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计算机视觉模型使混合线性建模能够使用真菌形态学来预测状菌根真菌殖民

Shufan Zhang¹, Yue Wu¹, Michael Skaro¹

¹Institute of Bioinformatics, University of Georgia, Athens, GA, USA.

Scientific reports

|May 13, 2024

概括

树状菌根真菌 (AMF) 的共生是古老的. 这项研究使用深度学习和建模来预测根中的AMF殖民,识别关键的真菌结构和营养交换的根区.

科学领域:

植物科学植物科学
菌类学菌类学是指菌类学.
计算生物学计算生物学

背景情况:

树枝状菌根真菌 (AMF) 与陆地植物形成古老的共生,对于碳的营养物质 (,) 交换至关重要.
了解AMF殖民对于改善作物和可持续农业至关重要.

研究的目的:

开发一种多尺度建模方法,以预测在 Sorghum bicolor 和 S. propinquum 重组杂交线 (RIL) 种群中的 AMF 殖民.
利用高通量表型来量化真菌结构及其与AMF殖民的关系.

主要方法:

用人面膜基于区域的卷积神经网络 (面膜R-CNN) 用于植物根中的真菌结构的像素细分.
利用混合线性模型分析了AMF殖民,根基和真菌结构分配之间的关系.
开发了多尺度模型来预测AMF殖民变化.

主要成果:

模型准确地捕获了超过95%的AMF殖民变化的变化,基于根部和真菌结构丰富度.
树分配是兄弟植物中AMF殖民的重要预测因素.
灌木和外状与上部根部的AMF殖民率最高有关,这表明它们在营养交换中的作用.

结论:

深度学习,特别是Mask R-CNN,对于植物根中AMF的高通量表型化是有效的.
混合线性建模为研究AMF殖民现象类型提供了强大的框架.
根基和真菌结构分配是AMF殖民模式和营养交换效率的关键决定因素.

关键词:

树状菌根菌的真菌树状菌根菌计算机视觉计算机视觉计算机视觉面具R-CNN是指一个R-CNN的面具.混合线性模型混合线性模型

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