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加快的Haustoria细分使得在谷物粉病理系统中的快速基因功能分析成为可能.

Stefanie Lück1, Deniz Demirhan1, Laura Agsten1

  • 1Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Seeland OT Gatersleben, Germany.

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概括

一个新的管道使用深度学习自动检测植物中的真菌感染. 这种工具准确地量化了大麦和小麦中的粉状菌,加速了基因功能研究.

关键词:
蓝视频蓝视频是什么意思格斯·格斯 (Gus Gus) 是一个很好的演员.大麦大麦大麦大麦大麦大麦大麦大麦谷物病原体的表型化深度学习是一种深度学习.这里是豪斯托里亚姆 (haustorium) 的所在地.高通量幻灯片成像技术微现象学就是微现象学.粉状菌是一种粉状菌.小麦小麦小麦小麦小麦小麦小麦.

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科学领域:

  • 植物病理学 植物病理学
  • 计算生物学 计算生物学
  • 遗传学 是一个遗传学.

背景情况:

  • 在植物中量化早期的真菌感染对于遗传研究至关重要,但目前是劳动密集型的.
  • 麦子和小麦等谷物中粉感染需要高效的分析方法.

研究的目的:

  • 开发和验证一个自动化管道,用于高吞吐量量化真菌感染事件.
  • 为了实现快速的功能验证屏幕和大规模的谷物粉相互作用的表型化.

主要方法:

  • 使用深度学习模型 (You Only Look Once网络和细分模型) 开发了一个公开可用的管道.
  • 该管道分析了大麦和小麦叶中GUS染色的表皮细胞和细胞内haustoria的全幻灯片图像.
  • 实现了自动聚焦层选择,以保持精细的结构细节.

主要成果:

  • 该管道准确地检测了大麦和小麦的β-glucuronidase (GUS) 染色细胞和haustoria.
  • 自动计数显示与手动计数几乎完全一致,证明了强大的跨物种可转移性.
  • 工作流过程在每个工作站的几分钟内滑动,用户输入最小.

结论:

  • 开发的管道大大降低了量化真菌感染的劳动强度.
  • 这种自动化系统可以快速验证植物和真菌相互作用的功能,并进行大规模的表型化.
  • 该管道支持对谷物粉相互作用的高效研究.