基于网络特征的叶片变化的表型强大地重建了潜在空间
Kohei Iwamasa1, Koji Noshita1,2
1Department of Biology, Kyushu University, Fukuoka, Fukuoka, Japan.
PLoS computational biology
|July 20, 2023
概括
我们开发了一种新的方法来量化叶脉网络,揭示了一个由运输效率和稳健性的功能性权衡驱动的单维形态空间. 这有助于理解血管精子静脉流动模式.
科学领域:
- 植物生物学 植物生物学
- 计算生物学是一种计算生物学.
- 形态测量 形态测量 形态测量
背景情况:
- 叶状静脉结构表现出血管种群中的等级模式.
- 功能需求约束了叶子组织网络,影响了它们的结构和多样性.
- 之前的研究使用了有限的形态测量来量化叶子变的表型.
研究的目的:
- 开发一种高通量表型化工作流程,用于量化叶脉网络.
- 识别特定于叶子变异的形态空间模式,了解形态多样性和约束.
- 为功能和结构分析量化表现叶状变化的拓学方面.
主要方法:
- 使用可行的系统获取叶子图像.
- 基于深度神经网络的叶静脉细分.
- 网络提取作为非定向图和计算网络特征.
- 缩小尺寸以识别形态空间模式.
主要成果:
- 五种物种的分类准确率为90.6%,仅基于网络特征.
- 识别与静脉循环度相关的一维形态空间.
- 观察到的一维分布模式与最佳运输效率,施工成本和强度的理论预测保持一致.
结论:
- 叶子风化模式是由功能性权衡形成的.
- 拟议的基于网络特征的方法为叶脉拓学提供了定量描述符.
- 这种方法对分析叶脉的功能和结构性质具有前景.
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