This study aimed at investigating the biological functions of long non-coding RNAs (lncRNAs) hox transcript antisense intergenic RNA (HOTAIR) in resistant ovarian cancer cells, exploring the regulation effect of HOTAIR on HOXA7, and investigating their influence on the chemosensitivity of ovarian cancer cells. Quantitative real-time polymerase chain reaction (qRT-PCR) was applied for the verification of HOTAIR expression in resistant and sensitive groups. How HOTAIR downregulation affected cell proliferation, migration and invasion, and apoptosis were determined using the MTT assay and the colony formation assay, the Transwell assay and flow cytometry analysis, respectively. Immunohistochemistry was used to inspect the protein expression of HOXA7 in resistant and sensitive ovarian cancer tissues. The regulation relationship between HOTAIR and HOXA7 was investigated by qRT-PCR and Western blot. The effect of HOTAIR and HOXA7 on tumor growth was confirmed by the tumor xenograft model of nude mice. By knocking down HOXA7, HOTAIR downregulation restrained the ovarian cancer deterioration in functional experiments. Silencing of HOTAIR and HOXA7 could effectively inhibit tumor growth and increase chemosensitivity of ovarian tumors in nude mice. Downregulation of HOTAIR negatively affected the survival and activity of resistant ovarian cancer cells, and suppressed the expression of HOXA7. Silencing of HOTAIR and HOXA7 could increase the chemosensitivity of ovarian cancer cells, thus suppressing tumor development.
Bridges are critical to economic and social development of a country. In order to ensure the safe operation of bridges, it is of great significance to accurately predict displacement of monitoring points from bridge Structural Health System (SHM). In the paper, a CEEMDAN-KELM model is proposed to improve the accuracy of displacement prediction of bridge. Firstly, the original displacement monitoring time series of bridge is accurately and effectively decomposed into multiple components called intrinsic mode functions (IMFs) and one residual component using a method named complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then, these components are forecasted by establishing appropriate kernel extreme learning machine (KELM) prediction models, respectively. At last, the prediction results of all components including residual component are summed as the final prediction results. A case study using global navigation satellite system (GNSS) monitoring data is used to illustrate the feasibility and validity of the proposed model. Practical results show that prediction accuracy using CEEMDAN-KELM model is superior to BP neural network model, EMD-ELM model and EMD-KELM model in terms of the same monitoring data.