ABSTRACT
: For a diesel engine, operating conditions have extreme importance in fault detection and diagnosis. Limited to various special circumstances, the multi-factor operating conditions of a diesel engine are difficult to measure, and the demand of automatic condition recognition based on vibration signals is urgent. In this paper, multi-factor operating condition recognition using a one-dimensional (1D) convolutional long short-term network (1D-CLSTM) is proposed. Firstly, a deep neural network framework is proposed based on a 1D convolutional neural network (CNN) and long short-Term network (LSTM). According to the characteristics of vibration signals of a diesel engine, batch normalization is introduced to regulate the input of each convolutional layer by fixing the mean value and variance. Subsequently, adaptive dropout is proposed to improve the model sparsity and prevent overfitting in model training. Moreover, the vibration signals measured under 12 operating conditions were used to verify the performance of the trained 1D-CLSTM classifier. Lastly, the vibration signals measured from another kind of diesel engine were applied to verify the generalizability of the proposed approach. Experimental results show that the proposed method is an effective approach for multi-factor operating condition recognition. In addition, the adaptive dropout can achieve better training performance than the constant dropout ratio. Compared with some state-of-the-art methods, the trained 1D-CLSTM classifier can predict new data with higher generalization accuracy.
ABSTRACT
Cervical cancer pathogenesis is regulated by numerous factors, including microRNAs. MicroRNA 1246 (miR-1246) has been shown to serve a role in cervical cancer tumorigenesis. However, the mechanisms through which miR-1246 exerts its oncogenic effects are largely unknown. The aim of the current study was to evaluate the effects of lentivirus-mediated miR-1246 knockdown on the biological characteristics and behavior of cervical cancer cells, and to identify the downstream signaling pathways affected by miR-1246 knockdown. Short hairpins inhibiting miR-1246 were synthesized and cloned into a recombinant lentiviral vector (LV-miR-1246-Inh), which was then used to infect SiHa cervical cancer cells. The effects of LV-miR-1246-Inh infection on cell invasion, proliferation and apoptosis were evaluated by Transwell assay, Cell Counting Kit-8 assay and flow cytometry, respectively. Thrombospondin-2 (THBS2), matrix metalloproteinase 2 (MMP2), MMP9 and extracellular matrix (ECM) component expression levels were evaluated, and the growth of xenograft tumors formed following injection of SiHa cells with knockdown of miR-1246 was assessed. miR-1246 downregulation in SiHa cells decreased proliferation, induced apoptosis and upregulated THBS2 expression. Furthermore, MMP2 and MMP9 levels were downregulated, whereas components of the ECM were upregulated subsequent to miR-1246 knockdown, indicating that this miRNA regulates cervical cancer cell pathogenesis via the THBS2/MMP/ECM pathway. Notably, SiHa cells with miR-1246 downregulation had a markedly decreased ability to form tumors in vivo. These results suggest that miR-1246 functions during cervical cancer pathogenesis and tumor formation via the THBS2/MMP/ECM signaling pathway. These findings support the future use of miR-1246 suppression in the treatment of cervical cancer.
ABSTRACT
This systematic review is written to investigate the outcome of cervical cancer. A comprehensive search of PubMed and EMBASE was performed to identify eligible studies. Nineteen studies from thirteen articles with a total of 1,310 participants were included in this meta-analysis. Overall survival (OS), disease-free survival (DFS), and recurrence-free survival (RFS) as a prognosis for cervical cancer were extracted and calculated, if available. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using STATA (version 12.0), resulting in the pooled HRs 0.70 (95% CI: 0.51-0.97) for OS, 1.02 (95% CI: 0.53-1.98) for DFS, and 0.56 (95% CI: 0.40-0.77) for RFS. The results indicated that cervical cancer patients with decreased microRNA expression were associated with shorter OS and RFS. It suggested that microRNAs might be promising markers for predicting the survival rate of cervical cancer.
