An efficient and rapid Agrobacterium tumefaciens-mediated transformation protocol was developed to generate activation-tagged mutant lines with the aim of large-scale functional analysis of the potato genome. The expl...An efficient and rapid Agrobacterium tumefaciens-mediated transformation protocol was developed to generate activation-tagged mutant lines with the aim of large-scale functional analysis of the potato genome. The explants were inoculated with an Agrobacterium strain harboring the binary plasmid pSKI074 containing four CaMV 35S enhancers in the T-DNA region which activates the downstream genes in the host plant after its integration. Various parameters investigated to increase transformation efficiency were the type and age of explant, cultivar, hormone combinations, preculture of explants, period of co-cultivation with bacteria and concentration of bacterial cultures used for transformation. Stem explants from 5 week old plantlets of cv. Bintje which had undergone phytohormone pretreatment for 4 days, inoculation with diluted bacterial concentration of OD600 = 0.2 containing acetosyringone followed by 2 days of co-cultivation and selection in media with IAA and trans-zeatin all helped in greatly improving the transformation efficiency. The total time required from infection to rooted shoots was 6-7 weeks. Initial evidence for stable integration and expression of the transgenes by PCR analysis showed that over 93% of the regenerated lines were transgenic and this was confirmed by Southern hybridization.展开更多
针对无人机场景下行人重识别所呈现的多视角多尺度特点,以及传统的基于卷积神经网络的行人重识别算法受限于局部感受野结构和下采样操作,很难对行人图像的全局特征进行提取且图像空间特征分辨率不高。提出一种无人机场景下基于Transfor...针对无人机场景下行人重识别所呈现的多视角多尺度特点,以及传统的基于卷积神经网络的行人重识别算法受限于局部感受野结构和下采样操作,很难对行人图像的全局特征进行提取且图像空间特征分辨率不高。提出一种无人机场景下基于Transformer的轻量化行人重识别(Lightweight Transformer-based Person Re-Identification,LTReID)算法,利用多头多注意力机制从全局角度提取人体不同部分特征,使用Circle损失和边界样本挖掘损失,以提高图像特征提取和细粒度图像检索性能,并利用快速掩码搜索剪枝算法对Transformer模型进行训练后轻量化,以提高模型的无人机平台部署能力。更进一步,提出一种可学习的面向无人机场景的空间信息嵌入,在训练过程中通过学习获得优化的非视觉信息,以提取无人机多视角下行人的不变特征,提升行人特征识别的鲁棒性。最后,在实际的无人机行人重识别数据库中,讨论了在不同量级主干网和不同剪枝率情况下所提LTReID算法的行人重识别性能,并与多种行人重识别算法进行了性能对比,结果表明了所提算法的有效性和优越性。展开更多
文摘An efficient and rapid Agrobacterium tumefaciens-mediated transformation protocol was developed to generate activation-tagged mutant lines with the aim of large-scale functional analysis of the potato genome. The explants were inoculated with an Agrobacterium strain harboring the binary plasmid pSKI074 containing four CaMV 35S enhancers in the T-DNA region which activates the downstream genes in the host plant after its integration. Various parameters investigated to increase transformation efficiency were the type and age of explant, cultivar, hormone combinations, preculture of explants, period of co-cultivation with bacteria and concentration of bacterial cultures used for transformation. Stem explants from 5 week old plantlets of cv. Bintje which had undergone phytohormone pretreatment for 4 days, inoculation with diluted bacterial concentration of OD600 = 0.2 containing acetosyringone followed by 2 days of co-cultivation and selection in media with IAA and trans-zeatin all helped in greatly improving the transformation efficiency. The total time required from infection to rooted shoots was 6-7 weeks. Initial evidence for stable integration and expression of the transgenes by PCR analysis showed that over 93% of the regenerated lines were transgenic and this was confirmed by Southern hybridization.
文摘针对无人机场景下行人重识别所呈现的多视角多尺度特点,以及传统的基于卷积神经网络的行人重识别算法受限于局部感受野结构和下采样操作,很难对行人图像的全局特征进行提取且图像空间特征分辨率不高。提出一种无人机场景下基于Transformer的轻量化行人重识别(Lightweight Transformer-based Person Re-Identification,LTReID)算法,利用多头多注意力机制从全局角度提取人体不同部分特征,使用Circle损失和边界样本挖掘损失,以提高图像特征提取和细粒度图像检索性能,并利用快速掩码搜索剪枝算法对Transformer模型进行训练后轻量化,以提高模型的无人机平台部署能力。更进一步,提出一种可学习的面向无人机场景的空间信息嵌入,在训练过程中通过学习获得优化的非视觉信息,以提取无人机多视角下行人的不变特征,提升行人特征识别的鲁棒性。最后,在实际的无人机行人重识别数据库中,讨论了在不同量级主干网和不同剪枝率情况下所提LTReID算法的行人重识别性能,并与多种行人重识别算法进行了性能对比,结果表明了所提算法的有效性和优越性。