Abstract: A method and apparatus is provided to reconstruct a computed tomography image using iterative reconstruction (IR) that is accelerated using various combinations of ordered subsets, conjugate gradient, preconditioning, resetting/restarting, and/or gradient approximation techniques. For example, when restarting criteria are satisfied the IR algorithm can be reset by setting conjugate-gradient parameters to initial values and/or by changing the number of ordered subsets. The IR algorithm can be accelerated by approximately calculating the gradients, by using a diagonal or Fourier preconditioner, and by selectively updating the preconditioner based on the regularization function. The update direction and step size can be calculated using the preconditioner and a surrogate function, which is not necessarily separable.

Abstract: A node interconnection apparatus includes a computing node and a resource control node, and a device interconnection interface connecting the two. Each of the computing node and the resource control node includes a processing unit and a storage unit, and the resource control node further includes a resource interface for connecting with a network storage device. The resource control node manages storage resource of the network storage device, and when the computing node needs to start up, the resource control node obtains operating system startup information from the network storage device and provides the information to the computing node. The computing node can start up without the need for storing startup information locally.

Abstract: An IL6R block CAR-T transgenic vector for alleviating CRS includes: AmpR sequence containing ampicillin resistance gene (SEQ ID NO: 1); prokaryotic replicon pUC Ori sequence (SEQ ID NO: 2); virus replicon SV40 Ori sequence (SEQ ID NO: 3); eWPRE enhanced posttranscriptional regulatory element of hepatitis B virus (SEQ ID NO: 11); human EF1a promoter (SEQ ID NO: 12); lentiviral packaging cis-elements for lentiviral packaging; humanized single-chain antibody fragment IL6RscFv1 (SEQ ID NO: 21), IL6RscFv2 (SEQ ID NO: 22), or IL6RscFv3 (SEQ ID NO: 23) of human IL6R; IRES ribosome binding sequence (SEQ ID NO: 25); IL6 signal peptide (SEQ ID NO: 26); human antibody Fc segment (SEQ ID NO: 27); and chimeric antigen receptors of the second or third generation CAR for integrating recognition, transmission and initiation. A preparation method of the IL6R block CAR-T transgenic vector and an application thereof in a preparation of drugs for alleviating CRS.

Abstract: X-ray scatter simulations to correct computed tomography (CT) data can be accelerated using a non-uniform discretization of the RTE, reducing the number of computations without sacrificing precision. For example, a coarser discretization can be used for higher-order/multiple-scatter flux, than for first-order-scatter flux. Similarly, precision is preserved when coarser angular resolution is used to simulate scatter within a patient, and finer angular resolution used for the scatter flux incident on detectors. Finer energy resolution is more beneficial at lower X-ray energies, and coarser spatial resolution can be applied to regions exhibiting less X-ray scatter (e.g., air and regions with low radiodensity). Further, predefined non-uniform discretization can be learned from scatter simulations on training data (e.g., a priori compressed grids learned from non-uniform grids generated by adaptive mesh methods).

Abstract: A machine learning model for predicting a size fit satisfaction for a variable size component is trained using at least sizing profiles of a plurality of items and feedbacks of subjects regarding sizing of the plurality of items. The machine learning model is used to determine a value for the variable size component that corresponds to an optimal predicted size fit satisfaction. The determined value of the variable size component is provided for use in creating a new item with a sizing variation based on the determined value.

Abstract: A multi-node data synchronous acquisition system and a method for real-time monitoring of underwater surface deformation. The system includes at least four sensor arrays, where each of the sensor array consists of a plurality of ribbon-like rigid substrates connected by movable joints. On each section of rigid substrate, three sensor units are respectively connected to a slave station data acquisition unit through cables. The slave station data acquisition unit is connected with a central controller through a cable. The central controller includes a compressive cabin outside and an embedded controller and a power supply inside. Each slave station data acquisition unit acquires data from an MEMS attitude sensor and then transmits it to the embedded controller. The present invention may realize synchronous acquisition of underwater or even underwater multi-node data, implement three-dimensional surface reconstruction, and may be used for improving the ocean observation capability.

Abstract: A deep learning (DL) network corrects/performs sinogram completion in computed tomography (CT) images based on complementary high- and low-kV projection data generated from a sparse (or fast) kilo-voltage (kV)-switching CT scan. The DL network is trained using inputs and targets, which respectively generated with and without kV switching. Another DL network can be trained to correct sinogram-completion errors in the projection data after a basis/material decomposition. A third DL network can be trained to correct sinogram-completion errors in reconstructed images based on the kV-switching projection data. Performance of the DL network can be improved by dividing a 3D convolutional neural network (CNN) into two steps performed by respective 2D CNNs. Further, the projection data and DLL can be divided into high- and low-frequency components to improve performance.

Abstract: A method and apparatuses are provided that use a neural network to correct artifacts in computed tomography (CT) images, especially cone-beam CT (CBCT) artifacts. The neural network is trained using a training dataset of artifact-minimized images paired with respective artifact-exhibiting images. In some embodiments, the artifact-minimized images are acquired using a small cone angle for the X-ray beam, and the artifact-exhibiting images are acquired either by forwarding projecting the artifact-minimized images using a large-cone-angle CBCT configuration or by performing a CBCT scan. In some embodiments, the network is a 2D convolutional neural network, and an artifact-exhibiting image is applied to the neural network as 2D slices taken for the coronal and/or sagittal views. Then the 2D image results from the neural network are reassembled as a 3D imaging having reduced imaging artifacts.

Abstract: Embodiments of application provide a node interconnection apparatus, and a method implemented by the node interconnection apparatus. The node interconnection apparatus includes a computing node and a resource control node, and a device interconnection interface connecting the two. Each of the computing node and the resource control node includes a processing unit and a storage unit, and the resource control node further includes a resource interface for connecting with a network storage device. The resource control node manages storage resource of the network storage device, and when the computing node needs started up, the resource control node obtains operating system startup information from the network storage device and provide the information to the computing node. The computing node can start up without the need for storing startup information locally. Therefore, storage resource inside the computing node is saved.

Abstract: An apparatus and method are described using a forward model to correct pulse pileup in spectrally resolved X-ray projection data from photon-counting detectors (PCDs). The forward model represents pulse pileup effects using an integral in which the integrand includes a term that is a function of a count rate, which term is called a spectrum distortion correction function. This correction function can be represented as superposition of basis energy functions and corresponding polynomials of the count rate, which are defined by the polynomial coefficients. To calibrate the forward model, the polynomial coefficients are adjusted to optimize an objective function, which uses calibration data having known projections lengths for the material components of a material decomposition. To determine projection lengths for projection data from a computed tomography scan, the calibrated polynomial coefficients are held constant and the projection lengths are adjusted to optimize an objective function.

Abstract: A packet processing method in a computing system is disclosed. The computing system comprises a host, wherein at least one network interface card is connected to the host. The network interface card includes switching equipment and at least two network ports. A first network port is corresponding to at least one physical function PF and multiple virtual functions VFs. At least one VF of the first network port is provided for a first virtual machine on the host in a passthrough manner. The first virtual machine sends a data packet from the VF that is connected to the first virtual machine. Switching equipment of the first network port forwards the data packet according to a destination MAC address of the data packet, and sends the data packet to a virtual bridge on VMM of the host. The VMM provides abundant network function processing for the data packet.

Abstract: An apparatus and method are described using a forward model to correct pulse pileup in spectrally resolved X-ray projection data from photon-counting detectors (PCDs). The forward model represents pulse pileup effects using an integral in which the integrand includes a term that is a function of a count rate, which term is called a spectrum distortion correction function. This correction function can be represented as superposition of basis energy functions and corresponding polynomials of the count rate, which are defined by the polynomial coefficients. To calibrate the forward model, the polynomial coefficients are adjusted to optimize an objective function, which uses calibration data having known projections lengths for the material components of a material decomposition. To determine projection lengths for projection data from a computed tomography scan, the calibrated polynomial coefficients are held constant and the projection lengths are adjusted to optimize an objective function.

Abstract: A machine learning model for predicting a size fit satisfaction for a variable size component is trained using at least sizing profiles of a plurality of items and feedbacks of subjects regarding sizing of the plurality of items. The machine learning model is used to determine a value for the variable size component that corresponds to an optimal predicted size fit satisfaction. The determined value of the variable size component is provided for use in creating a new item with a sizing variation based on the determined value.

Abstract: A trencher includes a trencher body; a cable detection mechanism disposed at a front portion of the trencher body; a chain mechanism and a jet mechanism disposed in the center of a bottom portion of the trencher body; a first track mechanism and a second track mechanism, the first track mechanism being disposed on a first side of the bottom portion of the trencher body, and the second track mechanism being disposed on a second side of the bottom portion of the trencher body; and a soil discharging component disposed at a rear portion of the trencher body.

Abstract: A deep learning (DL) network reduces artifacts in computed tomography (CT) images based on complementary sparse-view projection data generated from a sparse kilo-voltage peak (kVp)-switching CT scan. The DL network is trained using input images exhibiting artifacts and target images exhibiting little to no artifacts. Another DL network can be trained to perform image-domain material decomposition of the artifact-mitigated images by being trained using target images in which beam hardening is corrected and spatial variations in the X-ray beam are accounted for. Further, material decomposition and artifact mitigation can be integrated in a single DL network that is trained using as inputs reconstructed images having artifacts and as targets material images without artifacts with beam-hardening corrections, etc. Further, the target material images can be transformed using a whitening transform to decorrelate noise.

Abstract: An anastomosis clamp (1) and a delivery system thereof, where the anastomosis clamp (1) includes inner lancets (102, 602), round corners (103, 603), and outer rings (101, 601) that are sequentially connected; the delivery system may be used in cooperation with the anastomosis clamp (1); and the delivery system includes a distal end (2), a middle flexible sheath (3), and a handle (4).

Abstract: A method and apparatus are provided for using a neural network to estimate scatter in X-ray projection images and then correct for the X-ray scatter. For example, the neural network is a three-dimensional convolutional neural network 3D-CNN to which are applied projection images, at a given view, for respective energy bins and/or material components. The projection images can be obtained by material decomposing spectral projection data, or by segmenting a reconstructed CT image into material-component images, which are then forward projected to generate energy-resolved material-component projections. The result generated by the 3D-CNN is an estimated scatter flux. To train the 3D-CNN, the target scatter flux in the training data can be simulated using a radiative transfer equation method.

Abstract: An OCTS-based CAR-T vector for treating malignant glioma includes lentiviral skeleton plasmid, human EF1? promoter (SEQ ID NO.14), OCTS chimeric receptor structural domain, and PDL1 single-chain antibody; the OCTS chimeric receptor structural domain consists of CD8 leader chimeric receptor signal peptide (SEQ ID NO.15), PDL1 single-chain antibody light chain VL (SEQ ID NO.16), PDL1 single-chain antibody heavy chain VH (SEQ ID NO.17), EGFRvIII single-chain antibody light chain VL (SEQ ID NO.18), EGFRvIII single-chain antibody heavy chain VH (SEQ ID NO.19), antibody Inner-Linker (SEQ ID NO.20), single-chain antibody Inter-Linker (SEQ ID NO.21), CD8 Hinge chimeric receptor linker (SEQ ID NO.22), CD8 Transmembrane chimeric receptor transmembrane domain (SEQ ID NO.23), TCR chimeric receptor T cell activation domain (SEQ ID NO.26) and chimeric receptor co-stimulator domain.

Abstract: A method and apparatus is provided that uses a deep learning (DL) network to improve the image quality of computed tomography (CT) images, which were reconstructed using an analytical reconstruction method. The DL network is trained to use physical-model information in addition to the analytical reconstructed images to generate the improved images. The physical-model information can be generated, e.g., by estimating a gradient of the objective function (or just the data-fidelity term) of a model-based iterative reconstruction (MBIR) method (e.g., by performing one or more iterations of the MBIR method). The MBIR method can incorporate physical models for X-ray scatter, detector resolution/noise/non-linearities, beam-hardening, attenuation, and/or the system geometry. The DL network can be trained using input data comprising images reconstructed using the analytical reconstruction method and target data comprising images reconstructed using the MBIR method.

Abstract: Systems and methods for determining location-based bid modifier suggestions include determining a content placement cost based in part on a likelihood of a user that has entered a physical establishment completing a transaction, an average transaction amount for the establishment, and an expected return on investment (ROI). A location-based bid modifier may be determined using the computed cost and a base bid amount. In some implementations, the location-based bid modifier may also be based on a probability model that models the probability of the user visiting the establishment.