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    Optimized plant expression systems
    4.
    发明授权
    Optimized plant expression systems 有权

    公开(公告)号：US12297443B2

    公开(公告)日：2025-05-13

    申请号：US17190745

    申请日：2021-03-03

    Applicant: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY

    Inventor： Hugh Mason ,  Mary Pardhe ,  Andrew Diamos

    IPC: C12N15/86 ,  C12N15/82

    Abstract: Improved plant transient expression systems using optimized geminiviral vectors that efficiently produce heteromultimeric proteins are described herein. Examples of high yields are shown herein, including two, three, or four fluorescent proteins coexpressed simultaneously. Various antibodies were produced using the optimized vectors with special focus given to the creation and production of a chimeric broadly neutralizing anti-flavivirus antibody. The variable regions of this murine antibody, 2A10G6, were codon optimized and fused to a human IgG1. Analysis of the chimeric antibody showed that it was efficiently expressed in plants, can be purified to near homogeneity by a simple one-step purification process, retains its ability to recognize the Zika virus envelope protein, and induce an immune response against Zika virus. Two other monoclonal antibodies were produced at similar levels. This technology is versatile tool for the production of a wide spectrum of pharmaceutical multi-protein complexes in a fast, powerful, and cost-effective way.

    SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING TRANSPARENT MODELS FOR COMPUTER VISION AND IMAGE RECOGNITION UTILIZING DEEP LEARNING NON-TRANSPARENT BLACK BOX MODELS
    7.
    发明申请
    SYSTEMS, METHODS, AND APPARATUSES FOR IMPLEMENTING TRANSPARENT MODELS FOR COMPUTER VISION AND IMAGE RECOGNITION UTILIZING DEEP LEARNING NON-TRANSPARENT BLACK BOX MODELS 有权

    公开(公告)号：US20250095347A1

    公开(公告)日：2025-03-20

    申请号：US18293315

    申请日：2022-08-24

    Applicant: Arizona Board of Regents on behalf of Arizona State University

    Inventor： Roy Asim

    IPC: G06V10/82 ,  G06V10/778

    Abstract: Described herein are means for systematically generating transparent models for computer vision and image recognition utilizing deep learning non-transparent black box models. According to a particular embodiment, there is a specially configured system for generating an explainable AI model by performing operations, including: training a Convolutional Neural Network (CNN) to classify objects; training a Convolutional Neural Network (CNN) to classify objects from training data having a set of training images; training a multi-layer perceptron (MLP) to recognize both the objects and parts of the objects; generating the explainable AI model based on the training of the MLP; receiving an image having an object embedded therein, wherein the image forms no portion of the training data for the explainable AI model; executing the CNN and the explainable AI model within an image recognition system, and generating a prediction of the object in the image via the explainable AI model; recognizing parts of the object; providing the parts recognized within the object as evidence for the prediction of the object; and generating a description of why the image recognition system predicted the object in the image based on the evidence comprising the recognized parts.

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