Abstract: Provided is a recombinant adeno-associated virus (rAAV) or recombinant lentivirus, comprising an expression cassette in the genome, the expression cassette comprises a polynucleotide encoding thiamine pyrophosphokinase (TPK), which is operatively linked to a promoter. Also provided are also a pharmaceutical composition comprising the rAAV or recombinant lentivirus, and use of the rAAV, recombinant lentivirus and the pharmaceutical composition in the preparation of a medicament for treating or preventing Alzheimer's disease.

Abstract: A system for precisely locating abnormal areas of brain fiber bundles. The system extracts fiber connections of the whole brain from diffusion magnetic resonance data, and fiber bundle pathways extracts through self-defined fiber bundle pathways or based on brain fiber bundle templates. A selected fiber bundle pathway is projected on a fiber connection result of the whole brain and finely segmented. The imaging indexes such as fractional anisotropy, mean diffusivity, intra-neurite volume fraction and orientation dispersion index are calculated from diffusion magnetic resonance data, so as to obtain the imaging index of each node of each fiber bundle pathway. These imaging indexes are configured to classify the disease group and the healthy group by a machine learning method, and which nodes on which fiber bundle pathways have abnormal changes with different diseases can be precisely located.

Abstract: The invention provides a carbon dioxide-based polyurethane elastomer with damping and anti-fatigue aging properties and its preparation method. It is obtained from Component A and Component B; Component A includes: PTMG-1000 45˜60 parts, carbon dioxide-based polyol 25˜30 parts, poly-DOPO-ITA-pentanediol ester 10˜20 parts, BDO 3˜5 parts, water 0.3˜0.5 parts, N-methylimidazole 0.5˜0.8 parts, bis(2-dimethylaminoethyl) ether 0.1˜0.2 parts, needle-shaped nano-titanium dioxide 0.5 parts, hindered phenol 0.2˜0.5 parts, dibutyltin dilaurate 0.1˜0.3 parts, strontium chloride 0.005˜0.01 parts, rhodium chloride 0.02˜0.03 parts, foaming agent 0.2˜0.5 parts; Component B includes: carbon dioxide-based polyol 50˜55 parts, MDI-50 45˜50 parts, organic zinc 0.01˜0.02 parts, organic bismuth 0.01˜0.02 parts.

Abstract: A method of identifying an attack comprising receiving an input of one or more images, wherein the one or more images includes a patch size and size, divide the image into a first sub-image and a second sub-image, classify the first sub-image and the second sub-image, wherein classifying is accomplished via introducing a variable in a pixel location associated with the first and second sub-image, and in response to classifying the first and second sub-image and identifying an adversarial patch, output a notification indicating that the input is not certified.

Abstract: Provided is a compound of formula I or II, a stereoisomer, a tautomer, a geometrical isomer, a solvate, an active metabolite, a hydrate, a prodrug or a pharmaceutically acceptable salt thereof, a pharmaceutical composition thereof, and the medical uses thereof in treating diseases benefiting from the capsid protein assembly inhibitor, particularly diseases caused by hepatitis B virus infection.

Abstract: Provided are a lithium adsorbent and a preparation method therefor. A raw material for preparing the lithium adsorbent includes a lithium adsorbent active material and an auxiliary material, where the auxiliary material includes a hydrophilic binder and reinforcing fibers, and the use of the reinforcing fibers and the hydrophilic binder strongly prevents the propagation of brittle cracks caused by volume expansion and contraction of the lithium adsorbent active material during use, significantly increasing the strength and cycle life of the lithium adsorbent, while the use of the hydrophilic binder increases the adsorption rate and capacity of the lithium adsorbent.

Abstract: A system for predicting disease with graph convolutional neural network based on multimodal magnetic resonance imaging, which extracts the radiomics information of multiple brain regions across modals as the features of nodes from multimodal magnetic resonance data, and extracts the connectomics information between brain regions to form an adjacency matrix. T1-weighted structural images extract cortical information through cortical reconstruction, and resting-state magnetic resonance data are used to calculate amplitude of low frequency fluctuations, regional homogeneity and functional connectivity. Through multimodal data preprocessing, image index extraction and structured data integration, multimodal unstructured magnetic resonance image data are integrated into unified graph-structured data, and the disease is predicted by a graph convolutional neural network method.

Abstract: Provided are a lithium adsorbent and a preparation method therefor. A raw material for preparing the lithium adsorbent includes a lithium adsorbent active material and an auxiliary material, where the auxiliary material includes a hydrophilic binder and reinforcing fibers, and the use of the reinforcing fibers and the hydrophilic binder strongly prevents the propagation of brittle cracks caused by volume expansion and contraction of the lithium adsorbent active material during use, significantly increasing the strength and cycle life of the lithium adsorbent, while the use of the hydrophilic binder increases the adsorption rate and capacity of the lithium adsorbent.

Abstract: A system includes a machine-learning network. The network includes an input interface configured to receive input data from a sensor. The processor is programmed to receive the input data, generate a perturbed input data set utilize the input data, wherein the perturbed input data set includes perturbations of the input data, denoise the perturbed input data set utilizing a denoiser, wherein the denoiser is configured to generate a denoised data set, send the denoised data set to both a pre-trained classifier and a rejector, wherein the pre-trained classifier is configured to classify the denoised data set and the rejector is configured to reject a classification of the denoised data set, train, utilizing the denoised input data set, the a rejector to achieve a trained rejector, and in response to obtaining the trained rejector, output an abstain classification associated with the input data, wherein the abstain classification is ignored for classification.

Abstract: A module (10) adapted for fitting to a luminaire (100), comprising: a main module housing (50); a rotate plate (60) coupled to the main module housing, and rotatable relative to the main module housing; a connection interface (70) for connecting the module to a corresponding connection interface of the luminaire, wherein the connection interface (70) is connected to the rotate plate; and a rotatable electrical connection (80) between the rotate plate and the main module housing, wherein the rotate plate (60) comprises a locking feature (62) which engages with a retaining feature (52) of the main module housing to lock the rotate plate with a selected relative rotational position between the rotate plate and the main module housing, wherein the locking feature (62) is provided on an elastically deformable region (64) of the rotate plate such that the locking feature can be brought out of engagement from the retaining feature by manual deformation of the deformable region (64) thereby to enable selection of the

Abstract: Methods, systems, and computer programs encoded on computer storage media, for processing user-defined functions in containers. One example system receives a query written in a primary query language that calls a user-defined function. The system assigns primary segment hosts to process portions of the query written in the primary query language and assigns first containers to process the call to the user-defined function. The primary segment hosts provide to the first containers the user-defined function and one or more arguments in the call to the user-defined function. The first containers execute the user-defined function. The primary segment hosts receive an initial result of the call to the user-defined function computed by the first containers. The primary segment hosts compute a final result of the query using the initial result. The primary segment hosts provide the final result of the query in response to receiving the query.

Abstract: A modeling method for a low-loss transport technology with directed force application and slow landing is provided, including determining relevant parameters, such as friction coefficients between a crop and grid bars, performing theoretical analysis, modeling an auxiliary device for simulating a motion trail of the crop, and modeling a three-dimensional diagram according to structural parameters. In this way, the crop and the soil are effectively separated, and situations, such as high surface scratching rate and internal bruising caused due to a motion manner of a conventional low-loss transport device with directed force application and slow landing, in a separation process of the crop and the soil is greatly reduced.

Abstract: A rotor is provided and includes a central hub, an outer ring assembly comprising magnetic elements and back-to-back curved webs radially interposed between the central hub and the outer ring assembly.

Abstract: A system for classifying working memory task magnetoencephalography based on machine learning, including: the magnetoencephalography data acquisition module configured to acquire magnetoencephalography data of a subject in different working memory task states; the magnetoencephalography data preprocessing module configured to control the quality of magnetoencephalography data in different working memory tasks and separate noises and artifacts; the magnetoencephalography source reconstruction module configured for sensor signal analysis and source reconstruction analysis for the data processed by the magnetoencephalography data preprocessing module; and the machine learning classification module is configured to classify the working memory tasks to which the subjects belong by taking power time series as features.

Abstract: A system for precisely locating abnormal areas of brain fiber bundles. The system extracts fiber connections of the whole brain from diffusion magnetic resonance data, and fiber bundle pathways extracts through self-defined fiber bundle pathways or based on brain fiber bundle templates. A selected fiber bundle pathway is projected on a fiber connection result of the whole brain and finely segmented. The imaging indexes such as fractional anisotropy, mean diffusivity, intra-neurite volume fraction and orientation dispersion index are calculated from diffusion magnetic resonance data, so as to obtain the imaging index of each node of each fiber bundle pathway. These imaging indexes are configured to classify the disease group and the healthy group by a machine learning method, and which nodes on which fiber bundle pathways have abnormal changes with different diseases can be precisely located.

Abstract: A method and a system for simulating magnetic resonance echo-planar imaging artifacts. Firstly, for K-space artifacts, K-space data are restored through normal magnetic resonance images, and the K-space data are modified pertinently, and then images with artifacts are reconstructed; for susceptibility artifacts, a susceptibility model is constructed through normal magnetic resonance images, and the magnetic field distribution is reconstructed, and then the images with distortion artifacts are reconstructed. According to the present disclosure, a large number of artifact data sets with different artifact types and artifact degrees can be quickly created through a small number of normal images, thus laying a foundation for the research of identifying artifacts, eliminating or weakening artifacts.

Abstract: A system for predicting disease with graph convolutional neural network based on multimodal magnetic resonance imaging, which extracts the radiomics information of multiple brain regions across modals as the features of nodes from multimodal magnetic resonance data, and extracts the connectomics information between brain regions to form an adjacency matrix. T1-weighted structural images extract cortical information through cortical reconstruction, and resting-state magnetic resonance data are used to calculate amplitude of low frequency fluctuations, regional homogeneity and functional connectivity. Through multimodal data preprocessing, image index extraction and structured data integration, multimodal unstructured magnetic resonance image data are integrated into unified graph-structured data, and the disease is predicted by a graph convolutional neural network method.

Abstract: A cognitive training material generation method, a cognitive training method, a device, and a medium are provided. The cognitive training material generation method includes: acquiring a first feature and a second feature, the first feature including a multimedia material and semantic information corresponding to the multimedia material, the second feature including a magnetic resonance representation; fitting the first feature and the second feature, obtaining a semantic map according to a fitting result and a preset brain map, and acquiring target semantic information corresponding to a target point according to the semantic map; taking the first feature as input of a deep learning model and the second feature as a constraint of the deep learning model, training the deep learning model, and determining a weight parameter of the deep learning model; generating a cognitive training material according to the target semantic information and the weight parameter of the deep learning model.

Abstract: A laser wireless power transmission device and method based on power acquisition on power line. The device includes a power transmitter and a power receiver both installed on a power transmission line. The power transmitter includes an online induction power acquisition module, a semiconductor laser, a communication module, a two-dimensional pan-tilt, a beam shaping module, a control unit, a video image module, and an IMU module. The power receiver includes a four-quadrant photovoltaic panel, a DCDC module, a wireless monitoring module, and a battery.

Abstract: The present invention discloses an air duct shielding device and an air-cooled refrigerator; the air duct shielding device includes: a fan base having a plurality of air outlets; a first adjusting part having a rotary disc portion and a plurality of first shielding sheets arranged at intervals, the first adjusting part being configured to controllably rotate around an axis of the rotary disc portion; and a second adjusting part provided between the first adjusting part and the fan base, the second adjusting part having a plurality of second shielding sheets arranged at intervals; wherein when the first adjusting part rotates around the axis of the rotary disc portion, the second adjusting part is driven to rotate, such that the first shielding sheet and/or the second shielding sheet completely shield(s), partially shield(s) or completely expose(s) each air outlet, thereby adjusting an air outlet area of each of the plural air outlets.