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“Rolled-upness”: phenotyping leaf rolling in cereals using computer vision and functional data analysis approaches

Overview of attention for article published in Plant Methods, November 2015
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Title
“Rolled-upness”: phenotyping leaf rolling in cereals using computer vision and functional data analysis approaches
Published in
Plant Methods, November 2015
DOI 10.1186/s13007-015-0095-1
Pubmed ID
Authors

X. R. R. Sirault, A. G. Condon, J. T. Wood, G. D. Farquhar, G. J. Rebetzke

Abstract

The flag leaf of a wheat (Triticum aestivum L.) plant rolls up into a cylinder in response to drought conditions and then unrolls when leaf water relations improve. This is a desirable trait for extending leaf area duration and improving grain size particularly under drought. But how do we quantify this phenotype so that different varieties of wheat or different treatments can be compared objectively since this phenotype can easily be confounded with inter-genotypic differences in root-water uptake and/or transpiration at the leaf level if using traditional methods? We present a new method to objectively test a range of lines/varieties/treatments for their propensity of leaves to roll. We have designed a repeatable protocol and defined an objective measure of leaf curvature called "rolled-upness" which minimises confounding factors in the assessment of leaf rolling in grass species. We induced leaf rolling by immersing leaf strips in an osmoticum of known osmotic pressure. Using micro-photographs of individual leaf cross-sections at equilibrium in the osmoticum, two approaches were used to quantify leaf rolling. The first was to use some properties of the convex hull of the leaf cross-section. The second was to use cubic smoothing splines to approximate the transverse leaf shape mathematically and then use a statistic derived from the splines for comparison. Both approaches resulted in objective measurements that could differentiate clearly between breeding lines and varieties contrasting genetically in their propensity for leaf rolling under water stress. The spline approach distinguished between upward and downward curvature and allowed detailed properties of the rolling to be examined, such as the position on the strip where maximum curvature occurs. A method applying smoothing splines to skeletonised images of transverse wheat leaf sections enabled objective measurements of inter-genotypic variation for hydronastic leaf rolling in wheat. Mean-curvature of the leaf cross-section was the measure selected to discriminate between genotypes, as it was straightforward to calculate and easily construed. The method has broad applicability and provides an avenue to genetically dissect the trait in cereals.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 1 2%
Brazil 1 2%
Unknown 52 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 20%
Researcher 9 17%
Other 4 7%
Student > Master 4 7%
Student > Bachelor 3 6%
Other 6 11%
Unknown 17 31%
Readers by discipline Count As %
Agricultural and Biological Sciences 26 48%
Unspecified 2 4%
Arts and Humanities 1 2%
Biochemistry, Genetics and Molecular Biology 1 2%
Environmental Science 1 2%
Other 5 9%
Unknown 18 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 20 November 2015.
All research outputs
#14,241,439
of 22,833,393 outputs
Outputs from Plant Methods
#715
of 1,082 outputs
Outputs of similar age
#145,439
of 281,503 outputs
Outputs of similar age from Plant Methods
#7
of 13 outputs
Altmetric has tracked 22,833,393 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,082 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.3. This one is in the 30th percentile – i.e., 30% 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 281,503 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.