BackgroundmiR-141 is up-regulated and plays crucial roles in nasopharyngeal carcinoma (NPC). However, the molecular mechanism underlying the dysregulation of miR-141 is still obscure.MethodsThus, the ChIP-PCR was performed to identify the c-Myc-binding sites in miR-141 and BRD7. qRT-PCR, western blot and immunohistochemistry assays were used to detect the expression of miR-141 and its up/down stream molecules. The rescue experiments on the c-Myc/miR-141 axis were performed in vitro and in vivo.ResultsOur results showed that the levels of mature miR-141, pre-miR-141 and pri-miR-141 were downregulated in c-Myc knockdown NPC cells. Meanwhile, c-Myc transactivates the expression of miR-141 by binding its promoter region. Moreover, BRD7 was identified as a co-factor of c-Myc to negatively regulate the activation of c-Myc/miR-141 axis, as well as a direct target of c-Myc. Moreover, restoration of miR-141 in c-Myc knockdown NPC cells notably rescued the effect of c-Myc on cell proliferation and tumor growth, as well as the blocking of PTEN/AKT pathway. Additionally, the expression of c-Myc was positively correlated with that of miR-141 and the clinical stages of NPC patients and negatively associated with the expression of BRD7. Our findings demonstrated that BRD7 expression and c-Myc activation forms a negative feedback loop to control the cell proliferation and tumor growth by targeting miR-141.ConclusionsThese observations provide new mechanistic insights into the dysregulation of miR-141 expression and a promising therapeutic option for NPC.Electronic supplementary materialThe online version of this article (10.1186/s13046-018-0734-2) contains supplementary material, which is available to authorized users.
Remote sensing is an effective method of evaluating building damage after a large-scale natural disaster, such as an earthquake or a typhoon. In recent years, with the development of computer vision technology, deep learning algorithms have been used for damage assessment from aerial images. In April 2016, a series of earthquakes hit the Kyushu region, Japan, and caused severe damage in the Kumamoto and Oita Prefectures. Numerous buildings collapsed because of the strong and continuous shaking. In this study, a deep learning model called Mask R-CNN was modified to extract residential buildings and estimate their damage levels from post-event aerial images. Our Mask R-CNN model employs an improved feature pyramid network and online hard example mining. Furthermore, a non-maximum suppression algorithm across multiple classes was also applied to improve prediction. The aerial images captured on 29 April 2016 (two weeks after the main shock) in Mashiki Town, Kumamoto Prefecture, were used as the training and test sets. Compared with the field survey results, our model achieved approximately 95% accuracy for building extraction and over 92% accuracy for the detection of severely damaged buildings. The overall classification accuracy for the four damage classes was approximately 88%, demonstrating acceptable performance.
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