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Semi-automated 3D Leaf Reconstruction and Analysis of Trichome Patterning from Light Microscopic Images

Overview of attention for article published in PLoS Computational Biology, April 2013
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (62nd percentile)

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8 X users

Citations

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23 Dimensions

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Title
Semi-automated 3D Leaf Reconstruction and Analysis of Trichome Patterning from Light Microscopic Images
Published in
PLoS Computational Biology, April 2013
DOI 10.1371/journal.pcbi.1003029
Pubmed ID
Authors

Henrik Failmezger, Benjamin Jaegle, Andrea Schrader, Martin Hülskamp, Achim Tresch

Abstract

Trichomes are leaf hairs that are formed by single cells on the leaf surface. They are known to be involved in pathogen resistance. Their patterning is considered to emerge from a field of initially equivalent cells through the action of a gene regulatory network involving trichome fate promoting and inhibiting factors. For a quantitative analysis of single and double mutants or the phenotypic variation of patterns in different ecotypes, it is imperative to statistically evaluate the pattern reliably on a large number of leaves. Here we present a method that enables the analysis of trichome patterns at early developmental leaf stages and the automatic analysis of various spatial parameters. We focus on the most challenging young leaf stages that require the analysis in three dimensions, as the leaves are typically not flat. Our software TrichEratops reconstructs 3D surface models from 2D stacks of conventional light-microscope pictures. It allows the GUI-based annotation of different stages of trichome development, which can be analyzed with respect to their spatial distribution to capture trichome patterning events. We show that 3D modeling removes biases of simpler 2D models and that novel trichome patterning features increase the sensitivity for inter-accession comparisons.

X Demographics

X Demographics

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

Mendeley readers

The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 2%
Germany 1 2%
Belgium 1 2%
Unknown 53 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 21%
Researcher 11 20%
Student > Master 9 16%
Student > Bachelor 6 11%
Professor 2 4%
Other 7 13%
Unknown 9 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 45%
Biochemistry, Genetics and Molecular Biology 7 13%
Engineering 3 5%
Environmental Science 2 4%
Computer Science 2 4%
Other 8 14%
Unknown 9 16%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 31 July 2014.
All research outputs
#6,997,226
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#4,733
of 8,960 outputs
Outputs of similar age
#56,065
of 209,837 outputs
Outputs of similar age from PLoS Computational Biology
#54
of 145 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 46th percentile – i.e., 46% 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 209,837 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 73% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 62% of its contemporaries.