Abstract: The present disclosure provides a detector substrate and a flat panel detector, the detector substrate includes a substrate base and detector pixel units on the substrate base, each detector pixel unit includes: a driver circuit; a photoelectric conversion device disposed on a side, away from the substrate base, of the driver circuit, the photoelectric conversion device including at least two photoelectric conversion structures connected in series, a bottom electrode of a first photoelectric conversion structure being electrically connected with the driver circuit, and a top electrode of an nth photoelectric conversion structure being electrically connected with a bottom electrode of an (n+1)th photoelectric conversion structure, with n being greater than or equal to 1; and a bias voltage line on a side of the photoelectric conversion device away from the substrate base, the bias voltage line being electrically connected to a top electrode of a last photoelectric conversion structure.

Abstract: Provided herein is a technology for an Autonomous Vehicle Cloud System (AVCS). This AVCS provides sensing, data fusion, prediction, decision-making, and/or control instructions for specific vehicles at a microscopic level based on data from one or more of other vehicles, roadside unit (RSU), cloud-based platform, and traffic control center/traffic control unit (TCC/TCU). Specifically, the AVs can be effectively and efficiently operated and controlled by the AVCS. The AVCS provides individual vehicles with detailed time-sensitive control instructions for fulfilling driving tasks, including car following, lane changing, route guidance, and other related information. The AVCS is configured to predict individual vehicle behavior and provide planning and decision-making at a microscopic level. In addition, the AVCS is configured to provide one or more of virtual traffic light management, travel demand assignment, traffic state estimation, and platoon control.

Abstract: Provided herein is a technology for an Autonomous Vehicle Intelligent System (AVIS), which facilitates vehicle operations and control for autonomous driving. The AVIS and related methods provide vehicles with vehicle-specific information for a vehicle to perform driving tasks such as car following, lane changing, and route guidance. The AVIS comprises an onboard unit (OBU), wherein the OBU comprises a communication module communicating with one or more of other autonomous vehicles (AV), a roadside unit (RSU), a cloud platform, and a traffic control center/traffic control unit (TCC/TCU). The AVIS implements one or more of the following functions: sensing, prediction, decision-making, and vehicle control using onboard information and vehicle-specific information received from other AVs, the RSU, the cloud platform, and/or the TCC/TCU.

Abstract: The invention provides systems and methods for a computing power allocation system for autonomous driving (CPAS-AD), which is a component of an Intelligent Road Infrastructure System (IRIS). The CPAS-AD incorporates advanced computing capabilities that effectively allocate computational power for sensing, prediction, planning, decision-making, and control functions to enable end-to-end driving functions. In addition to the vehicle, the CPAS-AD can acquire additional computation resources from one or more of: (a) a roadside unit (RSU) network, (b) a cloud platform, (c) a traffic control center/traffic control unit (TCC/TCU), and (d) a traffic operations center (TOC). Additionally, tailored to different traffic scenarios, the CPAS-AD can allocate data and computation resources (including but not limited to CPU and GPU) for vehicle sensing, prediction, planning, decision-making, and control functions, thereby enabling safe and efficient autonomous driving.

Abstract: The invention provides systems and methods for a function-based computing power allocation system (FCPAS), which is a component of an Intelligent Road Infrastructure System (IRIS). The FCPAS incorporates advanced computing capabilities that effectively allocate computational power for prediction, planning, and decision making functions. Specifically, through the FCPAS, an AV can acquire additional computational resources for vehicle prediction, planning, and decision-making functions, thereby enabling safe and efficient autonomous driving. Additionally, tailored to different traffic scenarios, the FCPAS can allocate data and computational resources (including but not limited to CPU and GPU) for vehicle automation.

Abstract: Antibodies that bind to ?v?8 are provided.

Abstract: New antibodies and methods of use are described.

Abstract: New antibodies and methods of use are described.

Abstract: New antibodies and methods of use are described.

Abstract: A method for positioning tea-bud picking points based on fused thermal images and RGB images is provided and includes: firstly, image pairs of several tea buds are acquired by using an image acquisition device, and are each labeled to obtain a tea-bud object detection database and a tea-bud keypoint detection database; secondly, in order to obtain a trained object detection model and a trained keypoint detection model, the tea-bud object detection database and the tea-bud keypoint detection database are inputted into an object detection model and a keypoint detection model for training, respectively; finally, the trained object detection model and keypoint detection model are used to sequentially process the tea-bud image pairs to obtain tea-bud keypoint positions, and then tea-bud picking point positions are obtained in combination with the tea-bud growth characteristics. The positioning accuracy and efficiency of the tea-bud picking points can be improved.

Abstract: Antibodies that bind to ?v?8 are provided.

Abstract: The present invention discloses a physical embedded deep learning formation pressure prediction method, device, medium and equipment, the present invention characterizes seismic attenuation by logging impedance quality factor Q, based on the Q value and rock physics model of formation pressure, the physical mechanism of this kind of certainty replace Caianiello convolution neurons of the nonlinear activation function, using the convolution neurons, build deep learning convolution neural networks (CCNNs), can greatly increase the stress inversion precision and learning efficiency, get accurate formation pressure prediction results. Compared with the prior art, the present invention uses acoustic attenuation instead of the traditional acoustic velocity to characterize formation pressure, and solves the problem that the traditional pressure prediction method based on velocity has strong multiple solutions due to high gas content and complex structure.

Abstract: Antibodies that bind to ???8 are provided.

Abstract: New antibodies and methods of use are described.

Abstract: Provided herein are compositions including lipids and copolymers in the form of a nanodisc assembly. The subject copolymers include monomer units of styrene and monomer units selected from acrylic acid and an acrylic acid derivative. In certain cases, the copolymer is a copolymer of styrene and acrylic acid. Also provided herein, is an aqueous solution comprising the subject composition. Also provided herein, are methods for producing a nanodisc assembly, including incubation of a lipid and a subject copolymer. Further provided herein, are methods for solubilizing a membrane protein in an aqueous solution, wherein the method includes forming a nanodisc assembly of a lipid bilayer having one or more membrane proteins embedded therein, and a subject copolymer. Also provided are methods of solubilizing a hydrophobic constituent in an aqueous solution, including forming a nanodisc assembly of a lipid, a hydrophobic constituent, and a subject copolymer.

Abstract: Herein are innovations that enable facile cryo-EM analysis of diverse samples. Methods of functionalizing sample grids for cryo-EM are described, including methods of creating high quality graphene oxide films on cryo-EM substrates. The cryo-EM sample substrates are functionalized with affinity molecules that efficiently concentrate sample molecules and other specimen types on the grid, away from the air-water interface. Affinity groups include amines and proteins such as tagging system proteins and peptides that can be used to capture diverse sample types with high affinity. Optionally, spacers such as PEG chains are used to place sample particles away from the substrate surface, reducing substrate-induced artifacts.

Abstract: Antibodies that bind to ?v?8 are provided.

Abstract: New antibodies and methods of use are described.

Abstract: Provided is an antibody that specifically binds human ?v?? and blocks binding of TGFp peptide to ?v?8, wherein the antibody binds to the specificity determining loop (SDL) of human ?8. In some embodiments, the antibody further binds to one, two, or all three of the human av-head domain, the al helix of human ?8, or the al helix of human ?8. In some embodiments, the antibody is humanized or chimeric. In some embodiments, the antibody is linked to a detectable label. Also provided is a method of enhancing an immune response in a human individual, comprising administering a sufficient amount of the antibody to the individual, thereby enhancing an immune response. Also provided are pharmaceutical compositions comprising the anti-?v?? antibodies or antigen-binding molecules thereof.

Abstract: Provided is an antibody that specifically binds human ?v?? and blocks binding of TGFp peptide to ?v?8, wherein the antibody binds to the specificity determining loop (SDL) of human ?8. In some embodiments, the antibody further binds to one, two, or all three of the human av-head domain, the al helix of human ?8, or the al helix of human ?8. In some embodiments, the antibody is humanized or chimeric. In some embodiments, the antibody is linked to a detectable label. Also provided is a method of enhancing an immune response in a human individual, comprising administering a sufficient amount of the antibody to the individual, thereby enhancing an immune response. Also provided are pharmaceutical compositions comprising the anti-?v?? antibodies or antigen-binding molecules thereof.