Objective: To investigate the expression and correlation of transforming growth factor-β1 (TGF-β1) and fibroblast growth factor receptor 4 (FGFR4) in human hepatocellular carcinoma (HCC) and the relationship with clinicopathological features and prognosis.Materials and methods: The expression of TGF-β1 and FGFR4 in 126 HCC samples was detected immunohistochemically. Combined with clinical postoperative follow-up data, the expression of TGF-β1 and FGFR4 in HCC and the relationship with the prognosis of patients were analyzed by statistically.Results: The positive expression rate of TGF-β1 was 84.1% (106/126) in tumors, and that in peritumoral liver tissues was 64.3% (81/126); the positive expression rate of FGFR4 in tumors was 74.6% (94/126) and that in peritumoral liver tissues was 57.1% (72/126). The expression of TGF-β1 and FGFR4 in the carcinoma tissues was significantly higher than that in peritumoral liver tissues (p < 0.05). Intratumoral TGF-β1 and FGFR4 expression was associated with TNM stage (p < 0.05). TGF-β1 and FGFR4 expression levels didn't significantly correlate with other clinicopathological parameters, including age, sex, tumor size, serum AFP level, tumor differentiation, lymph node metastasis, etc. (p > 0.05). TGF-β1 expression was positively correlated with FGFR4 expression (r = 0.595, p < 0.05). Patients with positive FGFR4 or TGF-β1 expression had shorter overall survival compared with negative expression (p < 0.05).Conclusions: The expression of TGF-β1 and FGFR4 could make synergy on the occurrence and progression of HCC, and may be used as prognosis indicators for HCC patients.

Human activity recognition has been growing for decades in a variety of technological disciplines. However, in the existing WiFi-based human activity recognition systems, there are the following problems: Firstly, in the processing of channel state information (CSI) data, mainly for the removal of noise in the superimposed signal, there is no effective removal of useless multipath signals; Secondly, the data segmentation algorithm based on the empirical threshold requires manual adjustment of the threshold in different environments, resulting in poor robustness and unstable segmentation; Thirdly, simple learning classification is applied without specific design for CSI data structure and sufficiently abstracting information features. In this paper, a device-free human activity recognition system with a temporalfrequency attention mechanism is proposed, which can be deployed on commercial WiFi devices to identify human's daily activities. Firstly, the multipath signal affected by the channel change is extracted by using the difference of the propagation delay of different multipath, thereby eliminating the delay and invalid multipath signals that have undergone multiple reflections and refractions. Secondly, a neural network model based on attention mechanism is proposed, which assigns different weights to different characteristics and sequences by imitating the human brain to dedicate more attention to important information. Then, the long short-term memory (LSTM) model is used to learn the correlation features of different dimensions to realize human activity recognition. Finally, the system performance is evaluated in different environments, and the experimental results show that our syetem holds a better performance in both line-of-sight (LOS) and non-line-of-sight (NLOS) than the existing human activity recognition systems.

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AbstractIn this study, the propagation characteristics of electromagnetic waves in a parity-time (PT)-symmetrical 1D photonic crystal comprising dispersed silver layers are investigated. Based on the transmission matrix theory, the total reflection and transmission coefficients of the structure are obtained. It was found that, due to the PT-symmetrical structure, the reflections of the left and right incident waves are nonreciprocal. Numerical simulations indicated that the width of the band gap decreases with the increase in the gain and loss factor ρ in the PT medium, and the band gap ultimately disappears when ρ reaches a critical value, i. e., ${\rho }_{PT}$. With the increase in $\rho { >}{\rho }_{PT}$, anomalous transmittance and reflection occur within the original bang gap. As the gain and loss factor ρ continue to increase, the abnormal transmittance and reflectivity exhibit a trend of oscillatory decline, and perfect transmission can be achieved at larger values of ρ.

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