Abstract: A bispecific antibody and a monoclonal antibody against human thymic stromal lymphopoietin (TSLP), and a use thereof.

Abstract: The present disclosure provides a chip packaging method and a chip packaging structure. The chip packaging method includes: providing an encapsulated grain and a packaging substrate, the encapsulated grain including a first hybrid bonding structure; wherein the packaging substrate includes a front side and a back side opposite to each other; a second hybrid bonding structure is formed on the front side of the packaging substrate and a connection pin is formed on the back side of the packing substrate; the second hybrid bonding structure and the connection pin are electrically connected through a third connecting metal column penetrating the packaging substrate; and bonding together the first hybrid bonding structure of the encapsulated grain and the second hybrid bonding structure of the packaging substrate by medium-to-medium and metal-to-metal aligned bonding such that the encapsulated grain is bonded to the packaging substrate.

Abstract: Some embodiments of the disclosure provide a quality control method for data annotation on a fundus image. In some examples, the method includes: acquiring a plurality of fundus images; performing standardization processing on the fundus images to obtain a plurality of standardized fundus images; performing preliminary filtering on quality of the standardized fundus images to acquire a plurality of qualified fundus images; preparing a target fundus image set; a plurality of first annotation doctors respectively annotating the images of the target fundus image set, to acquire a plurality of groups of doctor annotation results; calculating, on the basis of the doctor annotation results, self-consistency and gold-standard consistency of the corresponding first annotation doctors, to acquire the doctor annotation results of the first annotation doctors satisfying a preset condition as target annotation results; and gathering a plurality of groups of target annotation results to acquire a final annotation result.

Abstract: Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: Provided are a CAR-T cell specifically recognizing CD70, and a preparation method therefor. The method comprises expressing a SCFV(Anti CD70)-CD8-4-1BB-CD3? molecule in a T cell. The CAR-T cell has tumor killing activity and may be used for preparing a medication in the treatment of a tumor with the high expression level of CD70 molecules.

Abstract: Some embodiments of the disclosure provide an artificial neural network system for recognizing a lesion in a fundus image. The system includes a pre-processing module configured to pre-process a target fundus image and a reference fundus image taken from one person separately, a first neural network (12) configured to generate a first advanced feature set from the target fundus image, a second neural network (22) configured to generate a second advanced feature set from the reference fundus image, a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set, and a third neural network (14) configured to generate, according to the feature combination set, a judgmental result of lesions.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: The present disclosure provides an artificial neural network system for identifying a lesion in a retinal fundus image that comprises a pre-processing module configured to separately pre-process a target retinal fundus image and a reference retinal fundus image taken from a same person; a first neural network (12) configured to generate a first advanced feature set from the target retinal fundus image; a second neural network (22) configured to generate a second advanced feature set from the reference retinal fundus image; a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set; and a third neural network (14) configured to generate, according to the feature combination set, a diagnosis result.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: A conductive primer composition comprising: (a) a resinous component comprising: a chlorinated polyolefin resin, an acrylic resin, and a polyester resin; (b) conductive titanium dioxide; (c) a non-conductive pigmented filler; and (d) an organic solvent is disclosed. A method of preparing a conductive primer composition is also disclosed.

Abstract: A method and apparatus for acquiring people flow information, and a method and apparatus for processing people flow information are provided.

Abstract: The present application discloses a method and apparatus for obtaining an image of a form sheet, which belongs to the field of image processing technology. The method includes: obtaining graphic code information from an image to be processed, wherein the graphic code information comprises at least position information of a graphic code; determining form sheet image feature information in the image to be processed based on the position information of the graphic code; and obtaining an image of a form sheet from the image to be processed based on the form sheet image feature information. In the present application, there is no manual operation of obtaining the image of the form sheet is needed, and thus the resources are saved and the efficiency of obtaining the image of the form sheet is improved.

Abstract: A sensor assembly includes a housing and a circuit board assembly. The housing includes a first accommodation cavity, a second accommodation cavity, a first connection portion, a second connection portion, and a third accommodation cavity. The first connection portion is connected to the first accommodation cavity and one end of the third accommodation cavity, and the second connection portion is connected to the second accommodation cavity and the other end of the third accommodation cavity. The circuit board assembly is accommodated within the first accommodation cavity, the second accommodation cavity, the first connection portion, the second connection portion and the third accommodation cavity. The width of the first connection portion and the second connection portion is less than the width of the first accommodation cavity, the second accommodation cavity and the third accommodation cavity.

Abstract: Systems and methods are provided for hybrid video encoding. An example method includes: acquiring image information; extracting a background image based at least in part on the image information; detecting whether the background image is stable; and performing encoding switching between a background-based encoding method and a non-background encoding method based at least in part on the detection.

Abstract: The present application discloses a method and apparatus for obtaining an image of a form sheet, which belongs to the field of image processing technology. The method includes: obtaining graphic code information from an image to be processed, wherein the graphic code information comprises at least position information of a graphic code; determining form sheet image feature information in the image to be processed based on the position information of the graphic code; and obtaining an image of a form sheet from the image to be processed based on the form sheet image feature information. In the present application, there is no manual operation of obtaining the image of the form sheet is needed, and thus the resources are saved and the efficiency of obtaining the image of the form sheet is improved.

Abstract: Some embodiments of the disclosure provide a diabetic retinopathy recognition system (S) based on fundus image. According to an embodiment, the system includes an image acquisition apparatus (1) configured to collect fundus images. The fundus images include target fundus images and reference fundus images taken from a person. The system further includes an automatic recognition apparatus (2) configured to process the fundus images from the image acquisition apparatus by using a deep learning method. The automatic recognition apparatus automatically determines whether a fundus image has a lesion and outputs the diagnostic result. According to another embodiment, the diabetic retinopathy recognition system (S) utilizes a deep learning method to automatically determine the fundus images and output the diagnostic result.

Abstract: The present disclosure provides an artificial neural network system for identifying a lesion in a retinal fundus image that comprises a pre-processing module configured to separately pre-process a target retinal fundus image and a reference retinal fundus image taken from a same person; a first neural network (12) configured to generate a first advanced feature set from the target retinal fundus image; a second neural network (22) configured to generate a second advanced feature set from the reference retinal fundus image; a feature combination module (13) configured to combine the first advanced feature set and the second advanced feature set to form a feature combination set; and a third neural network (14) configured to generate, according to the feature combination set, a diagnosis result.

Abstract: Systems and methods are provided for hybrid video encoding. An example method includes: acquiring image information; extracting a background image based at least in part on the image information; detecting whether the background image is stable; and performing encoding switching between a background-based encoding method and a non-background encoding method based at least in part on the detection.

Abstract: An apparatus includes a first circuit and a second circuit. The first circuit may be configured to generate a plurality of delayed signals each as a copy of an input signal shifted in time by a sequence of respective delays based on a control signal. At least two of the respective delays may have a different duration. The first circuit may also be configured to change a number of driver signals that are active during each delay in the sequence of respective delays based on the input signal and the plurality of delayed signals to control a slew rate of an output signal. The second circuit may be configured to drive the output signal in response to the driver signals.

Abstract: Systems and methods are provided for hybrid video encoding. An example method includes: acquiring image information; extracting a background image based at least in part on the image information; detecting whether the background image is stable; and performing encoding switching between a background-based encoding method and a non-background encoding method based at least in part on the detection.