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AIM:To detect the expression of CD44v6 mRNA and nm23-H1 mRNA in hepatocellular carcinoma (HCC) by in situ hybridization, and to evaluate the relationship between their expression and also relationship between their expressions and tumor invasion and metastasis.METHODS:CD44v6 cDNA probe was synthesized with PCR technique and the nm23-H1 cRNA probe by in vitro transcription. The expression of CD44v6 mRNA and nm23-H1 mRNA was detected by in situ hybridization. RESULTS:In group with high invasion and metastasis potential, the positive rates of CD44v6 mRNA and nm23-H1 mRNA were 80% (8/10) and 40% (4/10),in group with poor invasion and metastasis potential, they were 21.7% (5/23) and 91.3% (21/23).There was a positive correlation between the expression of CD44v6 mRNA and tumor invasion and metastasis potential in HCC (P < 0.01), and a reverse correlation between the expression of nm23-H1 mRNA and tumor invasion and metastasis potential (P < 0.01) and a reverse correlation in the expression between CD44v6 mRNA and nm23-H1 mRNA in HCC (P < 0.01).CONCLUSION: Detection of CD44v6 mRNA and nm23-H1 mRNA may be useful for tumor invasion and metastasis in HCC.

The extraction and classification of crops is the core issue of agricultural remote sensing. The precise classification of crop types is of great significance to the monitoring and evaluation of crops planting area, growth, and yield. Based on the Google Earth Engine and Google Colab cloud platform, this study takes the typical agricultural oasis area of Xiangride Town, Qinghai Province, as an example. It compares traditional machine learning (random forest, RF), object-oriented classification (object-oriented, OO), and deep neural networks (DNN), which proposes a random forest combined with deep neural network (RF+DNN) classification framework. In this study, the spatial characteristics of band information, vegetation index, and polarization of main crops in the study area were constructed using Sentinel-1 and Sentinel-2 data. The temporal characteristics of crops phenology and growth state were analyzed using the curve curvature method, and the data were screened in time and space. By comparing and analyzing the accuracy of the four classification methods, the advantages of RF+DNN model and its application value in crops classification were illustrated. The results showed that for the crops in the study area during the period of good growth and development, a better crop classification result could be obtained using RF+DNN classification method, whose model accuracy, training, and predict time spent were better than that of using DNN alone. The overall accuracy and Kappa coefficient of classification were 0.98 and 0.97, respectively. It is also higher than the classification accuracy of random forest (OA = 0.87, Kappa = 0.82), object oriented (OA = 0.78, Kappa = 0.70) and deep neural network (OA = 0.93, Kappa = 0.90). The scalable and simple classification method proposed in this paper gives full play to the advantages of cloud platform in data and operation, and the traditional machine learning combined with deep learning can effectively improve the classification accuracy. Timely and accurate extraction of crop types at different spatial and temporal scales is of great significance for crops pattern change, crops yield estimation, and crops safety warning.

The rapidly rising demand for fiber-reinforced plastics (FRPs) has led to large volumes of manufacturing and end-of-life waste. Recycling fiber-reinforced thermosets is very difficult owing to their complex structure and heterogeneity. Landfill and incineration have become the most commonly used methods for eliminating non-degradable FRP waste, which adversely affects the environment and ecology. The purpose of this review is to evaluate end-of-life FRP recycling technologies in terms of optimizing the reuse/recycling of resources and eliminating waste, thereby improving FRP waste management. The technical progress made in the recycling of thermosetting composites is reviewed, including mechanical, thermal (pyrolysis and fluidized-bed), and chemical (critical fluid and low-temperature solvent) methods. The technical feasibility of each method was compared, and the economic and environmental impacts were considered. The challenges and opportunities facing the establishment of a composite recycling market in the future are examined. Finally, we provide a comprehensive summary of the scope of each recycling method.

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