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Here, we report the discovery of two viruses associated with a disease characterized by severe diarrhea on a large-scale goat farm in Jilin province. Electron Microscopy observations revealed two kinds of virus particles with the sizes of 150–210 nm and 20–30 nm, respectively. Detection of 276 fecal specimens from the diseased herds showed the extensive infection of peste des petits ruminants virus (63.77%, 176/276) and caprine enterovirus (76.81%, 212/276), with a co-infection rate of 57.97% (160/276). These results were partially validated with RT-PCR, where all five PPRV-positive and CEV-positive specimens yielded the expected size of fragments, respectively, while no fragments were amplified from PPRV-negative and CEV-negative specimens. Moreover, corresponding PPRV and CEV fragments were amplified in PPRV and CEV double-positive specimens. Histopathological examinations revealed severe microscopic lesions such as degeneration, necrosis, and detachment of epithelial cells in the bronchioles and intestine. An immunohistochemistry assay detected PPRV antigens in bronchioles, cartilage tissue, intestine, and lymph nodes. Simultaneously, caprine enterovirus antigens were detected in lung, kidney, and intestinal tissues from the goats infected by the peste des petits ruminants virus. These results demonstrated the co-infection of peste des petits ruminants virus with caprine enterovirus in goats, revealing the tissue tropism for these two viruses, thus laying a basis for the future diagnosis, prevention, and epidemiological survey for these two virus infections. [ABSTRACT FROM AUTHOR]

Variable selection is one of the most important contents of machine learning research and a large number of variable selection methods have been proposed at present. From deep learning perspective, we propose a new variable selection method based on multi-layer artificial neural networks in this paper. The method propagates errors gained form a trained network model through the network from output layer to input layer and the values gained by input layer give information about the relative contribution. The variables with big contribution value are selected, which we call neural inverse propagation (NIP). Specifically, after training a neural network, the gained errors between the actual network outputs and the desired outputs go through the network from top to down and arrive at the input layer. The absolute values gained by input units provide information about the relative importance (contribution) of input units, which is that the larger the value is, the more important the corresponding input variable is. We remove k variables corresponding to the first k minimum absolute value gained by input units to the predictive variables, to perform variable selection. The efficiency of the proposed method is illustrated through its application to classification and regression tasks on a number of synthetic datasets and a real-world image classification dataset. [ABSTRACT FROM AUTHOR]

This work investigated the mechanical decay mechanism and high‐temperature corrosion mechanism of high‐strength steel via high‐temperature mechanical experiments, and corrosion and stress corrosion cracking tests were performed in simulated working conditions where water, oil, H2S, and CO2 coexist. The results show that the tensile strength and yield strength of high‐strength steel decrease with increasing temperature; the tensile strength and yield strength at 350°C reduce 11.91% and 19.08%, respectively, compared with 25°C. Moreover, the corrosion rate of high‐strength steel gradually increased from 0.0914 mm/a at 150°C to 1.2288 mm/a at 350°C. In addition, the influence of high temperature on the corrosion mechanism was inferred through thermodynamic calculations, and the reason for the presence of Fe3O4 in the corrosion products at 350°C was analyzed. At 350°C, stress corrosion cracking was observed in high‐strength steels when the applied stress reached 90% of the yield strength. Based on the aforementioned studies, the relevant mechanisms related to the decay of mechanical properties, corrosion, and stress corrosion cracking are also proposed. [ABSTRACT FROM AUTHOR]