Our study aimed to determine whether Krüppel-like factor 14 (KLF14) inhibits the proliferation and promotes the apoptosis of cervical cancer cells through integrin β1 (ITGB1). Immunohistochemistry was performed to determine the expression of KLF14. The effect of KLF14 on the proliferation of cervical cancer cells was verified by Cell Counting Kit-8 (CCK-8) assays, colony formation assays and in vivo experiments. The effect of KLF14 on cervical cancer cell apoptosis was detected by flow cytometry. The targeting relationship between KLF14 and ITGB1 was evaluated by Western blotting and a dual-luciferase reporter assay. Moreover, Flow cytometry was performed to verify the relationship between KLF14 and ITGB1 on the apoptosis of cervical cancer cells. Additionally, Western blot analysis was performed to investigate the relationship between KLF14 and ITGB1 on the expression of downstream related molecules. As a result, the expression of KLF14 in cervical cancer tissues was lower than that in paracancerous tissues. KLF14 inhibited proliferation and promoted apoptosis in cervical cancer cells. Mechanistically, ITGB1 expression was significantly downregulated in KLF14-overexpressing cervical cancer cells. At the same time, we found that the effects of KLF14 and ITGB1 on apoptosis of cervical cancer cells could be mutually affected. KLF14 directly targeted ITGB1 to regulate its downstream PI3K/AKT signalling pathway. In summary, KLF14 inhibits the progression of cervical cancer by targeting ITGB1 via the PI3K/AKT signalling pathway.

Rolling bearings are critical components that are incredibly prone to failure in the operation of mechanical equipment. Due to the complexity of the actual working conditions, multiple types, positions and scales of bearings are problematic to accurately and completely classify using conventional classification methods. In this study, a novel end-to-end deep learning framework consisting of a one-dimensional convolutional neural network (1D-CNN) and a module fused by long short-term memory (LSTM) and gated recurrent unit (GRU) is proposed to diagnose bearing failures, thus solving the problem of the poor accuracy of traditional fault identification. First, 1D-CNN is used to extract local features from bearing data thanks to its excellent local feature extraction capabilities. Second, global features are extracted from bearing data using LSTM and GRU, and classification is performed with Softmax. Finally, the proposed model is evaluated using Case Western Reserve University and the University of Cincinnati data, with accuracy rates of 99.99% and 99.83%, respectively. The experimental results indicate that the proposed model has good feasibility and performance.

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