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X Demographics
Mendeley readers
Attention Score in Context
| 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 |
Aoife 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. |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 20% |
| United Kingdom | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 1 | 20% |
| Science communicators (journalists, bloggers, editors) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 109 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 109 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 22 | 20% |
| Researcher | 15 | 14% |
| Student > Master | 8 | 7% |
| Student > Bachelor | 8 | 7% |
| Student > Doctoral Student | 7 | 6% |
| Other | 16 | 15% |
| Unknown | 33 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 42 | 39% |
| Engineering | 7 | 6% |
| Computer Science | 6 | 6% |
| Biochemistry, Genetics and Molecular Biology | 5 | 5% |
| Earth and Planetary Sciences | 3 | 3% |
| Other | 9 | 8% |
| Unknown | 37 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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
#5,474,715
of 25,473,687 outputs
Outputs from Plant Methods
#335
of 1,269 outputs
Outputs of similar age
#90,519
of 341,059 outputs
Outputs of similar age from Plant Methods
#8
of 39 outputs
Altmetric has tracked 25,473,687 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,269 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one has gotten more attention than average, scoring higher than 72% 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 341,059 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.