Burkitt lymphoma (BL) is a highly malignant non-Hodgkin's lymphoma that is closely related to the abnormal expression of genes. Familial acute myelogenous leukemia related factor (FAMLF; GenBank accession No. EF413001.1) is a novel gene that was cloned by our research group, and miR-181b is located in the intron of the FAMLF gene. To verify the role of miR-181b and FAMLF in BL, RNAhybrid software was used to predict target site of miR-181b on FAMLF and real-time quantitative PCR (RQ-PCR) was used to detect expression of miR-181b and FAMLF in BL patients, Raji cells and unaffected individuals. miR-181b was then transfected into Raji and CA46 cell lines and FAMLF expression was examined by RQ-PCR and western blotting. Further, Raji cells viability and proliferation were detected by MTT and clone formation, and Raji cell cycle and apoptosis were detected by flow cytometry. The results showed that miR-181b can bind to bases 21–42 of the FAMLF 5′ untranslated region (UTR), FAMLF was highly expressed and miR-181b was lowly expressed in BL patients compared with unaffected individuals. FAMLF expression was significantly and inversely correlated to miR-181b expression, and miR-181b negatively regulated FAMLF at posttranscriptional and translational levels. A dual-luciferase reporter gene assay identified that the 5′ UTR of FAMLF mRNA contained putative binding sites for miR-181b. Down-regulation of FAMLF by miR-181b arrested cell cycle, inhibited cell viability and proliferation in a BL cell line model. Our findings explain a new mechanism of BL pathogenesis and may also have implications in the therapy of FAMLF-overexpressing BL.
Modern low-altitude unmanned aircraft (UA) detection and surveillance systems mostly adopt the multi-sensor fusion technology scheme of radar, visible light, infrared, acoustic and radio detection. Firstly, this paper summarises the latest research progress of UA and bird target detection and recognition technology based on radar, and provides an effective way of detection and recognition from the aspects of echo modeling and micro motion characteristic cognition, manoeuver feature enhancement and extraction, motion trajectory difference, deep learning intelligent classification, etc. Furthermore, this paper also analyses the target feature extraction and recognition algorithms represented by deep learning for other kinds of sensor data. Finally, after a comparison of the detection ability of various detection technologies, a technical scheme for low-altitude UA surveillance system based on four types of sensors is proposed, with a detailed description of its main performance indicators.
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