BackgroundLong noncoding RNAs (lncRNAs) have been identified as a novel class of regulators implicated in diverse biological processes in human cancers. Currently, evidence have shown that SNHG6, a cancer-associated lncRNA, exerts critical functions in gastric cancer and hepatocellular carcinoma; however, its role in colorectal cancer (CRC) remains unclear.MethodsThe expression of SNHG6 was determined by quantitative real-time PCR in CRC tissues and cells. SNHG6 was downregulated by using RNAi technology. Cell proliferation was examined by MTT and clone formation assays. Cell migration and invasion were determined by wound healing and transwell assays. Fluorescence in situ hybridization assays were performed to examine subcellular localization of SNHG6 in CRC cells. Fluorescence reporter and Western blot assays were used to explore the potential mechanisms of SNHG6 in CRC progression.ResultsIn this study, we found that SNHG6 was significantly upregulated in CRC tissues and cell lines, compared with normal tissues and normal colorectal epithelial cell line NCM460, respectively. High expression of SNHG6 was positively correlated with tumor size, advanced TNM stage, and distant metastasis. Survival analyses revealed that SHNG6 was significantly associated with poor clinical outcomes and could serve as an independent prognostic factor. Loss-of-function studies demonstrated that SNHG6 knockdown inhibited CRC cell proliferation, induced G0/G1 arrest, promoted apoptosis, suppressed CRC cell migration and invasion, and restrained tumor growth. Mechanistic investigations showed that SNHG6 acted as a competing endogenous RNA for miR-181a-5p and attenuated the inhibitory effect of miR-181a-5p on E2F5.ConclusionTaken together, these results demonstrated that SNHG6 plays a crucial role in CRC progression via miR-181a-5p/E2F5 axis. Therefore, SNHG6 may serve as a prognostic and therapeutic biomarker in CRC.
An ultra-broadband single polarization filter based on plasmonic photonic crystal fiber with a liquid crystal core is investigated using the full-vectorial finite element method. Numerical simulations show that the loss of x-polarized core mode is better than 248.95 dB/cm in a 1.25-2.1 μm wavelength range, while the corresponding loss of y-polarized core mode is lower than 0.21 dB/cm. The high polarization extinction ratio is obtained at the communication wavelength of 1.3 μm, where the losses of x-and y-polarized core modes are 433.25 and 0.0064 dB/cm, respectively. When the fiber length is 1 mm, a broad bandwidth of 850 nm with an extinction ratio lower than −20 dB is obtained. Moreover, the proposed single polarization filter exhibits a better tolerance of realistic fabrication.
Vehicle detection based on unmanned aerial vehicle (UAV) images is a challenging task for the small size of objects, complex background, and the imbalance of various vehicle samples. This paper proposes a high-performance UAV vehicle detector. We use the single-shot refinement neural network (RefineDet) as a base network, which employs the top-down architecture to offer contextual information, achieving accurate detection. However, for the small size of vehicles, the top-down architecture introduces too much context, which brings surrounding interference. We present a multi-scale adjacent connection module (ACM) to provide effective contextual information and reduce interference for vehicle detection. In addition, we adopt an alternate double loss training strategy (ADT) to solve the problem of imbalance between hard and easy examples during training, and we design suitable default boxes according to the distribution of the UAV dataset to improve the recall rate. Our method achieves 92.0% and 90.4% accuracy on the collected UAV dataset and the publicly available Stanford drone dataset, respectively. And, the proposed detector can run at 58 FPS on a single GPU. INDEX TERMS Adjacent connection module, effective contexts, alternate double loss, unmanned aerial vehicle, detection.
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