Abstract: The present disclosure provides compositions and methods for gene therapy. Further, the present disclosure provides compositions and methods for treatment of Wilson's Disease through novel gene therapy mechanisms. Wherein a composition of a closed circular cDNA integrating gene therapy construct, the gene therapy construct comprising, from 5? to 3?, a polynucleotide sequence is disclosed.

Abstract: Disclosed in the present disclosure is an intelligent browsing apparatus based on railway centralized signaling monitoring system data, including: an interface module, configured to send out an analysis command and display a received analysis result; and a data analysis module, connected to the interface module, and configured to acquire railway centralized signaling monitoring system data to be analyzed according to the received analysis command and analyze the railway centralized signaling monitoring system data to obtain and feed the analysis result back to the interface module. The intelligent browsing apparatus can automatically analyze the railway centralized signaling monitoring system data.

Abstract: Similarity-based deformable registration of medical images that contain tumor regions often undesirably leads to disproportionate volume changes to tumor regions. An image-registration framework for preserving tumor volumes while promoting similarity in non-tumor part is provided. The framework involves a two-stage process. In the first stage, similarity-based registration is used to identify potential tumor regions by their volume change, generating a soft tumor mask accordingly. In the second stage, a volume-preserving registration network is used. The network uses a novel adaptive volume-preserving loss that penalizes the change in size adaptively based on the masks calculated from the previous stage. The framework balances image similarity and volume preservation in different regions, i.e. non-tumor and tumor regions, by using soft tumor masks to adjust the imposition of volume-preserving loss on each one. It ensures that the tumor volumes are preserved during the registration process.

Abstract: A method for advanced nitrogen and phosphorus removal in sewage treatment includes the following steps: feeding raw water and return sludge into a pre-denitrification zone for denitrification; allowing a sludge-containing mixed liquor discharged from the pre-denitrification zone to enter an anaerobic zone to undergo a biological phosphorus removal reaction; allowing a sludge-containing mixed liquor discharged from the anaerobic zone and a return nitrification liquid to enter an anoxic zone for denitrification; allowing a sludge-containing mixed liquor discharged from the anoxic zone to enter an aerobic zone for nitrification and excessive phosphorus uptake, and allowing part of a nitrification liquid to be returned to the anoxic zone; allowing a sludge-containing mixed liquor discharged from the aerobic zone to enter a sedimentation zone for separation; passing a resulting supernatant through a biological filtration zone; returning part of resulting sludge to the pre-denitrification zone; and the like.

Abstract: Disclosed are a model reconstruction method, a model processing method and apparatus, a device, a system, and a medium. The model reconstruction method includes: acquiring a human body image; generating a first three-dimensional facial model based on a first partial image in the human body image and generating a three-dimensional body model, wherein the first partial image includes facial features; preprocessing the first three-dimensional facial model and obtaining a second three-dimensional facial model based on a preprocessing result; and splicing the second three-dimensional facial model and the three-dimensional body model to obtain a spliced three-dimensional human body model.

Abstract: A weakly-supervised framework for segmenting a three-dimensional (3D) medical image incorporates information of geometric prior and contrastive similarity in a loss-based fashion for enhancing distinguishability of an organ in presence of complex organ shape and image artifacts (e.g., low-contrast tissues) in the image. Training images with bounding-box annotations are used to train the framework. The geometric prior is advantageously built on point-cloud representations, which are much more fine-grained and flexible than volume representations, thus serving as better supervision than an inherent property of the bounding-box annotations (i.e. height and width). Furthermore, using contrastive similarity encourages organ pixels to gather around in a contrastive embedding space. It helps more-effectively distinguish low-contrast tissues from the background than using the conventionally-used gray space.

Abstract: A method of providing an image to image translation using a style based generator architecture for generative adversarial networks. The method includes the step of applying a feature alignment loss function defined by: LossFA=?i=mnwi·L1(?(GSM)i,?(GTM)i) wherein GSM and GTM are respective generators of a source model and a target model, ?(·)i is the extraction of the ith layer of features and wi represents the weight of L1 loss at the ith layer, the L1 loss weight wi being reduced as layers decrease such that L1 loss at higher layers has a higher weight than the L1 loss at lower layers.

Abstract: Presented herein are technologies and methods for improved production of AAV vectors.

Abstract: Presented herein are technologies and methods for improved production of AAV vectors.

Abstract: A method of providing an image to image translation using a style based generator architecture for generative adversarial networks. The method includes the step of applying a feature alignment loss function defined by: LossFA=?i=mnwi·L1(?(GSM)i, ?(GTM)i) wherein GSM and GTM are respective generators of a source model and a target model, ?(·)i is the extraction of the ith layer of features and wi represents the weight of L1 loss at the ith layer, the L1 loss weight wi being reduced as layers decrease such that L1 loss at higher layers has a higher weight than the L1 loss at lower layers.

Abstract: Presented herein are technologies and methods for improved production of AAV vectors.

Abstract: Image stylization is based on a learning network. A learning network is trained with a plurality of images and a reference image with a particular texture style. A plurality of different sub-networks of the learning network is trained, respectively. Specifically, one of the sub-networks is trained to extract one or more feature maps from the source image and transform the feature maps with the texture style applied thereon to a target image. Each of the feature maps indicates part of feature information of the source image. Another sub-network is trained to apply a specified texture style to the extracted feature maps, such that the target image generated based on the processed feature maps can embody the specified texture style.

Abstract: According to implementations of the subject matter described herein, there is provided an image colorization solution. The solution includes determining a similarity between contents presented in a grayscale source image and a color reference image and determining a col or target image corresponding to the source image based on the similarity. Specifically, a first and a second sets of blocks similar and dissimilar to the reference image are determined based on the similarity; a first color for the first set of blocks is determined based on a color of corresponding blocks in the reference image; a second color for the second set of blocks is determined independently of the reference image. Through this solution, it is possible to provide user controllability and customized effects in colorization, and there is no strict requirement on correspondence between the color image and grayscale image, achieving more robustness to selection of color reference images.

Abstract: Implementations of the present disclosure provide a solution for cross-domain image translation. In this solution, a first learning network for geometric deformation from a first to a second image domain is determined based on first and second images in the first and second domains, images in the two domains having different styles and objects in the images having geometric deformation with respect to each other. Geometric deformation from the second to the first domains is performed on the second image or geometric deformation from the first to the second domains is performed on the first image, to generate an intermediate image. A second learning network for style transfer from the first to the second domains is determined based on the first and intermediate images or based on the second and intermediate images generated. Accordingly, processing accuracy of leaning networks for cross-domain image translation can be improved and complexity is lowered.

Abstract: The present disclosure provides, among other things, technologies for improving gene therapy. Among other things, the present disclosure provides technologies that permit monitoring and/or assessment one or more characteristics of a gene therapy treatment such as, for example, extent, level, and/or persistence of payload expression. In some embodiments, provided technologies particularly useful with integrating gene therapy.

Abstract: Methods and technologies for the treatment of methylmalonic acidemia.

Abstract: A filtering device of a cooker hood is configured in a V shape with an upward opening as a whole, and includes a V-shaped inner surface and a V-shaped outer surface. A tube portion is embedded between the inner surface and the outer surface, both ends of the tube portion being closed. The tube portion has a V shape with an upward opening. The tube portion includes an air inlet and an air outlet disposed in an interleaved manner, with the air inlet being located on the outer surface, and the air outlet being located on the inner surface. In this way, a rotating airflow is suitable to be formed in the tube portion of the filtering device, thereby improving a grease separation effect.

Abstract: In accordance with implementations of the subject matter described herein, there is proposed a solution of visual stylization of stereoscopic images. In the solution, a first feature map for a first source image and a second feature map for a second source image are extracted. The first and second source images correspond to first and second views of a stereoscopic image, respectively. A first unidirectional disparity from the first source image to the second source image is determined based on the first and second source images. First and second target images having a specified visual style are generated by processing the first and second feature maps based on the first unidirectional disparity. Through the solution, a disparity between two source images of a stereoscopic image are taken into account when performing the visual style transfer, thereby maintaining a stereoscopic effect in the stereoscopic image consisting of the target images.

Abstract: Implementations of the present disclosure provide a solution for cross-domain image translation. In this solution, a first learning network for geometric deformation from a first to a second image domain is determined based on first and second images in the first and second domains, images in the two domains having different styles and objects in the images having geometric deformation with respect to each other. Geometric deformation from the second to the first domains is performed on the second image or geometric deformation from the first to the second domains is performed on the first image, to generate an intermediate image. A second learning network for style transfer from the first to the second domains is determined based on the first and intermediate images or based on the second and intermediate images generated. Accordingly, processing accuracy of leaning networks for cross-domain image translation can be improved and complexity is lowered.

Abstract: A method for advanced nitrogen and phosphorus removal in sewage treatment includes the following steps: feeding raw water and return sludge into a pre-denitrification zone for denitrification; allowing a sludge-containing mixed liquor discharged from the pre-denitrification zone to enter an anaerobic zone to undergo a biological phosphorus removal reaction; allowing a sludge-containing mixed liquor discharged from the anaerobic zone and a return nitrification liquid to enter an anoxic zone for denitrification; allowing a sludge-containing mixed liquor discharged from the anoxic zone to enter an aerobic zone for nitrification and excessive phosphorus uptake, and allowing part of a nitrification liquid to be returned to the anoxic zone; allowing a sludge-containing mixed liquor discharged from the aerobic zone to enter a sedimentation zone for separation; passing a resulting supernatant through a biological filtration zone; returning part of resulting sludge to the pre-denitrification zone; and the like.