Abstract: The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving the three-dimensional image acquired by an imaging device, and creating a first stack of two-dimensional images from a first plane of the three-dimensional image and a second stack of two-dimensional images from a second plane of the three-dimensional image. The method may further include segmenting, by a processor, the first stack and the second stack of two-dimensional images using at least one neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the first stack and second stack.

Abstract: Systems and methods are provided for generating a pseudo-CT prediction model that can be used to generate pseudo-CT images. An exemplary system may include a processor configured to retrieve training data including at least one MR image and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may extract a plurality of features from each image point of the at least one MR image, create a feature vector for each image point based on the extracted features, and extract a CT value from each image point of the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the feature vectors and the CT values of the plurality of training subjects.

Abstract: A preparation method for polypeptide nano-bubbles, comprising the following steps: constructing a recombinant plasmid, which includes a Flag tag, an adipose tissue-targeting polypeptide and the coding gene of nano-bubble marker membrane protein CD63; and transfecting the recombinant plasmid into cells which secrete nano-bubbles through lipidosome for culturing, collecting a cell culture solution, and extracting the polypeptide nano-bubbles by ultracentrifugation. The present invention further discloses the polypeptide nano-bubbles and application thereof in the preparation of drugs for treating obesity. The polypeptide nano-bubbles bring great convenience for targeted therapy of anti-obesity drugs. The polypeptide nano-bubbles have good biocompatibility and are capable of carrying different kinds of bioactive substances.

Abstract: The present invention discloses a strain S. cerevisiae M 2016785 producing high concentration of ?-phenylethanol and application thereof, and belongs to the technical field of industrial microorganisms. The strain S. cerevisiae M 2016785 producing high concentration of ?-phenylethanol according to the present invention was deposited in China Center for Type Culture Collection (CCTCC) on Dec. 26, 2016 with the accession number OF CCTCC NO: M 2016785. The strain of the present invention has the high ?-phenylethanol production capacity, and is applied in the fermentation of Huangjiu, the fermentation of cooking wine, the brewing of vinegar, the fermentation of soybean sauce and the fermentation of Baijiu, in which the content of ?-phenylethanol can reach 410 mg/L, 450 mg/L, 300 mg/L, 200 mg/L, 110 mg/L and 370 mg/L respectively, and effectively increases the concentration of the flavor substance such as 2-phenylethyl acetate.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical image. Embodiments of the present disclosure may locate and track a moving, three-dimensional (3D) target in a patient undergoing image-guided radiation therapy. For example, an adaptive filter model for a region of interest in the patient may be received, wherein the adaptive filter model is based on the target to be tracked. An image acquisition device may obtain a two-dimensional (2D) slice of a region of interest in the patient. A processor may then apply the adaptive filter model to the 2D slice, wherein the adaptive filter model includes an offset value. The processor may also determine a location of the target in the 2D slice based on the adaptive filter model. The processor may also estimate a potential location of the target based on the offset value. The processor may then repeat one or more of the above steps to track the moving target during image-guided radiation therapy of the patient.

Abstract: This invention relates to pharmaceutical composition and methods of using RXR agonist and/or RAR agonist for the treatment or prevention of head and neck cancer.

Abstract: Embodiments disclose a method and system for segmenting medical images. In certain embodiments, the system comprises a database configured to store a plurality of medical images acquired by an image acquisition device. The plurality of images include at least one first medical image of an object, and a second medical image of the object, each first medical image associated with a first structure label map. The system further comprises a processor that is configured to register the at least one first medical image to the second medical image, determine a classifier model using the registered first medical image and the corresponding first structure label map, and determine a second structure label map associated with the second medical image using the classifier model.

Abstract: Embodiments of this application provide an information transmission method, a network device, and a user equipment. The method provided in this application includes: sending, by a network device, a broadcast frame that includes first duration information and second duration information that are of A-BFT (Association Beamforming Training); receiving a frame sent by a first user equipment in a timeslot randomly selected from a first time range; and receiving a frame sent by a second user equipment in a timeslot randomly selected from a second time range. According to the embodiments of this application, beamforming training efficiency can be improved.

Abstract: Embodiments of this application describe a communication method, a first station (STA), a second STA, and an access point (AP). The method includes: determining, by a first STA, a first basic service set (BSS) color; generating, by the first STA, a data frame when the first station STA and a second STA perform D2D transmission, where the data frame includes the first BSS color; and sending, by the first STA, the data frame to the second STA. The first BSS color is carried in the D2D data frame, so that when receiving the data frame, another receiving station in a BSS to which the first STA and the second STA belong can determine that the frame is a frame from a neighboring cell.

Abstract: The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving the three-dimensional image acquired by an imaging device, and creating a first stack of two-dimensional images from a first plane of the three-dimensional image and a second stack of two-dimensional images from a second plane of the three-dimensional image. The method may further include segmenting, by a processor, the first stack and the second stack of two-dimensional images using at least one neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the first stack and second stack.

Abstract: Systems and methods are provided for generating a pseudo-CT prediction model using multi-channel MR images. An exemplary system may include a processor configured to retrieve training data including multiple MR images and at least one CT image for each of a plurality of training subjects. For each training subject, the processor may determine at least one tissue parameter map based on the multiple MR images and obtain CT values based on the at least one CT image. The processor may also generate the pseudo-CT prediction model based on the tissue parameter maps and the CT values of the plurality of training subjects.

Abstract: Features, such as anatomical features, may be automatically segmented from medical imaging information, using a computer-implemented method. In an example, three-dimensional (3D) medical imaging information may be received, such as defining a first volume. A first trained convolutional neural network (CNN) may be applied to the three-dimensional medical imaging information. An output from the first trained CNN may be used to determine a region-of-interest within the first volume, the region-of-interest defining a lesser, second volume. A different, second trained CNN may be applied to the region-of-interest, a segmented representation of the 3D medical imaging information may be provided using the outputs from the first and second CNNs, where the second CNN provides enhanced segmentation detail in the region-of-interest without requiring application of the second CNN to an entirety of the first volume. Techniques are also described from training one or more of the first and second CNNs.

Abstract: This invention relates to pharmaceutical composition and methods of using vitamin A and/or RAR? agonist for the treatment or prevention of diseases or conditions associated with high fat diet and/or vitamin deficiency.

Abstract: The invention relates to a polyurethane (PU) reactive hot melt adhesive removable at elevated temperature as well as having a high bonding strength at room temperature.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical image. Embodiments of the present disclosure may locate and track a moving, three-dimensional (3D ) target in a patient undergoing image-guided radiation therapy. For example, an adaptive filter model for a region of interest in the patient may be received, wherein the adaptive filter model is based on the target to be tracked. An image acquisition device may obtain a two-dimensional (2D) slice of a region of interest in the patient. A processor may then apply the adaptive filter model to the 2D slice, wherein the adaptive filter model includes an offset value. The processor may also determine a location of the target in the 2D slice based on the adaptive filter model. The processor may also estimate a potential location of the target based on the offset value. The processor may then repeat one or more of the above steps to track the moving target during image-guided radiation therapy of the patient.

Abstract: Embodiments disclose a method and system for segmenting medical images. In certain embodiments, the system comprises a database configured to store a plurality of medical images acquired by an image acquisition device. The plurality of images include at least one first medical image of an object, and a second medical image of the object, each first medical image associated with a first structure label map. The system further comprises a processor that is configured to register the at least one first medical image to the second medical image, determine a classifier model using the registered first medical image and the corresponding first structure label map, and determine a second structure label map associated with the second medical image using the classifier model.

Abstract: Systems and methods include training a deep convolutional neural network (DCNN) to reduce one or more artifacts using a projection space or an image space approach. In a projection space approach, a method can include collecting at least one artifact contaminated cone beam computed tomography (CBCT) projection space image, and at least one corresponding artifact reduced, CBCT projection space image from each patient in a group of patients, and using the artifact contaminated and artifact reduced CBCT projection space images to train a DCNN to reduce artifacts in a projection space image. In an image space approach, a method can include collecting a plurality of CBCT patient anatomical images and corresponding registered computed tomography anatomical images from a group of patients, and using the plurality of CBCT anatomical images and corresponding artifact reduced computed tomography anatomical images to train a DCNN to remove artifacts from a CBCT anatomical image.

Abstract: The present disclosure relates to systems, methods, and computer-readable storage media for segmenting medical image. Embodiments of the present disclosure may locate a target in a three-dimensional (3D) volume. For example, an image acquisition device may provide a 3D medical image containing a region of interest of the target. A processor may then extract a plurality of two-dimensional (2D) slices from the 3D image. The processor may also determine a 2D patch for each 2D slice, wherein the 2D patch corresponds to an area of the 2D slice associated with the target. The processor may also convert the 2D patch to an adaptive filter model for determining a location of the region of interest.

Abstract: Embodiments of the present invention provide an information transmission method and a node. The information transmission method provided in the present invention may include: receiving, by a first node, a request message sent by a second node, where the first node and the second node belong to a same BSS; and sending, by the first node, a response message to the second node, where the response message includes an identifier of a BSS to which the first node belongs. The embodiments of the present invention can improve a network throughput.