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
中国山水画风格迁移的目标是在保持原有山水真实场景图像内容的前提下,引入传统中国画作特征,以生成具有中国山水画艺术特征的图像。近年,由于深度学习的快速发展,卷积神经网络(CNN)和对抗生成网络(GAN)几乎主导了包括风格迁移在内的大...中国山水画风格迁移的目标是在保持原有山水真实场景图像内容的前提下,引入传统中国画作特征,以生成具有中国山水画艺术特征的图像。近年,由于深度学习的快速发展,卷积神经网络(CNN)和对抗生成网络(GAN)几乎主导了包括风格迁移在内的大部分图像生成任务,但也存在一些问题,如真实场景在风格迁移过程中易丢失语义,GAN网络训练出现模型坍塌,CNN风格迁移方法出现棋盘效应等。视觉Transformer模型为图像处理任务提供了新的解决方案,但训练需大量数据且计算复杂。为了解决生成中国画过程中由上述因素引起的图像质量低及细节特征丢失等问题,本文提出一种能基于细节特征提取融合的中国山水画风格迁移网络,即SSTR(swin style transfer transformer)。该网络在StyTr^(2)网络的基础上,引入了Swin–Transformer模型,利用视觉Transformer的强语义性保留山水场景的特征;同时利用Swin–Transformer模型的分层体系结构及滑窗操作计算注意力机制,提取更多的山水画艺术风格细节,同时降低模型训练复杂度;最后,引入一个CNN解码器细化生成目标图像。本文利用公开视觉数据集COCO 2014与公开山水画数据集进行训练、验证与测试,并将结果与基线方法进行比较。结果表明,SSTR在处理中国山水画风格迁移任务中,风格损失和内容损失分别为1.35和1.88,在风格损失上优于StyTr^(2),表现出了优异的特征提取能力和图像生成能力。展开更多
Some studies have confirmed the neuroprotective effect of remote ischemic conditioning against stroke. Although numerous animal researches have shown that the neuroprotective effect of remote ischemic conditioning may...Some studies have confirmed the neuroprotective effect of remote ischemic conditioning against stroke. Although numerous animal researches have shown that the neuroprotective effect of remote ischemic conditioning may be related to neuroinflammation, cellular immunity, apoptosis, and autophagy, the exact underlying molecular mechanisms are unclear. This review summarizes the current status of different types of remote ischemic conditioning methods in animal and clinical studies and analyzes their commonalities and differences in neuroprotective mechanisms and signaling pathways. Remote ischemic conditioning has emerged as a potential therapeutic approach for improving stroke-induced brain injury owing to its simplicity, non-invasiveness, safety, and patient tolerability. Different forms of remote ischemic conditioning exhibit distinct intervention patterns, timing, and application range. Mechanistically, remote ischemic conditioning can exert neuroprotective effects by activating the Notch1/phosphatidylinositol 3-kinase/Akt signaling pathway, improving cerebral perfusion, suppressing neuroinflammation, inhibiting cell apoptosis, activating autophagy, and promoting neural regeneration. While remote ischemic conditioning has shown potential in improving stroke outcomes, its full clinical translation has not yet been achieved.展开更多
为提高档案管理的效率和准确性,提出了一种基于智能优化深度网络的档案数据分析方法。该方法结合了Transformer网络的特征提取能力和灰狼优化算法(Grey Wolf Optimizer,GWO)的参数优化能力,显著提高了档案管理的效率和准确性。将提出的...为提高档案管理的效率和准确性,提出了一种基于智能优化深度网络的档案数据分析方法。该方法结合了Transformer网络的特征提取能力和灰狼优化算法(Grey Wolf Optimizer,GWO)的参数优化能力,显著提高了档案管理的效率和准确性。将提出的方法运用到档案数据的多分类任务中,实验结果表明,通过GWO算法优化后的Transformer-GWO模型在与xgboost分类模型结合时取得了最佳性能,其宏观精确度、宏观召回率和宏观F1分数分别达到0.893、0.878以及0.885,提出的方法有效提升了档案管理的智能化水平。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.
文摘中国山水画风格迁移的目标是在保持原有山水真实场景图像内容的前提下,引入传统中国画作特征,以生成具有中国山水画艺术特征的图像。近年,由于深度学习的快速发展,卷积神经网络(CNN)和对抗生成网络(GAN)几乎主导了包括风格迁移在内的大部分图像生成任务,但也存在一些问题,如真实场景在风格迁移过程中易丢失语义,GAN网络训练出现模型坍塌,CNN风格迁移方法出现棋盘效应等。视觉Transformer模型为图像处理任务提供了新的解决方案,但训练需大量数据且计算复杂。为了解决生成中国画过程中由上述因素引起的图像质量低及细节特征丢失等问题,本文提出一种能基于细节特征提取融合的中国山水画风格迁移网络,即SSTR(swin style transfer transformer)。该网络在StyTr^(2)网络的基础上,引入了Swin–Transformer模型,利用视觉Transformer的强语义性保留山水场景的特征;同时利用Swin–Transformer模型的分层体系结构及滑窗操作计算注意力机制,提取更多的山水画艺术风格细节,同时降低模型训练复杂度;最后,引入一个CNN解码器细化生成目标图像。本文利用公开视觉数据集COCO 2014与公开山水画数据集进行训练、验证与测试,并将结果与基线方法进行比较。结果表明,SSTR在处理中国山水画风格迁移任务中,风格损失和内容损失分别为1.35和1.88,在风格损失上优于StyTr^(2),表现出了优异的特征提取能力和图像生成能力。
基金supported partly by the National Natural Science Foundation of China,No.82071332the Chongqing Natural Science Foundation Joint Fund for Innovation and Development,No.CSTB2023NSCQ-LZX0041 (both to ZG)。
文摘Some studies have confirmed the neuroprotective effect of remote ischemic conditioning against stroke. Although numerous animal researches have shown that the neuroprotective effect of remote ischemic conditioning may be related to neuroinflammation, cellular immunity, apoptosis, and autophagy, the exact underlying molecular mechanisms are unclear. This review summarizes the current status of different types of remote ischemic conditioning methods in animal and clinical studies and analyzes their commonalities and differences in neuroprotective mechanisms and signaling pathways. Remote ischemic conditioning has emerged as a potential therapeutic approach for improving stroke-induced brain injury owing to its simplicity, non-invasiveness, safety, and patient tolerability. Different forms of remote ischemic conditioning exhibit distinct intervention patterns, timing, and application range. Mechanistically, remote ischemic conditioning can exert neuroprotective effects by activating the Notch1/phosphatidylinositol 3-kinase/Akt signaling pathway, improving cerebral perfusion, suppressing neuroinflammation, inhibiting cell apoptosis, activating autophagy, and promoting neural regeneration. While remote ischemic conditioning has shown potential in improving stroke outcomes, its full clinical translation has not yet been achieved.
文摘为提高档案管理的效率和准确性,提出了一种基于智能优化深度网络的档案数据分析方法。该方法结合了Transformer网络的特征提取能力和灰狼优化算法(Grey Wolf Optimizer,GWO)的参数优化能力,显著提高了档案管理的效率和准确性。将提出的方法运用到档案数据的多分类任务中,实验结果表明,通过GWO算法优化后的Transformer-GWO模型在与xgboost分类模型结合时取得了最佳性能,其宏观精确度、宏观召回率和宏观F1分数分别达到0.893、0.878以及0.885,提出的方法有效提升了档案管理的智能化水平。
