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Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography

Overview of attention for article published in Plant Methods, November 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

twitter
8 tweeters
wikipedia
1 Wikipedia page
reddit
1 Redditor

Citations

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24 Dimensions

Readers on

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52 Mendeley
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Title
Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography
Published in
Plant Methods, November 2017
DOI 10.1186/s13007-017-0229-8
Pubmed ID
Authors

Nathan Hughes, Karen Askew, Callum P. Scotson, Kevin Williams, Colin Sauze, Fiona Corke, John H. Doonan, Candida Nibau

Abstract

Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations. In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress. Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.

Twitter Demographics

The data shown below were collected from the profiles of 8 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Researcher 8 15%
Student > Master 6 12%
Student > Bachelor 6 12%
Student > Doctoral Student 4 8%
Other 9 17%
Unknown 6 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 37%
Engineering 6 12%
Biochemistry, Genetics and Molecular Biology 5 10%
Computer Science 4 8%
Earth and Planetary Sciences 2 4%
Other 6 12%
Unknown 10 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 19 March 2019.
All research outputs
#2,521,964
of 15,890,858 outputs
Outputs from Plant Methods
#134
of 769 outputs
Outputs of similar age
#69,061
of 323,370 outputs
Outputs of similar age from Plant Methods
#18
of 89 outputs
Altmetric has tracked 15,890,858 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 769 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one has done well, scoring higher than 82% of its peers.
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 323,370 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 89 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.