Abstract: A computer device performs feature extraction on two-dimensional medical images included in a three-dimensional medical image, to obtain image features corresponding to the two-dimensional medical images. The three-dimensional medical image are obtained by continuously scanning a target tissue structure. The computer device determines offsets of the two-dimensional medical images in a target direction based on the image features. The computer device performs feature alignment on the image features based on the offsets, to obtain aligned image features. The computer device performs three-dimensional segmentation on the three-dimensional medical image based on the aligned image features, to obtain three-dimensional layer distribution of the target tissue structure in the three-dimensional medical image.

Abstract: Methods of semiconductor processing may include contacting a substrate with a first slurry and a first platen. The substrate may include silicon oxide defining one or more features, a liner extending across the silicon oxide and within the one or more features, and a copper-containing layer deposited on the liner and extending within the one or more features. The first slurry and the first platen may remove a first portion of the copper-containing layer. The methods may include contacting the substrate with a second slurry and a second platen, which may remove at least a portion of the liner. The methods may include contacting the substrate with a third slurry and a third platen, which may remove a second portion of the copper-containing layer. The methods may include contacting the substrate with a fourth slurry and a fourth platen, which may remove at least a portion of the silicon oxide.

Abstract: A method and an apparatus for classifying an electroencephalogram signal, a device and a computer-readable storage medium. The method includes: obtaining an electroencephalogram signal; performing feature extraction on the electroencephalogram signal to obtain a signal feature corresponding to the electroencephalogram signal; obtaining a difference distribution ratio, the difference distribution ratio being used for representing impacts of difference distributions of different types on distributions of the signal feature and a source domain feature in a feature domain, the source domain feature being a feature corresponding to a source domain electroencephalogram signal; aligning the signal feature with the source domain feature according to the difference distribution ratio to obtain an aligned signal feature; and classifying the aligned signal feature to obtain a motor imagery type corresponding to the electroencephalogram signal.

Abstract: Methods and apparatuses for image processing are provided. A first image belonging to a first image domain is acquired and input to an image processing model to be trained to obtain a second image belonging to a second image domain. A first correlation degree between an image feature of the first image and an image feature of the second image to obtain a target feature correlation degree is calculated. A second correlation degree between feature value distribution of the image feature of the first image and feature value distribution of the image feature of the second image is calculated to obtain a distribution correlation degree. Model parameters of an image processing model are adjusted to a direction in which the target feature correlation degree is increased and a direction in which the distribution correlation degree is increased to obtain a trained image processing.

Abstract: Disclosed herein are nanoparticle immunoconjugates useful for therapeutics and/or diagnostics. The immunoconjugates have diameter (e.g., average diameter) no greater than 20 nanometers (e.g., as measured by dynamic light scattering (DLS) in aqueous solution, e.g., saline solution). In certain embodiments, the conjugates are silica-based nanoparticles with single chain antibody fragments attached thereto.

Abstract: An image classification model training method and apparatus are provided. Classification results of each image outputted by an image classification model are obtained. When the classification results outputted by the image classification model do not meet a reference condition, a reference classification result is constructed based on the classification results outputted by the image classification model. Because the reference classification result can indicate a probability that images belong to each class, a parameter of the image classification model is updated to obtain a trained image classification model based on a total error value between the classification results of the each image and the reference classification result.

Abstract: Provided are a scheduling method and apparatus based on a deep learning node computation, and a storage medium. The scheduling method includes: a to-be-computed node of a preset neural network computation graph is acquired; a node type of the to-be-computed node is determined, where the node type includes a hardware computation node and a software computation node; in a case where the node type is the hardware computation node, the hardware computation node is scheduled to a first queue, and whether a hardware computing power module corresponding to the hardware computation node is occupied or not is determined; and in a case where the hardware computing power module is not occupied, the hardware computation node is input into the hardware computing power module for computing.

Abstract: This application discloses a method for reconstructing a dendritic tissue in an image performed by a computer device. The method includes: acquiring original image data corresponding to a target image of a target dendritic tissue and corresponding reconstruction reference data determined based on a local reconstruction result of the target dendritic tissue in the target image; applying a target segmentation model to the original image data and the reconstruction reference data to acquire a target segmentation result for indicating a target category of each pixel in the target image, and the target category of any pixel being used for indicating whether the pixel belongs to the target dendritic tissue or not; and reconstructing the target dendritic tissue in the target image based on the target segmentation result to obtain a complete reconstruction result of the target dendritic tissue in the target image.

Abstract: Provided are a method for the parallel processing of data, a device, and a storage medium. The method includes: identifying, from multiple first computing nodes, at least three first computing nodes which have a logical relationship are identified and defining the at least three first computing nodes which have the logical relationship as a first parallel node group, where the first parallel node group includes a first preceding node and at least two first subsequent nodes; acquiring a first input data model of the first preceding node and generating a first input tensor of the first preceding node; computing a first output tensor of the first preceding node according to the first input data model and the first input tensor; and acquiring a second input data model of the at least two first subsequent nodes and using the first output tensor as a second input tensor.

Abstract: A method for training an image segmentation model includes calling an encoder to perform feature extraction on a sample image and a scale image to obtain a sample image feature and a scale image feature. The method also includes performing class activation graph calculation to obtain a sample class activation graph and a scale class activation graph. The method also includes calling a decoder to obtain a sample segmentation result of the sample image, and calling the decoder to obtain a scale segmentation result of the scale image. The method also includes calculating a class activation graph loss and calculating a scale loss. The method also includes training the decoder based on the class activation graph loss and the scale loss.

Abstract: Described herein are nanoprobes comprising ultrasmall aminated and cRGDY-conjugated nanoparticles labeled with Zirconium-89 (89Zr) and methods of their use. The provided compositions are renally clearable and possess suitable blood circulation half-time, high tumor active targeting capability, dominant renal clearance, low liver accumulation, and a high tumor-to-background ratio. The described nanoprobes exhibit great potential as “target-or-clear” tracers to human subjects for systemic targeted imaging (or treatment) of cancer.

Abstract: For training for and performance of patient modeling from surface data in a medical system, a progressive multi-task model is used. Different tasks for scanning are provided, such as landmark estimation and patient pose estimation. One or more features learned for one task are used as fixed or constant features in the other task. This progressive approach based on shared features increases efficiency while avoiding reductions in accuracy for any given task.

Abstract: This disclosure relates to methods of functionalizing a nanoparticle, e.g., for conjugation to a targeting ligand and/or payload moiety, such as for the production of a nanoparticle drug conjugate (NDC).

Abstract: The disclosure relates to nanoparticle drug conjugates (NDC) that comprise ultrasmall nanoparticles, folate receptor (FR) targeting ligands, and linker-drug conjugates, and methods of making and using them to treat cancer.

Abstract: Porous compositions and methods of making and using same. The compositions may be one or more layer(s) of mesoporous inorganic materials. The mesoporous inorganic material(s) may be a plurality of inorganic nanocages, which may be microporous. A composition may include homostacks of layers of the same inorganic mesoporous materials. A composition may include heterostacks of layers of inorganic mesoporous materials, where at least two of the layers are different. The compositions may be surface functionalized. The compositions may be formed in a reaction mixture including one or more precursor(s), one or more surfactant(s), water, and one or more organic solvent(s). The compositions may be formed at the liquid-liquid interface between the water and the one or more organic solvent(s). A composition may be used as a catalyst, in a catalytic method, as a separation medium, in a separation method, in nanomedicine applications, or the like.

Abstract: A method for controlling a scanner comprises: sensing an outer surface of a body of a subject to collect body surface data, using machine learning to predict a surface of an internal organ of the subject based on the body surface data, and controlling the scanner based on the predicted surface of the internal organ.

Abstract: A display panel including a substrate, multiple display pixels, an encapsulation structure, and an auxiliary layer is provided. The substrate includes a display region, a bendable region, and a buffer region positioned therebetween. The display pixels are disposed in the display region. The encapsulation structure is overlapped with the display region and covers the display pixels. The auxiliary layer is overlapped with the bendable region and has a top surface. A first height is included between a top surface of the auxiliary layer and a surface of the substrate. The auxiliary layer and the encapsulation structure define a recess overlapped with the buffer region. A second height is included between a bottom surface of the recess positioned in the buffer region and the surface of the substrate. A difference between the first height and the second height is greater than 0 ?m and less than or equal to 4 ?m.

Abstract: The present invention provides a display panel comprising a base substrate, wherein the base substrate comprises a display area and a non-display area disposed around the display area; a light emitting layer disposed on the display area; a first inorganic layer disposed on the base substrate and covering the light emitting layer; a guiding structure disposed on the first inorganic layer; an organic layer disposed on the guiding structure, wherein the guiding structure is used to guide vapor in the organic layer into the non-display area; and a second inorganic layer disposed on the guiding structure and covering the organic layer.

Abstract: Described is a versatile surface modification approach to, for example, modularly and orthogonally functionalize nanoparticles (NPs) such as, for example, PEGylated nanoparticles, ith various types of different functional ligands (functional groups) on the NP surface. It enables the synthesis of, for example, penta-functional PEGylated nanoparticles integrating a variety of properties into a single NP, e.g., fluorescence detection, specific cell targeting, radioisotope chelating/labeling, ratiometric pH sensing, and drug delivery, while the overall NP size remains, for example, below 10 nm.

Abstract: The present invention relates to a recombinant nonstructural protein 1 (NS 1), a recombinant influenza virus and an immunological composition including the same, as well as a method of treating or preventing a disease or condition caused by or associated with an influenza virus in a subject in need thereof. At least one amino acid residue is mutated or deleted in 4 contiguous amino acid residues of TRAF3-interacting motifs (TIMs) within an effector domain of the recombinant NS 1. A recombinant influenza virus including the recombinant NS1 is comparable to the wild-type influenza virus in virus replication ability, and the recombinant influenza virus can elevate interferon activation of a subject for achieving better immune response and better immunological protection, leading in a method of treating or preventing a disease or condition caused by or associated with an influenza virus in a subject in need thereof.