Abstract: An assembled cabinet and power connection boards thereof, in which side board assemblies of the assembled cabinet are stacked in a way that upper power connectors of a power connection board of a lower side board assembly among two adjacent stacked up side board assemblies are electrically connected with lower power connectors of a power connection board of the an upper side board assembly among the two adjacent stacked up side board assemblies, so as to preserve the versatility and flexibility of the installation of the assembled cabinet while allowing stable power supply to the electronic locks of the assembled cabinet.

Abstract: The present invention relates to the field of biomedicine, to an improved T cell receptor-costimulatory molecule chimera, in particular to a T cell receptor (TCR) or TCR complex comprising a costimulatory molecule, a T cell comprising the TCR or TCR complex, and the use thereof.

Abstract: The invention relates to the field of biomedicine, in particular to a T cell co-expressing CXCR2 and a Synthetic T-Cell Receptor and Antigen Receptor (STAR) against GPC3, and uses thereof.

Abstract: An electronic lock for an assembled cabinet, including a lock shell, a locking and unlocking assembly mounted on the lock shell, an electronic driving device, a wireless communication device, a control circuit board, and a power supply. The control circuit board is electrically connected to the electronic driving device, the wireless communication device, and the power supply. The electronic driving device transmits motion to the locking and unlocking assembly. The electronic lock is adaptable to free combination of an assembled cabinet to provide easy installation and convenient use.

Abstract: An automatic abnormal cell recognition method, the method including: 1) scanning a slide using a digital pathological scanner and obtaining a cytological slide image; 2) obtaining a set of centroid coordinates of all nuclei that is denoted as CentroidOfNucleus by automatically localizing nuclei of all cells in the cytological slide image using a feature fusion based localizing method; 3) obtaining a set of cell square region of interest (ROI) images that are denoted as ROI_images; 4) grouping all cell images in the ROI_images into different groups based on sampling without replacement, where each group contains ROW×COLUMN cell images with preset ROW and COLUMN parameters; obtaining a set of splice images; and 5) classifying all cell images in the splice image simultaneously by using the splice image as an input of a trained deep neural network; and recognizing cells classified as abnormal categories.

Abstract: An automatic abnormal cell recognition method, the method including: 1) scanning a slide using a digital pathological scanner and obtaining a cytological slide image; 2) obtaining a set of centroid coordinates of all nuclei that is denoted as CentroidOfNucleus by automatically localizing nuclei of all cells in the cytological slide image using a feature fusion based localizing method; 3) obtaining a set of cell square region of interest (ROI) images that are denoted as ROI_images,; 4) grouping all cell images in the ROI_images into different groups based on sampling without replacement, where each group contains ROW×COLUMN cell images with preset ROW and COLUMN parameters; obtaining a set of splice images; and 5) classifying all cell images in the splice image simultaneously by using the splice image as an input of a trained deep neural network; and recognizing cells classified as abnormal categories.