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High-Throughput Analysis of Arabidopsis Stem Vibrations to Identify Mutants With Altered Mechanical Properties

Overview of attention for article published in Frontiers in Plant Science, June 2018
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Title
High-Throughput Analysis of Arabidopsis Stem Vibrations to Identify Mutants With Altered Mechanical Properties
Published in
Frontiers in Plant Science, June 2018
DOI 10.3389/fpls.2018.00780
Pubmed ID
Authors

Miyuki T. Nakata, Masahiro Takahara, Shingo Sakamoto, Kouki Yoshida, Nobutaka Mitsuda

Abstract

Mechanical properties are rarely used as quantitative indices for the large-scale mutant screening of plants, even in the model plant Arabidopsis thaliana. The mechanical properties of plant stems generally influence their vibrational characteristics. Here, we developed Python-based software, named AraVib, for the high-throughput analysis of free vibrations of plant stems, focusing specifically on Arabidopsis stem vibrations, and its extended version, named AraVibS, to identify mutants with altered mechanical properties. These programs can be used without knowledge of Python and require only an inexpensive handmade setting stand and an iPhone/iPad with a high-speed shooting function for data acquisition. Using our system, we identified an nst1 nst3 double-mutant lacking secondary cell walls in fiber cells and a wrky12 mutant displaying ectopic formation of secondary cell wall compared with wild type by employing only two growth traits (stem height and fresh weight) in addition to videos of stem vibrations. Furthermore, we calculated the logarithmic decrement, the damping ratio, the natural frequency and the stiffness based on the spring-mass-damper model from the video data using AraVib. The stiffness was estimated to be drastically decreased in nst1 nst3, which agreed with previous tensile test results. However, in wrky12, the stiffness was significantly increased. These results demonstrate the effectiveness of our new system. Because our method can be applied in a high-throughput manner, it can be used to screen for mutants with altered mechanical properties.

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Mendeley readers

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Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 4 20%
Student > Doctoral Student 2 10%
Professor 2 10%
Student > Master 2 10%
Lecturer 1 5%
Other 1 5%
Unknown 8 40%
Readers by discipline Count As %
Agricultural and Biological Sciences 4 20%
Engineering 3 15%
Biochemistry, Genetics and Molecular Biology 2 10%
Computer Science 2 10%
Materials Science 1 5%
Other 0 0%
Unknown 8 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 November 2023.
All research outputs
#16,833,528
of 24,752,377 outputs
Outputs from Frontiers in Plant Science
#12,473
of 23,601 outputs
Outputs of similar age
#214,636
of 334,069 outputs
Outputs of similar age from Frontiers in Plant Science
#290
of 472 outputs
Altmetric has tracked 24,752,377 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 23,601 research outputs from this source. They receive a mean Attention Score of 3.9. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 334,069 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 472 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.