Abstract: Provided herein are trispecific and/or trivalent binding proteins comprising four polypeptide chains that form three antigen binding sites that specifically bind one or more target proteins, wherein a first pair of polypeptides forming the binding protein possess dual variable domains having a cross-over orientation, and wherein and a second pair of polypeptides possess a single variable domain forming a single antigen binding site. In some embodiments, the binding proteins comprise a binding site that binds a CD28 polypeptide, a binding site that binds a CD3 polypeptide, and a binding site that binds a third polypeptide, such as a tumor target protein. In some embodiments, the binding proteins comprise four polypeptide chains that form three antigen binding sites that specifically bind one or more HIV target proteins. The disclosure also relates to methods for making trispecific and/or trivalent binding proteins and uses of such binding proteins.

Abstract: Provided is a method for processing video calls. The method includes: determining a layer bit rate corresponding to each frame layer in video data sent by a transmitter, wherein a plurality of video frames in the video data are organized into a plurality of frame layers based on an inter-frame dependency relationship, and the layer bit rate comprises a bit rate acquired by counting bit rates of video frames in a corresponding frame layer; determining, based on uplink bit rates corresponding to a plurality of receivers and a total downlink detected bandwidth, downlink allocated bandwidths corresponding to the receivers; determining, based on the downlink allocated bandwidths corresponding to the receivers and the layer bit rates, target frame layers corresponding to the receivers; and forwarding video data subjected to frame extraction processing for the target frame layers to corresponding receivers.

Abstract: The present disclosure provides an automatic driving test method. The method includes: acquiring test configuration information input by a user; generating test cases according to the test configuration information, and allocating the test cases to corresponding test types, wherein test types comprise a virtual simulation test, a whole vehicle in-loop test, a closed site test, and an open road test; and under each test type, testing a to be tested vehicle based on the corresponding test cases to obtain test results corresponding to each of the test type, and the test results comprise a simulation test result, a whole vehicle in-loop test result, a closed road test result, and an open road test result. Thus obtaining rich comprehensive test evaluation results makes the test results for autonomous vehicles more accurate.

Abstract: A class of benzoxazinone derivatives and a preparation method therefor, specifically related to a compound shown in formula (II), or a stereoisomer or a pharmaceutically acceptable salt thereof.

Abstract: An isolated cell membrane potential detection system can measure a cell membrane current change when light having different parameters such as wavelength, energy density, pulse width, and repetition rate stimulates a cell to be detected, to detect whether the biological electrical activity of the cell can be regulated by light, and to detect the specificity of the light regulation parameters of the cell. In the light regulation cell membrane potential detection system, a method of progressively adjusting and detecting with multiple optical parameters from small to large is used to measure a cell membrane current change in different periods under a certain photostimulation parameter for each group of cells, so as to quickly and efficiently detect which type of optical signal can regulate the characteristics of the cell such as the generation of a membrane current, photostimulation parameter specificity, and an energy threshold.

Abstract: The present disclosure features useful methods to treat cancer, e.g., in a subject in need thereof. In some embodiments, the methods described include administering an agent that reduces the level and/or activity of BRM/BRG1 in combination with an immunotherapy. In some embodiments, the immunotherapy is administered concurrently with the agent that reduces the level and/or activity of BRM and/or BRG1 in the subject. In some embodiments, the immunotherapy is administered prior to the agent that reduces the level and/or activity of BRM and/or BRG1 in the subject. In some embodiments, the immunotherapy is administered subsequent to the agent that reduces the level and/or activity of BRM and/or BRG1 in the subject.

Abstract: The present provides an electrospinning apparatus for fabricating a nanofiber of core-shell structure, including a high-voltage electrostatic generator, a frame and a liquid brusher. A horizontally moving means is connected to the frame, and the horizontally moving means is connected with the liquid brusher. A chain-drive mechanism and a first reservoir are provided below the liquid brusher, the chain-drive mechanism includes a first sprocket and a second sprocket, and a chain is connected between the first sprocket and the second sprocket. The chain is engaged with a spray sprocket, and the spray sprocket is connected with a first motor via a revolving shaft. A core solution is contained in the first reservoir, and the lower portion of the spray sprocket is immersed in the core solution. The horizontally moving means drives the liquid brusher to move along the chain, and the liquid brusher brushes the shell solution over the chain.

Abstract: A computer-implemented method of generating editable free-viewport videos is provided. A plurality of video of a scene from a plurality of views is obtained. The scene comprises includes an environment and one or more dynamic entities. A 3D bounding-box is generated for each dynamic entity in the scene. A computer device encodes a machine learning model including an environment layer and a dynamic entity layer for each dynamic entity in the scene. The environment layer represents a continuous function of space and time of the environment. The dynamic entity layer represents a continuous function of space and time of the dynamic entity. The dynamic entity layer includes a deformation module and a neural radiance module. The deformation module is configured to deform a spatial coordinate in accordance with a timestamp and a trained deformation weight. The neural radiance module is configured to derive a density value and a color.

Abstract: This disclosure relates to antibodies, antigen-binding fragments, protein constructs, and chimeric antigen receptors that bind to a complex comprising an AFP peptide and an MHC molecule, and the uses thereof.

Abstract: Described herein are methods and non-transitory computer-readable media configured to obtain a plurality of images from a plurality of image scanning orientations for an object. A rigid registration is performed to the plurality of images to obtain a transformation matrix to normalize the plurality of images from their respective image spaces to a normalized image space. Each normalized image comprises a plurality of voxels. A machine learning model comprising an implicit representation of a high-resolution image is trained using the normalized images, wherein the high-resolution image comprises more voxels than the voxels in the normalized images. The high-resolution image is generated based on the trained machine learning model. The plurality of images are a plurality of anisotropic 2D images, while the high resolution image can be a 2D or 3D high resolution image.

Abstract: The present disclosure provides MEK inhibitors, compositions thereof, and methods of using the same.

Abstract: A deep neural network based hair rendering system is presented to model high frequency component of furry objects. Compared with existing approaches, the present method can generate photo-realistic rendering results. An acceleration method is applied in our framework, which can speed up training and rendering processes. In addition, a patch-based training scheme is introduced, which significantly increases the quality of outputs and preserves high frequency details.

Abstract: Provided are a three-dimensional reconstruction method, apparatus and system of a dynamic scene, a server and a medium. The method includes: acquiring multiple continuous depth image sequences of the dynamic scene, where the multiple continuous depth image sequences are captured by an array of drones equipped with depth cameras; fusing the multiple continuous depth image sequences to establish a three-dimensional reconstruction model of the dynamic scene; obtaining target observation points of the array of drones through calculation according to the three-dimensional reconstruction model and current poses of the array of drones; and instructing the array of drones to move to the target observation points to capture, and updating the three-dimensional reconstruction model according to multiple continuous depth image sequences captured by the array of drones at the target observation points.

Abstract: A server determines, at a first predetermined time, a default decision as to whether to provide a first media content clip after the end of a first media content item. At a second predetermined time, after the first predetermined time, the server initiates a determination of a first decision as to whether to provide the first media content clip after the end of the first media content item. In accordance with the first decision being reached within a predetermined latency period, the server provides the first media content clip to the first electronic device in accordance with the first decision. In accordance with a determination that the predetermined latency period has elapsed without the first decision being reached, provides the first media content clip to the first electronic device in accordance with the default decision.

Abstract: The present disclosure provides MEK inhibitors, compositions thereof, and methods of using the same.

Abstract: The present disclosure provides MEK inhibitors, compositions thereof, and methods of using the same.

Abstract: Systems, methods, and non-transitory computer-readable media are configured to obtain a set of content items to train a neural radiance field-based (NeRF-based) machine learning model for object recognition. Depth maps of objects depicted in the set of content items can be determined. A first set of training data comprising reconstructed content items depicting only the objects can be generated based on the depth maps. A second set of training data comprising one or more optimal training paths associated with the set of content items can be generated based on the depth maps. The one or more optimal training paths are generated based at least in part on a dissimilarity matrix associated with the set of content items. The NeRF-based machine learning model can be trained based on the first set of training data and the second set of training data.

Abstract: The present provides an electrospinning apparatus for fabricating a nanofiber of core-shell structure, including a high-voltage electrostatic generator, a frame and a liquid brusher. A horizontally moving means is connected to the frame, and the horizontally moving means is connected with the liquid brusher. A chain-drive mechanism and a first reservoir are provided below the liquid brusher, the chain-drive mechanism includes a first sprocket and a second sprocket, and a chain is connected between the first sprocket and the second sprocket. The chain is engaged with a spray sprocket, and the spray sprocket is connected with a first motor via a revolving shaft. A core solution is contained in the first reservoir, and the lower portion of the spray sprocket is immersed in the core solution. The horizontally moving means drives the liquid brusher to move along the chain, and the liquid brusher brushes the shell solution over the chain.

Abstract: Described herein are systems and methods for training machine learning models to generate three-dimensional (3D) motions based on light detection and ranging (LiDAR) point clouds. In various embodiments, a computing system can encode a machine learning model representing an object in a scene. The computing system can train the machine learning model using a dataset comprising synchronous LiDAR point clouds captured by monocular LiDAR sensors and ground-truth three-dimensional motions obtained from IMU devices. The machine learning model can be configured to generate a three-dimensional motion of the object based on an input of a plurality of point cloud frames captured by a monocular LiDAR sensor.

Abstract: Described herein are systems and methods of capturing motions of humans in a scene. A plurality of IMU devices and a LiDAR sensor are mounted on a human. IMU data is captured by the IMU devices and LiDAR data is captured by the LiDAR sensor. Motions of the human are estimated based on the IMU data and the LiDAR data. A three-dimensional scene map is built based on the LiDAR data. An optimization is performed to obtain optimized motions of the human and optimized scene map.