This paper investigates the problem of anti-jamming communication in dynamic and intelligent jamming environment. A sequential deep reinforcement learning algorithm (SDRLA) without prior information is proposed, and raw spectrum information is used as the input of SDRLA. The proposed SDRLA algorithm mainly contains two parts: Firstly, deep learning and sliding window principle are introduced to identify jamming patterns; Secondly, reinforcement learning is carried out to make on-line channel selection based on recognized jamming patterns. In addition, channel switching cost is introduced for the purpose of formulating the trade-off relationship between throughput and overhead. Taking advantage of both deep learning and reinforcement learning, this method can realize rapid and effective anti-jamming channel selection with no need for modeling the jammer's characteristics. Simulation results show the convergence and effectiveness of the proposed SDRLA algorithm. Compared with single-mode reinforcement learning, our approach can reach better convergence performance and higher utility. INDEX TERMS Pattern-aware, reinforcement learning (RL), deep learning (DL), anti-jamming communication, channel switching cost.
Gastric cancer (GC) development trends have identified multiple processes ranging from inflammation to carcinogenesis, however, key pathogenic mechanisms remain unclear. Tissue microenvironment (TME) cells are critical for the progression of malignant tumors. Here, we generated a dynamic transcriptome map of various TME cells during multi-disease stages using single-cell sequencing analysis. We observed a set of key transition markers related to TME cell carcinogenic evolution, and delineated landmark dynamic carcinogenic trajectories of these cells. Of these, macrophages, fibroblasts, and endothelial cells exerted considerable effects toward epithelial cells, suggesting these cells may be key TME factors promoting GC occurrence and development. Our results suggest a phenotypic convergence of different TME cell types toward tumor formation processes in GC. We believe our data would pave the way for early GC detection, diagnosis, and treatment therapies.
In this paper, we present a novel universal coupled theory for metamaterial bound states in the continuum (BIC) or quasi-bound states in the continuum (quasi-BIC) which provides ultra-high Q resonance for metamaterial devices. Our theory analytically calculates the coupling of two bright modes with phase information. Our method has much more accuracy for ultra-strong coupling comparing with the previous theories (the coupling of one bright mode and one dark mode and the two bright-mode coupling). Therefore, our theory is much more suitable for BIC or quasi-BIC and we can accurately predict the transmission spectrum of metamaterial BIC or quasi-BIC for the first time.
Background Interleukin 20 receptor A (IL20RA) has been shown to play a role in the establishment and progression of multiple tumors. However, the expression of this protein in colorectal cancer (CRC) and its correlation with the clinicopathological parameters of CRC have remained unclear. Methods A total of 323 paraffin sections including CRC tissues and adjacent normal tissues after surgery were collected. IL20RA protein expression was detected by immunohistochemical staining. The difference expression of IL20RA mRNA between CRC and normal tissues was also explored in the Oncomine and GEO databases. In addition, the IL20RA-related differentially expressed genes were analyzed in TCGA database and enrichment analysis was conducted to explore the cell functions and pathways related to IL20RA expression. Results There was increased IL20RA expression in CRC compared with that in normal tissues. High IL20RA expression was associated with greater tumor diameter, lymph node metastasis, and poor TNM stage in CRC, while also being suggestive of poor prognosis. The main pathways of IL20RA-related differentially expressed genes in TCGA were protein heterodimerization activity, oxygen binding, oxygen transporter activity, hormone activity, and lipid transporter activity. Meanwhile, IL20RA-related differentially expressed genes were mainly enriched in peroxidase, nucleotide stimulant repair, fatty acid metabolism, basal transcription factor, and RNA degradation. Conclusions IL20RA might have a role as a biomarker for CRC. Its upregulation might contribute to an aggressive phenotype in CRC. IL20RA’s involvement in the development and progression of CRC might occur through it affecting fatty acid metabolism, oxygen binding, oxygen transport, and hormone activity.
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