Specific emitter identification (SEI) enables the discrimination of individual radio emitters with the external features carried by the received waveforms. This identification technique has been widely adopted in military and civil applications. However, many previous methods based on hand-crafted features are subject to the present expertise. To remedy these shortcomings, this paper presents a novel SEI algorithm using deep learning architecture. First, we perform Hilbert-Huang transform on the received signal and convert the resulting Hilbert spectrum into a grayscale image. As a signal representation, the Hilbert spectrum image has high information integrity and can provide abundant information about the nonlinear and non-stationary characteristics of signals for identifying emitters. Thereafter, we construct a deep residual network for learning the visual differences reflected in the Hilbert spectrum images. By using the residual architectures, we effectively address the degradation problem, which improves efficiency and generalization. From our analysis, the proposed approach combines high information integrity with low complexity, which outperforms previous studies in the literature. The simulation results validate that the Hilbert spectrum image is a successful signal representation, and also demonstrate that the fingerprints extracted from raw images using deep learning are more effective and robust than the expert ones. Furthermore, our method has the capability of adapting to signals collected under various conditions.
Purpose: Esophageal cancer is a deadly malignancy with a 5-year survival rate of only 5% to 20%, which has remained unchanged for decades. Esophageal cancer possesses a high frequency of TP53 mutations leading to dysfunctional G 1 cell-cycle checkpoint, which likely makes esophageal cancer cells highly reliant upon G 2 -M checkpoint for adaptation to DNA replication stress and DNA damage after radiation. We aim to explore whether targeting Wee1 kinase to abolish G 2 -M checkpoint sensitizes esophageal cancer cells to radiotherapy.Experimental Design: Cell viability was assessed by cytotoxicity and colony-forming assays, cell-cycle distribution was analyzed by flow cytometry, and mitotic catastrophe was assessed by immunofluorescence staining. Human esophageal cancer xenografts were generated to explore the radiosensitizing effect of AZD1775 in vivo.Results: The IC 50 concentrations of AZD1775 on esophageal cancer cell lines were between 300 and 600 nmol/L. AZD1775 (100 nmol/L) as monotherapy did not alter the viability of esophageal cancer cells, but significantly radiosensitized esophageal cancer cells. AZD1775 significantly abrogated radiationinduced G 2 -M phase arrest and attenuation of p-CDK1-Y15. Moreover, AZD1775 increased radiation-induced mitotic catastrophe, which was accompanied by increased gH2AX levels, and subsequently reduced survival after radiation. Importantly, AZD1775 in combination with radiotherapy resulted in marked tumor regression of esophageal cancer tumor xenografts.Conclusions: Abrogation of G 2 -M checkpoint by targeting Wee1 kinase with AZD1775 sensitizes esophageal cancer cells to radiotherapy in vitro and in mouse xenografts. Our findings suggest that inhibition of Wee1 by AZD1775 is an effective strategy for radiosensitization in esophageal cancer and warrants clinical testing.
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