Abstract: A fitting assembly includes a fitting comprising a housing and a connector assembly arranged within the housing. The connector assembly comprising a hollow interior and configured to be movable between an unactuated position and an actuated position. In the actuated position, the connector assembly is connectable to a pipe receivable within the hollow interior. An adapter coupled to the fitting includes a biasing mechanism configured to bias the connector assembly from the actuated position to the unactuated position.

Abstract: In an embodiment, a computer-implemented method comprises deploying a dual-active database on a first active database site and a second active database site. The first active database site comprises a first group of disks including a first sub-group of disks and a second sub-group of disks respectively corresponding to a third sub-group of disks and a fourth sub-group of disks included in a second group of disks in the second active database site. The method further comprises storing a first set of database logs on the first sub-group of disks while a second set of database logs is being written on the second sub-group of disks. Contents of the second set of database logs are consistent with contents of the first set of database logs and function as a duplication of the first set of database logs.

Abstract: A secondary battery includes a positive electrode, a negative electrode, and an electrolyte. The positive electrode includes a positive-electrode active substance layer, the positive-electrode active substance layer contains a pre-lithiation agent, and a molecular formula of the pre-lithiation agent is LixNiaCu1?a?bMbO2, where 1?x?2, 0<a<1, and 0?b <0.1, and M is selected from one or more of Zn, Sn, Mg, Fe, and Mn. The negative electrode includes a negative-electrode active substance layer including graphite and silicon-containing material. The electrolyte contains fluoroethylene carbonate (FEC). A weight percentage of the pre-lithiation agent in the positive-electrode active substance layer, a weight percentage of silicon content in the negative-electrode active substance layer, and a weight percentage of FEC in the electrolyte satisfy 0.2×WSi?WFEC?7.5%?0.6×WL.

Abstract: Metadata is replicated. For instance, in response to receiving a request for replicating metadata to a target node, information of an index structure associated with the metadata is obtained. The index structure may include terminal nodes for storing the metadata and index nodes for storing index information of the terminal nodes. Next, the metadata from the terminal nodes is extracted based on the information of the index structure. Further, a sequence is generated to be replicated of the metadata. The sequence to be replicated includes items of the metadata, and locations of the items in the sequence to be replicated are determined based on key information corresponding to the items. In addition, the items of the metadata, in the sequence to be replicated, are replicated to the target node. In this way, only the metadata in the index structure is replicated, thereby reducing the data amount of replication.

Abstract: Disclosed herein include systems, devices, and methods for identifying recombinant variants (e.g., gene conversion variants) of genes such as GBA gene and CYP21A2 gene, the copy numbers of recombinant variants, and gene variant status (e.g., carrier, compound heterozygous, or homozygous).

Abstract: Techniques, systems, and devices are described for providing a computational frame for estimating high-dimensional stochastic behaviors. In one exemplary aspect, a method for performing numerical estimation includes receiving a set of measurements of a stochastic behavior. The set of correlated measurements follows a non-standard probability distribution and is non-linearly correlated. Also, a non-linear relationship exists between a set of system variables that describes the stochastic behavior and a corresponding set of measurements. The method includes determining, based on the set of measurements, a numerical model of the stochastic behavior. The numerical model comprises a feature space comprising non-correlated features corresponding to the stochastic behavior. The non-correlated features have a dimensionality of M and the set of measurements has a dimensionality of N, M being smaller than N.

Abstract: For machine training and application of a trained complex-valued machine learning model, an activation function of the machine learning model, such as a neural network, includes a learnable parameter that is complex or defined in a complex domain with two dimensions, such as real and imaginary or magnitude and phase dimensions. The complex learnable parameter is trained for any of various applications, such as MR fingerprinting, other medical imaging, or non-medical uses.

Abstract: An approach is provided for evaluating a performance of a query. A risk of selecting a low performance access path for a query is determined. The risk is determined to exceed a risk threshold. Based on the risk exceeding the risk threshold and using a machine learning optimizer, first costs of access paths for the query are determined. Using a cost-based database optimizer, second costs of the access paths are determined. Using a strong classifier operating on the first costs and the second costs, a final access path for the query is selected from the access paths.

Abstract: Disclosed herein are systems, methods, and instrumentalities associated with reconstructing magnetic resonance (MR) images based on under-sampled MR data. The MR data include 2D or 3D information, and may encompass multiple contrasts and multiple coils. The MR images are reconstructed using deep learning (DL) methods, which may accelerate the scan and/or image generation process. Challenges imposed by the large quantity of the MR data and hardware limitations are overcome by separately reconstructing MR images based on respective subsets of contrasts, coils, and/or readout segments, and then combining the reconstructed MR images to obtain desired multi-contrast results.

Abstract: A method of forming graphene layers is disclosed. The method includes precleaning the substrate with a plasma formed from an argon- and hydrogen-containing gas, followed by forming a graphene layer by exposing the substrate to a microwave plasma to form a graphene layer on the substrate. The microwave plasma comprises hydrocarbon and hydrogen radicals. The substrate is then cooled. A capping layer may also be formed.

Abstract: Neural network based systems, methods, and instrumentalities may be used to remove motion artifacts from magnetic resonance (MR) images. Such a neural network based system may be trained to perform the motion artifact removal tasks without reference (e.g., without using paired motion-contaminated and motion-free MR images). Various training techniques are described herein including one that feeds the neural network with pairs of MR images with different levels of motion contamination and forces the neural network learn to correct the motion contamination by transforming a first image of a contaminated pair into a second image of the contaminated pair. Other neural network training techniques are also described with an aim to reduce the reliance on training data that is difficult to obtain.

Abstract: A drain programmed read-only memory includes a diffusion region that spans a width of a bitcell and forms a drain of a first transistor and a second transistor. A bit line lead in a metal layer adjacent the diffusion region extends across the width of the bitcell. A first via extends from an upper half of the bit line lead and couples to a drain of the first transistor. Similarly, a second via extends from a lower half of the bit line and couples to a drain of the second transistor.

Abstract: A method for non-invasive quantification of organ fat using a magnetic resonance approach includes: constructing a detection system; connecting a detection area; detection system startup; acquiring data; analyzing data; and performing horizontal data analysis. An external computer, a radio frequency (RF) subsystem, and a portable magnet module are used to construct a system for non-invasive quantification of organ fat based on low-field nuclear magnetic resonance (LF-NMR,), which causes no damage, and achieves accurate and non-invasive quantification of organ fat. Specific pulse sequences are used to excite nuclear spin in a target region to generate LF-NMR, so as to achieve “one-click” detection, which is used for fast screening of related diseases such as non-alcoholic fatty liver disease (NAFLD). The system has accurate quantification, and is easy to operate without constraints of operator qualifications.

Abstract: Motion contaminated magnetic resonance (MR) images for training an artificial neural network to remove motion artifacts from the MR images are difficult to obtain. Described herein are systems, methods, and instrumentalities for injecting motion artifacts into clean MR images and using the artificially contaminated images for machine learning and neural network training. The motion contaminated MR images may be created based on clean source MR images that are associated with multiple physiological cycles of a scanned object, and by deriving MR data segments for the multiple physiological cycles based on the source MR images. The MR data segments thus derived may be combined to obtain a simulated MR data set, from which one or more target MR images may be generated to exhibit a motion artifact. The motion artifact may be created by manipulating the source MR images and/or controlling the manner in which the MR data set or the target MR images are generated.

Abstract: A bullseyes plot may be generated based on cardiac magnetic resonance imaging (CMRI) to facilitate the diagnosis and treatment of heart diseases. Described herein are systems, methods, and instrumentalities associated with bullseyes plot generation. A plurality of myocardial segments may be obtained for constructing the bullseye plot based on landmark points detected in short-axis and long-axis magnetic resonance (MR) slices of the heart and by arranging the short-axis MR slices sequentially in accordance with the order in which the slices are generated during the CMRI. The sequential order of the short-axis MR slices may be determined utilizing projected locations of the short-axis MR slices on a long-axis MR slice and respective distances of the projected locations to a landmark point of the long-axis MR slice. The myocardium and/or landmark points may be identified in the short-axis and/or long-axis MR slices using artificial neural networks.

Abstract: A pharmaceutical composition comprising a small molecule EGFR inhibitor and a preparation method therefor, the composition comprising N-(5-((4-(1-cyclopropyl-1H-indol-3-yl)pyrimidin-2-yl)amino)-2-((2-(dimethylamino)ethyl)(methyl)amino)-4-methoxyphenyl)acrylamide, an isomer, solvate, hydrate, or pharmaceutically acceptable salt thereof, or a combination thereof that acts as an active ingredient, and at least one pharmaceutically acceptable excipient.

Abstract: Immunogenic peptide fragments of metalloprotease ADAMTS-7 including a first short peptide, which is any one of the followings: a short peptide having the amino acid sequence shown in SEQ ID NO: 1 in the sequence listing; a short peptide having the amino acid sequence shown in SEQ ID NO: 2 in the sequence listing; a short peptide having the amino acid sequence shown in SEQ ID NO: 3 in the sequence listing; a short peptide having the amino acid sequence shown in SEQ ID NO: 4 in the sequence listing. The description includes uses of conjugates containing the above short peptides and vaccines containing the conjugates. The vaccines containing the short peptides can remarkably inhibit the intimal neogenesis in the vascular restenosis mouse models and the occurrence of atherosclerosis in high-fat-fed mice, and can be used for the prevention or treatment of atherosclerosis and/or vascular restenosis.

Abstract: An unsupervised machine learning method with self-supervision losses improves a slice-wise spatial resolution of 3D medical images with thick slices, and does not require high resolution images as the ground truth for training. The method utilizes information from high-resolution dimensions to increase a resolution of another desired dimension.

Abstract: A secondary battery includes a positive electrode, a negative electrode, and an electrolyte. The positive electrode includes a positive-electrode active substance layer, the positive-electrode active substance layer contains a pre-lithiation agent, and a molecular formula of the pre-lithiation agent is LixNiaCu1?a?bMbO2, where 1?x?2, 0<a<1, and 0?b <0.1, and M is selected from one or more of Zn, Sn, Mg, Fe, and Mn. The negative electrode includes a negative-electrode active substance layer including graphite and silicon-containing material. The electrolyte contains fluoroethylene carbonate (FEC). A weight percentage of the pre-lithiation agent in the positive-electrode active substance layer, a weight percentage of silicon content in the negative-electrode active substance layer, and a weight percentage of FEC in the electrolyte satisfy 0.2×WSi?WFEC?7.5%-0.6×WL.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with image segmentation that may be implementing using an encoder neural network and a decoder neural network. The encoder network may be configured to receive a medical image comprising a visual representation of an anatomical structure and generate a latent representation of the medical image indicating a plurality of features of the medical image. The latent representation may be used by the decoder network to generate a mask for segmenting the anatomical structure from the medical image. The decoder network may be pre-trained to learn a shape prior associated with the anatomical structure and once trained, the decoder network may be used to constrain an output of the encoder network during training of the encoder network.