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In recent years, circular RNAs (circRNAs) have been found to play an essential regulatory role in hepatocellular carcinoma (HCC) through various mechanisms, particularly the endogenous competitive RNA (ceRNA) mechanism. Therefore, it is significant to explore the circRNAs in hepatoma. In this study, we constructed the ceRNA and survival network using Cytoscape. We also used R, Perl software, and multiple online databases and platforms, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), to perform overall survival, immune cell infiltration, immune checkpoints, pathway activity, and anticancer drug sensitivity analysis of the genes. Finally, the receiver operator characteristic curve (ROC) analysis was performed to identify the diagnosis value of the genes. KEGG analysis revealed the T cell receptor signaling pathway as the main enrichment pathway. A total of 29 genes related to survival and prognosis were screened out. The findings suggest that ZNF544, WDR76, ACTG1, RASSF3, E2F3, ASRGL1, and POGK are associated with multilevel immune cell infiltration. Additionally, immune checkpoint analysis screened out the ACTG1, E2F3, RASSF3, and WDR76. It was also revealed that the WDR76, E2F3, ASRGL1, and POGK mainly activated the cell cycle and DNA damage response (DDR) pathway. The results suggest that the sensitivity toward trametinib, refametinib (RDEA119), and selumetinib correlates to the expression of WDR76. ROC analysis showed that the area under the curve (AUC) of all genes in the regulatory axis was greater than 0.7. The identified hsa_circ_0000417/hsa_circ_0002688/hsa_circ_0001387--hsa-miR-199a-5p--WDR76 regulatory axis may provide new insights into the progression, clinical diagnosis, and treatment of HCC. [ABSTRACT FROM AUTHOR]

The ECOC technique is a powerful tool to learn and combine multiple binary learners for multi-class classification. It generally involves three steps: coding, dichotomizers learning, and decoding. In previous ECOC methods, the coding step and the dichotomizers learning step are usually performed independently. This simplifies the learning problem but may lead to unsatisfactory decoding results. To solve this problem, we propose a novel model for learning the ECOC matrix and dichotomizers jointly from data. We formulate the model as a nonlinear programming problem and develop an efficient alternating minimization algorithm to solve it. Specifically, for fixed ECOC matrix, our model is decomposed into a group of mutually independent quadratic programming problems; while for fixed dichotomizers, it is a difference of convex functions problem and can be easily solved using the concave--convex procedure algorithm. Our experimental results on ten data sets from the UCI machine learning repository demonstrated the advantage of our model over state-of-the-art ECOC methods. [ABSTRACT FROM AUTHOR]

The quantum convolutional neural networks (QCNNs) are emerging as a promising solution for image classification problems on near-term quantum devices. While QCNNs have shown encouraging results in binary classification tasks, their effectiveness in more complex multiclass classification tasks remains to be fully understood. In this study, we propose three distinct QCNN architectures, each inspired by the configuration of two-qubit entangling blocks available in quantum hardware. These QCNNs are designed based on different sliding modes of quantum filters. We investigate the impact of quantum filter structure, filter arrangement and parameter sharing among filters on the performance of QCNNs in multiclass classification. Our findings indicate that the specific structure of the quantum filter significantly affects the models' performance. Moreover, we observed that unsharring parameters and more complex filter arrangements can significantly enhance the performance of QCNNs. These results contribute to the development of powerful quantum classifiers for multiclass image classification. [ABSTRACT FROM AUTHOR]

Atrial fibrosis is a crucial contributor to initiation and perpetuation of atrial fibrillation (AF). This study aimed to identify a circRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) regulatory network related to atrial fibrosis in AF, especially to validate hsa_circ_0000672/hsa_miR-516a-5p/TRAF6 ceRNA axis in AF preliminarily. The circRNA-miRNA-mRNA ceRNA network associated with AF fibrosis was constructed using bioinformatic tools and literature reviews. Left atrium (LA) low voltage was used to represent LA fibrosis by using LA voltage matrix mapping. Ten controls with sinus rhythm (SR), and 20 patients with persistent AF including 12 patients with LA low voltage and 8 patients with LA normal voltage were enrolled in this study. The ceRNA regulatory network associated with atrial fibrosis was successfully constructed, which included up-regulated hsa_circ_0000672 and hsa_circ_0003916, down-regulated miR-516a-5p and five up-regulated hub genes (KRAS, SMAD2, TRAF6, MAPK11 and SMURF1). In addition, according to the results of Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, these hub genes were clustered in TGF-beta and MAPK signaling pathway. In the patients with persistent AF, hsa_circ_0000672 expression in peripheral blood monocytes was significantly higher than those in controls with SR by quantitative real-time polymerase chain reaction (p-value < 0.001). Furthermore, hsa_circ_0000672 expression was higher in peripheral blood monocytes of persistent AF patients with LA low voltage than those with LA normal voltage (p-value = 0.002). The dual-luciferase activity assay confirmed that hsa_circ_0000672 exerted biological functions as a sponge of miR-516a-5p to regulate expression of its target gene TRAF6. Hsa_circ_0000672 expression in peripheral blood monocytes may be associated with atrial fibrosis. The hsa_circ_0000672 may be involved in atrial fibrosis by indirectly regulating TRAF6 as a ceRNA by sponging miR-516a-5p. [ABSTRACT FROM AUTHOR]