↓ Skip to main content

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

Overview of attention for article published in BMC Bioinformatics, January 2016
Altmetric Badge

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 (66th percentile)

Mentioned by

twitter
7 tweeters

Citations

dimensions_citation
3 Dimensions

Readers on

mendeley
26 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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.

Twitter Demographics

The data shown below were collected from the profiles of 7 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 31%
Researcher 7 27%
Professor > Associate Professor 3 12%
Student > Master 2 8%
Librarian 1 4%
Other 2 8%
Unknown 3 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 31%
Neuroscience 6 23%
Biochemistry, Genetics and Molecular Biology 2 8%
Computer Science 2 8%
Nursing and Health Professions 1 4%
Other 4 15%
Unknown 3 12%

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
#1,170,703
of 6,982,524 outputs
Outputs from BMC Bioinformatics
#825
of 3,255 outputs
Outputs of similar age
#66,744
of 313,945 outputs
Outputs of similar age from BMC Bioinformatics
#49
of 147 outputs
Altmetric has tracked 6,982,524 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 3,255 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 74% 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 313,945 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 147 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 66% of its contemporaries.