eigenvalues and eigenvectors of the evaluation indicators to determine the contribution of the English teaching quality assessment indicators. Finally, create the English teaching evaluation weight calculation model using the machine learning approach, and acquire the English teaching evaluation’s complete results using the fuzzy evaluation method. The experimental results show that when the number of iterations is 60, the normalized mean square error of the effectiveness of the method is 0.03, and when the number of tests is 30, the effectiveness of the method is 0.99; this shows that the method can improve the effectiveness of the English teaching evaluation. It can effectively reduce the error of the English teaching evaluation.

With the actual demand of international communication, intelligent translation of English has become a key direction of artificial intelligence development in the current English field. For the existing English intelligent translation, how to deal with the massive data effectively is always a big problem. Therefore, it is necessary to use the principle of machine learning to optimize the English intelligent translation model. The purpose of this paper is to optimize the existing English intelligent translation model through spectral clustering to remove outliers, so as to make it more suitable for the use of massive data. Moreover, this paper uses deep learning methods to improve on the basis of the PoseNet network structure and adds regularization to the convolutional layer, which reduces the problem of gradient disappearance and reduces the computational complexity. In addition, this paper uses adaptive weighting to remove invalid model assumptions. In the conceptual space of the similarity matrix, the interior point is farther from the origin than the outlier point. The algorithm in this paper can detect and eliminate the outliers in a paragraph of English. At the same time, the normal English content will be classified into data categories, and the final translation result will be obtained. Through the experimental test, it can be seen that the model proposed in this paper has good performance and can cope with massive data, so it has certain superiority.