Abstract: The present disclosure discloses a tobacco leaf foliar spraying substance for reducing harmful ingredients in cheroot, a method for reducing carcinogenic components in flue-cured tobacco leaves and flue-cured tobacco shreds. The tobacco leaf foliar spraying substance contains a lotus leaf extract, the harmful chemical ingredients comprise N-nitrosonornicotine, 4-(N-methyl-nitrosamine)-1-(3-pyridinyl)-1-butanone, N-nitrosoanabasine and N-nitrosoanatabine. Foliar spraying is performed on a fertile field by using the lotus leaf extract before tobacco leaves are harvested and modulated, which not only significantly promotesagronomic characters and economic traits of tobacco and alleviates tobacco leaf browning but also effectively reduces harmful chemical ingredients unique to tobacco leaves, such as nitrosamine and nicotine.

Abstract: Embodiments of the present disclosure disclose a method for processing tasks in parallel, a device and a storage medium, and relate to a field of artificial intelligent technologies. The method includes: determining at least one parallel computing graph of a target task; determining a parallel computing graph and an operator scheduling scheme based on a hardware execution cost of each operator task of each of the at least one parallel computing graph in a cluster, in which the cluster includes a plurality of nodes for executing the plurality of operator tasks, and each parallel computing graph corresponds to at least one operator scheduling scheme; and scheduling and executing the plurality of operator tasks of the determined parallel computing graph in the cluster based on the determined parallel computing graph and the determined operator scheduling scheme.

Abstract: The present disclosure provides a method for generating a backbone network, an apparatus for generating a backbone network, a device, and a storage medium. The method includes: acquiring a set of a training image, a set of an inference image, and a set of an initial backbone network; training and inferring, for each initial backbone network in the set of the initial backbone network, the initial backbone network by using the set of the training image and the set of the inference image, to obtain an inference time and an inference accuracy of a trained backbone network in an inference process; determining a basic backbone network based on the inference time and the inference accuracy of the trained backbone network in the inference process; and obtaining a target backbone network based on the basic backbone network and a preset target network.

Abstract: Use of DKK-1 protein or the nucleic acid sequence in preparation of cancer diagnostic agents or kits, method to detect liver cancer with the monoclonal antibody thereof, the kit comprising anti-DKK-1 antibody or protein specific nucleic acid probes, together with a label, and method to detecting specific DKK-1 protein expression are disclosed.

Abstract: Use of DKK-1 protein or the nucleic acid sequence in preparation of cancer diagnostic agents or kits, method to detect liver cancer with the monoclonal antibody thereof, the kit comprising anti-DKK-1 antibody or protein specific nucleic acid probes, together with a label, and method to detecting specific DKK-1 protein expression are disclosed.

Abstract: Use of DKK-1 protein or the nucleic acid sequence in preparation of cancer diagnostic agents or kits, method to detect liver cancer with the monoclonal antibody thereof, the kit comprising anti-DKK-1 antibody or protein specific nucleic acid probes, together with a label, and method to detecting specific DKK-1 protein expression are disclosed.

Abstract: Use of DKK-1 protein or the nucleic acid sequence in preparation of cancer diagnostic agents or kits, method to detect liver cancer with the monoclonal antibody thereof, the kit comprising anti-DKK-1 antibody or protein specific nucleic acid probes, together with a label, and method to detecting specific DKK-1 protein expression are disclosed.