5 statistical analysis using ggpubr. Machine learning classification was implemented using 1 Sci-kit learn in Python (Pedregosa et al., 2011). The script used for data analysis in R is 2 publicly available as an R-notebook (http://doi.org/10.5281/zenodo.3534239), as well as 3 the Jupyter notebook containing the command lines used for machine learning 4 (http://doi.org/10.5281/zenodo.3534148).5 6 3. Results 7 8 3.1 Extended exposure to heat stress results in a proportional decrease of the rosette 9 size and photosynthetic efficiency 10 11 To assess whether high-throughput phenotyping c
Open resource ↗zenodo · 10.5281/zenodo.3534239 · pdf-raw-page:5 lines:1-56Unverified paper record
The use of high throughput phenotyping for assessment of heat stress-induced changes in Arabidopsis
bioRxiv (Cold Spring Harbor Laboratory) · 11 Nov 2019 · 10.1101/838102
Abstract
The worldwide rise in heatwave frequency poses a threat to plant survival and productivity. Determining the new marker phenotypes that show reproducible response to heat stress and contribute to heat stress tolerance is becoming a priority. In this study, we describe a protocol focusing on the daily changes in plant morphology and photosynthetic performance after exposure to heat stress using an automated non-invasive phenotyping system. Heat stress exposure resulted in an acute reduction of quantum yield of photosystem II and increased leaf angle. In the longer term, exposure to heat also affected plant growth and morphology. By tracking the recovery period of WT and mutants impaired in thermotolerance (hsp101), we observed that the difference in maximum quantum yield, quenching, rosette size, and morphology. By examining the correlation across the traits throughout time, we observed that early changes in photochemical quenching corresponded with the rosette size at later stages, which suggests the contribution of quenching to overall heat tolerance. We also determined that 6h of heat stress provides the most informative insight in plant responses to heat, as it shows a clear separation between treated and non-treated plants as well as WT and hsp101. Our work streamlines future discoveries by providing an experimental protocol, data analysis pipeline and new phenotypes that could be used as targets in thermotolerance screenings.
Plant phenotyping relevance
自動化・非破壊フェノタイピングシステムを用いた形態・光合成表現型の取得プロトコル、データ解析パイプライン、新規表現型を中心的に提示しており、耐暑性スクリーニングへの再利用可能な方法論である。
abstractwe describe a protocol focusing on the daily changes in plant morphology and photosynthetic performance after exposure to heat stress using an automated non-invasive phenotyping system.
abstractOur work streamlines future discoveries by providing an experimental protocol, data analysis pipeline and new phenotypes that could be used as targets in thermotolerance screenings.
Code and data availability
The paper publicly deposits its authors' analysis code: an R-notebook for data analysis and a Jupyter notebook for machine learning, both on Zenodo. No phenotype dataset or image deposit is stated in the supplied blocks.
ng ggpubr. Machine learning classification was implemented using 1 Sci-kit learn in Python (Pedregosa et al., 2011). The script used for data analysis in R is 2 publicly available as an R-notebook (http://doi.org/10.5281/zenodo.3534239), as well as 3 the Jupyter notebook containing the command lines used for machine learning 4 (http://doi.org/10.5281/zenodo.3534148).5 6 3. Results 7 8 3.1 Extended exposure to heat stress results in a proportional decrease of the rosette 9 size and photosynthetic efficiency 10 11 To assess whether high-throughput phenotyping can capture significant alterations in plant 12 physiology caused by exposure to heat stress, we exposed three weeks old Arabidopsis
Open resource ↗zenodo · 10.5281/zenodo.3534148 · pdf-raw-page:5 lines:1-56This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.