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    SEMI-DOMINANT MOLECULAR MARKER RELATED TO MAIZE DWARF AND APPLICATION THEREOF
    4.
    发明公开
    SEMI-DOMINANT MOLECULAR MARKER RELATED TO MAIZE DWARF AND APPLICATION THEREOF 审中-公开

    公开(公告)号：US20240114861A1

    公开(公告)日：2024-04-11

    申请号：US18233341

    申请日：2023-08-14

    申请人： Anhui Agricultural University

    发明人： Yang Zhao ,  Qing Ma ,  Xiaoduo Lu ,  Yixiao Wang ,  Hongying Wu

    IPC分类号： A01H1/04 ,  C12Q1/6895

    CPC分类号： A01H1/045 ,  C12Q1/6895 ,  C12Q2600/13 ,  C12Q2600/156

    摘要： The present application discloses a semi-dominant molecular marker related to maize dwarf and an application thereof, and relates to the technical field of maize molecular breeding. The molecular marker related to the maize dwarf is caused by a non-synonymous mutation from G to A in a maize gene Zm00001d013465, and the molecular marker is a nucleotide sequence composed of the non-synonymous mutation site and its upstream and downstream bases. The present application identifies a dwarf mutant, coded as E5779, from an EMS mutation population with maize B73 as the genetic background. By genome resequencing and Mutmap mapping, it is found that the mutant is caused by the non-synonymous mutation from G to A in the gene Zm00001d013465. The molecular marker is developed based on this, and the analysis and utilization of E5779 are helpful to improve the maize plant height and density-tolerant breeding, it has an important breeding application potential.

    SYSTEMS AND METHODS RELATING TO PROTOCOLS IN PLANT BREEDING PIPELINES
    8.
    发明公开
    SYSTEMS AND METHODS RELATING TO PROTOCOLS IN PLANT BREEDING PIPELINES 审中-公开

    公开(公告)号：US20240032493A1

    公开(公告)日：2024-02-01

    申请号：US18028173

    申请日：2021-09-17

    申请人： MONSANTO TECHNOLOGY LLC

    发明人： Xin SHEN ,  Aviral SHUKLA ,  Slobodan TRIFUNOVIC ,  Yiduo ZHAN ,  Zihao ZHAO

    IPC分类号： A01H1/02 ,  A01H1/04

    CPC分类号： A01H1/02 ,  A01H1/04

    摘要： Systems and methods are provided for automatically allocating test protocols to a plurality of test locations. Once such method includes a computing device executing a first stage machine learning prediction model (MLPM) based on protocol data for multiple test protocols for a test experiment to generate a first stage output. The first stage MLPM is trained based on historical allocation data for one or more prior test experiments. Multiple test sets are associated with the test protocols, and the first stage output includes, for multiple test locations, allocation prediction scores for the test protocols. Based on the first stage output, the computing device executes a second stage optimization model to generate a second stage output. The second stage output includes an allocation plan for the test protocols. The allocation plan identifies one or more of the test locations for each of the test protocols.