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IPLaminator: an ImageJ plugin for automated binning and quantification of retinal lamination

Overview of attention for article published in BMC Bioinformatics, January 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)
  • Good Attention Score compared to outputs of the same age and source (67th percentile)

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7 X users

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Title
IPLaminator: an ImageJ plugin for automated binning and quantification of retinal lamination
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-016-0876-1
Pubmed ID
Authors

Shuai Li, Michael Woodfin, Seth S. Long, Peter G. Fuerst

Abstract

Information in the brain is often segregated into spatially organized layers that reflect the function of the embedded circuits. This is perhaps best exemplified in the layering, or lamination, of the retinal inner plexiform layer (IPL). The neurites of the retinal ganglion, amacrine and bipolar cell subtypes that form synapses in the IPL are precisely organized in highly refined strata within the IPL. Studies focused on developmental organization and cell morphology often use this layered stratification to characterize cells and identify the function of genes in development of the retina. A current limitation to such analysis is the lack of standardized tools to quantitatively analyze this complex structure. Most previous work on neuron stratification in the IPL is qualitative and descriptive. In this study we report the development of an intuitive platform to rapidly and reproducibly assay IPL lamination. The novel ImageJ based software plugin we developed: IPLaminator, rapidly analyzes neurite stratification patterns in the retina and other neural tissues. A range of user options allows researchers to bin IPL stratification based on fixed points, such as the neurites of cholinergic amacrine cells, or to define a number of bins into which the IPL will be divided. Options to analyze tissues such as cortex were also added. Statistical analysis of the output then allows a quantitative value to be assigned to differences in laminar patterning observed in different models, genotypes or across developmental time. IPLaminator is an easy to use software application that will greatly speed and standardize quantification of neuron organization.

X Demographics

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The data shown below were collected from the profiles of 7 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 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 36 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 25%
Researcher 9 25%
Professor > Associate Professor 3 8%
Student > Master 2 6%
Student > Bachelor 1 3%
Other 4 11%
Unknown 8 22%
Readers by discipline Count As %
Agricultural and Biological Sciences 10 28%
Neuroscience 9 25%
Computer Science 3 8%
Nursing and Health Professions 1 3%
Mathematics 1 3%
Other 4 11%
Unknown 8 22%
Attention Score in Context

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 17 January 2016.
All research outputs
#5,643,544
of 22,840,638 outputs
Outputs from BMC Bioinformatics
#2,076
of 7,288 outputs
Outputs of similar age
#90,382
of 392,526 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 141 outputs
Altmetric has tracked 22,840,638 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 7,288 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 71% 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 392,526 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 76% of its contemporaries.
We're also able to compare this research output to 141 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 67% of its contemporaries.