6results about How to "Good test result" patented technology

The application relates to the technical field of biological medicine, in particular to a medicine for combined treatment of tumors by using a recombinant oncolytic virus and a small-molecule anticancer drug. The method comprises the following steps: combined treatment of tumors by using a recombinant oncolytic virus and a small-molecule anticancer drug; the small-molecule anticancer drug is a small-molecule anticancer drug targeting Parp; the recombinant oncolytic virus comprises M protein, G protein, N protein, P protein and L protein after site-directed mutagenesis. The application can kill tumor cells by using a recombinant oncolytic virus and a small-molecule anticancer drug, and the curative effect is 1+1>2.

This application relates to the field of biomedicine, specifically to a drug for the combined treatment of tumors using a recombinant oncolytic virus and a small-molecule anticancer drug. The specific steps include: treating tumors using a combination of a recombinant oncolytic virus and a small-molecule anticancer drug; the small-molecule anticancer drug is a BTK-targeting small-molecule anticancer drug; the recombinant oncolytic virus includes site-directed mutated M, G, N, P, and L proteins. This application utilizes the combined attack and killing of tumor cells by a recombinant oncolytic virus and a small-molecule anticancer drug, achieving a therapeutic effect greater than the sum of its parts (1+1>2).

The utility model discloses a cloth inspecting machine, relates to the technical field of textile equipment, and adopts the technical scheme that the cloth inspecting machine comprises a cloth inspecting machine body, a cloth inspecting table and a winding device are arranged on the cloth inspecting machine body, a cloth inspecting area is arranged on the cloth inspecting table, a plurality of cloth inspecting lamps which are distributed at equal intervals are arranged in the cloth inspecting area, and a partition plate is fixedly connected between every two adjacent cloth inspecting lamps; limiting pieces which are symmetrically distributed are arranged on the portions, located on the two sides of the cloth inspecting area, of the cloth inspecting table, and the limiting pieces are arranged in a telescopic adjusting mode. According to the cloth inspecting device, the cloth inspecting lamps are arranged in the cloth inspecting area, the cloth inspecting lamps in the corresponding area can be turned on according to the size of cloth needing to be inspected, energy waste is reduced, the limiting pieces are arranged on the two sides of the cloth inspecting area, the cloth can be limited to a certain degree in the cloth inspecting process, left-right sliding of the cloth is avoided, and the cloth inspecting efficiency is improved. The cloth inspecting machine has a better inspecting effect, meanwhile, the limiting pieces can also play a role in shielding redundant light sources on the two sides of the cloth, and operators can observe the cloth conveniently.

The application discloses a kind of construction method and application of intelligent signal detection model, belong to mobile communication technical field;The application constructs a new type of graph neural network containing aggregation update module, the global feature of each node is aggregated with its neighbor node information, so as to extract the hidden feature state of data according to the feature information of neighbor node and itself, and then realize the effective capture of the feature of communication signal such non-euclidean space data;In addition, after each aggregation is completed, the intermediate feature obtained by aggregation is further extracted in the application Feature, to improve the expression ability of intermediate feature, so that the designed graph neural network has more efficient information interaction ability and more powerful relationship reasoning ability, greatly improve the precision of signal detection.The application effectively solves the problem that the performance and computational complexity of traditional signal detection method are difficult to balance, and has strong robustness to channel estimation error.

This invention provides a road obstacle detection method based on an improved version of YOLOv4. Improvements have been made to YOLOv4, making the network more lightweight, enhancing its robustness and ability to handle complex environments, and enabling more accurate obstacle detection, thus improving the detection performance for small targets. The method includes: using the lightweight MobileNetV3 as the backbone network to provide feature extraction operations; employing a linear bottleneck inverse residual structure combined with depthwise separable convolution and SE attention mechanisms; improving the feature fusion network; using an efficient channel attention module in the model, which avoids dimensionality reduction and effectively captures cross-channel interaction information, maintaining the number of channel layers after global pooling, and using a non-dimensionality-reduced local cross-channel interaction strategy; focusing on the relationships between different channels through band matrix learning, selectively learning different information in different channels; and proposing a new DBR module, which consists of DepthwiseConv2D, BatchNormalization, and ReLU6.

This application relates to the field of biomedicine, specifically to a drug for the combined treatment of tumors using a recombinant oncolytic virus and a small-molecule anticancer drug. The specific steps include: treating tumors using a combination of a recombinant oncolytic virus and a small-molecule anticancer drug; the small-molecule anticancer drug is a CDK4 / 6-targeting small-molecule anticancer drug; the recombinant oncolytic virus includes site-directedly mutated M, G, N, P, and L proteins. This application utilizes the combined attack and killing of tumor cells by a recombinant oncolytic virus and a small-molecule anticancer drug, achieving a therapeutic effect greater than the sum of its parts (1+1>2).