Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container includes capturing a plurality of images of the specimen container from multiple viewpoints, stacking the multiple viewpoint images along a channel dimension into a single stacked input, and processing the stacked input with a single deep convolutional neural network (SDNN). The SDNN includes a segmentation convolutional neural network that receives the stacked input and outputs multiple label maps simultaneously. The SDNN also includes a classification convolutional neural network that processes the multiple label maps and outputs an HILN determination (Hemolysis, Icterus, and/or Lipemia, or Normal) of the serum or plasma portion of the specimen. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: This application provides a gesture information processing method and apparatus, an electronic device, and a storage medium. The method includes: determining an electromyography signal collection target object in a gesture information usage environment; dividing the electromyography signal sample through a sliding window having a fixed window value and a fixed stride into different electromyography signals of the target object, and denoising the different electromyography signals of the target object; recognizing the denoised different electromyography signals, and determining probabilities of gesture information represented by the different electromyography signals; and weighting the probabilities of the gesture information represented by the different electromyography signals, so as to determine gesture information matching the target object.

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: 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: 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: The disclosure relates to carrier particle-drug conjugates, including nanoparticle drug conjugates (NDC), that can be used in the delivery of a drug to a biological target (e.g., for targeted delivery of a cytotoxic drug to a cancer cell or tumor). Also disclosed are self-immolative linkers and linker-payload conjugates suitable for use in a carrier particle drug conjugate, and methods of making the same, and methods for treating cancer.

Abstract: The present disclosure provides a high-plasticity rapidly-degradable Mg-Li-Gd-Ni alloy, including the following chemical elements by mass percentage: 1.0-10.0% of Gd, 0.2-2.0% of Ni, 5.5-10% of Li, and the rest of Mg and inevitable impurities. The impurities have a total content less than or equal to 0.3%. The present disclosure further provides a preparation method of the high-plasticity rapidly-degradable Mg-Li-Gd-Ni alloy. The high-plasticity rapidly-degradable Mg-Li-Gd-Ni alloy provided by the present disclosure constructs an ?-Mg+?-Li dual-phase matrix structure by introducing ?-Li with a body-centered cubic (BCC) structure with relatively more slip systems to improve plasticity of the alloy, then adds a certain amount of Gd element to weaken texture and promote non-basal plane slip, and further improves plasticity. In addition, by introducing the high-potential Ni-containing LPSO phase, a large potential difference with ?-Mg and ?-Li is formed to increase the degradation performance.

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: Described herein are novel conjugates containing an inhibitor (e.g., a PSMA inhibitor, e.g., a gastrin-releasing peptide receptor inhibitor) and metal chelator that are covalently attached to a macromolecule (e.g., a nanoparticle, a polymer, a protein). Such conjugates exhibit distinct properties over the free, unbound inhibitor/chelator construct.

Abstract: A neural network-based method for quantifying a volume of a specimen. The method includes providing a specimen, capturing images of the specimen, and directly classifying to one of a plurality of volume classes or volumes using a trained neural network. Quality check modules and specimen testing apparatus adapted to carry out the volume quantification method are described, as are other aspects.

Abstract: An aqueous synthesis methodology for the preparation of silica nanoparticles (SNPs), core-shell SNPs having, for example, a size of 2 to 15 nm and narrow size-dispersion with size control below 1 nm, i.e. at the level of a single atomic layer. Different types of dyes, including near infrared (NIR) emitters, can be covalently encapsulated within and brightness can be enhanced via addition of extra silica shells. The surface may be functionalized with polyethylene glycol (PEG) groups and, optionally, specific surface ligands. This aqueous synthesis methodology also enables synthesis of 2 to 15 nm sized fluorescent core and core-shell aluminosilicate nanoparticles (ASNPs) which may also be surface functionalized. Encapsulation efficiency and brightness of highly negatively charged NIR fluorophores is enhanced relative to the corresponding SNPs without aluminum.

Abstract: A data classification and recognition method includes: obtaining a first data set and a second data set, the second data set including second data, samples in the second data being labeled; performing training using first data in an unsupervised training mode and using the second data in a supervised training mode to obtain a first classification model; obtaining a second classification model; performing distillation training on a model parameter of the second classification model to obtain a data classification model; and performing class prediction on target data by using the data classification model.

Abstract: A data processing method includes: acquiring an initial sample angiography image set; performing data expansion processing on a first sample angiography image based on physical characteristics of blood vessels at a target site to obtain a processed sample angiography image, performing label conversion processing on a first label based on the physical characteristics of the blood vessels at the target site to obtain a second label of the processed sample angiography image, and adding the processed sample angiography image and the second label to a target sample angiography image set; and training an angiography image recognition model using the initial sample angiography image set and the target sample angiography image set to obtain a trained angiography image recognition model. The performance of the trained angiography image recognition model is improved by increasing the number of samples.

Abstract: An image segmentation method includes: encoding an original image containing a target object based on a prior knowledge vector, to obtain a target feature map, the prior knowledge vector comprising a plurality of prior knowledge weights each representing accuracy of a corresponding rater labeling a region of an object in an image; decoding the target feature map, to obtain a first segmented image of the original image, the first segmented image indicating a target region in which the target object is located in the original image; performing image reconstruction on the first segmented image based on the prior knowledge vector, to obtain labeled segmented images, wherein one labeled segmented image corresponds to one prior knowledge weight and indicates a target region labeled by a corresponding rater; and processing the target feature map based on the labeled segmented images, to obtain a second segmented image of the original image.

Abstract: This present disclosure relates to the technical field of artificial intelligence, and provides a vessel image classification method and apparatus, a device, and a storage medium. The method includes: inputting a first vessel image sample into a first image processing model, and obtaining a predicted enhanced image and predicted vessel location information; and training the first image processing model based on a second vessel image sample, vessel location labeling information, the predicted enhanced image, and the predicted vessel location information. In the above solution, the impact of image quality on the vessel classification is considered during training of the vessel classification model, so that an end-to-end vessel classification model subsequently generated based on the trained first image processing model can realize a higher classification accuracy for a low quality vessel image, thereby improving the accuracy of classifying vessels in the vessel image by artificial intelligence.

Abstract: A physiological electric signal classification processing method includes: performing data alignment on an initial physiological electric signal corresponding to a target user identity based on target signal spatial information corresponding to the target user identify to obtain a target physiological electric signal; performing spatial feature extraction on the target physiological electric signal based on a target spatial filtering matrix to obtain a target spatial feature, the target spatial filtering matrix being generated based on target training physiological electric signals corresponding to a plurality of training user identities respectively and training labels corresponding to the target training physiological electric signals, the target training physiological electric signals being obtained by performing data alignment on initial training physiological electric signals based on training signal spatial information corresponding to the training user identities; and obtaining a classification result

Abstract: An image processing method can reduce costs related to manual labeling, improve training efficiency, and increase a quantity of training samples, thereby improving the accuracy of an image classification model. First images and second images are processed using an image classification model to obtain predicted classification results. The first images include a classification label and the second images include a pseudo classification label. A first loss value indicating accuracy is acquired based on the predicted classification results, the corresponding classification labels, and the corresponding pseudo classification labels. A second loss value indicating accuracy is acquired based on the predicted classification results and the corresponding pseudo classification labels. A model parameter of the image classification model is updated based on the first loss value and the second loss value. Classification processing and acquisition is performed until a target image classification model is obtained.

Abstract: An image classification method includes: performing image segmentation on an unlabeled sample image to obtain image blocks and performing feature extraction on each image block to obtain an initial image feature set including an initial image feature corresponding to each image block, rearranging and combining initial image features in the initial image feature set to obtain a first image feature set and a second image feature set, first image features in the first image feature set and second image features in the second image feature set corresponding to different rearrangement and combination manners, pre-training an image classification model based on the first image feature set and the second image feature set, the image classification model being configured to classify content in an image, and fine-tuning the pre-trained image classification model based on a labeled sample image.

Abstract: A 3DP preparation process of a high-strength rapid-dissolving magnesium alloy for an underground temporary plugging tool is disclosed by the present disclosure, comprising the following steps: 1) evenly mixing ingredients of material components; 2) importing the shape of a product needing to be printed into a computer control system, and printing alloy powder and glue in a 3D printer in an alternate spraying molding mode to obtain a blank with the needed shape; 3) drying the blank obtained in the step 2) and then carrying out degreasing and sintering in a protective atmosphere or vacuum; and 4) sintering the blank obtained in the step 3) at a high temperature of 570° C.-680° C. in the protective atmosphere or vacuum and then cooling to a room temperature.

Abstract: An electroencephalogram signal classification method includes: obtaining a first electroencephalogram signal; processing the first electroencephalogram signal using at least two electroencephalogram signal classification models to obtain respective motor imagery probability distributions from the at least two electroencephalogram signal classification models; and determining a motor imagery type of the first electroencephalogram signal based on the motor imagery probability distributions. A plurality of electroencephalogram signal classification models is respectively trained using an augmented data set obtained through augmentation. During prediction, by combining the plurality of electroencephalogram signal classification models, the accuracy of classifying an electroencephalogram signal to determine a motor imagery type may be improved, when using a model trained with a relatively small number of training samples.