INTRON-CONTAINING PROMOTERS AND USES THEREOF
    23.
    发明申请

    公开(公告)号:US20200377898A1

    公开(公告)日:2020-12-03

    申请号:US16070648

    申请日:2017-02-02

    Abstract: The present invention relates to the field of molecular biology and more particularly to promoters useful for metabolic engineering in yeast or fungi for the production of biobased chemicals with broad applications. Intron-containing promoters with strong activity during oil-accumulation stages are particularly useful for genetic engineering in yeast and fungi, particularly Rhodosporidium or Rhodotorula genera. Such promoter are capable of driving strong expression of RNA or proteins in species of the Rhodosporidium or Rhodotorula genera.

    Agrobacterium strains for plant transformation

    公开(公告)号:US10570402B2

    公开(公告)日:2020-02-25

    申请号:US16190386

    申请日:2018-11-14

    Abstract: The present invention relates to the field of the production of transgenic plants through Agrobacterium-mediated transformation of cells of somatic embryogenic calli or embryogenic suspension cultures and regeneration of the transformed cells into fruit-setting plants. In particular, the present invention relates to the production of transgenic plants in the Euphorbiaceae family. The present invention further relates to media compositions, selection methods and engineered Agrobacterium tumefaciens strains that improve Agrobacterium-mediated transformation efficiency.

    Poisson-binomial based image recognition method and system

    公开(公告)号:US10325183B2

    公开(公告)日:2019-06-18

    申请号:US15511089

    申请日:2015-09-15

    Abstract: An improved system and method for digital image classification is provided. A host computer having a processor is coupled to a memory storing thereon reference feature data. A graphics processing unit (GPU) having a processor is coupled to the host computer and is configured to obtain, from the host computer, feature data corresponding to the digital image; to access, from the memory, the one or more reference feature data; and to determine a semi-metric distance based on a Poisson-Binomial distribution between the feature data and the one or more reference feature data. The host computer is configured to classify the digital image using the determined semi-metric distance.

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