Abstract: The present invention relates to the technical field of model establishment, and provides a dimethylarsinous acid-induced malignantly transformed cell line of human keratinocytes and application thereof. In the present invention, human keratinocytes are persistently exposed to and incubated with a low dose of dimethylarsinous acid, to construct an inorganic arsenic metabolite dimethylarsinous acid (DMAIII)-induced malignantly transformed cell model of human keratinocytes. The malignantly transformed cell model of the present invention promotes the identification of carcinogenicity of arsenic methylated metabolites, and indicates that long-term exposure to low-dose arsenic metabolite dimethylarsinous acid (DMAIII) causes malignant transformation of skin cells, thus providing a new cell model basis and new research idea for the study of carcinogenic mechanism of arsenic.

Abstract: The present invention relates to the technical field of model establishment, and provides a dimethylmonothioarsinic acid-induced malignantly transformed cell line of human keratinocytes and use thereof. In the present invention, human keratinocytes are persistently exposed to and incubated with dimethylmonothioarsinic acid, to construct an inorganic arsenic metabolite dimethylmonothioarsinic acid (DMMTAv)-induced malignantly transformed cell model of human keratinocytes. The malignantly transformed cell model of the present invention promotes the identification of carcinogenicity of arsenic methylated metabolites, and indicates that long-term exposure to low-dose arsenic metabolite dimethylmonothioarsinic acid (DMMTAv) causes malignant transformation of skin cells, thus providing a new cell model basis and new research idea for the study of carcinogenic mechanism of arsenic.

Abstract: This application provides a target detection method and device based on a neural network and training method and device of a neural network for target detection. The target detection method comprises: acquiring a to-be-detected image that contains a target; acquiring first feature information of the to-be-detected image by use of a first neural network, acquiring second feature information of the to-be-detected image by use of a second neural network; combining the first feature information and the second feature information to acquire combined feature information; and acquiring a target detection result by use of the second neural network and based on the combined feature information, wherein the number of layers of the second neural network is larger than the number of layers of the first neural network, the first feature information is heatmap feature information, and the second feature information is picture feature information.

Abstract: This application provides a target detection method and device based on a neural network and training method and device of a neural network for target detection. The target detection method comprises: acquiring a to-be-detected image that contains a target;acquiring first feature information of the to-be-detected image by use of a first neural network, acquiring second feature information of the to-be-detected image by use of a second neural network;combining the first feature information and the second feature information to acquire combined feature information; and acquiring a target detection result by use of the second neural network and based on the combined feature information, wherein the number of layers of the second neural network is larger than the number of layers of the first neural network, the first feature information is heatmap feature information, and the second feature information is picture feature information.