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利用延时成像和深度神经网络对侧根发育进行定量分析。

Quantitative analysis of lateral root development with time-lapse imaging and deep neural network.

作者信息

Uemura Yuta, Tsukagoshi Hironaka

机构信息

Faculty of Agriculture, Meijo University, Nagoya, Japan.

出版信息

Quant Plant Biol. 2024 Feb 13;5:e1. doi: 10.1017/qpb.2024.2. eCollection 2024.

Abstract

During lateral root (LR) development, morphological alteration of the developing single LR primordium occurs continuously. Precise observation of this continuous alteration is important for understanding the mechanism involved in single LR development. Recently, we reported that very long-chain fatty acids are important signalling molecules that regulate LR development. In the study, we developed an efficient method to quantify the transition of single LR developmental stages using time-lapse imaging followed by a deep neural network (DNN) analysis. In this 'insight' paper, we discuss our DNN method and the importance of time-lapse imaging in studies on plant development. Integrating DNN analysis and imaging is a powerful technique for the quantification of the timing of the transition of organ morphology; it can become an important method to elucidate spatiotemporal molecular mechanisms in plant development.

摘要

在侧根(LR)发育过程中,单个LR原基发育过程中的形态变化持续发生。精确观察这种持续变化对于理解单个LR发育所涉及的机制很重要。最近,我们报道了超长链脂肪酸是调节LR发育的重要信号分子。在这项研究中,我们开发了一种有效的方法,通过延时成像结合深度神经网络(DNN)分析来量化单个LR发育阶段的转变。在这篇“见解”文章中,我们讨论了我们的DNN方法以及延时成像在植物发育研究中的重要性。将DNN分析与成像相结合是一种用于量化器官形态转变时间的强大技术;它可以成为阐明植物发育中时空分子机制的重要方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b83/10877138/306b7d455474/S263288282400002X_figAb.jpg

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