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Fig. 3 | Plant Methods

Fig. 3

From: Root Hair Sizer: an algorithm for high throughput recovery of different root hair and root developmental parameters

Fig. 3

RHS processed on roots submitted to different treatments. Example of application of RHS to analyse the effect of water, NF or IAA treatment on M. truncatula roots development. Roots were immersed 1 h in water (pink data), 10 nM NF (magenta data) or 10 µm IAA (orange data) and observed 18 h after immersion. A batch of untreated roots where also observed at the same time (green data). a Pictures of representative roots for different tested conditions. Red contours highlight root hairs detected with RHS. Yellow lines indicate root regions considered for the first sigmoidal fit. For untreated and NF treated roots, data obtained between 0 and 6000 µm from the RT were used for the adjustment. For water and IAA treated roots, data from 0 to 5000 µm and from 0 to 2000 µm from the RT were used respectively. Green lines indicate regions used for the second consecutive sigmoidal fit at d50_1 ± 5δ_1. Cyan dots point out d50 − 2δ_2 and d50 + 2δ_2, the initiation and termination of RHs growth. Scale: 500 µm. b RH length and sigmoidal curve adjustment of data obtained with pictures presented in a. Black and red curves present the two consecutive fits achieved. Grey areas highlight data used to perform the second fit. The first dashed lines mark d50 − 2δ_2 the initiation of RHs growth, the second dashed line mark d50 + 2δ_2 the arrest of RH growth. ce Whisker plot comparing, for tested conditions: Lmax parameter (c), estimated RHs growth rate (d), the length between d50 ± 2δ_2 and distance from RT at d50 − 2δ_2 (f). Crosses indicate mean value of the corresponding data, dots present outliers according to Tukey method. Data were obtained from two biological replicates, using 7 to 8 M. truncatula roots per replicate. For RH growth rate estimation using root growth rate, see “Material and methods”. Letters present the significative groups obtained from a one-way ANOVA test with Bonferroni multiple comparison post-test (p < 0.05)

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