The deep convolutional neural network has strong representative ability, which can learn latent information repeatedly from signal samples and improve the accuracy of automatic modulation classification (AMC). In this paper, a novel compressive convolutional neural network (CCNN) is proposed for AMC, where different constellation images, i.e., regular constellation images (RCs) and contrast enhanced grid constellation images (CGCs), are generated as network inputs from received signals. Moreover, a compressive loss constraint is proposed to train the CCNN, which aims at capturing high-dimensional features for modulation classification. Additionally, CCNN utilizes intra-class compactness and inter-class separability to enhance the classification and robustness performance for the different orders of modulations. The simulation results demonstrate that CCNN displays superior classification and robustness performance than existing AMC methods.INDEX TERMS Automatic modulation classification, compressive loss constraint, deep convolutional neural network, multiple constellation images.

Background/Aims: Arsenic trioxide (ATO) is a known anti-acute promyelocytic leukemia (APL) reagent, whose clinical applications are limited by its serious cardiac toxicity and fatal adverse effects, such as sudden cardiac death resulting from long QT syndrome (LQTS). The mechanisms of cardiac arrhythmia due to ATO exposure still need to be elucidated. Long non-coding RNAs (lncRNAs) are emerging as major regulators of various pathophysiological processes. This study aimed to explore the involvement of lncRNAs in ATO-induced LQTS in vivo and in vitro. Methods: For in vivo experiments, mice were administered ATO through the tail vein. For in vitro experiments, ATO was added to the culture medium of primary cultured neonatal mouse cardiomyocytes. To evaluate the effect of lncRNA Kcnq1ot1, siRNA and lentivirus-shRNA were synthesized to knockdown lncRNA Kcnq1ot1. Results: After ATO treatment, the Kcnq1ot1 and Kcnq1 expression levels were down regulated. lncRNA Kcnq1ot1 knockdown prolonged the action potential duration (APD) in vitro and exerted LQTS in vivo. Correspondingly, Kcnq1 expression was decreased after silencing lncRNA Kcnq1ot1. However, the knockdown of Kcnq1 exerted no effect on lncRNA Kcnq1ot1 expression. Conclusions: To our knowledge, this report is the first to demonstrate that lncRNA Kcnq1ot1 downregulation is responsible for QT interval prolongation induced by ATO at least partially by repressing Kcnq1 expression. lncRNA Kcnq1ot1 has important pathophysiological functions in the heart and could become a novel antiarrhythmic target.

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The human immunodeficiency virus (HIV) is a major global public health issue. HIV-related cardiovascular disease remains a leading cause of morbidity and mortality in HIV positive patients. HIV Tat is a regulatory protein encoded by tat gene of HIV-1, which not only promotes the transcription of HIV, but it is also involved in the pathogenesis of HIV-related complications. This review is aimed at summarizing the current understanding of Tat in HIV-related cardiovascular diseases.

Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the correlation between TFs and surveyed the TF interaction networks associated with gene expression in GM12878 and K562 cell lines. We identified six TF modules associated with gene expression in each cell line. Furthermore, according to the enrichment characteristics of TFs in these TF modules around a target gene, a convolutional neural network model, called TFCNN, was constructed to identify gene expression level. Results showed that the TFCNN model achieved a good prediction performance for gene expression. The average of the area under receiver operating characteristics curve (AUC) can reach up to 0.975 and 0.976, respectively in GM12878 and K562 cell lines. By comparison, we found that the TFCNN model outperformed the prediction models based on SVM and LDA. This is due to the TFCNN model could better extract the combinatorial interaction among TFs. Further analysis indicated that the abundant binding of regulatory TFs dominates expression of target genes, while the cooperative interaction between TFs has a subtle regulatory effects. And gene expression could be regulated by different TF combinations in a nonlinear way. These results are helpful for deciphering the mechanism of TF combination regulating gene expression.

Hepatocellular carcinoma (HCC) is among the most common and lethal form of cancer worldwide. However, its diagnosis and treatment are still dissatisfactory, due to limitations in the understanding of its pathogenic mechanism. Therefore, it is important to elucidate the molecular mechanisms and identify novel therapeutic targets for HCC. Circadian rhythm-related genes control a variety of biological processes. These genes play pivotal roles in the initiation and progression of HCC and are potential diagnostic markers and therapeutic targets. This review gives an update on the research progress of circadian rhythms, their effects on the initiation, progression, and prognosis of HCC, in a bid to provide new insights for the research and treatment of HCC.

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